From d861498ae36d4d09c34643e1b368c47b9ef14c12 Mon Sep 17 00:00:00 2001
From: czk32611 <czk32611@hotmail.com>
Date: Tue, 2 Apr 2024 17:56:58 +0800
Subject: [PATCH] initial_commit

---
 .gitignore                                    |    10 +
 LICENSE                                       |    21 +
 README.md                                     |   243 +
 assets/BBOX_SHIFT.md                          |    26 +
 assets/demo/monalisa/monalisa.png             |   Bin 0 -> 275396 bytes
 assets/demo/sun1/sun.png                      |   Bin 0 -> 773418 bytes
 assets/demo/sun2/sun.png                      |   Bin 0 -> 773418 bytes
 assets/demo/yongen/yongen.jpeg                |   Bin 0 -> 95653 bytes
 assets/figs/landmark_ref.png                  |   Bin 0 -> 92936 bytes
 assets/figs/musetalk_arc.jpg                  |   Bin 0 -> 804782 bytes
 configs/inference/test.yaml                   |     9 +
 data/audio/monalisa.wav                       |   Bin 0 -> 6971436 bytes
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 data/video/monalisa.mp4                       |   Bin 0 -> 6906049 bytes
 data/video/sun.mp4                            |   Bin 0 -> 2217973 bytes
 musetalk/models/unet.py                       |    47 +
 musetalk/models/vae.py                        |   148 +
 musetalk/utils/__init__.py                    |     5 +
 musetalk/utils/blending.py                    |    60 +
 musetalk/utils/dwpose/default_runtime.py      |    54 +
 ...8xb32-270e_coco-ubody-wholebody-384x288.py |   257 +
 musetalk/utils/face_detection/README.md       |     1 +
 musetalk/utils/face_detection/__init__.py     |     7 +
 musetalk/utils/face_detection/api.py          |   240 +
 .../face_detection/detection/__init__.py      |     1 +
 .../utils/face_detection/detection/core.py    |   130 +
 .../face_detection/detection/sfd/__init__.py  |     1 +
 .../face_detection/detection/sfd/bbox.py      |   129 +
 .../face_detection/detection/sfd/detect.py    |   114 +
 .../face_detection/detection/sfd/net_s3fd.py  |   129 +
 .../detection/sfd/sfd_detector.py             |    59 +
 musetalk/utils/face_detection/models.py       |   261 +
 musetalk/utils/face_detection/utils.py        |   313 +
 musetalk/utils/face_parsing/__init__.py       |    50 +
 musetalk/utils/face_parsing/model.py          |   283 +
 musetalk/utils/face_parsing/resnet.py         |   109 +
 musetalk/utils/preprocessing.py               |   113 +
 musetalk/utils/utils.py                       |    61 +
 musetalk/whisper/audio2feature.py             |   125 +
 musetalk/whisper/requirements.txt             |     6 +
 musetalk/whisper/setup.py                     |    24 +
 musetalk/whisper/whisper.egg-info/PKG-INFO    |     5 +
 musetalk/whisper/whisper.egg-info/SOURCES.txt |    18 +
 .../whisper.egg-info/dependency_links.txt     |     1 +
 .../whisper/whisper.egg-info/entry_points.txt |     2 +
 .../whisper/whisper.egg-info/requires.txt     |     9 +
 .../whisper/whisper.egg-info/top_level.txt    |     1 +
 musetalk/whisper/whisper/__init__.py          |   116 +
 musetalk/whisper/whisper/__main__.py          |     4 +
 .../whisper/whisper/assets/gpt2/merges.txt    | 50001 ++++++++++++++++
 .../assets/gpt2/special_tokens_map.json       |     1 +
 .../whisper/assets/gpt2/tokenizer_config.json |     1 +
 .../whisper/whisper/assets/gpt2/vocab.json    |     1 +
 .../whisper/whisper/assets/mel_filters.npz    |   Bin 0 -> 2048 bytes
 .../assets/multilingual/added_tokens.json     |     1 +
 .../whisper/assets/multilingual/merges.txt    | 50000 +++++++++++++++
 .../multilingual/special_tokens_map.json      |     1 +
 .../assets/multilingual/tokenizer_config.json |     1 +
 .../whisper/assets/multilingual/vocab.json    |     1 +
 musetalk/whisper/whisper/audio.py             |   125 +
 musetalk/whisper/whisper/decoding.py          |   729 +
 musetalk/whisper/whisper/model.py             |   290 +
 .../whisper/whisper/normalizers/__init__.py   |     2 +
 musetalk/whisper/whisper/normalizers/basic.py |    71 +
 .../whisper/whisper/normalizers/english.json  |  1742 +
 .../whisper/whisper/normalizers/english.py    |   543 +
 musetalk/whisper/whisper/tokenizer.py         |   331 +
 musetalk/whisper/whisper/transcribe.py        |   207 +
 musetalk/whisper/whisper/utils.py             |    87 +
 requirements.txt                              |    11 +
 scripts/inference.py                          |   142 +
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 create mode 100755 musetalk/models/unet.py
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 create mode 100644 musetalk/utils/dwpose/default_runtime.py
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 create mode 100644 musetalk/utils/face_detection/README.md
 create mode 100644 musetalk/utils/face_detection/__init__.py
 create mode 100644 musetalk/utils/face_detection/api.py
 create mode 100644 musetalk/utils/face_detection/detection/__init__.py
 create mode 100644 musetalk/utils/face_detection/detection/core.py
 create mode 100644 musetalk/utils/face_detection/detection/sfd/__init__.py
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 create mode 100644 musetalk/utils/face_detection/detection/sfd/sfd_detector.py
 create mode 100644 musetalk/utils/face_detection/models.py
 create mode 100644 musetalk/utils/face_detection/utils.py
 create mode 100755 musetalk/utils/face_parsing/__init__.py
 create mode 100755 musetalk/utils/face_parsing/model.py
 create mode 100755 musetalk/utils/face_parsing/resnet.py
 create mode 100644 musetalk/utils/preprocessing.py
 create mode 100644 musetalk/utils/utils.py
 create mode 100644 musetalk/whisper/audio2feature.py
 create mode 100644 musetalk/whisper/requirements.txt
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 create mode 100644 musetalk/whisper/whisper.egg-info/PKG-INFO
 create mode 100644 musetalk/whisper/whisper.egg-info/SOURCES.txt
 create mode 100644 musetalk/whisper/whisper.egg-info/dependency_links.txt
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 create mode 100644 musetalk/whisper/whisper.egg-info/requires.txt
 create mode 100644 musetalk/whisper/whisper.egg-info/top_level.txt
 create mode 100644 musetalk/whisper/whisper/__init__.py
 create mode 100644 musetalk/whisper/whisper/__main__.py
 create mode 100644 musetalk/whisper/whisper/assets/gpt2/merges.txt
 create mode 100644 musetalk/whisper/whisper/assets/gpt2/special_tokens_map.json
 create mode 100644 musetalk/whisper/whisper/assets/gpt2/tokenizer_config.json
 create mode 100644 musetalk/whisper/whisper/assets/gpt2/vocab.json
 create mode 100755 musetalk/whisper/whisper/assets/mel_filters.npz
 create mode 100644 musetalk/whisper/whisper/assets/multilingual/added_tokens.json
 create mode 100644 musetalk/whisper/whisper/assets/multilingual/merges.txt
 create mode 100644 musetalk/whisper/whisper/assets/multilingual/special_tokens_map.json
 create mode 100644 musetalk/whisper/whisper/assets/multilingual/tokenizer_config.json
 create mode 100644 musetalk/whisper/whisper/assets/multilingual/vocab.json
 create mode 100644 musetalk/whisper/whisper/audio.py
 create mode 100644 musetalk/whisper/whisper/decoding.py
 create mode 100644 musetalk/whisper/whisper/model.py
 create mode 100644 musetalk/whisper/whisper/normalizers/__init__.py
 create mode 100644 musetalk/whisper/whisper/normalizers/basic.py
 create mode 100644 musetalk/whisper/whisper/normalizers/english.json
 create mode 100644 musetalk/whisper/whisper/normalizers/english.py
 create mode 100644 musetalk/whisper/whisper/tokenizer.py
 create mode 100644 musetalk/whisper/whisper/transcribe.py
 create mode 100644 musetalk/whisper/whisper/utils.py
 create mode 100644 requirements.txt
 create mode 100644 scripts/inference.py

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..6d812f3
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,10 @@
+.DS_Store
+*.log
+.idea/
+.vscode/
+*.pyc
+.ipynb_checkpoints
+models/
+results/
+data/audio/*.WAV
+data/video/*.mp4
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..ef10aae
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 TMElyralab
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/README.md b/README.md
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index 0000000..d7825b2
--- /dev/null
+++ b/README.md
@@ -0,0 +1,243 @@
+# MuseTalk
+
+MuseTalk: Real-Time High Quality Lip Synchronization with Latent Space Inpainting
+</br>
+Yue Zhang <sup>\*</sup>,
+Minhao Liu<sup>\*</sup>,
+Zhaokang Chen,
+Bin Wu<sup>†</sup>,
+Yingjie He,
+Chao Zhan,
+Wenjiang Zhou
+(<sup>*</sup>Equal Contribution, <sup>†</sup>Corresponding Author, benbinwu@tencent.com)
+
+**[github](https://github.com/TMElyralab/MuseTalk)**    **[huggingface](https://huggingface.co/TMElyralab/MuseTalk)**    **Project(comming soon)**    **Technical report (comming soon)**
+
+We introduce `MuseTalk`, a **real-time high quality** lip-syncing model (30fps+ on an NVIDIA Tesla V100). MuseTalk can be applied with virtual human videos, e.g., generated by [MuseV](https://github.com/TMElyralab/MuseV), as a complete solution.
+
+# Overview
+`MuseTalk` is a real-time high quality audio-driven lip-syncing model trained in the latent space of `ft-mse-vae`, which
+
+1. modifies an unseen face according to the input audio, with a size of face region of `256 x 256`.
+1. supports audio in various languages, such as Chinese, English, and Japanese.
+1. supports real-time inference with 30fps+ on an NVIDIA Tesla V100.
+1. supports modification of the center point of the face region proposes, which **SIGNIFICANTLY** affects generation results. 
+1. checkpoint available trained on the HDTF dataset.
+1. training codes (comming soon).
+
+# News
+- [04/02/2024] Released MuseTalk project and pretrained models.
+
+## Model
+![Model Structure](assets/figs/musetalk_arc.jpg)
+MuseTalk was trained in latent spaces, where the images were encoded by a freezed VAE. The audio was encoded by a freezed `whisper-tiny` model. The architecture of the generation network was borrowed from the UNet of the `stable-diffusion-v1-4`, where the audio embeddings were fused to the image embeddings by cross-attention.
+
+## Cases
+### MuseV + MuseTalk make human photos alive！
+<table class="center">
+  <tr style="font-weight: bolder;text-align:center;">
+        <td width="33%">Image</td>
+        <td width="33%">MuseV </td>
+        <td width="33%"> +MuseTalk</td>
+  </tr>
+  <tr>
+    <td>
+      <img src=assets/demo/yongen/yongen.jpeg width="95%">
+    </td>
+    <td >
+      <video src=assets/demo/yongen/yongen_musev.mp4 controls preload></video>
+    </td>
+    <td >
+      <video src=assets/demo/yongen/yongen_musetalk.mp4 controls preload></video>
+    </td>
+  </tr>
+  <tr>
+    <td>
+      <img src=assets/demo/monalisa/monalisa.png width="95%">
+    </td>
+    <td >
+      <video src=assets/demo/monalisa/monalisa_musev.mp4 controls preload></video>
+    </td>
+    <td >
+      <video src=assets/demo/monalisa/monalisa_musetalk.mp4 controls preload></video>
+    </td>
+  </tr>
+  <tr>
+    <td>
+      <img src=assets/demo/sun1/sun.png width="95%">
+    </td>
+    <td >
+      <video src=assets/demo/sun1/sun_musev.mp4 controls preload></video>
+    </td>
+    <td >
+      <video src=assets/demo/sun1/sun_musetalk.mp4 controls preload></video>
+    </td>
+  </tr>
+  <tr>
+    <td>
+      <img src=assets/demo/sun2/sun.png width="95%">
+    </td>
+    <td >
+      <video src=assets/demo/sun2/sun_musev.mp4 controls preload></video>
+    </td>
+    <td >
+      <video src=assets/demo/sun2/sun_musetalk.mp4 controls preload></video>
+    </td>
+  </tr>
+</table >
+
+* The character of the last two rows, `Xinying Sun`, is a supermodel KOL. You can follow her on [douyin](https://www.douyin.com/user/MS4wLjABAAAAWDThbMPN_6Xmm_JgXexbOii1K-httbu2APdG8DvDyM8).
+
+## Video dubbing
+<table class="center">
+  <tr style="font-weight: bolder;text-align:center;">
+        <td width="70%">MuseTalk</td>
+        <td width="30%">Original videos</td>
+  </tr>
+  <tr>
+    <td>
+      <video src=assets/demo/video_dubbing/Let_the_Bullets_Fly.mp4 controls preload></video>
+    </td>
+    <td>
+      <a href="//www.bilibili.com/video/BV1wT411b7HU">Link</a>
+      <href src=""></href>
+    </td>
+  </tr>
+</table>
+
+* For video dubbing, we applied a self-developed tool which can detect the talking person. 
+
+
+# TODO:
+- [x] trained models and inference codes.
+- [ ] technical report.
+- [ ] training codes.
+- [ ] online UI.
+- [ ] a better model (may take longer).
+
+
+# Getting Started
+We provide a detailed tutorial about the installation and the basic usage of MuseTalk for new users:
+## Installation
+To prepare the Python environment and install additional packages such as opencv, diffusers, mmcv, etc., please follow the steps below:
+### Build environment
+
+We recommend a python version >=3.10 and cuda version =11.7. Then build environment as follows:
+
+```shell
+pip install -r requirements.txt
+```
+### whisper
+install whisper to extract audio feature (only encoder)
+```
+pip install --editable ./musetalk/whisper
+```
+
+### mmlab packages
+```bash
+pip install --no-cache-dir -U openmim 
+mim install mmengine 
+mim install "mmcv>=2.0.1" 
+mim install "mmdet>=3.1.0" 
+mim install "mmpose>=1.1.0" 
+```
+
+### Download ffmpeg-static
+Download the ffmpeg-static and
+```
+export FFMPEG_PATH=/path/to/ffmpeg
+```
+for example:
+```
+export FFMPEG_PATH=/musetalk/ffmpeg-4.4-amd64-static
+```
+### Download weights
+You can download weights manually as follows:
+
+1. Download our trained [weights](https://huggingface.co/TMElyralab/MuseTalk).
+
+2. Download the weights of other components:
+   - [sd-vae-ft-mse](https://huggingface.co/stabilityai/sd-vae-ft-mse)
+   - [whisper](https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt)
+   - [dwpose](https://huggingface.co/yzd-v/DWPose/tree/main)
+   - [face-parse-bisent](https://github.com/zllrunning/face-parsing.PyTorch)
+   - [resnet18](https://download.pytorch.org/models/resnet18-5c106cde.pth)
+
+
+Finally, these weights should be organized in `models` as follows:
+```
+./models/
+├── musetalk
+│   └── musetalk.json
+│   └── pytorch_model.bin
+├── dwpose
+│   └── dw-ll_ucoco_384.pth
+├── face-parse-bisent
+│   ├── 79999_iter.pth
+│   └── resnet18-5c106cde.pth
+├── sd-vae-ft-mse
+│   ├── config.json
+│   └── diffusion_pytorch_model.bin
+└── whisper
+    └── tiny.pt
+```
+## Quickstart
+
+### Inference
+Here, we provide the inference script. 
+```
+python -m scripts.inference --inference_config configs/inference/test.yaml 
+```
+configs/inference/test.yaml is the path to the inference configuration file, including video_path and audio_path.
+The video_path should be either a video file or a directory of images.
+
+#### Use of bbox_shift to have adjustable results
+:mag_right: We have found that upper-bound of the mask has an important impact on mouth openness. Thus, to control the mask region, we suggest using the `bbox_shift` parameter. Positive values (moving towards the lower half) increase mouth openness, while negative values (moving towards the upper half) decrease mouth openness.
+
+You can start by running with the default configuration to obtain the adjustable value range, and then re-run the script within this range. 
+
+For example, in the case of `Xinying Sun`, after running the default configuration, it shows that the adjustable value rage is [-9, 9]. Then, to decrease the mouth openness, we set the value to be `-7`. 
+```
+python -m scripts.inference --inference_config configs/inference/test.yaml --bbox_shift -7 
+```
+:pushpin: More technical details can be found in [bbox_shift](assets/BBOX_SHIFT.md).
+
+#### Combining MuseV and MuseTalk
+
+You are suggested to first apply [MuseV](https://github.com/TMElyralab/MuseV) to generate a video by referring [this](https://github.com/TMElyralab/MuseV?tab=readme-ov-file#text2video). Then, you can use `MuseTalk` by referring [this]().
+
+# Note
+
+If you want to launch online video chats, you are suggested to generate videos using MuseV and apply necessary pre-processing such as face detection in advance. During online chatting, only UNet and the VAE decoder are involved, which makes MuseTalk real-time.
+
+
+# Acknowledgement
+1. We thank open-source components like [whisper](https://github.com/isaacOnline/whisper/tree/extract-embeddings), [dwpose](https://github.com/IDEA-Research/DWPose), [face-alignment](https://github.com/1adrianb/face-alignment), [face-parsing](https://github.com/zllrunning/face-parsing.PyTorch), [S3FD](https://github.com/yxlijun/S3FD.pytorch). 
+1. MuseTalk has referred much to [diffusers](https://github.com/huggingface/diffusers).
+1. MuseTalk has been built on `HDTF` datasets.
+
+Thanks for open-sourcing!
+
+# Limitations
+- Resolution: Though MuseTalk uses a face region size of 256 x 256, which make it better than other open-source methods, it has not yet reached the theoretical resolution bound. We will continue to deal with this problem.  
+If you need higher resolution, you could apply super resolution models such as [GFPGAN](https://github.com/TencentARC/GFPGAN) in combination with MuseTalk.
+
+- Identity preservation: Some details of the original face are not well preserved, such as mustache, lip shape and color.
+
+- Jitter: There exists some jitter as the current pipeline adopts single-frame generation.
+
+# Citation
+```bib
+@article{musetalk,
+  title={MuseTalk: Real-Time High Quality Lip Synchorization with Latent Space Inpainting},
+  author={Zhang, Yue and Liu, Minhao and Chen, Zhaokang and Wu, Bin and He, Yingjie and Zhan, Chao and Zhou, Wenjiang},
+  journal={arxiv},
+  year={2024}
+}
+```
+# Disclaimer/License
+1. `code`: The code of MuseTalk is released under the MIT License. There is no limitation for both academic and commercial usage.
+1. `model`: The trained model are available for any purpose, even commercially.
+1. `other opensource model`: Other open-source models used must comply with their license, such as `whisper`, `ft-mse-vae`, `dwpose`, `S3FD`, etc..
+1. The testdata are collected from internet, which are available for non-commercial research purposes only.
+1. `AIGC`: This project strives to impact the domain of AI-driven video generation positively. Users are granted the freedom to create videos using this tool, but they are expected to comply with local laws and utilize it responsibly. The developers do not assume any responsibility for potential misuse by users.
diff --git a/assets/BBOX_SHIFT.md b/assets/BBOX_SHIFT.md
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--- /dev/null
+++ b/assets/BBOX_SHIFT.md
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+## Why is there a "bbox_shift" parameter?
+When processing training data, we utilize the combination of face detection results (bbox) and facial landmarks to determine the region of the head segmentation box. Specifically, we use the upper bound of the bbox as the upper boundary of the segmentation box, the maximum y value of the facial landmarks coordinates as the lower boundary of the segmentation box, and the minimum and maximum x values of the landmarks coordinates as the left and right boundaries of the segmentation box. By processing the dataset in this way, we can ensure the integrity of the face.
+
+However, we have observed that the masked ratio on the face varies across different images due to the varying face shapes of subjects. Furthermore, we found that the upper-bound of the mask mainly lies close to the 27th, 28th and 30th landmark points (as shown in Fig.1), which correspond to proportions of 15%, 63%, and 22% in the dataset, respectively.
+
+During the inference process, we discovered that as the upper-bound of the mask gets closer to the mouth (30th), the audio features contribute more to lip motion. Conversely, as the upper-bound of the mask moves away from the mouth (28th), the audio features contribute more to generating details of facial disappearance. Hence, we define this characteristic as a parameter that can adjust the effect of generating mouth shapes, which users can adjust according to their needs in practical scenarios.
+
+![landmark](figs/landmark_ref.png)
+
+Fig.1. Facial landmarks
+### Step 0.
+Running with the default configuration to obtain the adjustable value range, and then re-run the script within this range. 
+```
+python -m scripts.inference --inference_config configs/inference/test.yaml 
+```
+```
+********************************************bbox_shift parameter adjustment**********************************************************
+Total frame:「838」 Manually adjust range : [ -9~9 ] , the current value: 0
+*************************************************************************************************************************************
+```
+### Step 1.
+re-run the script within the above range. 
+```
+python -m scripts.inference --inference_config configs/inference/test.yaml --bbox_shift xx  # where xx is in [-9, 9].
+```
+In our experimental observations, we found that positive values (moving towards the lower half) generally increase mouth openness, while negative values (moving towards the upper half) generally decrease mouth openness. However, it's important to note that this is not an absolute rule, and users may need to adjust the parameter according to their specific needs and the desired effect.
\ No newline at end of file
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diff --git a/configs/inference/test.yaml b/configs/inference/test.yaml
new file mode 100644
index 0000000..0aa6d76
--- /dev/null
+++ b/configs/inference/test.yaml
@@ -0,0 +1,9 @@
+task_0:
+ video_path: "data/video/monalisa.mp4"
+ audio_path: "data/audio/monalisa.wav"
+
+task_1:
+ video_path: "data/video/sun.mp4"
+ audio_path: "data/audio/sun.wav"
+  
+
diff --git a/data/audio/monalisa.wav b/data/audio/monalisa.wav
new file mode 100644
index 0000000000000000000000000000000000000000..bf7328e531296c8bc3562d67a74c7d645979a744
GIT binary patch
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diff --git a/musetalk/models/unet.py b/musetalk/models/unet.py
new file mode 100755
index 0000000..02a9408
--- /dev/null
+++ b/musetalk/models/unet.py
@@ -0,0 +1,47 @@
+import torch
+import torch.nn as nn
+import math
+import json
+
+from diffusers import UNet2DConditionModel
+import sys
+import time
+import numpy as np
+import os
+
+class PositionalEncoding(nn.Module):
+    def __init__(self, d_model=384, max_len=5000):
+        super(PositionalEncoding, self).__init__()
+        pe = torch.zeros(max_len, d_model)
+        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
+        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+        pe = pe.unsqueeze(0)
+        self.register_buffer('pe', pe)
+
+    def forward(self, x):
+        b, seq_len, d_model = x.size()
+        pe = self.pe[:, :seq_len, :]
+        x = x + pe.to(x.device)
+        return x
+    
+class UNet():
+    def __init__(self, 
+                 unet_config,
+                 model_path,
+                 use_float16=False,
+        ):
+        with open(unet_config, 'r') as f:
+            unet_config = json.load(f)
+        self.model = UNet2DConditionModel(**unet_config)
+        self.pe = PositionalEncoding(d_model=384)
+        self.weights = torch.load(model_path)
+        self.model.load_state_dict(self.weights)
+        if use_float16:
+            self.model = self.model.half()
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.model.to(self.device)
+    
+if __name__ == "__main__":
+    unet = UNet()
\ No newline at end of file
diff --git a/musetalk/models/vae.py b/musetalk/models/vae.py
new file mode 100755
index 0000000..51efef4
--- /dev/null
+++ b/musetalk/models/vae.py
@@ -0,0 +1,148 @@
+from diffusers import AutoencoderKL
+import torch
+import torchvision.transforms as transforms
+import torch.nn.functional as F
+import cv2
+import numpy as np
+from PIL import Image
+import os
+
+class VAE():
+    """
+    VAE (Variational Autoencoder) class for image processing.
+    """
+
+    def __init__(self, model_path="./models/sd-vae-ft-mse/", resized_img=256, use_float16=False):
+        """
+        Initialize the VAE instance.
+
+        :param model_path: Path to the trained model.
+        :param resized_img: The size to which images are resized.
+        :param use_float16: Whether to use float16 precision.
+        """
+        self.model_path = model_path
+        self.vae = AutoencoderKL.from_pretrained(self.model_path)
+
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.vae.to(self.device)
+
+        if use_float16:
+            self.vae = self.vae.half()
+            self._use_float16 = True
+        else:
+            self._use_float16 = False
+
+        self.scaling_factor = self.vae.config.scaling_factor
+        self.transform = transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+        self._resized_img = resized_img
+        self._mask_tensor = self.get_mask_tensor()
+        
+    def get_mask_tensor(self):
+        """
+        Creates a mask tensor for image processing.
+        :return: A mask tensor.
+        """
+        mask_tensor = torch.zeros((self._resized_img,self._resized_img))
+        mask_tensor[:self._resized_img//2,:] = 1
+        mask_tensor[mask_tensor< 0.5] = 0
+        mask_tensor[mask_tensor>= 0.5] = 1
+        return mask_tensor
+            
+    def preprocess_img(self,img_name,half_mask=False):
+        """
+        Preprocess an image for the VAE.
+
+        :param img_name: The image file path or a list of image file paths.
+        :param half_mask: Whether to apply a half mask to the image.
+        :return: A preprocessed image tensor.
+        """
+        window = []
+        if isinstance(img_name, str):
+            window_fnames = [img_name]
+            for fname in window_fnames:
+                img = cv2.imread(fname)
+                img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+                img = cv2.resize(img, (self._resized_img, self._resized_img),
+                                     interpolation=cv2.INTER_LANCZOS4)
+                window.append(img)
+        else:
+            img = cv2.cvtColor(img_name, cv2.COLOR_BGR2RGB)
+            window.append(img)
+            
+        x = np.asarray(window) / 255.
+        x = np.transpose(x, (3, 0, 1, 2))
+        x = torch.squeeze(torch.FloatTensor(x))
+        if half_mask:
+            x = x * (self._mask_tensor>0.5)
+        x = self.transform(x)
+        
+        x = x.unsqueeze(0) # [1, 3, 256, 256] torch tensor
+        x = x.to(self.vae.device)
+
+        return x
+
+    def encode_latents(self,image):
+        """
+        Encode an image into latent variables.
+
+        :param image: The image tensor to encode.
+        :return: The encoded latent variables.
+        """
+        with torch.no_grad():
+            init_latent_dist = self.vae.encode(image.to(self.vae.dtype)).latent_dist
+        init_latents = self.scaling_factor * init_latent_dist.sample()
+        return init_latents
+    
+    def decode_latents(self, latents):
+        """
+        Decode latent variables back into an image.
+        :param latents: The latent variables to decode.
+        :return: A NumPy array representing the decoded image.
+        """
+        latents = (1/  self.scaling_factor) * latents
+        image = self.vae.decode(latents.to(self.vae.dtype)).sample
+        image = (image / 2 + 0.5).clamp(0, 1)
+        image = image.detach().cpu().permute(0, 2, 3, 1).float().numpy()
+        image = (image * 255).round().astype("uint8")
+        image = image[...,::-1] # RGB to BGR
+        return image
+    
+    def get_latents_for_unet(self,img):
+        """
+        Prepare latent variables for a U-Net model.
+        :param img: The image to process.
+        :return: A concatenated tensor of latents for U-Net input.
+        """
+        
+        ref_image = self.preprocess_img(img,half_mask=True) # [1, 3, 256, 256] RGB, torch tensor
+        masked_latents = self.encode_latents(ref_image) # [1, 4, 32, 32], torch tensor
+        ref_image = self.preprocess_img(img,half_mask=False) # [1, 3, 256, 256] RGB, torch tensor
+        ref_latents = self.encode_latents(ref_image) # [1, 4, 32, 32], torch tensor
+        latent_model_input = torch.cat([masked_latents, ref_latents], dim=1)
+        return latent_model_input
+
+if __name__ == "__main__":
+    vae_mode_path = "./models/sd-vae-ft-mse/"
+    vae = VAE(model_path = vae_mode_path,use_float16=False)
+    img_path = "./results/sun001_crop/00000.png"
+    
+    crop_imgs_path = "./results/sun001_crop/"
+    latents_out_path = "./results/latents/"
+    if not os.path.exists(latents_out_path):
+        os.mkdir(latents_out_path)
+
+    files = os.listdir(crop_imgs_path)
+    files.sort()
+    files = [file for file in files if file.split(".")[-1] == "png"]
+
+    for file in files:
+        index = file.split(".")[0]
+        img_path = crop_imgs_path + file
+        latents = vae.get_latents_for_unet(img_path)
+        print(img_path,"latents",latents.size())
+        #torch.save(latents,os.path.join(latents_out_path,index+".pt"))
+        #reload_tensor = torch.load('tensor.pt')
+        #print(reload_tensor.size())
+        
+
+    
\ No newline at end of file
diff --git a/musetalk/utils/__init__.py b/musetalk/utils/__init__.py
new file mode 100644
index 0000000..b58c41d
--- /dev/null
+++ b/musetalk/utils/__init__.py
@@ -0,0 +1,5 @@
+import sys
+from os.path import abspath, dirname
+current_dir = dirname(abspath(__file__))
+parent_dir = dirname(current_dir)
+sys.path.append(parent_dir+'/utils')
diff --git a/musetalk/utils/blending.py b/musetalk/utils/blending.py
new file mode 100644
index 0000000..d9d8930
--- /dev/null
+++ b/musetalk/utils/blending.py
@@ -0,0 +1,60 @@
+from PIL import Image
+import numpy as np
+import cv2
+from face_parsing import FaceParsing
+
+fp = FaceParsing()
+
+def get_crop_box(box, expand):
+    x, y, x1, y1 = box
+    x_c, y_c = (x+x1)//2, (y+y1)//2
+    w, h = x1-x, y1-y
+    s = int(max(w, h)//2*expand)
+    crop_box = [x_c-s, y_c-s, x_c+s, y_c+s]
+    return crop_box, s
+
+def face_seg(image):
+    seg_image = fp(image)
+    if seg_image is None:
+        print("error, no person_segment")
+        return None
+
+    seg_image = seg_image.resize(image.size)
+    return seg_image
+
+def get_image(image,face,face_box,upper_boundary_ratio = 0.5,expand=1.2):
+    #print(image.shape)
+    #print(face.shape)
+    
+    body = Image.fromarray(image[:,:,::-1])
+    face = Image.fromarray(face[:,:,::-1])
+
+    x, y, x1, y1 = face_box 
+    #print(x1-x,y1-y)
+    crop_box, s = get_crop_box(face_box, expand)
+    x_s, y_s, x_e, y_e = crop_box
+    face_position = (x, y)
+
+    face_large = body.crop(crop_box)
+    ori_shape = face_large.size
+
+    mask_image = face_seg(face_large)
+    mask_small = mask_image.crop((x-x_s, y-y_s, x1-x_s, y1-y_s))
+    mask_image = Image.new('L', ori_shape, 0)
+    mask_image.paste(mask_small, (x-x_s, y-y_s, x1-x_s, y1-y_s))
+
+    # keep upper_boundary_ratio of talking area
+    width, height = mask_image.size
+    top_boundary = int(height * upper_boundary_ratio)
+    modified_mask_image = Image.new('L', ori_shape, 0)
+    modified_mask_image.paste(mask_image.crop((0, top_boundary, width, height)), (0, top_boundary))
+
+    blur_kernel_size = int(0.1 * ori_shape[0] // 2 * 2) + 1
+    mask_array = cv2.GaussianBlur(np.array(modified_mask_image), (blur_kernel_size, blur_kernel_size), 0)
+    mask_image = Image.fromarray(mask_array)
+    mask_image.save("./debug_mask.png")
+    
+    face_large.paste(face, (x-x_s, y-y_s, x1-x_s, y1-y_s))
+    body.paste(face_large, crop_box[:2], mask_image)
+    body = np.array(body)
+    return body[:,:,::-1]
diff --git a/musetalk/utils/dwpose/default_runtime.py b/musetalk/utils/dwpose/default_runtime.py
new file mode 100644
index 0000000..6f27c03
--- /dev/null
+++ b/musetalk/utils/dwpose/default_runtime.py
@@ -0,0 +1,54 @@
+default_scope = 'mmpose'
+
+# hooks
+default_hooks = dict(
+    timer=dict(type='IterTimerHook'),
+    logger=dict(type='LoggerHook', interval=50),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    checkpoint=dict(type='CheckpointHook', interval=10),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    visualization=dict(type='PoseVisualizationHook', enable=False),
+    badcase=dict(
+        type='BadCaseAnalysisHook',
+        enable=False,
+        out_dir='badcase',
+        metric_type='loss',
+        badcase_thr=5))
+
+# custom hooks
+custom_hooks = [
+    # Synchronize model buffers such as running_mean and running_var in BN
+    # at the end of each epoch
+    dict(type='SyncBuffersHook')
+]
+
+# multi-processing backend
+env_cfg = dict(
+    cudnn_benchmark=False,
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
+    dist_cfg=dict(backend='nccl'),
+)
+
+# visualizer
+vis_backends = [
+    dict(type='LocalVisBackend'),
+    # dict(type='TensorboardVisBackend'),
+    # dict(type='WandbVisBackend'),
+]
+visualizer = dict(
+    type='PoseLocalVisualizer', vis_backends=vis_backends, name='visualizer')
+
+# logger
+log_processor = dict(
+    type='LogProcessor', window_size=50, by_epoch=True, num_digits=6)
+log_level = 'INFO'
+load_from = None
+resume = False
+
+# file I/O backend
+backend_args = dict(backend='local')
+
+# training/validation/testing progress
+train_cfg = dict(by_epoch=True)
+val_cfg = dict()
+test_cfg = dict()
diff --git a/musetalk/utils/dwpose/rtmpose-l_8xb32-270e_coco-ubody-wholebody-384x288.py b/musetalk/utils/dwpose/rtmpose-l_8xb32-270e_coco-ubody-wholebody-384x288.py
new file mode 100644
index 0000000..293efad
--- /dev/null
+++ b/musetalk/utils/dwpose/rtmpose-l_8xb32-270e_coco-ubody-wholebody-384x288.py
@@ -0,0 +1,257 @@
+#_base_ = ['../../../_base_/default_runtime.py']
+_base_ = ['default_runtime.py']
+
+# runtime
+max_epochs = 270
+stage2_num_epochs = 30
+base_lr = 4e-3
+train_batch_size = 32
+val_batch_size = 32
+
+train_cfg = dict(max_epochs=max_epochs, val_interval=10)
+randomness = dict(seed=21)
+
+# optimizer
+optim_wrapper = dict(
+    type='OptimWrapper',
+    optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05),
+    paramwise_cfg=dict(
+        norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))
+
+# learning rate
+param_scheduler = [
+    dict(
+        type='LinearLR',
+        start_factor=1.0e-5,
+        by_epoch=False,
+        begin=0,
+        end=1000),
+    dict(
+        # use cosine lr from 150 to 300 epoch
+        type='CosineAnnealingLR',
+        eta_min=base_lr * 0.05,
+        begin=max_epochs // 2,
+        end=max_epochs,
+        T_max=max_epochs // 2,
+        by_epoch=True,
+        convert_to_iter_based=True),
+]
+
+# automatically scaling LR based on the actual training batch size
+auto_scale_lr = dict(base_batch_size=512)
+
+# codec settings
+codec = dict(
+    type='SimCCLabel',
+    input_size=(288, 384),
+    sigma=(6., 6.93),
+    simcc_split_ratio=2.0,
+    normalize=False,
+    use_dark=False)
+
+# model settings
+model = dict(
+    type='TopdownPoseEstimator',
+    data_preprocessor=dict(
+        type='PoseDataPreprocessor',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        bgr_to_rgb=True),
+    backbone=dict(
+        _scope_='mmdet',
+        type='CSPNeXt',
+        arch='P5',
+        expand_ratio=0.5,
+        deepen_factor=1.,
+        widen_factor=1.,
+        out_indices=(4, ),
+        channel_attention=True,
+        norm_cfg=dict(type='SyncBN'),
+        act_cfg=dict(type='SiLU'),
+        init_cfg=dict(
+            type='Pretrained',
+            prefix='backbone.',
+            checkpoint='https://download.openmmlab.com/mmpose/v1/projects/'
+            'rtmpose/cspnext-l_udp-aic-coco_210e-256x192-273b7631_20230130.pth'  # noqa: E501
+        )),
+    head=dict(
+        type='RTMCCHead',
+        in_channels=1024,
+        out_channels=133,
+        input_size=codec['input_size'],
+        in_featuremap_size=(9, 12),
+        simcc_split_ratio=codec['simcc_split_ratio'],
+        final_layer_kernel_size=7,
+        gau_cfg=dict(
+            hidden_dims=256,
+            s=128,
+            expansion_factor=2,
+            dropout_rate=0.,
+            drop_path=0.,
+            act_fn='SiLU',
+            use_rel_bias=False,
+            pos_enc=False),
+        loss=dict(
+            type='KLDiscretLoss',
+            use_target_weight=True,
+            beta=10.,
+            label_softmax=True),
+        decoder=codec),
+    test_cfg=dict(flip_test=True, ))
+
+# base dataset settings
+dataset_type = 'UBody2dDataset'
+data_mode = 'topdown'
+data_root = 'data/UBody/'
+
+backend_args = dict(backend='local')
+
+scenes = [
+    'Magic_show', 'Entertainment', 'ConductMusic', 'Online_class', 'TalkShow',
+    'Speech', 'Fitness', 'Interview', 'Olympic', 'TVShow', 'Singing',
+    'SignLanguage', 'Movie', 'LiveVlog', 'VideoConference'
+]
+
+train_datasets = [
+    dict(
+        type='CocoWholeBodyDataset',
+        data_root='data/coco/',
+        data_mode=data_mode,
+        ann_file='annotations/coco_wholebody_train_v1.0.json',
+        data_prefix=dict(img='train2017/'),
+        pipeline=[])
+]
+
+for scene in scenes:
+    train_dataset = dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_mode=data_mode,
+        ann_file=f'annotations/{scene}/train_annotations.json',
+        data_prefix=dict(img='images/'),
+        pipeline=[],
+        sample_interval=10)
+    train_datasets.append(train_dataset)
+
+# pipelines
+train_pipeline = [
+    dict(type='LoadImage', backend_args=backend_args),
+    dict(type='GetBBoxCenterScale'),
+    dict(type='RandomFlip', direction='horizontal'),
+    dict(type='RandomHalfBody'),
+    dict(
+        type='RandomBBoxTransform', scale_factor=[0.5, 1.5], rotate_factor=90),
+    dict(type='TopdownAffine', input_size=codec['input_size']),
+    dict(type='mmdet.YOLOXHSVRandomAug'),
+    dict(
+        type='Albumentation',
+        transforms=[
+            dict(type='Blur', p=0.1),
+            dict(type='MedianBlur', p=0.1),
+            dict(
+                type='CoarseDropout',
+                max_holes=1,
+                max_height=0.4,
+                max_width=0.4,
+                min_holes=1,
+                min_height=0.2,
+                min_width=0.2,
+                p=1.0),
+        ]),
+    dict(type='GenerateTarget', encoder=codec),
+    dict(type='PackPoseInputs')
+]
+val_pipeline = [
+    dict(type='LoadImage', backend_args=backend_args),
+    dict(type='GetBBoxCenterScale'),
+    dict(type='TopdownAffine', input_size=codec['input_size']),
+    dict(type='PackPoseInputs')
+]
+
+train_pipeline_stage2 = [
+    dict(type='LoadImage', backend_args=backend_args),
+    dict(type='GetBBoxCenterScale'),
+    dict(type='RandomFlip', direction='horizontal'),
+    dict(type='RandomHalfBody'),
+    dict(
+        type='RandomBBoxTransform',
+        shift_factor=0.,
+        scale_factor=[0.5, 1.5],
+        rotate_factor=90),
+    dict(type='TopdownAffine', input_size=codec['input_size']),
+    dict(type='mmdet.YOLOXHSVRandomAug'),
+    dict(
+        type='Albumentation',
+        transforms=[
+            dict(type='Blur', p=0.1),
+            dict(type='MedianBlur', p=0.1),
+            dict(
+                type='CoarseDropout',
+                max_holes=1,
+                max_height=0.4,
+                max_width=0.4,
+                min_holes=1,
+                min_height=0.2,
+                min_width=0.2,
+                p=0.5),
+        ]),
+    dict(type='GenerateTarget', encoder=codec),
+    dict(type='PackPoseInputs')
+]
+
+# data loaders
+train_dataloader = dict(
+    batch_size=train_batch_size,
+    num_workers=10,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dict(
+        type='CombinedDataset',
+        metainfo=dict(from_file='configs/_base_/datasets/coco_wholebody.py'),
+        datasets=train_datasets,
+        pipeline=train_pipeline,
+        test_mode=False,
+    ))
+
+val_dataloader = dict(
+    batch_size=val_batch_size,
+    num_workers=10,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False, round_up=False),
+    dataset=dict(
+        type='CocoWholeBodyDataset',
+        data_root=data_root,
+        data_mode=data_mode,
+        ann_file='data/coco/annotations/coco_wholebody_val_v1.0.json',
+        bbox_file='data/coco/person_detection_results/'
+        'COCO_val2017_detections_AP_H_56_person.json',
+        data_prefix=dict(img='coco/val2017/'),
+        test_mode=True,
+        pipeline=val_pipeline,
+    ))
+test_dataloader = val_dataloader
+
+# hooks
+default_hooks = dict(
+    checkpoint=dict(
+        save_best='coco-wholebody/AP', rule='greater', max_keep_ckpts=1))
+
+custom_hooks = [
+    dict(
+        type='EMAHook',
+        ema_type='ExpMomentumEMA',
+        momentum=0.0002,
+        update_buffers=True,
+        priority=49),
+    dict(
+        type='mmdet.PipelineSwitchHook',
+        switch_epoch=max_epochs - stage2_num_epochs,
+        switch_pipeline=train_pipeline_stage2)
+]
+
+# evaluators
+val_evaluator = dict(
+    type='CocoWholeBodyMetric',
+    ann_file='data/coco/annotations/coco_wholebody_val_v1.0.json')
+test_evaluator = val_evaluator
diff --git a/musetalk/utils/face_detection/README.md b/musetalk/utils/face_detection/README.md
new file mode 100644
index 0000000..c073376
--- /dev/null
+++ b/musetalk/utils/face_detection/README.md
@@ -0,0 +1 @@
+The code for Face Detection in this folder has been taken from the wonderful [face_alignment](https://github.com/1adrianb/face-alignment) repository. This has been modified to take batches of faces at a time. 
\ No newline at end of file
diff --git a/musetalk/utils/face_detection/__init__.py b/musetalk/utils/face_detection/__init__.py
new file mode 100644
index 0000000..31c3e22
--- /dev/null
+++ b/musetalk/utils/face_detection/__init__.py
@@ -0,0 +1,7 @@
+# -*- coding: utf-8 -*-
+
+__author__ = """Adrian Bulat"""
+__email__ = 'adrian.bulat@nottingham.ac.uk'
+__version__ = '1.0.1'
+
+from .api import FaceAlignment, LandmarksType, NetworkSize, YOLOv8_face
diff --git a/musetalk/utils/face_detection/api.py b/musetalk/utils/face_detection/api.py
new file mode 100644
index 0000000..0a6a8d6
--- /dev/null
+++ b/musetalk/utils/face_detection/api.py
@@ -0,0 +1,240 @@
+from __future__ import print_function
+import os
+import torch
+from torch.utils.model_zoo import load_url
+from enum import Enum
+import numpy as np
+import cv2
+try:
+    import urllib.request as request_file
+except BaseException:
+    import urllib as request_file
+
+from .models import FAN, ResNetDepth
+from .utils import *
+
+
+class LandmarksType(Enum):
+    """Enum class defining the type of landmarks to detect.
+
+    ``_2D`` - the detected points ``(x,y)`` are detected in a 2D space and follow the visible contour of the face
+    ``_2halfD`` - this points represent the projection of the 3D points into 3D
+    ``_3D`` - detect the points ``(x,y,z)``` in a 3D space
+
+    """
+    _2D = 1
+    _2halfD = 2
+    _3D = 3
+
+
+class NetworkSize(Enum):
+    # TINY = 1
+    # SMALL = 2
+    # MEDIUM = 3
+    LARGE = 4
+
+    def __new__(cls, value):
+        member = object.__new__(cls)
+        member._value_ = value
+        return member
+
+    def __int__(self):
+        return self.value
+
+
+
+class FaceAlignment:
+    def __init__(self, landmarks_type, network_size=NetworkSize.LARGE,
+                 device='cuda', flip_input=False, face_detector='sfd', verbose=False):
+        self.device = device
+        self.flip_input = flip_input
+        self.landmarks_type = landmarks_type
+        self.verbose = verbose
+
+        network_size = int(network_size)
+
+        if 'cuda' in device:
+            torch.backends.cudnn.benchmark = True
+#             torch.backends.cuda.matmul.allow_tf32 = False
+#             torch.backends.cudnn.benchmark = True
+#             torch.backends.cudnn.deterministic = False
+#             torch.backends.cudnn.allow_tf32 = True
+            print('cuda start')
+
+
+        # Get the face detector
+        face_detector_module = __import__('face_detection.detection.' + face_detector,
+                                          globals(), locals(), [face_detector], 0)
+        
+        self.face_detector = face_detector_module.FaceDetector(device=device, verbose=verbose)
+
+    def get_detections_for_batch(self, images):
+        images = images[..., ::-1]
+        detected_faces = self.face_detector.detect_from_batch(images.copy())
+        results = []
+
+        for i, d in enumerate(detected_faces):
+            if len(d) == 0:
+                results.append(None)
+                continue
+            d = d[0]
+            d = np.clip(d, 0, None)
+            
+            x1, y1, x2, y2 = map(int, d[:-1])
+            results.append((x1, y1, x2, y2))
+
+        return results
+    
+    
+class YOLOv8_face:
+    def __init__(self, path = 'face_detection/weights/yolov8n-face.onnx', conf_thres=0.2, iou_thres=0.5):
+        self.conf_threshold = conf_thres
+        self.iou_threshold = iou_thres
+        self.class_names = ['face']
+        self.num_classes = len(self.class_names)
+        # Initialize model
+        self.net = cv2.dnn.readNet(path)
+        self.input_height = 640
+        self.input_width = 640
+        self.reg_max = 16
+
+        self.project = np.arange(self.reg_max)
+        self.strides = (8, 16, 32)
+        self.feats_hw = [(math.ceil(self.input_height / self.strides[i]), math.ceil(self.input_width / self.strides[i])) for i in range(len(self.strides))]
+        self.anchors = self.make_anchors(self.feats_hw)
+
+    def make_anchors(self, feats_hw, grid_cell_offset=0.5):
+        """Generate anchors from features."""
+        anchor_points = {}
+        for i, stride in enumerate(self.strides):
+            h,w = feats_hw[i]
+            x = np.arange(0, w) + grid_cell_offset  # shift x
+            y = np.arange(0, h) + grid_cell_offset  # shift y
+            sx, sy = np.meshgrid(x, y)
+            # sy, sx = np.meshgrid(y, x)
+            anchor_points[stride] = np.stack((sx, sy), axis=-1).reshape(-1, 2)
+        return anchor_points
+
+    def softmax(self, x, axis=1):
+        x_exp = np.exp(x)
+        # 如果是列向量，则axis=0
+        x_sum = np.sum(x_exp, axis=axis, keepdims=True)
+        s = x_exp / x_sum
+        return s
+    
+    def resize_image(self, srcimg, keep_ratio=True):
+        top, left, newh, neww = 0, 0, self.input_width, self.input_height
+        if keep_ratio and srcimg.shape[0] != srcimg.shape[1]:
+            hw_scale = srcimg.shape[0] / srcimg.shape[1]
+            if hw_scale > 1:
+                newh, neww = self.input_height, int(self.input_width / hw_scale)
+                img = cv2.resize(srcimg, (neww, newh), interpolation=cv2.INTER_AREA)
+                left = int((self.input_width - neww) * 0.5)
+                img = cv2.copyMakeBorder(img, 0, 0, left, self.input_width - neww - left, cv2.BORDER_CONSTANT,
+                                         value=(0, 0, 0))  # add border
+            else:
+                newh, neww = int(self.input_height * hw_scale), self.input_width
+                img = cv2.resize(srcimg, (neww, newh), interpolation=cv2.INTER_AREA)
+                top = int((self.input_height - newh) * 0.5)
+                img = cv2.copyMakeBorder(img, top, self.input_height - newh - top, 0, 0, cv2.BORDER_CONSTANT,
+                                         value=(0, 0, 0))
+        else:
+            img = cv2.resize(srcimg, (self.input_width, self.input_height), interpolation=cv2.INTER_AREA)
+        return img, newh, neww, top, left
+
+    def detect(self, srcimg):
+        input_img, newh, neww, padh, padw = self.resize_image(cv2.cvtColor(srcimg, cv2.COLOR_BGR2RGB))
+        scale_h, scale_w = srcimg.shape[0]/newh, srcimg.shape[1]/neww
+        input_img = input_img.astype(np.float32) / 255.0
+
+        blob = cv2.dnn.blobFromImage(input_img)
+        self.net.setInput(blob)
+        outputs = self.net.forward(self.net.getUnconnectedOutLayersNames())
+        # if isinstance(outputs, tuple):
+        #     outputs = list(outputs)
+        # if float(cv2.__version__[:3])>=4.7:
+        #     outputs = [outputs[2], outputs[0], outputs[1]] ###opencv4.7需要这一步，opencv4.5不需要
+        # Perform inference on the image
+        det_bboxes, det_conf, det_classid, landmarks = self.post_process(outputs, scale_h, scale_w, padh, padw)
+        return det_bboxes, det_conf, det_classid, landmarks
+
+    def post_process(self, preds, scale_h, scale_w, padh, padw):
+        bboxes, scores, landmarks = [], [], []
+        for i, pred in enumerate(preds):
+            stride = int(self.input_height/pred.shape[2])
+            pred = pred.transpose((0, 2, 3, 1))
+            
+            box = pred[..., :self.reg_max * 4]
+            cls = 1 / (1 + np.exp(-pred[..., self.reg_max * 4:-15])).reshape((-1,1))
+            kpts = pred[..., -15:].reshape((-1,15)) ### x1,y1,score1, ..., x5,y5,score5
+
+            # tmp = box.reshape(self.feats_hw[i][0], self.feats_hw[i][1], 4, self.reg_max)
+            tmp = box.reshape(-1, 4, self.reg_max)
+            bbox_pred = self.softmax(tmp, axis=-1)
+            bbox_pred = np.dot(bbox_pred, self.project).reshape((-1,4))
+
+            bbox = self.distance2bbox(self.anchors[stride], bbox_pred, max_shape=(self.input_height, self.input_width)) * stride
+            kpts[:, 0::3] = (kpts[:, 0::3] * 2.0 + (self.anchors[stride][:, 0].reshape((-1,1)) - 0.5)) * stride
+            kpts[:, 1::3] = (kpts[:, 1::3] * 2.0 + (self.anchors[stride][:, 1].reshape((-1,1)) - 0.5)) * stride
+            kpts[:, 2::3] = 1 / (1+np.exp(-kpts[:, 2::3]))
+
+            bbox -= np.array([[padw, padh, padw, padh]])  ###合理使用广播法则
+            bbox *= np.array([[scale_w, scale_h, scale_w, scale_h]])
+            kpts -= np.tile(np.array([padw, padh, 0]), 5).reshape((1,15))
+            kpts *= np.tile(np.array([scale_w, scale_h, 1]), 5).reshape((1,15))
+
+            bboxes.append(bbox)
+            scores.append(cls)
+            landmarks.append(kpts)
+
+        bboxes = np.concatenate(bboxes, axis=0)
+        scores = np.concatenate(scores, axis=0)
+        landmarks = np.concatenate(landmarks, axis=0)
+    
+        bboxes_wh = bboxes.copy()
+        bboxes_wh[:, 2:4] = bboxes[:, 2:4] - bboxes[:, 0:2]  ####xywh
+        classIds = np.argmax(scores, axis=1)
+        confidences = np.max(scores, axis=1)  ####max_class_confidence
+        
+        mask = confidences>self.conf_threshold
+        bboxes_wh = bboxes_wh[mask]  ###合理使用广播法则
+        confidences = confidences[mask]
+        classIds = classIds[mask]
+        landmarks = landmarks[mask]
+        
+        indices = cv2.dnn.NMSBoxes(bboxes_wh.tolist(), confidences.tolist(), self.conf_threshold,
+                                   self.iou_threshold).flatten()
+        if len(indices) > 0:
+            mlvl_bboxes = bboxes_wh[indices]
+            confidences = confidences[indices]
+            classIds = classIds[indices]
+            landmarks = landmarks[indices]
+            return mlvl_bboxes, confidences, classIds, landmarks
+        else:
+            print('nothing detect')
+            return np.array([]), np.array([]), np.array([]), np.array([])
+
+    def distance2bbox(self, points, distance, max_shape=None):
+        x1 = points[:, 0] - distance[:, 0]
+        y1 = points[:, 1] - distance[:, 1]
+        x2 = points[:, 0] + distance[:, 2]
+        y2 = points[:, 1] + distance[:, 3]
+        if max_shape is not None:
+            x1 = np.clip(x1, 0, max_shape[1])
+            y1 = np.clip(y1, 0, max_shape[0])
+            x2 = np.clip(x2, 0, max_shape[1])
+            y2 = np.clip(y2, 0, max_shape[0])
+        return np.stack([x1, y1, x2, y2], axis=-1)
+    
+    def draw_detections(self, image, boxes, scores, kpts):
+        for box, score, kp in zip(boxes, scores, kpts):
+            x, y, w, h = box.astype(int)
+            # Draw rectangle
+            cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 255), thickness=3)
+            cv2.putText(image, "face:"+str(round(score,2)), (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), thickness=2)
+            for i in range(5):
+                cv2.circle(image, (int(kp[i * 3]), int(kp[i * 3 + 1])), 4, (0, 255, 0), thickness=-1)
+                # cv2.putText(image, str(i), (int(kp[i * 3]), int(kp[i * 3 + 1]) - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), thickness=1)
+        return image
+    
+ROOT = os.path.dirname(os.path.abspath(__file__))
\ No newline at end of file
diff --git a/musetalk/utils/face_detection/detection/__init__.py b/musetalk/utils/face_detection/detection/__init__.py
new file mode 100644
index 0000000..1a6b040
--- /dev/null
+++ b/musetalk/utils/face_detection/detection/__init__.py
@@ -0,0 +1 @@
+from .core import FaceDetector
\ No newline at end of file
diff --git a/musetalk/utils/face_detection/detection/core.py b/musetalk/utils/face_detection/detection/core.py
new file mode 100644
index 0000000..0f8275e
--- /dev/null
+++ b/musetalk/utils/face_detection/detection/core.py
@@ -0,0 +1,130 @@
+import logging
+import glob
+from tqdm import tqdm
+import numpy as np
+import torch
+import cv2
+
+
+class FaceDetector(object):
+    """An abstract class representing a face detector.
+
+    Any other face detection implementation must subclass it. All subclasses
+    must implement ``detect_from_image``, that return a list of detected
+    bounding boxes. Optionally, for speed considerations detect from path is
+    recommended.
+    """
+
+    def __init__(self, device, verbose):
+        self.device = device
+        self.verbose = verbose
+
+        if verbose:
+            if 'cpu' in device:
+                logger = logging.getLogger(__name__)
+                logger.warning("Detection running on CPU, this may be potentially slow.")
+
+        if 'cpu' not in device and 'cuda' not in device:
+            if verbose:
+                logger.error("Expected values for device are: {cpu, cuda} but got: %s", device)
+            raise ValueError
+
+    def detect_from_image(self, tensor_or_path):
+        """Detects faces in a given image.
+
+        This function detects the faces present in a provided BGR(usually)
+        image. The input can be either the image itself or the path to it.
+
+        Arguments:
+            tensor_or_path {numpy.ndarray, torch.tensor or string} -- the path
+            to an image or the image itself.
+
+        Example::
+
+            >>> path_to_image = 'data/image_01.jpg'
+            ...   detected_faces = detect_from_image(path_to_image)
+            [A list of bounding boxes (x1, y1, x2, y2)]
+            >>> image = cv2.imread(path_to_image)
+            ...   detected_faces = detect_from_image(image)
+            [A list of bounding boxes (x1, y1, x2, y2)]
+
+        """
+        raise NotImplementedError
+
+    def detect_from_directory(self, path, extensions=['.jpg', '.png'], recursive=False, show_progress_bar=True):
+        """Detects faces from all the images present in a given directory.
+
+        Arguments:
+            path {string} -- a string containing a path that points to the folder containing the images
+
+        Keyword Arguments:
+            extensions {list} -- list of string containing the extensions to be
+            consider in the following format: ``.extension_name`` (default:
+            {['.jpg', '.png']}) recursive {bool} -- option wherever to scan the
+            folder recursively (default: {False}) show_progress_bar {bool} --
+            display a progressbar (default: {True})
+
+        Example:
+        >>> directory = 'data'
+        ...   detected_faces = detect_from_directory(directory)
+        {A dictionary of [lists containing bounding boxes(x1, y1, x2, y2)]}
+
+        """
+        if self.verbose:
+            logger = logging.getLogger(__name__)
+
+        if len(extensions) == 0:
+            if self.verbose:
+                logger.error("Expected at list one extension, but none was received.")
+            raise ValueError
+
+        if self.verbose:
+            logger.info("Constructing the list of images.")
+        additional_pattern = '/**/*' if recursive else '/*'
+        files = []
+        for extension in extensions:
+            files.extend(glob.glob(path + additional_pattern + extension, recursive=recursive))
+
+        if self.verbose:
+            logger.info("Finished searching for images. %s images found", len(files))
+            logger.info("Preparing to run the detection.")
+
+        predictions = {}
+        for image_path in tqdm(files, disable=not show_progress_bar):
+            if self.verbose:
+                logger.info("Running the face detector on image: %s", image_path)
+            predictions[image_path] = self.detect_from_image(image_path)
+
+        if self.verbose:
+            logger.info("The detector was successfully run on all %s images", len(files))
+
+        return predictions
+
+    @property
+    def reference_scale(self):
+        raise NotImplementedError
+
+    @property
+    def reference_x_shift(self):
+        raise NotImplementedError
+
+    @property
+    def reference_y_shift(self):
+        raise NotImplementedError
+
+    @staticmethod
+    def tensor_or_path_to_ndarray(tensor_or_path, rgb=True):
+        """Convert path (represented as a string) or torch.tensor to a numpy.ndarray
+
+        Arguments:
+            tensor_or_path {numpy.ndarray, torch.tensor or string} -- path to the image, or the image itself
+        """
+        if isinstance(tensor_or_path, str):
+            return cv2.imread(tensor_or_path) if not rgb else cv2.imread(tensor_or_path)[..., ::-1]
+        elif torch.is_tensor(tensor_or_path):
+            # Call cpu in case its coming from cuda
+            return tensor_or_path.cpu().numpy()[..., ::-1].copy() if not rgb else tensor_or_path.cpu().numpy()
+        elif isinstance(tensor_or_path, np.ndarray):
+            return tensor_or_path[..., ::-1].copy() if not rgb else tensor_or_path
+        else:
+            raise TypeError
diff --git a/musetalk/utils/face_detection/detection/sfd/__init__.py b/musetalk/utils/face_detection/detection/sfd/__init__.py
new file mode 100644
index 0000000..5a63ecd
--- /dev/null
+++ b/musetalk/utils/face_detection/detection/sfd/__init__.py
@@ -0,0 +1 @@
+from .sfd_detector import SFDDetector as FaceDetector
\ No newline at end of file
diff --git a/musetalk/utils/face_detection/detection/sfd/bbox.py b/musetalk/utils/face_detection/detection/sfd/bbox.py
new file mode 100644
index 0000000..4bd7222
--- /dev/null
+++ b/musetalk/utils/face_detection/detection/sfd/bbox.py
@@ -0,0 +1,129 @@
+from __future__ import print_function
+import os
+import sys
+import cv2
+import random
+import datetime
+import time
+import math
+import argparse
+import numpy as np
+import torch
+
+try:
+    from iou import IOU
+except BaseException:
+    # IOU cython speedup 10x
+    def IOU(ax1, ay1, ax2, ay2, bx1, by1, bx2, by2):
+        sa = abs((ax2 - ax1) * (ay2 - ay1))
+        sb = abs((bx2 - bx1) * (by2 - by1))
+        x1, y1 = max(ax1, bx1), max(ay1, by1)
+        x2, y2 = min(ax2, bx2), min(ay2, by2)
+        w = x2 - x1
+        h = y2 - y1
+        if w < 0 or h < 0:
+            return 0.0
+        else:
+            return 1.0 * w * h / (sa + sb - w * h)
+
+
+def bboxlog(x1, y1, x2, y2, axc, ayc, aww, ahh):
+    xc, yc, ww, hh = (x2 + x1) / 2, (y2 + y1) / 2, x2 - x1, y2 - y1
+    dx, dy = (xc - axc) / aww, (yc - ayc) / ahh
+    dw, dh = math.log(ww / aww), math.log(hh / ahh)
+    return dx, dy, dw, dh
+
+
+def bboxloginv(dx, dy, dw, dh, axc, ayc, aww, ahh):
+    xc, yc = dx * aww + axc, dy * ahh + ayc
+    ww, hh = math.exp(dw) * aww, math.exp(dh) * ahh
+    x1, x2, y1, y2 = xc - ww / 2, xc + ww / 2, yc - hh / 2, yc + hh / 2
+    return x1, y1, x2, y2
+
+
+def nms(dets, thresh):
+    if 0 == len(dets):
+        return []
+    x1, y1, x2, y2, scores = dets[:, 0], dets[:, 1], dets[:, 2], dets[:, 3], dets[:, 4]
+    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
+    order = scores.argsort()[::-1]
+
+    keep = []
+    while order.size > 0:
+        i = order[0]
+        keep.append(i)
+        xx1, yy1 = np.maximum(x1[i], x1[order[1:]]), np.maximum(y1[i], y1[order[1:]])
+        xx2, yy2 = np.minimum(x2[i], x2[order[1:]]), np.minimum(y2[i], y2[order[1:]])
+
+        w, h = np.maximum(0.0, xx2 - xx1 + 1), np.maximum(0.0, yy2 - yy1 + 1)
+        ovr = w * h / (areas[i] + areas[order[1:]] - w * h)
+
+        inds = np.where(ovr <= thresh)[0]
+        order = order[inds + 1]
+
+    return keep
+
+
+def encode(matched, priors, variances):
+    """Encode the variances from the priorbox layers into the ground truth boxes
+    we have matched (based on jaccard overlap) with the prior boxes.
+    Args:
+        matched: (tensor) Coords of ground truth for each prior in point-form
+            Shape: [num_priors, 4].
+        priors: (tensor) Prior boxes in center-offset form
+            Shape: [num_priors,4].
+        variances: (list[float]) Variances of priorboxes
+    Return:
+        encoded boxes (tensor), Shape: [num_priors, 4]
+    """
+
+    # dist b/t match center and prior's center
+    g_cxcy = (matched[:, :2] + matched[:, 2:]) / 2 - priors[:, :2]
+    # encode variance
+    g_cxcy /= (variances[0] * priors[:, 2:])
+    # match wh / prior wh
+    g_wh = (matched[:, 2:] - matched[:, :2]) / priors[:, 2:]
+    g_wh = torch.log(g_wh) / variances[1]
+    # return target for smooth_l1_loss
+    return torch.cat([g_cxcy, g_wh], 1)  # [num_priors,4]
+
+
+def decode(loc, priors, variances):
+    """Decode locations from predictions using priors to undo
+    the encoding we did for offset regression at train time.
+    Args:
+        loc (tensor): location predictions for loc layers,
+            Shape: [num_priors,4]
+        priors (tensor): Prior boxes in center-offset form.
+            Shape: [num_priors,4].
+        variances: (list[float]) Variances of priorboxes
+    Return:
+        decoded bounding box predictions
+    """
+
+    boxes = torch.cat((
+        priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:],
+        priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1)
+    boxes[:, :2] -= boxes[:, 2:] / 2
+    boxes[:, 2:] += boxes[:, :2]
+    return boxes
+
+def batch_decode(loc, priors, variances):
+    """Decode locations from predictions using priors to undo
+    the encoding we did for offset regression at train time.
+    Args:
+        loc (tensor): location predictions for loc layers,
+            Shape: [num_priors,4]
+        priors (tensor): Prior boxes in center-offset form.
+            Shape: [num_priors,4].
+        variances: (list[float]) Variances of priorboxes
+    Return:
+        decoded bounding box predictions
+    """
+
+    boxes = torch.cat((
+        priors[:, :, :2] + loc[:, :, :2] * variances[0] * priors[:, :, 2:],
+        priors[:, :, 2:] * torch.exp(loc[:, :, 2:] * variances[1])), 2)
+    boxes[:, :, :2] -= boxes[:, :, 2:] / 2
+    boxes[:, :, 2:] += boxes[:, :, :2]
+    return boxes
diff --git a/musetalk/utils/face_detection/detection/sfd/detect.py b/musetalk/utils/face_detection/detection/sfd/detect.py
new file mode 100644
index 0000000..15ccdc5
--- /dev/null
+++ b/musetalk/utils/face_detection/detection/sfd/detect.py
@@ -0,0 +1,114 @@
+import torch
+import torch.nn.functional as F
+
+import os
+import sys
+import cv2
+import random
+import datetime
+import math
+import argparse
+import numpy as np
+
+import scipy.io as sio
+import zipfile
+from .net_s3fd import s3fd
+from .bbox import *
+
+
+def detect(net, img, device):
+    img = img - np.array([104, 117, 123])
+    img = img.transpose(2, 0, 1)
+    img = img.reshape((1,) + img.shape)
+
+    if 'cuda' in device:
+        torch.backends.cudnn.benchmark = True
+
+    img = torch.from_numpy(img).float().to(device)
+    BB, CC, HH, WW = img.size()
+    with torch.no_grad():
+        olist = net(img)
+
+    bboxlist = []
+    for i in range(len(olist) // 2):
+        olist[i * 2] = F.softmax(olist[i * 2], dim=1)
+    olist = [oelem.data.cpu() for oelem in olist]
+    for i in range(len(olist) // 2):
+        ocls, oreg = olist[i * 2], olist[i * 2 + 1]
+        FB, FC, FH, FW = ocls.size()  # feature map size
+        stride = 2**(i + 2)    # 4,8,16,32,64,128
+        anchor = stride * 4
+        poss = zip(*np.where(ocls[:, 1, :, :] > 0.05))
+        for Iindex, hindex, windex in poss:
+            axc, ayc = stride / 2 + windex * stride, stride / 2 + hindex * stride
+            score = ocls[0, 1, hindex, windex]
+            loc = oreg[0, :, hindex, windex].contiguous().view(1, 4)
+            priors = torch.Tensor([[axc / 1.0, ayc / 1.0, stride * 4 / 1.0, stride * 4 / 1.0]])
+            variances = [0.1, 0.2]
+            box = decode(loc, priors, variances)
+            x1, y1, x2, y2 = box[0] * 1.0
+            # cv2.rectangle(imgshow,(int(x1),int(y1)),(int(x2),int(y2)),(0,0,255),1)
+            bboxlist.append([x1, y1, x2, y2, score])
+    bboxlist = np.array(bboxlist)
+    if 0 == len(bboxlist):
+        bboxlist = np.zeros((1, 5))
+
+    return bboxlist
+
+def batch_detect(net, imgs, device):
+    imgs = imgs - np.array([104, 117, 123])
+    imgs = imgs.transpose(0, 3, 1, 2)
+
+    if 'cuda' in device:
+        torch.backends.cudnn.benchmark = True
+
+    imgs = torch.from_numpy(imgs).float().to(device)
+    BB, CC, HH, WW = imgs.size()
+    with torch.no_grad():
+        olist = net(imgs)
+#     print(olist)
+    
+    bboxlist = []
+    for i in range(len(olist) // 2):
+        olist[i * 2] = F.softmax(olist[i * 2], dim=1)
+    
+    olist = [oelem.cpu() for oelem in olist]
+    for i in range(len(olist) // 2):
+        ocls, oreg = olist[i * 2], olist[i * 2 + 1]
+        FB, FC, FH, FW = ocls.size()  # feature map size
+        stride = 2**(i + 2)    # 4,8,16,32,64,128
+        anchor = stride * 4
+        poss = zip(*np.where(ocls[:, 1, :, :] > 0.05))
+        for Iindex, hindex, windex in poss:
+            axc, ayc = stride / 2 + windex * stride, stride / 2 + hindex * stride
+            score = ocls[:, 1, hindex, windex]
+            loc = oreg[:, :, hindex, windex].contiguous().view(BB, 1, 4)
+            priors = torch.Tensor([[axc / 1.0, ayc / 1.0, stride * 4 / 1.0, stride * 4 / 1.0]]).view(1, 1, 4)
+            variances = [0.1, 0.2]
+            box = batch_decode(loc, priors, variances)
+            box = box[:, 0] * 1.0
+            # cv2.rectangle(imgshow,(int(x1),int(y1)),(int(x2),int(y2)),(0,0,255),1)
+            bboxlist.append(torch.cat([box, score.unsqueeze(1)], 1).cpu().numpy())
+    bboxlist = np.array(bboxlist)
+    if 0 == len(bboxlist):
+        bboxlist = np.zeros((1, BB, 5))
+
+    return bboxlist
+
+def flip_detect(net, img, device):
+    img = cv2.flip(img, 1)
+    b = detect(net, img, device)
+
+    bboxlist = np.zeros(b.shape)
+    bboxlist[:, 0] = img.shape[1] - b[:, 2]
+    bboxlist[:, 1] = b[:, 1]
+    bboxlist[:, 2] = img.shape[1] - b[:, 0]
+    bboxlist[:, 3] = b[:, 3]
+    bboxlist[:, 4] = b[:, 4]
+    return bboxlist
+
+
+def pts_to_bb(pts):
+    min_x, min_y = np.min(pts, axis=0)
+    max_x, max_y = np.max(pts, axis=0)
+    return np.array([min_x, min_y, max_x, max_y])
diff --git a/musetalk/utils/face_detection/detection/sfd/net_s3fd.py b/musetalk/utils/face_detection/detection/sfd/net_s3fd.py
new file mode 100644
index 0000000..fc64313
--- /dev/null
+++ b/musetalk/utils/face_detection/detection/sfd/net_s3fd.py
@@ -0,0 +1,129 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class L2Norm(nn.Module):
+    def __init__(self, n_channels, scale=1.0):
+        super(L2Norm, self).__init__()
+        self.n_channels = n_channels
+        self.scale = scale
+        self.eps = 1e-10
+        self.weight = nn.Parameter(torch.Tensor(self.n_channels))
+        self.weight.data *= 0.0
+        self.weight.data += self.scale
+
+    def forward(self, x):
+        norm = x.pow(2).sum(dim=1, keepdim=True).sqrt() + self.eps
+        x = x / norm * self.weight.view(1, -1, 1, 1)
+        return x
+
+
+class s3fd(nn.Module):
+    def __init__(self):
+        super(s3fd, self).__init__()
+        self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
+        self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
+
+        self.conv2_1 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
+        self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)
+
+        self.conv3_1 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
+        self.conv3_2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
+        self.conv3_3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
+
+        self.conv4_1 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
+        self.conv4_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+        self.conv4_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+
+        self.conv5_1 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+        self.conv5_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+        self.conv5_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+
+        self.fc6 = nn.Conv2d(512, 1024, kernel_size=3, stride=1, padding=3)
+        self.fc7 = nn.Conv2d(1024, 1024, kernel_size=1, stride=1, padding=0)
+
+        self.conv6_1 = nn.Conv2d(1024, 256, kernel_size=1, stride=1, padding=0)
+        self.conv6_2 = nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1)
+
+        self.conv7_1 = nn.Conv2d(512, 128, kernel_size=1, stride=1, padding=0)
+        self.conv7_2 = nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1)
+
+        self.conv3_3_norm = L2Norm(256, scale=10)
+        self.conv4_3_norm = L2Norm(512, scale=8)
+        self.conv5_3_norm = L2Norm(512, scale=5)
+
+        self.conv3_3_norm_mbox_conf = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1)
+        self.conv3_3_norm_mbox_loc = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1)
+        self.conv4_3_norm_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1)
+        self.conv4_3_norm_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1)
+        self.conv5_3_norm_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1)
+        self.conv5_3_norm_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1)
+
+        self.fc7_mbox_conf = nn.Conv2d(1024, 2, kernel_size=3, stride=1, padding=1)
+        self.fc7_mbox_loc = nn.Conv2d(1024, 4, kernel_size=3, stride=1, padding=1)
+        self.conv6_2_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1)
+        self.conv6_2_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1)
+        self.conv7_2_mbox_conf = nn.Conv2d(256, 2, kernel_size=3, stride=1, padding=1)
+        self.conv7_2_mbox_loc = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1)
+
+    def forward(self, x):
+        h = F.relu(self.conv1_1(x))
+        h = F.relu(self.conv1_2(h))
+        h = F.max_pool2d(h, 2, 2)
+
+        h = F.relu(self.conv2_1(h))
+        h = F.relu(self.conv2_2(h))
+        h = F.max_pool2d(h, 2, 2)
+
+        h = F.relu(self.conv3_1(h))
+        h = F.relu(self.conv3_2(h))
+        h = F.relu(self.conv3_3(h))
+        f3_3 = h
+        h = F.max_pool2d(h, 2, 2)
+
+        h = F.relu(self.conv4_1(h))
+        h = F.relu(self.conv4_2(h))
+        h = F.relu(self.conv4_3(h))
+        f4_3 = h
+        h = F.max_pool2d(h, 2, 2)
+
+        h = F.relu(self.conv5_1(h))
+        h = F.relu(self.conv5_2(h))
+        h = F.relu(self.conv5_3(h))
+        f5_3 = h
+        h = F.max_pool2d(h, 2, 2)
+
+        h = F.relu(self.fc6(h))
+        h = F.relu(self.fc7(h))
+        ffc7 = h
+        h = F.relu(self.conv6_1(h))
+        h = F.relu(self.conv6_2(h))
+        f6_2 = h
+        h = F.relu(self.conv7_1(h))
+        h = F.relu(self.conv7_2(h))
+        f7_2 = h
+
+        f3_3 = self.conv3_3_norm(f3_3)
+        f4_3 = self.conv4_3_norm(f4_3)
+        f5_3 = self.conv5_3_norm(f5_3)
+
+        cls1 = self.conv3_3_norm_mbox_conf(f3_3)
+        reg1 = self.conv3_3_norm_mbox_loc(f3_3)
+        cls2 = self.conv4_3_norm_mbox_conf(f4_3)
+        reg2 = self.conv4_3_norm_mbox_loc(f4_3)
+        cls3 = self.conv5_3_norm_mbox_conf(f5_3)
+        reg3 = self.conv5_3_norm_mbox_loc(f5_3)
+        cls4 = self.fc7_mbox_conf(ffc7)
+        reg4 = self.fc7_mbox_loc(ffc7)
+        cls5 = self.conv6_2_mbox_conf(f6_2)
+        reg5 = self.conv6_2_mbox_loc(f6_2)
+        cls6 = self.conv7_2_mbox_conf(f7_2)
+        reg6 = self.conv7_2_mbox_loc(f7_2)
+
+        # max-out background label
+        chunk = torch.chunk(cls1, 4, 1)
+        bmax = torch.max(torch.max(chunk[0], chunk[1]), chunk[2])
+        cls1 = torch.cat([bmax, chunk[3]], dim=1)
+
+        return [cls1, reg1, cls2, reg2, cls3, reg3, cls4, reg4, cls5, reg5, cls6, reg6]
diff --git a/musetalk/utils/face_detection/detection/sfd/sfd_detector.py b/musetalk/utils/face_detection/detection/sfd/sfd_detector.py
new file mode 100644
index 0000000..8fbce15
--- /dev/null
+++ b/musetalk/utils/face_detection/detection/sfd/sfd_detector.py
@@ -0,0 +1,59 @@
+import os
+import cv2
+from torch.utils.model_zoo import load_url
+
+from ..core import FaceDetector
+
+from .net_s3fd import s3fd
+from .bbox import *
+from .detect import *
+
+models_urls = {
+    's3fd': 'https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth',
+}
+
+
+class SFDDetector(FaceDetector):
+    def __init__(self, device, path_to_detector=os.path.join(os.path.dirname(os.path.abspath(__file__)), 's3fd.pth'), verbose=False):
+        super(SFDDetector, self).__init__(device, verbose)
+
+        # Initialise the face detector
+        if not os.path.isfile(path_to_detector):
+            model_weights = load_url(models_urls['s3fd'])
+        else:
+            model_weights = torch.load(path_to_detector)
+
+        self.face_detector = s3fd()
+        self.face_detector.load_state_dict(model_weights)
+        self.face_detector.to(device)
+        self.face_detector.eval()
+
+    def detect_from_image(self, tensor_or_path):
+        image = self.tensor_or_path_to_ndarray(tensor_or_path)
+
+        bboxlist = detect(self.face_detector, image, device=self.device)
+        keep = nms(bboxlist, 0.3)
+        bboxlist = bboxlist[keep, :]
+        bboxlist = [x for x in bboxlist if x[-1] > 0.5]
+
+        return bboxlist
+
+    def detect_from_batch(self, images):
+        bboxlists = batch_detect(self.face_detector, images, device=self.device)
+        keeps = [nms(bboxlists[:, i, :], 0.3) for i in range(bboxlists.shape[1])]
+        bboxlists = [bboxlists[keep, i, :] for i, keep in enumerate(keeps)]
+        bboxlists = [[x for x in bboxlist if x[-1] > 0.5] for bboxlist in bboxlists]
+
+        return bboxlists
+
+    @property
+    def reference_scale(self):
+        return 195
+
+    @property
+    def reference_x_shift(self):
+        return 0
+
+    @property
+    def reference_y_shift(self):
+        return 0
diff --git a/musetalk/utils/face_detection/models.py b/musetalk/utils/face_detection/models.py
new file mode 100644
index 0000000..ee2dde3
--- /dev/null
+++ b/musetalk/utils/face_detection/models.py
@@ -0,0 +1,261 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+
+
+def conv3x3(in_planes, out_planes, strd=1, padding=1, bias=False):
+    "3x3 convolution with padding"
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3,
+                     stride=strd, padding=padding, bias=bias)
+
+
+class ConvBlock(nn.Module):
+    def __init__(self, in_planes, out_planes):
+        super(ConvBlock, self).__init__()
+        self.bn1 = nn.BatchNorm2d(in_planes)
+        self.conv1 = conv3x3(in_planes, int(out_planes / 2))
+        self.bn2 = nn.BatchNorm2d(int(out_planes / 2))
+        self.conv2 = conv3x3(int(out_planes / 2), int(out_planes / 4))
+        self.bn3 = nn.BatchNorm2d(int(out_planes / 4))
+        self.conv3 = conv3x3(int(out_planes / 4), int(out_planes / 4))
+
+        if in_planes != out_planes:
+            self.downsample = nn.Sequential(
+                nn.BatchNorm2d(in_planes),
+                nn.ReLU(True),
+                nn.Conv2d(in_planes, out_planes,
+                          kernel_size=1, stride=1, bias=False),
+            )
+        else:
+            self.downsample = None
+
+    def forward(self, x):
+        residual = x
+
+        out1 = self.bn1(x)
+        out1 = F.relu(out1, True)
+        out1 = self.conv1(out1)
+
+        out2 = self.bn2(out1)
+        out2 = F.relu(out2, True)
+        out2 = self.conv2(out2)
+
+        out3 = self.bn3(out2)
+        out3 = F.relu(out3, True)
+        out3 = self.conv3(out3)
+
+        out3 = torch.cat((out1, out2, out3), 1)
+
+        if self.downsample is not None:
+            residual = self.downsample(residual)
+
+        out3 += residual
+
+        return out3
+
+
+class Bottleneck(nn.Module):
+
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(Bottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
+                               padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(planes * 4)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class HourGlass(nn.Module):
+    def __init__(self, num_modules, depth, num_features):
+        super(HourGlass, self).__init__()
+        self.num_modules = num_modules
+        self.depth = depth
+        self.features = num_features
+
+        self._generate_network(self.depth)
+
+    def _generate_network(self, level):
+        self.add_module('b1_' + str(level), ConvBlock(self.features, self.features))
+
+        self.add_module('b2_' + str(level), ConvBlock(self.features, self.features))
+
+        if level > 1:
+            self._generate_network(level - 1)
+        else:
+            self.add_module('b2_plus_' + str(level), ConvBlock(self.features, self.features))
+
+        self.add_module('b3_' + str(level), ConvBlock(self.features, self.features))
+
+    def _forward(self, level, inp):
+        # Upper branch
+        up1 = inp
+        up1 = self._modules['b1_' + str(level)](up1)
+
+        # Lower branch
+        low1 = F.avg_pool2d(inp, 2, stride=2)
+        low1 = self._modules['b2_' + str(level)](low1)
+
+        if level > 1:
+            low2 = self._forward(level - 1, low1)
+        else:
+            low2 = low1
+            low2 = self._modules['b2_plus_' + str(level)](low2)
+
+        low3 = low2
+        low3 = self._modules['b3_' + str(level)](low3)
+
+        up2 = F.interpolate(low3, scale_factor=2, mode='nearest')
+
+        return up1 + up2
+
+    def forward(self, x):
+        return self._forward(self.depth, x)
+
+
+class FAN(nn.Module):
+
+    def __init__(self, num_modules=1):
+        super(FAN, self).__init__()
+        self.num_modules = num_modules
+
+        # Base part
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.conv2 = ConvBlock(64, 128)
+        self.conv3 = ConvBlock(128, 128)
+        self.conv4 = ConvBlock(128, 256)
+
+        # Stacking part
+        for hg_module in range(self.num_modules):
+            self.add_module('m' + str(hg_module), HourGlass(1, 4, 256))
+            self.add_module('top_m_' + str(hg_module), ConvBlock(256, 256))
+            self.add_module('conv_last' + str(hg_module),
+                            nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0))
+            self.add_module('bn_end' + str(hg_module), nn.BatchNorm2d(256))
+            self.add_module('l' + str(hg_module), nn.Conv2d(256,
+                                                            68, kernel_size=1, stride=1, padding=0))
+
+            if hg_module < self.num_modules - 1:
+                self.add_module(
+                    'bl' + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0))
+                self.add_module('al' + str(hg_module), nn.Conv2d(68,
+                                                                 256, kernel_size=1, stride=1, padding=0))
+
+    def forward(self, x):
+        x = F.relu(self.bn1(self.conv1(x)), True)
+        x = F.avg_pool2d(self.conv2(x), 2, stride=2)
+        x = self.conv3(x)
+        x = self.conv4(x)
+
+        previous = x
+
+        outputs = []
+        for i in range(self.num_modules):
+            hg = self._modules['m' + str(i)](previous)
+
+            ll = hg
+            ll = self._modules['top_m_' + str(i)](ll)
+
+            ll = F.relu(self._modules['bn_end' + str(i)]
+                        (self._modules['conv_last' + str(i)](ll)), True)
+
+            # Predict heatmaps
+            tmp_out = self._modules['l' + str(i)](ll)
+            outputs.append(tmp_out)
+
+            if i < self.num_modules - 1:
+                ll = self._modules['bl' + str(i)](ll)
+                tmp_out_ = self._modules['al' + str(i)](tmp_out)
+                previous = previous + ll + tmp_out_
+
+        return outputs
+
+
+class ResNetDepth(nn.Module):
+
+    def __init__(self, block=Bottleneck, layers=[3, 8, 36, 3], num_classes=68):
+        self.inplanes = 64
+        super(ResNetDepth, self).__init__()
+        self.conv1 = nn.Conv2d(3 + 68, 64, kernel_size=7, stride=2, padding=3,
+                               bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
+        self.avgpool = nn.AvgPool2d(7)
+        self.fc = nn.Linear(512 * block.expansion, num_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                m.weight.data.normal_(0, math.sqrt(2. / n))
+            elif isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.inplanes, planes * block.expansion,
+                          kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        x = self.avgpool(x)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+
+        return x
diff --git a/musetalk/utils/face_detection/utils.py b/musetalk/utils/face_detection/utils.py
new file mode 100644
index 0000000..3dc4cf3
--- /dev/null
+++ b/musetalk/utils/face_detection/utils.py
@@ -0,0 +1,313 @@
+from __future__ import print_function
+import os
+import sys
+import time
+import torch
+import math
+import numpy as np
+import cv2
+
+
+def _gaussian(
+        size=3, sigma=0.25, amplitude=1, normalize=False, width=None,
+        height=None, sigma_horz=None, sigma_vert=None, mean_horz=0.5,
+        mean_vert=0.5):
+    # handle some defaults
+    if width is None:
+        width = size
+    if height is None:
+        height = size
+    if sigma_horz is None:
+        sigma_horz = sigma
+    if sigma_vert is None:
+        sigma_vert = sigma
+    center_x = mean_horz * width + 0.5
+    center_y = mean_vert * height + 0.5
+    gauss = np.empty((height, width), dtype=np.float32)
+    # generate kernel
+    for i in range(height):
+        for j in range(width):
+            gauss[i][j] = amplitude * math.exp(-(math.pow((j + 1 - center_x) / (
+                sigma_horz * width), 2) / 2.0 + math.pow((i + 1 - center_y) / (sigma_vert * height), 2) / 2.0))
+    if normalize:
+        gauss = gauss / np.sum(gauss)
+    return gauss
+
+
+def draw_gaussian(image, point, sigma):
+    # Check if the gaussian is inside
+    ul = [math.floor(point[0] - 3 * sigma), math.floor(point[1] - 3 * sigma)]
+    br = [math.floor(point[0] + 3 * sigma), math.floor(point[1] + 3 * sigma)]
+    if (ul[0] > image.shape[1] or ul[1] > image.shape[0] or br[0] < 1 or br[1] < 1):
+        return image
+    size = 6 * sigma + 1
+    g = _gaussian(size)
+    g_x = [int(max(1, -ul[0])), int(min(br[0], image.shape[1])) - int(max(1, ul[0])) + int(max(1, -ul[0]))]
+    g_y = [int(max(1, -ul[1])), int(min(br[1], image.shape[0])) - int(max(1, ul[1])) + int(max(1, -ul[1]))]
+    img_x = [int(max(1, ul[0])), int(min(br[0], image.shape[1]))]
+    img_y = [int(max(1, ul[1])), int(min(br[1], image.shape[0]))]
+    assert (g_x[0] > 0 and g_y[1] > 0)
+    image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1]
+          ] = image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1]] + g[g_y[0] - 1:g_y[1], g_x[0] - 1:g_x[1]]
+    image[image > 1] = 1
+    return image
+
+
+def transform(point, center, scale, resolution, invert=False):
+    """Generate and affine transformation matrix.
+
+    Given a set of points, a center, a scale and a targer resolution, the
+    function generates and affine transformation matrix. If invert is ``True``
+    it will produce the inverse transformation.
+
+    Arguments:
+        point {torch.tensor} -- the input 2D point
+        center {torch.tensor or numpy.array} -- the center around which to perform the transformations
+        scale {float} -- the scale of the face/object
+        resolution {float} -- the output resolution
+
+    Keyword Arguments:
+        invert {bool} -- define wherever the function should produce the direct or the
+        inverse transformation matrix (default: {False})
+    """
+    _pt = torch.ones(3)
+    _pt[0] = point[0]
+    _pt[1] = point[1]
+
+    h = 200.0 * scale
+    t = torch.eye(3)
+    t[0, 0] = resolution / h
+    t[1, 1] = resolution / h
+    t[0, 2] = resolution * (-center[0] / h + 0.5)
+    t[1, 2] = resolution * (-center[1] / h + 0.5)
+
+    if invert:
+        t = torch.inverse(t)
+
+    new_point = (torch.matmul(t, _pt))[0:2]
+
+    return new_point.int()
+
+
+def crop(image, center, scale, resolution=256.0):
+    """Center crops an image or set of heatmaps
+
+    Arguments:
+        image {numpy.array} -- an rgb image
+        center {numpy.array} -- the center of the object, usually the same as of the bounding box
+        scale {float} -- scale of the face
+
+    Keyword Arguments:
+        resolution {float} -- the size of the output cropped image (default: {256.0})
+
+    Returns:
+        [type] -- [description]
+    """  # Crop around the center point
+    """ Crops the image around the center. Input is expected to be an np.ndarray """
+    ul = transform([1, 1], center, scale, resolution, True)
+    br = transform([resolution, resolution], center, scale, resolution, True)
+    # pad = math.ceil(torch.norm((ul - br).float()) / 2.0 - (br[0] - ul[0]) / 2.0)
+    if image.ndim > 2:
+        newDim = np.array([br[1] - ul[1], br[0] - ul[0],
+                           image.shape[2]], dtype=np.int32)
+        newImg = np.zeros(newDim, dtype=np.uint8)
+    else:
+        newDim = np.array([br[1] - ul[1], br[0] - ul[0]], dtype=np.int)
+        newImg = np.zeros(newDim, dtype=np.uint8)
+    ht = image.shape[0]
+    wd = image.shape[1]
+    newX = np.array(
+        [max(1, -ul[0] + 1), min(br[0], wd) - ul[0]], dtype=np.int32)
+    newY = np.array(
+        [max(1, -ul[1] + 1), min(br[1], ht) - ul[1]], dtype=np.int32)
+    oldX = np.array([max(1, ul[0] + 1), min(br[0], wd)], dtype=np.int32)
+    oldY = np.array([max(1, ul[1] + 1), min(br[1], ht)], dtype=np.int32)
+    newImg[newY[0] - 1:newY[1], newX[0] - 1:newX[1]
+           ] = image[oldY[0] - 1:oldY[1], oldX[0] - 1:oldX[1], :]
+    newImg = cv2.resize(newImg, dsize=(int(resolution), int(resolution)),
+                        interpolation=cv2.INTER_LINEAR)
+    return newImg
+
+
+def get_preds_fromhm(hm, center=None, scale=None):
+    """Obtain (x,y) coordinates given a set of N heatmaps. If the center
+    and the scale is provided the function will return the points also in
+    the original coordinate frame.
+
+    Arguments:
+        hm {torch.tensor} -- the predicted heatmaps, of shape [B, N, W, H]
+
+    Keyword Arguments:
+        center {torch.tensor} -- the center of the bounding box (default: {None})
+        scale {float} -- face scale (default: {None})
+    """
+    max, idx = torch.max(
+        hm.view(hm.size(0), hm.size(1), hm.size(2) * hm.size(3)), 2)
+    idx += 1
+    preds = idx.view(idx.size(0), idx.size(1), 1).repeat(1, 1, 2).float()
+    preds[..., 0].apply_(lambda x: (x - 1) % hm.size(3) + 1)
+    preds[..., 1].add_(-1).div_(hm.size(2)).floor_().add_(1)
+
+    for i in range(preds.size(0)):
+        for j in range(preds.size(1)):
+            hm_ = hm[i, j, :]
+            pX, pY = int(preds[i, j, 0]) - 1, int(preds[i, j, 1]) - 1
+            if pX > 0 and pX < 63 and pY > 0 and pY < 63:
+                diff = torch.FloatTensor(
+                    [hm_[pY, pX + 1] - hm_[pY, pX - 1],
+                     hm_[pY + 1, pX] - hm_[pY - 1, pX]])
+                preds[i, j].add_(diff.sign_().mul_(.25))
+
+    preds.add_(-.5)
+
+    preds_orig = torch.zeros(preds.size())
+    if center is not None and scale is not None:
+        for i in range(hm.size(0)):
+            for j in range(hm.size(1)):
+                preds_orig[i, j] = transform(
+                    preds[i, j], center, scale, hm.size(2), True)
+
+    return preds, preds_orig
+
+def get_preds_fromhm_batch(hm, centers=None, scales=None):
+    """Obtain (x,y) coordinates given a set of N heatmaps. If the centers
+    and the scales is provided the function will return the points also in
+    the original coordinate frame.
+
+    Arguments:
+        hm {torch.tensor} -- the predicted heatmaps, of shape [B, N, W, H]
+
+    Keyword Arguments:
+        centers {torch.tensor} -- the centers of the bounding box (default: {None})
+        scales {float} -- face scales (default: {None})
+    """
+    max, idx = torch.max(
+        hm.view(hm.size(0), hm.size(1), hm.size(2) * hm.size(3)), 2)
+    idx += 1
+    preds = idx.view(idx.size(0), idx.size(1), 1).repeat(1, 1, 2).float()
+    preds[..., 0].apply_(lambda x: (x - 1) % hm.size(3) + 1)
+    preds[..., 1].add_(-1).div_(hm.size(2)).floor_().add_(1)
+
+    for i in range(preds.size(0)):
+        for j in range(preds.size(1)):
+            hm_ = hm[i, j, :]
+            pX, pY = int(preds[i, j, 0]) - 1, int(preds[i, j, 1]) - 1
+            if pX > 0 and pX < 63 and pY > 0 and pY < 63:
+                diff = torch.FloatTensor(
+                    [hm_[pY, pX + 1] - hm_[pY, pX - 1],
+                     hm_[pY + 1, pX] - hm_[pY - 1, pX]])
+                preds[i, j].add_(diff.sign_().mul_(.25))
+
+    preds.add_(-.5)
+
+    preds_orig = torch.zeros(preds.size())
+    if centers is not None and scales is not None:
+        for i in range(hm.size(0)):
+            for j in range(hm.size(1)):
+                preds_orig[i, j] = transform(
+                    preds[i, j], centers[i], scales[i], hm.size(2), True)
+
+    return preds, preds_orig
+
+def shuffle_lr(parts, pairs=None):
+    """Shuffle the points left-right according to the axis of symmetry
+    of the object.
+
+    Arguments:
+        parts {torch.tensor} -- a 3D or 4D object containing the
+        heatmaps.
+
+    Keyword Arguments:
+        pairs {list of integers} -- [order of the flipped points] (default: {None})
+    """
+    if pairs is None:
+        pairs = [16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0,
+                 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 27, 28, 29, 30, 35,
+                 34, 33, 32, 31, 45, 44, 43, 42, 47, 46, 39, 38, 37, 36, 41,
+                 40, 54, 53, 52, 51, 50, 49, 48, 59, 58, 57, 56, 55, 64, 63,
+                 62, 61, 60, 67, 66, 65]
+    if parts.ndimension() == 3:
+        parts = parts[pairs, ...]
+    else:
+        parts = parts[:, pairs, ...]
+
+    return parts
+
+
+def flip(tensor, is_label=False):
+    """Flip an image or a set of heatmaps left-right
+
+    Arguments:
+        tensor {numpy.array or torch.tensor} -- [the input image or heatmaps]
+
+    Keyword Arguments:
+        is_label {bool} -- [denote wherever the input is an image or a set of heatmaps ] (default: {False})
+    """
+    if not torch.is_tensor(tensor):
+        tensor = torch.from_numpy(tensor)
+
+    if is_label:
+        tensor = shuffle_lr(tensor).flip(tensor.ndimension() - 1)
+    else:
+        tensor = tensor.flip(tensor.ndimension() - 1)
+
+    return tensor
+
+# From pyzolib/paths.py (https://bitbucket.org/pyzo/pyzolib/src/tip/paths.py)
+
+
+def appdata_dir(appname=None, roaming=False):
+    """ appdata_dir(appname=None, roaming=False)
+
+    Get the path to the application directory, where applications are allowed
+    to write user specific files (e.g. configurations). For non-user specific
+    data, consider using common_appdata_dir().
+    If appname is given, a subdir is appended (and created if necessary).
+    If roaming is True, will prefer a roaming directory (Windows Vista/7).
+    """
+
+    # Define default user directory
+    userDir = os.getenv('FACEALIGNMENT_USERDIR', None)
+    if userDir is None:
+        userDir = os.path.expanduser('~')
+        if not os.path.isdir(userDir):  # pragma: no cover
+            userDir = '/var/tmp'  # issue #54
+
+    # Get system app data dir
+    path = None
+    if sys.platform.startswith('win'):
+        path1, path2 = os.getenv('LOCALAPPDATA'), os.getenv('APPDATA')
+        path = (path2 or path1) if roaming else (path1 or path2)
+    elif sys.platform.startswith('darwin'):
+        path = os.path.join(userDir, 'Library', 'Application Support')
+    # On Linux and as fallback
+    if not (path and os.path.isdir(path)):
+        path = userDir
+
+    # Maybe we should store things local to the executable (in case of a
+    # portable distro or a frozen application that wants to be portable)
+    prefix = sys.prefix
+    if getattr(sys, 'frozen', None):
+        prefix = os.path.abspath(os.path.dirname(sys.executable))
+    for reldir in ('settings', '../settings'):
+        localpath = os.path.abspath(os.path.join(prefix, reldir))
+        if os.path.isdir(localpath):  # pragma: no cover
+            try:
+                open(os.path.join(localpath, 'test.write'), 'wb').close()
+                os.remove(os.path.join(localpath, 'test.write'))
+            except IOError:
+                pass  # We cannot write in this directory
+            else:
+                path = localpath
+                break
+
+    # Get path specific for this app
+    if appname:
+        if path == userDir:
+            appname = '.' + appname.lstrip('.')  # Make it a hidden directory
+        path = os.path.join(path, appname)
+        if not os.path.isdir(path):  # pragma: no cover
+            os.mkdir(path)
+
+    # Done
+    return path
diff --git a/musetalk/utils/face_parsing/__init__.py b/musetalk/utils/face_parsing/__init__.py
new file mode 100755
index 0000000..5bfddba
--- /dev/null
+++ b/musetalk/utils/face_parsing/__init__.py
@@ -0,0 +1,50 @@
+import torch
+import time
+import os
+import cv2
+import numpy as np
+from PIL import Image
+from .model import BiSeNet
+import torchvision.transforms as transforms
+
+class FaceParsing():
+    def __init__(self):
+        self.net = self.model_init()
+        self.preprocess = self.image_preprocess()
+
+    def model_init(self, 
+                   resnet_path='./models/face-parse-bisent/resnet18-5c106cde.pth', 
+                   model_pth='./models/face-parse-bisent/79999_iter.pth'):
+        net = BiSeNet(resnet_path)
+        net.cuda()
+        net.load_state_dict(torch.load(model_pth))
+        net.eval()
+        return net
+
+    def image_preprocess(self):
+        return transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
+        ])
+
+    def __call__(self, image, size=(512, 512)):
+        if isinstance(image, str):
+            image = Image.open(image)
+
+        width, height = image.size
+        with torch.no_grad():
+            image = image.resize(size, Image.BILINEAR)
+            img = self.preprocess(image)
+            img = torch.unsqueeze(img, 0).cuda()
+            out = self.net(img)[0]
+            parsing = out.squeeze(0).cpu().numpy().argmax(0)
+            parsing[np.where(parsing>13)] = 0
+            parsing[np.where(parsing>=1)] = 255
+        parsing = Image.fromarray(parsing.astype(np.uint8))
+        return parsing
+
+if __name__ == "__main__":
+    fp = FaceParsing()
+    segmap = fp('154_small.png')
+    segmap.save('res.png')
+    
diff --git a/musetalk/utils/face_parsing/model.py b/musetalk/utils/face_parsing/model.py
new file mode 100755
index 0000000..13d13e0
--- /dev/null
+++ b/musetalk/utils/face_parsing/model.py
@@ -0,0 +1,283 @@
+#!/usr/bin/python
+# -*- encoding: utf-8 -*-
+
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision
+
+from .resnet import Resnet18
+# from modules.bn import InPlaceABNSync as BatchNorm2d
+
+
+class ConvBNReLU(nn.Module):
+    def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, *args, **kwargs):
+        super(ConvBNReLU, self).__init__()
+        self.conv = nn.Conv2d(in_chan,
+                out_chan,
+                kernel_size = ks,
+                stride = stride,
+                padding = padding,
+                bias = False)
+        self.bn = nn.BatchNorm2d(out_chan)
+        self.init_weight()
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = F.relu(self.bn(x))
+        return x
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+class BiSeNetOutput(nn.Module):
+    def __init__(self, in_chan, mid_chan, n_classes, *args, **kwargs):
+        super(BiSeNetOutput, self).__init__()
+        self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1)
+        self.conv_out = nn.Conv2d(mid_chan, n_classes, kernel_size=1, bias=False)
+        self.init_weight()
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.conv_out(x)
+        return x
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module, nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+class AttentionRefinementModule(nn.Module):
+    def __init__(self, in_chan, out_chan, *args, **kwargs):
+        super(AttentionRefinementModule, self).__init__()
+        self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1)
+        self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size= 1, bias=False)
+        self.bn_atten = nn.BatchNorm2d(out_chan)
+        self.sigmoid_atten = nn.Sigmoid()
+        self.init_weight()
+
+    def forward(self, x):
+        feat = self.conv(x)
+        atten = F.avg_pool2d(feat, feat.size()[2:])
+        atten = self.conv_atten(atten)
+        atten = self.bn_atten(atten)
+        atten = self.sigmoid_atten(atten)
+        out = torch.mul(feat, atten)
+        return out
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+
+class ContextPath(nn.Module):
+    def __init__(self, resnet_path, *args, **kwargs):
+        super(ContextPath, self).__init__()
+        self.resnet = Resnet18(resnet_path)
+        self.arm16 = AttentionRefinementModule(256, 128)
+        self.arm32 = AttentionRefinementModule(512, 128)
+        self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
+        self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
+        self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0)
+
+        self.init_weight()
+
+    def forward(self, x):
+        H0, W0 = x.size()[2:]
+        feat8, feat16, feat32 = self.resnet(x)
+        H8, W8 = feat8.size()[2:]
+        H16, W16 = feat16.size()[2:]
+        H32, W32 = feat32.size()[2:]
+
+        avg = F.avg_pool2d(feat32, feat32.size()[2:])
+        avg = self.conv_avg(avg)
+        avg_up = F.interpolate(avg, (H32, W32), mode='nearest')
+
+        feat32_arm = self.arm32(feat32)
+        feat32_sum = feat32_arm + avg_up
+        feat32_up = F.interpolate(feat32_sum, (H16, W16), mode='nearest')
+        feat32_up = self.conv_head32(feat32_up)
+
+        feat16_arm = self.arm16(feat16)
+        feat16_sum = feat16_arm + feat32_up
+        feat16_up = F.interpolate(feat16_sum, (H8, W8), mode='nearest')
+        feat16_up = self.conv_head16(feat16_up)
+
+        return feat8, feat16_up, feat32_up  # x8, x8, x16
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, (nn.Linear, nn.Conv2d)):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module, nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+### This is not used, since I replace this with the resnet feature with the same size
+class SpatialPath(nn.Module):
+    def __init__(self, *args, **kwargs):
+        super(SpatialPath, self).__init__()
+        self.conv1 = ConvBNReLU(3, 64, ks=7, stride=2, padding=3)
+        self.conv2 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
+        self.conv3 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
+        self.conv_out = ConvBNReLU(64, 128, ks=1, stride=1, padding=0)
+        self.init_weight()
+
+    def forward(self, x):
+        feat = self.conv1(x)
+        feat = self.conv2(feat)
+        feat = self.conv3(feat)
+        feat = self.conv_out(feat)
+        return feat
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module, nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+class FeatureFusionModule(nn.Module):
+    def __init__(self, in_chan, out_chan, *args, **kwargs):
+        super(FeatureFusionModule, self).__init__()
+        self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0)
+        self.conv1 = nn.Conv2d(out_chan,
+                out_chan//4,
+                kernel_size = 1,
+                stride = 1,
+                padding = 0,
+                bias = False)
+        self.conv2 = nn.Conv2d(out_chan//4,
+                out_chan,
+                kernel_size = 1,
+                stride = 1,
+                padding = 0,
+                bias = False)
+        self.relu = nn.ReLU(inplace=True)
+        self.sigmoid = nn.Sigmoid()
+        self.init_weight()
+
+    def forward(self, fsp, fcp):
+        fcat = torch.cat([fsp, fcp], dim=1)
+        feat = self.convblk(fcat)
+        atten = F.avg_pool2d(feat, feat.size()[2:])
+        atten = self.conv1(atten)
+        atten = self.relu(atten)
+        atten = self.conv2(atten)
+        atten = self.sigmoid(atten)
+        feat_atten = torch.mul(feat, atten)
+        feat_out = feat_atten + feat
+        return feat_out
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module, nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+class BiSeNet(nn.Module):
+    def __init__(self, resnet_path='models/resnet18-5c106cde.pth', n_classes=19, *args, **kwargs):
+        super(BiSeNet, self).__init__()
+        self.cp = ContextPath(resnet_path)
+        ## here self.sp is deleted
+        self.ffm = FeatureFusionModule(256, 256)
+        self.conv_out = BiSeNetOutput(256, 256, n_classes)
+        self.conv_out16 = BiSeNetOutput(128, 64, n_classes)
+        self.conv_out32 = BiSeNetOutput(128, 64, n_classes)
+        self.init_weight()
+
+    def forward(self, x):
+        H, W = x.size()[2:]
+        feat_res8, feat_cp8, feat_cp16 = self.cp(x)  # here return res3b1 feature
+        feat_sp = feat_res8  # use res3b1 feature to replace spatial path feature
+        feat_fuse = self.ffm(feat_sp, feat_cp8)
+
+        feat_out = self.conv_out(feat_fuse)
+        feat_out16 = self.conv_out16(feat_cp8)
+        feat_out32 = self.conv_out32(feat_cp16)
+
+        feat_out = F.interpolate(feat_out, (H, W), mode='bilinear', align_corners=True)
+        feat_out16 = F.interpolate(feat_out16, (H, W), mode='bilinear', align_corners=True)
+        feat_out32 = F.interpolate(feat_out32, (H, W), mode='bilinear', align_corners=True)
+        return feat_out, feat_out16, feat_out32
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params = [], [], [], []
+        for name, child in self.named_children():
+            child_wd_params, child_nowd_params = child.get_params()
+            if isinstance(child, FeatureFusionModule) or isinstance(child, BiSeNetOutput):
+                lr_mul_wd_params += child_wd_params
+                lr_mul_nowd_params += child_nowd_params
+            else:
+                wd_params += child_wd_params
+                nowd_params += child_nowd_params
+        return wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params
+
+
+if __name__ == "__main__":
+    net = BiSeNet(19)
+    net.cuda()
+    net.eval()
+    in_ten = torch.randn(16, 3, 640, 480).cuda()
+    out, out16, out32 = net(in_ten)
+    print(out.shape)
+
+    net.get_params()
diff --git a/musetalk/utils/face_parsing/resnet.py b/musetalk/utils/face_parsing/resnet.py
new file mode 100755
index 0000000..e2e5d87
--- /dev/null
+++ b/musetalk/utils/face_parsing/resnet.py
@@ -0,0 +1,109 @@
+#!/usr/bin/python
+# -*- encoding: utf-8 -*-
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.model_zoo as modelzoo
+
+# from modules.bn import InPlaceABNSync as BatchNorm2d
+
+resnet18_url = 'https://download.pytorch.org/models/resnet18-5c106cde.pth'
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
+                     padding=1, bias=False)
+
+
+class BasicBlock(nn.Module):
+    def __init__(self, in_chan, out_chan, stride=1):
+        super(BasicBlock, self).__init__()
+        self.conv1 = conv3x3(in_chan, out_chan, stride)
+        self.bn1 = nn.BatchNorm2d(out_chan)
+        self.conv2 = conv3x3(out_chan, out_chan)
+        self.bn2 = nn.BatchNorm2d(out_chan)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = None
+        if in_chan != out_chan or stride != 1:
+            self.downsample = nn.Sequential(
+                nn.Conv2d(in_chan, out_chan,
+                          kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(out_chan),
+                )
+
+    def forward(self, x):
+        residual = self.conv1(x)
+        residual = F.relu(self.bn1(residual))
+        residual = self.conv2(residual)
+        residual = self.bn2(residual)
+
+        shortcut = x
+        if self.downsample is not None:
+            shortcut = self.downsample(x)
+
+        out = shortcut + residual
+        out = self.relu(out)
+        return out
+
+
+def create_layer_basic(in_chan, out_chan, bnum, stride=1):
+    layers = [BasicBlock(in_chan, out_chan, stride=stride)]
+    for i in range(bnum-1):
+        layers.append(BasicBlock(out_chan, out_chan, stride=1))
+    return nn.Sequential(*layers)
+
+
+class Resnet18(nn.Module):
+    def __init__(self, model_path):
+        super(Resnet18, self).__init__()
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
+                               bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1)
+        self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2)
+        self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2)
+        self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2)
+        self.init_weight(model_path)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = F.relu(self.bn1(x))
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        feat8 = self.layer2(x) # 1/8
+        feat16 = self.layer3(feat8) # 1/16
+        feat32 = self.layer4(feat16) # 1/32
+        return feat8, feat16, feat32
+
+    def init_weight(self, model_path):
+        state_dict = torch.load(model_path) #modelzoo.load_url(resnet18_url)
+        self_state_dict = self.state_dict()
+        for k, v in state_dict.items():
+            if 'fc' in k: continue
+            self_state_dict.update({k: v})
+        self.load_state_dict(self_state_dict)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, (nn.Linear, nn.Conv2d)):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module,  nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+if __name__ == "__main__":
+    net = Resnet18()
+    x = torch.randn(16, 3, 224, 224)
+    out = net(x)
+    print(out[0].size())
+    print(out[1].size())
+    print(out[2].size())
+    net.get_params()
diff --git a/musetalk/utils/preprocessing.py b/musetalk/utils/preprocessing.py
new file mode 100644
index 0000000..9eafe7c
--- /dev/null
+++ b/musetalk/utils/preprocessing.py
@@ -0,0 +1,113 @@
+import sys
+from face_detection import FaceAlignment,LandmarksType
+from os import listdir, path
+import subprocess
+import numpy as np
+import cv2
+import pickle
+import os
+import json
+from mmpose.apis import inference_topdown, init_model
+from mmpose.structures import merge_data_samples
+import torch
+from tqdm import tqdm
+
+# initialize the mmpose model
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+config_file = './musetalk/utils/dwpose/rtmpose-l_8xb32-270e_coco-ubody-wholebody-384x288.py'
+checkpoint_file = './models/dwpose/dw-ll_ucoco_384.pth'
+model = init_model(config_file, checkpoint_file, device=device)
+
+# initialize the face detection model
+device = "cuda" if torch.cuda.is_available() else "cpu"
+fa = FaceAlignment(LandmarksType._2D, flip_input=False,device=device)
+
+# maker if the bbox is not sufficient 
+coord_placeholder = (0.0,0.0,0.0,0.0)
+
+def resize_landmark(landmark, w, h, new_w, new_h):
+    w_ratio = new_w / w
+    h_ratio = new_h / h
+    landmark_norm = landmark / [w, h]
+    landmark_resized = landmark_norm * [new_w, new_h]
+    return landmark_resized
+
+def read_imgs(img_list):
+    frames = []
+    print('reading images...')
+    for img_path in tqdm(img_list):
+        frame = cv2.imread(img_path)
+        frames.append(frame)
+    return frames
+
+def get_landmark_and_bbox(img_list,upperbondrange =0):
+    frames = read_imgs(img_list)
+    batch_size_fa = 1
+    batches = [frames[i:i + batch_size_fa] for i in range(0, len(frames), batch_size_fa)]
+    coords_list = []
+    landmarks = []
+    if upperbondrange != 0:
+        print('get key_landmark and face bounding boxes with the bbox_shift:',upperbondrange)
+    else:
+        print('get key_landmark and face bounding boxes with the default value')
+    average_range_minus = []
+    average_range_plus = []
+    for fb in tqdm(batches):
+        results = inference_topdown(model, np.asarray(fb)[0])
+        results = merge_data_samples(results)
+        keypoints = results.pred_instances.keypoints
+        face_land_mark= keypoints[0][23:91]
+        face_land_mark = face_land_mark.astype(np.int32)
+        
+        # get bounding boxes by face detetion
+        bbox = fa.get_detections_for_batch(np.asarray(fb))
+        
+        # adjust the bounding box refer to landmark
+        # Add the bounding box to a tuple and append it to the coordinates list
+        for j, f in enumerate(bbox):
+            if f is None: # no face in the image
+                coords_list += [coord_placeholder]
+                continue
+            
+            half_face_coord =  face_land_mark[29]#np.mean([face_land_mark[28], face_land_mark[29]], axis=0)
+            range_minus = (face_land_mark[30]- face_land_mark[29])[1]
+            range_plus = (face_land_mark[29]- face_land_mark[28])[1]
+            average_range_minus.append(range_minus)
+            average_range_plus.append(range_plus)
+            if upperbondrange != 0:
+                half_face_coord[1] = upperbondrange+half_face_coord[1] #手动调整  + 向下（偏29）  - 向上（偏28）
+            half_face_dist = np.max(face_land_mark[:,1]) - half_face_coord[1]
+            upper_bond = half_face_coord[1]-half_face_dist
+            
+            f_landmark = (np.min(face_land_mark[:, 0]),int(upper_bond),np.max(face_land_mark[:, 0]),np.max(face_land_mark[:,1]))
+            x1, y1, x2, y2 = f_landmark
+            
+            if y2-y1<=0 or x2-x1<=0 or x1<0: # if the landmark bbox is not suitable, reuse the bbox
+                coords_list += [f]
+                w,h = f[2]-f[0], f[3]-f[1]
+                print("error bbox:",f)
+            else:
+                coords_list += [f_landmark]
+    
+    print("********************************************bbox_shift parameter adjustment**********************************************************")
+    print(f"Total frame:「{len(frames)}」 Manually adjust range : [ -{int(sum(average_range_minus) / len(average_range_minus))}~{int(sum(average_range_plus) / len(average_range_plus))} ] , the current value: {upperbondrange}")
+    print("*************************************************************************************************************************************")
+    return coords_list,frames
+    
+
+if __name__ == "__main__":
+    img_list = ["./results/lyria/00000.png","./results/lyria/00001.png","./results/lyria/00002.png","./results/lyria/00003.png"]
+    crop_coord_path = "./coord_face.pkl"
+    coords_list,full_frames = get_landmark_and_bbox(img_list)
+    with open(crop_coord_path, 'wb') as f:
+        pickle.dump(coords_list, f)
+        
+    for bbox, frame in zip(coords_list,full_frames):
+        if bbox == coord_placeholder:
+            continue
+        x1, y1, x2, y2 = bbox
+        crop_frame = frame[y1:y2, x1:x2]
+        print('Cropped shape', crop_frame.shape)
+        
+        #cv2.imwrite(path.join(save_dir, '{}.png'.format(i)),full_frames[i][0][y1:y2, x1:x2])
+    print(coords_list)
diff --git a/musetalk/utils/utils.py b/musetalk/utils/utils.py
new file mode 100644
index 0000000..6b2f02f
--- /dev/null
+++ b/musetalk/utils/utils.py
@@ -0,0 +1,61 @@
+import os
+import cv2
+import numpy as np
+import torch
+
+ffmpeg_path = os.getenv('FFMPEG_PATH')
+if ffmpeg_path is None:
+    print("please download ffmpeg-static and export to FFMPEG_PATH. \nFor example: export FFMPEG_PATH=/musetalk/ffmpeg-4.4-amd64-static")
+elif ffmpeg_path not in os.getenv('PATH'):
+    print("add ffmpeg to path")
+    os.environ["PATH"] = f"{ffmpeg_path}:{os.environ['PATH']}"
+
+    
+from musetalk.whisper.audio2feature import Audio2Feature
+from musetalk.models.vae import VAE
+from musetalk.models.unet import UNet,PositionalEncoding
+
+def load_all_model():
+    audio_processor = Audio2Feature(model_path="./models/whisper/tiny.pt")
+    vae = VAE(model_path = "./models/sd-vae-ft-mse/")
+    unet = UNet(unet_config="./models/musetalk/musetalk.json",
+                model_path ="./models/musetalk/pytorch_model.bin")
+    pe = PositionalEncoding(d_model=384)
+    return audio_processor,vae,unet,pe
+
+def get_file_type(video_path):
+    _, ext = os.path.splitext(video_path)
+
+    if ext.lower() in ['.jpg', '.jpeg', '.png', '.bmp', '.tif', '.tiff']:
+        return 'image'
+    elif ext.lower() in ['.avi', '.mp4', '.mov', '.flv', '.mkv']:
+        return 'video'
+    else:
+        return 'unsupported'
+
+def get_video_fps(video_path):
+    video = cv2.VideoCapture(video_path)
+    fps = video.get(cv2.CAP_PROP_FPS)
+    video.release()
+    return fps
+
+def datagen(whisper_chunks,vae_encode_latents,batch_size=8,delay_frame = 0):
+    whisper_batch, latent_batch = [], []
+    for i, w in enumerate(whisper_chunks):
+        idx = (i+delay_frame)%len(vae_encode_latents)
+        latent = vae_encode_latents[idx]
+        whisper_batch.append(w)
+        latent_batch.append(latent)
+
+        if len(latent_batch) >= batch_size:
+            whisper_batch = np.asarray(whisper_batch)
+            latent_batch = torch.cat(latent_batch, dim=0)
+            yield whisper_batch, latent_batch
+            whisper_batch, latent_batch = [], []
+
+    # the last batch may smaller than batch size
+    if len(latent_batch) > 0:
+        whisper_batch = np.asarray(whisper_batch)
+        latent_batch = torch.cat(latent_batch, dim=0)
+
+        yield whisper_batch, latent_batch
\ No newline at end of file
diff --git a/musetalk/whisper/audio2feature.py b/musetalk/whisper/audio2feature.py
new file mode 100644
index 0000000..8c12fcc
--- /dev/null
+++ b/musetalk/whisper/audio2feature.py
@@ -0,0 +1,125 @@
+import os
+#import whisper
+from whisper import load_model
+#import whisper.whispher as whiisper
+import soundfile as sf
+import numpy as np
+import time
+import sys
+sys.path.append("..")
+
+class Audio2Feature():
+    def __init__(self, whisper_model_type="tiny",model_path="./checkpoints/wisper_tiny.pt"):
+        self.whisper_model_type = whisper_model_type
+        self.model = load_model(model_path) #
+
+
+    def get_sliced_feature(self,feature_array, vid_idx, audio_feat_length= [2,2],fps = 25):
+        """
+        Get sliced features based on a given index
+        :param feature_array: 
+        :param start_idx: the start index of the feature
+        :param audio_feat_length:
+        :return: 
+        """
+        length = len(feature_array)
+        selected_feature = []
+        selected_idx = []
+        
+        center_idx = int(vid_idx*50/fps) 
+        left_idx = center_idx-audio_feat_length[0]*2
+        right_idx = center_idx + (audio_feat_length[1]+1)*2
+        
+        for idx in range(left_idx,right_idx):
+            idx = max(0, idx)
+            idx = min(length-1, idx)
+            x = feature_array[idx]
+            selected_feature.append(x)
+            selected_idx.append(idx)
+        
+        selected_feature = np.concatenate(selected_feature, axis=0)
+        selected_feature = selected_feature.reshape(-1, 384)# 50*384
+        return selected_feature,selected_idx
+
+    def get_sliced_feature_sparse(self,feature_array, vid_idx, audio_feat_length= [2,2],fps = 25):
+        """
+        Get sliced features based on a given index
+        :param feature_array: 
+        :param start_idx: the start index of the feature
+        :param audio_feat_length:
+        :return: 
+        """
+        length = len(feature_array)
+        selected_feature = []
+        selected_idx = []
+
+        for dt in range(-audio_feat_length[0],audio_feat_length[1]+1):
+            left_idx = int((vid_idx+dt)*50/fps)
+            if left_idx<1 or left_idx>length-1:
+                left_idx = max(0, left_idx)
+                left_idx = min(length-1, left_idx)
+
+                x = feature_array[left_idx]
+                x = x[np.newaxis,:,:]
+                x = np.repeat(x, 2, axis=0)
+                selected_feature.append(x)
+                selected_idx.append(left_idx)
+                selected_idx.append(left_idx)
+            else:
+                x = feature_array[left_idx-1:left_idx+1]
+                selected_feature.append(x)
+                selected_idx.append(left_idx-1)
+                selected_idx.append(left_idx)
+        selected_feature = np.concatenate(selected_feature, axis=0)
+        selected_feature = selected_feature.reshape(-1, 384)# 50*384
+        return selected_feature,selected_idx
+    
+
+    def feature2chunks(self,feature_array,fps,audio_feat_length = [2,2]):
+        whisper_chunks = []
+        whisper_idx_multiplier = 50./fps 
+        i = 0
+        print(f"video in {fps} FPS, audio idx in 50FPS")
+        while 1:
+            start_idx = int(i * whisper_idx_multiplier)
+            selected_feature,selected_idx = self.get_sliced_feature(feature_array= feature_array,vid_idx = i,audio_feat_length=audio_feat_length,fps=fps)
+            #print(f"i:{i},selected_idx {selected_idx}")
+            whisper_chunks.append(selected_feature)
+            i += 1
+            if start_idx>len(feature_array):
+                break
+
+        return whisper_chunks
+
+    def audio2feat(self,audio_path):
+        # get the sample rate of the audio
+        result = self.model.transcribe(audio_path)
+        embed_list = []
+        for emb in result['segments']:
+            encoder_embeddings = emb['encoder_embeddings']
+            encoder_embeddings = encoder_embeddings.transpose(0,2,1,3)
+            encoder_embeddings = encoder_embeddings.squeeze(0)
+            start_idx = int(emb['start'])
+            end_idx = int(emb['end'])
+            emb_end_idx = int((end_idx - start_idx)/2)
+            embed_list.append(encoder_embeddings[:emb_end_idx])
+        concatenated_array = np.concatenate(embed_list, axis=0)
+        return concatenated_array
+
+if __name__ == "__main__":
+    audio_processor = Audio2Feature(model_path="../../models/whisper/whisper_tiny.pt")
+    audio_path = "./test.mp3"
+    array = audio_processor.audio2feat(audio_path)
+    print(array.shape)
+    fps = 25
+    whisper_idx_multiplier = 50./fps 
+
+    i = 0
+    print(f"video in {fps} FPS, audio idx in 50FPS")
+    while 1:
+        start_idx = int(i * whisper_idx_multiplier)
+        selected_feature,selected_idx = audio_processor.get_sliced_feature(feature_array= array,vid_idx = i,audio_feat_length=[2,2],fps=fps)
+        print(f"video idx {i},\t audio idx {selected_idx},\t shape {selected_feature.shape}")
+        i += 1
+        if start_idx>len(array):
+            break
diff --git a/musetalk/whisper/requirements.txt b/musetalk/whisper/requirements.txt
new file mode 100644
index 0000000..a461403
--- /dev/null
+++ b/musetalk/whisper/requirements.txt
@@ -0,0 +1,6 @@
+numpy
+torch
+tqdm
+more-itertools
+transformers>=4.19.0
+ffmpeg-python==0.2.0
diff --git a/musetalk/whisper/setup.py b/musetalk/whisper/setup.py
new file mode 100644
index 0000000..a6d1fce
--- /dev/null
+++ b/musetalk/whisper/setup.py
@@ -0,0 +1,24 @@
+import os
+
+import pkg_resources
+from setuptools import setup, find_packages
+
+setup(
+    name="whisper",
+    py_modules=["whisper"],
+    version="1.0",
+    description="",
+    author="OpenAI",
+    packages=find_packages(exclude=["tests*"]),
+    install_requires=[
+        str(r)
+        for r in pkg_resources.parse_requirements(
+            open(os.path.join(os.path.dirname(__file__), "requirements.txt"))
+        )
+    ],
+    entry_points = {
+        'console_scripts': ['whisper=whisper.transcribe:cli'],
+    },
+    include_package_data=True,
+    extras_require={'dev': ['pytest']},
+)
diff --git a/musetalk/whisper/whisper.egg-info/PKG-INFO b/musetalk/whisper/whisper.egg-info/PKG-INFO
new file mode 100644
index 0000000..3e4da1f
--- /dev/null
+++ b/musetalk/whisper/whisper.egg-info/PKG-INFO
@@ -0,0 +1,5 @@
+Metadata-Version: 2.1
+Name: whisper
+Version: 1.0
+Author: OpenAI
+Provides-Extra: dev
diff --git a/musetalk/whisper/whisper.egg-info/SOURCES.txt b/musetalk/whisper/whisper.egg-info/SOURCES.txt
new file mode 100644
index 0000000..9f364f8
--- /dev/null
+++ b/musetalk/whisper/whisper.egg-info/SOURCES.txt
@@ -0,0 +1,18 @@
+setup.py
+whisper/__init__.py
+whisper/__main__.py
+whisper/audio.py
+whisper/decoding.py
+whisper/model.py
+whisper/tokenizer.py
+whisper/transcribe.py
+whisper/utils.py
+whisper.egg-info/PKG-INFO
+whisper.egg-info/SOURCES.txt
+whisper.egg-info/dependency_links.txt
+whisper.egg-info/entry_points.txt
+whisper.egg-info/requires.txt
+whisper.egg-info/top_level.txt
+whisper/normalizers/__init__.py
+whisper/normalizers/basic.py
+whisper/normalizers/english.py
\ No newline at end of file
diff --git a/musetalk/whisper/whisper.egg-info/dependency_links.txt b/musetalk/whisper/whisper.egg-info/dependency_links.txt
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/musetalk/whisper/whisper.egg-info/dependency_links.txt
@@ -0,0 +1 @@
+
diff --git a/musetalk/whisper/whisper.egg-info/entry_points.txt b/musetalk/whisper/whisper.egg-info/entry_points.txt
new file mode 100644
index 0000000..d4542b9
--- /dev/null
+++ b/musetalk/whisper/whisper.egg-info/entry_points.txt
@@ -0,0 +1,2 @@
+[console_scripts]
+whisper = whisper.transcribe:cli
diff --git a/musetalk/whisper/whisper.egg-info/requires.txt b/musetalk/whisper/whisper.egg-info/requires.txt
new file mode 100644
index 0000000..e1f7483
--- /dev/null
+++ b/musetalk/whisper/whisper.egg-info/requires.txt
@@ -0,0 +1,9 @@
+numpy
+torch
+tqdm
+more-itertools
+transformers>=4.19.0
+ffmpeg-python==0.2.0
+
+[dev]
+pytest
diff --git a/musetalk/whisper/whisper.egg-info/top_level.txt b/musetalk/whisper/whisper.egg-info/top_level.txt
new file mode 100644
index 0000000..0ccd48f
--- /dev/null
+++ b/musetalk/whisper/whisper.egg-info/top_level.txt
@@ -0,0 +1 @@
+whisper
diff --git a/musetalk/whisper/whisper/__init__.py b/musetalk/whisper/whisper/__init__.py
new file mode 100644
index 0000000..b925553
--- /dev/null
+++ b/musetalk/whisper/whisper/__init__.py
@@ -0,0 +1,116 @@
+import hashlib
+import io
+import os
+import urllib
+import warnings
+from typing import List, Optional, Union
+
+import torch
+from tqdm import tqdm
+
+from .audio import load_audio, log_mel_spectrogram, pad_or_trim
+from .decoding import DecodingOptions, DecodingResult, decode, detect_language
+from .model import Whisper, ModelDimensions
+from .transcribe import transcribe
+
+
+_MODELS = {
+    "tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt",
+    "tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt",
+    "base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt",
+    "base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt",
+    "small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt",
+    "small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt",
+    "medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt",
+    "medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
+    "large": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt",
+    "large-v1": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large-v1.pt",
+    "large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
+    "large-v3": "https://openaipublic.azureedge.net/main/whisper/models/e5b1a55b89c1367dacf97e3e19bfd829a01529dbfdeefa8caeb59b3f1b81dadb/large-v3.pt",
+}
+
+
+def _download(url: str, root: str, in_memory: bool) -> Union[bytes, str]:
+    os.makedirs(root, exist_ok=True)
+
+    expected_sha256 = url.split("/")[-2]
+    download_target = os.path.join(root, os.path.basename(url))
+
+    if os.path.exists(download_target) and not os.path.isfile(download_target):
+        raise RuntimeError(f"{download_target} exists and is not a regular file")
+
+    if os.path.isfile(download_target):
+        model_bytes = open(download_target, "rb").read()
+        if hashlib.sha256(model_bytes).hexdigest() == expected_sha256:
+            return model_bytes if in_memory else download_target
+        else:
+            warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file")
+
+    with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
+        with tqdm(total=int(source.info().get("Content-Length")), ncols=80, unit='iB', unit_scale=True, unit_divisor=1024) as loop:
+            while True:
+                buffer = source.read(8192)
+                if not buffer:
+                    break
+
+                output.write(buffer)
+                loop.update(len(buffer))
+
+    model_bytes = open(download_target, "rb").read()
+    if hashlib.sha256(model_bytes).hexdigest() != expected_sha256:
+        raise RuntimeError("Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.")
+
+    return model_bytes if in_memory else download_target
+
+
+def available_models() -> List[str]:
+    """Returns the names of available models"""
+    return list(_MODELS.keys())
+
+
+def load_model(name: str, device: Optional[Union[str, torch.device]] = None, download_root: str = None, in_memory: bool = False) -> Whisper:
+    """
+    Load a Whisper ASR model
+
+    Parameters
+    ----------
+    name : str
+        one of the official model names listed by `whisper.available_models()`, or
+        path to a model checkpoint containing the model dimensions and the model state_dict.
+    device : Union[str, torch.device]
+        the PyTorch device to put the model into
+    download_root: str
+        path to download the model files; by default, it uses "~/.cache/whisper"
+    in_memory: bool
+        whether to preload the model weights into host memory
+
+    Returns
+    -------
+    model : Whisper
+        The Whisper ASR model instance
+    """
+
+    if device is None:
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+    if download_root is None:
+        download_root = os.getenv(
+            "XDG_CACHE_HOME", 
+            os.path.join(os.path.expanduser("~"), ".cache", "whisper")
+        )
+
+    if name in _MODELS:
+        checkpoint_file = _download(_MODELS[name], download_root, in_memory)
+    elif os.path.isfile(name):
+        checkpoint_file = open(name, "rb").read() if in_memory else name
+    else:
+        raise RuntimeError(f"Model {name} not found; available models = {available_models()}")
+
+    with (io.BytesIO(checkpoint_file) if in_memory else open(checkpoint_file, "rb")) as fp:
+        checkpoint = torch.load(fp, map_location=device)
+    del checkpoint_file
+
+    dims = ModelDimensions(**checkpoint["dims"])
+    model = Whisper(dims)
+    model.load_state_dict(checkpoint["model_state_dict"])
+
+    return model.to(device)
diff --git a/musetalk/whisper/whisper/__main__.py b/musetalk/whisper/whisper/__main__.py
new file mode 100644
index 0000000..bc9b04a
--- /dev/null
+++ b/musetalk/whisper/whisper/__main__.py
@@ -0,0 +1,4 @@
+from .transcribe import cli
+
+
+cli()
diff --git a/musetalk/whisper/whisper/assets/gpt2/merges.txt b/musetalk/whisper/whisper/assets/gpt2/merges.txt
new file mode 100644
index 0000000..6636bda
--- /dev/null
+++ b/musetalk/whisper/whisper/assets/gpt2/merges.txt
@@ -0,0 +1,50001 @@
+#version: 0.2 - Trained by `huggingface/tokenizers`
+Ġ t
+Ġ a
+h e
+i n
+r e
+o n
+Ġt he
+e r
+Ġ s
+a t
+Ġ w
+Ġ o
+e n
+Ġ c
+i t
+i s
+a n
+o r
+e s
+Ġ b
+e d
+Ġ f
+in g
+Ġ p
+o u
+Ġa n
+a l
+a r
+Ġt o
+Ġ m
+Ġo f
+Ġ in
+Ġ d
+Ġ h
+Ġan d
+i c
+a s
+l e
+Ġt h
+i on
+o m
+l l
+en t
+Ġ n
+Ġ l
+s t
+Ġ re
+v e
+Ġ e
+r o
+l y
+Ġb e
+Ġ g
+Ġ T
+c t
+Ġ S
+i d
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+Ġ I
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+e t
+Ġ A
+Ġ is
+Ġ on
+i m
+a m
+o w
+a y
+a d
+s e
+Ġth at
+Ġ C
+i g
+Ġf or
+a c
+Ġ y
+v er
+u r
+Ġ u
+l d
+Ġs t
+Ġ M
+' s
+Ġ he
+Ġ it
+at ion
+it h
+i r
+c e
+Ġy ou
+i l
+Ġ B
+Ġw h
+o l
+Ġ P
+Ġw ith
+Ġ 1
+t er
+c h
+Ġa s
+Ġw e
+Ġ (
+n d
+i ll
+Ġ D
+i f
+Ġ 2
+a g
+er s
+k e
+Ġ "
+Ġ H
+e m
+Ġc on
+Ġ W
+Ġ R
+he r
+Ġw as
+Ġ r
+o d
+Ġ F
+u l
+at e
+Ġa t
+r i
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+o re
+ĠT he
+Ġs e
+u s
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+Ġh a
+u m
+Ġa re
+Ġd e
+a in
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+ig h
+es t
+is t
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+Ġ N
+t h
+Ġc om
+Ġ G
+u n
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+Ġ L
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+Ġ E
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+it y
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+Ġb y
+e l
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+or t
+o c
+q u
+Ġf rom
+Ġha ve
+Ġs u
+i ve
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+Ġs h
+Ġth is
+n t
+r a
+p e
+igh t
+ar t
+m ent
+Ġa l
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+Ġc h
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+Ġa b
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+Ġ j
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+ver y
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+, "
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+Ġbe en
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+Ġan y
+Ġa pp
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+ation s
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+Ġ 4
+ic al
+Ġthe m
+Ġhe r
+ou nt
+ĠC h
+Ġa r
+Ġ if
+Ġthe re
+Ġp e
+Ġy ear
+a v
+Ġm y
+Ġs ome
+Ġwhe n
+ou gh
+ac h
+Ġth an
+r u
+on d
+ic k
+Ġo ver
+ve l
+Ġ qu
+Ċ Ċ
+Ġs c
+re at
+re e
+ĠI t
+ou nd
+p ort
+Ġal so
+Ġp art
+f ter
+Ġk n
+Ġbe c
+Ġt ime
+en s
+Ġ 5
+op le
+Ġwh at
+Ġn o
+d u
+m er
+an g
+Ġn ew
+-- --
+Ġg et
+or y
+it ion
+ing s
+Ġj ust
+Ġint o
+Ġ 0
+ent s
+o ve
+t e
+Ġpe ople
+Ġp re
+Ġit s
+Ġre c
+Ġt w
+i an
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+Ġwor k
+ad e
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+Ġ ro
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+Ġa fter
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+o ld
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+oc k
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+) .
+id ent
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+Ġcon s
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+Ġw ant
+Ġse e
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+c ed
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+Ġf l
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+Ġsh ould
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+) ,
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+= =
+ib le
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+er t
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+ur ing
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+ic s
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+at ive
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+il ity
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+ers on
+Ġth ose
+Ġsu ch
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+as on
+t ing
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+e f
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+m s
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+t s
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+Ġg u
+Ġin st
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+in ce
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+Ġ |
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+ver n
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+u ally
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+Ġ2 00
+1 0
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+u res
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+sel f
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+u es
+c y
+Ġm in
+oll ow
+Ġc ol
+i o
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+he s
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+a ir
+i er
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+re ad
+an k
+at ch
+e ver
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+Ġpo int
+or k
+ĠN ew
+Ġs ur
+o ol
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+em ent
+Ġus ed
+ra ct
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+ou n
+ĠA l
+c i
+Ġdiff ere
+Ġwh ile
+---- ----
+Ġg ame
+ce pt
+Ġs im
+.. .
+Ġin ter
+e k
+Ġre port
+Ġpro du
+Ġst ill
+l ed
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+Ġhe re
+Ġwor ld
+Ġth ough
+Ġn um
+ar ch
+im es
+al e
+ĠS e
+ĠI f
+/ /
+ĠL e
+Ġre t
+Ġre f
+Ġtr ans
+n er
+ut ion
+ter s
+Ġt ake
+ĠC l
+Ġcon f
+w ay
+a ve
+Ġgo ing
+Ġs l
+u g
+ĠA meric
+Ġspe c
+Ġh and
+Ġbet ween
+ist s
+ĠD e
+o ot
+I t
+Ġe ar
+Ġagain st
+Ġh igh
+g an
+a z
+at her
+Ġex p
+Ġo p
+Ġin s
+Ġg r
+Ġhel p
+Ġre qu
+et s
+in s
+ĠP ro
+is m
+Ġf ound
+l and
+at a
+us s
+am es
+Ġp erson
+Ġg reat
+p r
+Ġs ign
+ĠA n
+' ve
+Ġs omet
+Ġs er
+h ip
+Ġr un
+Ġ :
+Ġt er
+ire ct
+Ġf ollow
+Ġd et
+ic es
+Ġf ind
+1 2
+Ġm em
+Ġc r
+e red
+e x
+Ġex t
+ut h
+en se
+c o
+Ġte am
+v ing
+ou se
+as h
+at t
+v ed
+Ġsy stem
+ĠA s
+d er
+iv es
+m in
+Ġle ad
+ĠB l
+c ent
+Ġa round
+Ġgo vern
+Ġc ur
+vel op
+an y
+Ġc our
+al th
+ag es
+iz e
+Ġc ar
+od e
+Ġl aw
+Ġre ad
+' m
+c on
+Ġre al
+Ġsupp ort
+Ġ1 2
+.. ..
+Ġre ally
+n ess
+Ġf act
+Ġd ay
+Ġb oth
+y ing
+Ġs erv
+ĠF or
+Ġth ree
+Ġw om
+Ġm ed
+od y
+ĠThe y
+5 0
+Ġex per
+t on
+Ġe ach
+ak es
+Ġc he
+Ġc re
+in es
+Ġre p
+1 9
+g g
+ill ion
+Ġg rou
+ut e
+i k
+W e
+g et
+E R
+Ġm et
+Ġs ays
+o x
+Ġd uring
+er n
+iz ed
+a red
+Ġf am
+ic ally
+Ġha pp
+ĠI s
+Ġch ar
+m ed
+v ent
+Ġg ener
+i ent
+p le
+i et
+re nt
+1 1
+v es
+pt ion
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diff --git a/musetalk/whisper/whisper/assets/multilingual/added_tokens.json b/musetalk/whisper/whisper/assets/multilingual/added_tokens.json
new file mode 100644
index 0000000..5c50999
--- /dev/null
+++ b/musetalk/whisper/whisper/assets/multilingual/added_tokens.json
@@ -0,0 +1 @@
+{"<|endoftext|>": 50257}
diff --git a/musetalk/whisper/whisper/assets/multilingual/merges.txt b/musetalk/whisper/whisper/assets/multilingual/merges.txt
new file mode 100644
index 0000000..afde9f4
--- /dev/null
+++ b/musetalk/whisper/whisper/assets/multilingual/merges.txt
@@ -0,0 +1,50000 @@
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diff --git a/musetalk/whisper/whisper/assets/multilingual/special_tokens_map.json b/musetalk/whisper/whisper/assets/multilingual/special_tokens_map.json
new file mode 100644
index 0000000..817762d
--- /dev/null
+++ b/musetalk/whisper/whisper/assets/multilingual/special_tokens_map.json
@@ -0,0 +1 @@
+{"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}
\ No newline at end of file
diff --git a/musetalk/whisper/whisper/assets/multilingual/tokenizer_config.json b/musetalk/whisper/whisper/assets/multilingual/tokenizer_config.json
new file mode 100644
index 0000000..0235ccf
--- /dev/null
+++ b/musetalk/whisper/whisper/assets/multilingual/tokenizer_config.json
@@ -0,0 +1 @@
+{"unk_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "eos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "add_prefix_space": false, "model_max_length": 1024, "special_tokens_map_file": null, "name_or_path": "multilingual", "errors": "replace", "tokenizer_class": "GPT2Tokenizer"}
\ No newline at end of file
diff --git a/musetalk/whisper/whisper/assets/multilingual/vocab.json b/musetalk/whisper/whisper/assets/multilingual/vocab.json
new file mode 100644
index 0000000..406c3f2
--- /dev/null
+++ b/musetalk/whisper/whisper/assets/multilingual/vocab.json
@@ -0,0 +1 @@
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7569, "Ñį": 7570, "ĠÐĿÑĥ": 7571, "music": 7572, "ìĽIJ": 7573, "Ġgan": 7574, "ìĭł": 7575, "Ġdefault": 7576, "Ġbem": 7577, "Ùī": 7578, "Ġforced": 7579, "ĠObviously": 7580, "Ġstone": 7581, "Ġtie": 7582, "Ġdrinking": 7583, "Ġserved": 7584, "Cause": 7585, "Ġconference": 7586, "ĠExactly": 7587, "ãĥĪ": 7588, "łľ": 7589, "ìĻĢ": 7590, "ĠRa": 7591, "Ġfake": 7592, "Ġdiff": 7593, "ãģ©": 7594, "Ġchallenging": 7595, "Ġì¤ij": 7596, "Ïĩ": 7597, "ä»Ģ麼": 7598, "Ġintelligence": 7599, "rete": 7600, "Ġstudying": 7601, "Ġappoint": 7602, "Ġtan": 7603, "Ġим": 7604, "Ġcurve": 7605, "ĠTeam": 7606, "ĠAz": 7607, "Ġзд": 7608, "ĠMusic": 7609, "field": 7610, "iration": 7611, "Ġfailed": 7612, "Ġnovel": 7613, "Ġdifferently": 7614, "Ġescape": 7615, "ĠYo": 7616, "ĠOctober": 7617, "ıyor": 7618, "Ġdescribed": 7619, "Ġconvert": 7620, "acement": 7621, "Ġhotel": 7622, "isation": 7623, "Ġsuis": 7624, "ãģij": 7625, "åŃIJ": 7626, "æĢİ": 7627, "Ġwalked": 7628, "200": 7629, "Ġneighborhood": 7630, "isp": 7631, "ĠLos": 7632, 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"hern": 7894, "idos": 7895, "Ġemphas": 7896, "Ġmessages": 7897, "Ġgender": 7898, "ĠWenn": 7899, "Ġво": 7900, "Ġprices": 7901, "olo": 7902, "Ġпон": 7903, "wing": 7904, "ĠFil": 7905, "аем": 7906, "ĠCur": 7907, "Ġfalse": 7908, "Ġfields": 7909, "Ġsé": 7910, "24": 7911, "Ġmac": 7912, "uÅŁ": 7913, "Ġlayers": 7914, "Ġadvoc": 7915, "wan": 7916, "Ġkar": 7917, "ĠÅŀ": 7918, "Ġdecor": 7919, "Ġwalls": 7920, "oe": 7921, "issions": 7922, "Ġresol": 7923, "×¢": 7924, "ĠCarol": 7925, "ĠVide": 7926, "leep": 7927, "ĠYOU": 7928, "Ġflip": 7929, "Ġsurgery": 7930, "Ġchop": 7931, "UR": 7932, ".,": 7933, "Ġagency": 7934, "Ġwanting": 7935, "Ġsolar": 7936, "Ġhoriz": 7937, "ĠAdam": 7938, "Ġstaying": 7939, "olic": 7940, "Ġgrateful": 7941, "Ġremark": 7942, "Ġtechnologies": 7943, "Ġprotein": 7944, "å¿ĥ": 7945, "дел": 7946, "ĠMont": 7947, "Ġshoulder": 7948, "Ġza": 7949, "rey": 7950, "ĠOoh": 7951, "Ġsty": 7952, "icar": 7953, "оÑĤÑĢ": 7954, "Ġroute": 7955, "ĠTurn": 7956, "Ġbom": 7957, "Ġdebate": 7958, "Ġpossibility": 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"igs": 17156, "ologically": 17157, "opp": 17158, "Ġdizer": 17159, "ĠAllen": 17160, "Ġgli": 17161, "Ġacceleration": 17162, "Ġvitamin": 17163, "ãĥŃ": 17164, "vä": 17165, "ĠAccess": 17166, "à®Ļ": 17167, "rás": 17168, "Ġappreciated": 17169, "Ġnah": 17170, "Ġposter": 17171, "Ġtale": 17172, "Ġhighlighted": 17173, "æĸĩ": 17174, "żeli": 17175, "Ġblockchain": 17176, "Ġmicrow": 17177, "Ġcinema": 17178, "ĠChang": 17179, "ĠSearch": 17180, "usters": 17181, "ĠZero": 17182, "ĠDivision": 17183, "ÑĢаÑģ": 17184, "Ġscare": 17185, "Ġjelly": 17186, "ĠAdministration": 17187, "SO": 17188, "Ġlined": 17189, "Ġê°Ħ": 17190, "Ġgeben": 17191, "Ġsoda": 17192, "Ġwinners": 17193, "³¼": 17194, "ÙĴ": 17195, "ĠAmb": 17196, "åķıé¡Į": 17197, "åĶ": 17198, "Ġpeg": 17199, "å·±": 17200, "43": 17201, "Ġraus": 17202, "Ġrewards": 17203, "Ġinclus": 17204, "Ġhighway": 17205, "Ġhah": 17206, "Ġmultiplied": 17207, "Ġsẽ": 17208, "Ġdisciples": 17209, "Ġning": 17210, "Ġdressing": 17211, "Ġattributes": 17212, "ĠMosc": 17213, "ĠGreece": 17214, "Ġsek": 17215, "ĠLearn": 17216, "Ġjus": 17217, "rendre": 17218, "Ġpersonne": 17219, "plete": 17220, "Ġplacing": 17221, "Ġluego": 17222, "illance": 17223, "ĠобÑī": 17224, "Ġprovision": 17225, "Ġlion": 17226, "tra": 17227, "boards": 17228, "Ġbehaviour": 17229, "hey": 17230, "Ġsubscription": 17231, "Ġprotagon": 17232, "ãĥ£": 17233, "Ġvara": 17234, "ĠÅŁu": 17235, "Ġhaha": 17236, "Ġteaspoon": 17237, "æŁ": 17238, "avoir": 17239, "Ġcrypto": 17240, "ĠÑģÑĤаÑĢ": 17241, "ĠStore": 17242, "abs": 17243, "ĠStudents": 17244, "Ġlaund": 17245, "into": 17246, "Ġapproached": 17247, "°ľ": 17248, "ÑĥÑİÑī": 17249, "ĠLabor": 17250, "otes": 17251, "iatric": 17252, "ĠgroÃŁ": 17253, "utive": 17254, "Ġид": 17255, "ĠGib": 17256, "Ġplacement": 17257, "ĠdifÃŃcil": 17258, "Ġfrog": 17259, "ĠвÑģеÑħ": 17260, "ĠJr": 17261, "azed": 17262, "ÑĥÑī": 17263, "Ġê¼": 17264, "frame": 17265, "аеÑĪÑĮ": 17266, "Ġlockdown": 17267, "åij³": 17268, "Ġmedi": 17269, "Ġ×Ķ×ŀ×": 17270, "ений": 17271, "emale": 17272, "ì¢ħ": 17273, "ateral": 17274, "Ġdistant": 17275, "Ġbears": 17276, "Ġjournalist": 17277, "解": 17278, "ĠMarshall": 17279, "ĠIhnen": 17280, "uetooth": 17281, "bag": 17282, "ĠÄijã": 17283, "ĠHighness": 17284, "Ġì°į": 17285, "ика": 17286, "ĠWu": 17287, "ĠFran": 17288, "Ġpeng": 17289, "Ġfon": 17290, "Ġhypothesis": 17291, "ĠÑĢÑĥ": 17292, "Ġly": 17293, "×ļ": 17294, "ìĽĶ": 17295, "ĠRadio": 17296, "à¸ŀ": 17297, "Dav": 17298, "Ġembarrassing": 17299, "ĠìŀĪìĸ´": 17300, "Ġcasting": 17301, "Ġcage": 17302, "ĠPsych": 17303, "ĠìĿ¼ëĭ¨": 17304, "Ġž": 17305, "imb": 17306, "Ġdirectors": 17307, "SH": 17308, "ĠÏĦην": 17309, "á»ģu": 17310, "ĠkonuÅŁ": 17311, "Ġoptional": 17312, "quarters": 17313, "iker": 17314, "ĠSant": 17315, "Ġverses": 17316, "ë¶Ģ": 17317, "Ġolar": 17318, "ĠÏĩ": 17319, "ãĥķ": 17320, "Ġγια": 17321, "ĠImm": 17322, "Ġcontroversial": 17323, "Ġersten": 17324, "Ġrecip": 17325, "ĠChristianity": 17326, "Ġê´ľ": 17327, "ordon": 17328, "×ķש": 17329, "Ġslash": 17330, "ĠPf": 17331, "ÑĥдÑĮ": 17332, "×ķ×Ŀ": 17333, "ĠPerry": 17334, "Ġmamy": 17335, "Ġbackgrounds": 17336, "Ġà®İன": 17337, "Ġpendant": 17338, "ĠColumbia": 17339, "Ġinverse": 17340, "ĠÑĩеÑĢез": 17341, "Ġsv": 17342, "Ġdigging": 17343, "41": 17344, "chem": 17345, "Ġnavigation": 17346, "ĠShin": 17347, "ĠFront": 17348, "PD": 17349, "Ġbearing": 17350, "ĠWasser": 17351, "Ġwax": 17352, "ĠCHRIS": 17353, "ching": 17354, "Ġpressed": 17355, "El": 17356, "ĠDal": 17357, "onsin": 17358, "Ġbinding": 17359, "Ñģкой": 17360, "poons": 17361, "Ġmock": 17362, "arest": 17363, "кÑĢа": 17364, "MM": 17365, "Ġcorrupt": 17366, "storm": 17367, "Ġrefres": 17368, "ĠCoach": 17369, "llä": 17370, "ĠTHIS": 17371, "Ġparag": 17372, "Ġìĵ°": 17373, "pool": 17374, "Ġbillions": 17375, "Ġê¹Ģ": 17376, "group": 17377, "Ġwelcoming": 17378, "cellence": 17379, "ĠDuke": 17380, "긴": 17381, "Ġprimera": 17382, "ìł¸": 17383, "Ġpond": 17384, "Ġstatue": 17385, "Ġ구ë": 17386, "Ġhatch": 17387, "Ġinstrumental": 17388, "Ġresidential": 17389, "커": 17390, "Ġaccepting": 17391, "oshi": 17392, "date": 17393, "ĠìĶ¨": 17394, "Ġplanted": 17395, "Ġjoking": 17396, "ĠìĦľ": 17397, "Ġhated": 17398, "ĠÑĢаÑģÑģк": 17399, "Ġslept": 17400, "Ġpackages": 17401, "Ġislands": 17402, "esen": 17403, "ģı": 17404, "Ġdiagon": 17405, "ĠOsc": 17406, "Ġmesh": 17407, "Ġscales": 17408, "arity": 17409, "ĠDefense": 17410, "ãģ¡ãĤĩ": 17411, "ĠLewis": 17412, "ĠÑģегоднÑı": 17413, "Ġflies": 17414, "uinely": 17415, "ĠConsider": 17416, "Ġstark": 17417, "hew": 17418, "ĠAsÃŃ": 17419, "³´ë": 17420, "Ġpropose": 17421, "Ġíķĺë©´": 17422, "odo": 17423, "ĠNormally": 17424, "Ġheeft": 17425, "ĠHarris": 17426, "gro": 17427, "ĠBlood": 17428, "base": 17429, "ĠiOS": 17430, "Ġtouches": 17431, "Ġinspir": 17432, "Ġ×ĵ": 17433, "Ġbinary": 17434, "Ġì¶Ķ": 17435, "Ġserial": 17436, "Ġion": 17437, "Ġunemployment": 17438, "Ġodds": 17439, "ĠFab": 17440, "ĠFBI": 17441, "BRUN": 17442, "Ġweights": 17443, "νο": 17444, "atile": 17445, "Ġnurses": 17446, "Ġinvolvement": 17447, "ĠíĶ¼": 17448, "Ġgovernance": 17449, "ĠâĤ¬": 17450, "ÑĢÑĥп": 17451, "ierra": 17452, "íĺķ": 17453, "ĠJerry": 17454, "Ġbeard": 17455, "Ġsalvation": 17456, "ĠAlong": 17457, "gentle": 17458, "ĠKi": 17459, "bol": 17460, "ĠPlat": 17461, "Ġhasht": 17462, "è¿ij": 17463, "Ġware": 17464, "Ġpartie": 17465, "ycz": 17466, "Ġintr": 17467, "Fih": 17468, "nent": 17469, "Ġcheat": 17470, "ilen": 17471, "Ġë¯": 17472, "orie": 17473, "Ġfácil": 17474, "etric": 17475, "Ġaffecting": 17476, "unciation": 17477, "Ġaffairs": 17478, "Ġbee": 17479, "Ġviewing": 17480, "Ġorang": 17481, "ĠLan": 17482, "ĠСÑĤ": 17483, "ä¸ĸ": 17484, "ĠMes": 17485, "ĥģ": 17486, "erie": 17487, "Ġespa": 17488, "Ġinterpre": 17489, "Ġpossess": 17490, "Ġpurely": 17491, "rito": 17492, "found": 17493, "asma": 17494, "ìłģìĿ¸": 17495, "Ġexamine": 17496, "ĠÑĥм": 17497, "Ġbesch": 17498, "ĠTomorrow": 17499, "ĠBlock": 17500, "Ġvariant": 17501, "Ġpreference": 17502, "Ġcoaches": 17503, "Ġmedications": 17504, "ĠíĺĦ": 17505, "Ġempire": 17506, "ëĦ¤": 17507, "ĠIllinois": 17508, "Ġcrispy": 17509, "Ġthì": 17510, "Ġbees": 17511, "77": 17512, "Ġglow": 17513, "èº": 17514, "ĠStudies": 17515, "åIJĦ": 17516, "ĠChallenge": 17517, "Ġunlikely": 17518, "Ч": 17519, "ıyorsun": 17520, "DIE": 17521, "Ġminimize": 17522, "izard": 17523, "Ġún": 17524, "Ġencontrar": 17525, "ĠKill": 17526, "å»": 17527, "Ġvanilla": 17528, "ĠGrant": 17529, "ĠGT": 17530, "sea": 17531, "Ġsought": 17532, "вод": 17533, "Ġnäm": 17534, "ĠAunt": 17535, "OWN": 17536, "Ġpumpkin": 17537, "stellen": 17538, "Ġrag": 17539, "егда": 17540, "Ġstoryt": 17541, "Ġforum": 17542, "æ©Ł": 17543, "Ġestaba": 17544, "uche": 17545, "Ġcongress": 17546, "ĠRey": 17547, "Ġdramatically": 17548, "ĠSport": 17549, "ĠYellow": 17550, "Ġê³ĦìĨį": 17551, "Ġdisgusting": 17552, "ĠRecent": 17553, "Ġacquired": 17554, "Ġcables": 17555, "çĶļ": 17556, "din": 17557, "Ġvisto": 17558, "Ġcommunicating": 17559, "ÑģÑĤавлÑı": 17560, "еÑģÑĤо": 17561, "ãĥ»ãĥ»ãĥ»": 17562, "Ġrég": 17563, "Ġsocks": 17564, "Ġproces": 17565, "because": 17566, "Ġutter": 17567, "Ġcolocar": 17568, "Ġnewest": 17569, "Ġgramm": 17570, "表": 17571, "ä¸įçŁ¥éģĵ": 17572, "Ġshifting": 17573, "Ġcarrier": 17574, "ĠÑģкоÑĢ": 17575, "ĠSchw": 17576, "Ġexecuted": 17577, "Ġmaintained": 17578, "ĠÏĨ": 17579, "ĠMoses": 17580, "Ġdisse": 17581, "Ġhorr": 17582, "ãĢľ": 17583, "Ġrally": 17584, "Ġallem": 17585, "ĠEventually": 17586, "Ġdiyor": 17587, "lvania": 17588, "Ġschnell": 17589, "Ġê³¼": 17590, "Ġ매": 17591, "Ġstruggles": 17592, "late": 17593, "Ġclarify": 17594, "ément": 17595, "Ġmultiplic": 17596, "ибо": 17597, "Ġjourn": 17598, "Ġfragr": 17599, "Ġsurprisingly": 17600, "Ġdesperate": 17601, "52": 17602, "Ġsul": 17603, "ĠRead": 17604, "ĠFried": 17605, "Ġmond": 17606, "woo": 17607, "Ġorganizing": 17608, "ãģĹãĤĩãģĨ": 17609, "ĠSoon": 17610, "ĠвопÑĢоÑģ": 17611, "ĠNur": 17612, "ĠÐĹд": 17613, "Ġspider": 17614, "еÑģÑı": 17615, "Ġtutorials": 17616, "Ġnutrients": 17617, "orer": 17618, "Ġcoefficient": 17619, "Ġarrangement": 17620, "Ġpricing": 17621, "nan": 17622, "yu": 17623, "BL": 17624, "Ġtribe": 17625, "ĠHoward": 17626, "unks": 17627, "Ġnewer": 17628, "Ġprovin": 17629, "Ġprediction": 17630, "hos": 17631, "Ġolsun": 17632, "ĠAround": 17633, "Ġvier": 17634, "ĠÑģÑĤоÑĢон": 17635, "Ġvalley": 17636, "ĠEla": 17637, "ifi": 17638, "Ġgalaxy": 17639, "Ġtranqu": 17640, "Ġadvers": 17641, "ĠTemple": 17642, "iffs": 17643, "igence": 17644, "èĩªå·±": 17645, "Ġkönnte": 17646, "ĠÄijó": 17647, "Did": 17648, "Ġphotographs": 17649, "ĠAWS": 17650, "ÑĨиÑı": 17651, "Ġguards": 17652, "Ġappointed": 17653, "ĠGil": 17654, "Ġмом": 17655, "Ġcod": 17656, "ĠUnlike": 17657, "Ġevenly": 17658, "isconsin": 17659, "Ġestou": 17660, "Ġmnie": 17661, "ĠExec": 17662, "ĠMV": 17663, "ĠEine": 17664, "ä¿¡": 17665, "ĠRoger": 17666, "ĠFac": 17667, "ĠList": 17668, "Ġfuer": 17669, "аеÑĤе": 17670, "omed": 17671, "Ġattraction": 17672, "èī²": 17673, "Ġterrain": 17674, "ĠDrop": 17675, "Ġcorporations": 17676, "Ġsciences": 17677, "Ġthrone": 17678, "ãģĦãģŁ": 17679, "Ġaj": 17680, "ĠRot": 17681, "çī¹": 17682, "Ġsupporters": 17683, "ĠBere": 17684, "Here": 17685, "Ġdiferentes": 17686, "Ġsignificance": 17687, "Ïĥη": 17688, "æĪij覺å¾Ĺ": 17689, "Ġclamp": 17690, "ĠëĮĢë": 17691, "Ġfabulous": 17692, "rez": 17693, "æĮģ": 17694, "Ġassumptions": 17695, "uther": 17696, "wid": 17697, "pot": 17698, "è¿İ": 17699, "Ġyan": 17700, "ulin": 17701, "ÑĢÑĭв": 17702, "ĠSlow": 17703, "ĠPennsy": 17704, "Ġíķ´ìĦľ": 17705, "Ġmeio": 17706, "Ġwealthy": 17707, "ĠEight": 17708, "Ġpulse": 17709, "Ġfriction": 17710, "idity": 17711, "ĠHoll": 17712, "iyorum": 17713, "Ġsounded": 17714, "ĠCarr": 17715, "Ġfork": 17716, "âĺ": 17717, "ĠPA": 17718, "Ġconspir": 17719, "Ġcoding": 17720, "rt": 17721, "ĠTyp": 17722, "Ġìĸij": 17723, "Ġпог": 17724, "Ġmiser": 17725, "ĠÑģмоÑĤÑĢ": 17726, "ĠSweden": 17727, "Ġolarak": 17728, "ĠZhang": 17729, "ĠChi": 17730, "ĠTitan": 17731, "Ġscreening": 17732, "ĠSpider": 17733, "ĠÅŀimdi": 17734, "Ġobstacles": 17735, "lara": 17736, "Ġchallenged": 17737, "pse": 17738, "TON": 17739, "ụ": 17740, "ĠPi": 17741, "Ġlagi": 17742, "ieurs": 17743, "Ġhurting": 17744, "Ġneglect": 17745, "Ġgenerating": 17746, "Ġyoungest": 17747, "Ġaudit": 17748, "ĠÑĢез": 17749, "Ïģά": 17750, "Ġdonate": 17751, "ĠPDF": 17752, "Ġvisits": 17753, "Ġcruise": 17754, "PP": 17755, "aser": 17756, "Ġwsp": 17757, "backs": 17758, "ivals": 17759, "ãģĨãĤĵ": 17760, "Ġdeve": 17761, "Ġproport": 17762, "Ġcath": 17763, "ĠEffect": 17764, "Ġwinds": 17765, "ĠìĻĶ": 17766, "Ġcharts": 17767, "Ġsama": 17768, "Ġautomation": 17769, "Ġпока": 17770, "Ġolan": 17771, "Ġboats": 17772, "Ġcafe": 17773, "Ġdenied": 17774, "ĠMama": 17775, "Ġblocking": 17776, "ĠThor": 17777, "Ġphenomenal": 17778, "Ġstakeholders": 17779, "Ġunos": 17780, "ÑĥеÑĤ": 17781, "ĠAbraham": 17782, "ãģ§ãĤĤ": 17783, "Ġdetection": 17784, "Ġjuris": 17785, "Ġpowered": 17786, "zial": 17787, "Ġwelfare": 17788, "Ġupgrad": 17789, "Ġmożna": 17790, "ĠCase": 17791, "cular": 17792, "ĶìĿ´": 17793, "ãĥģ": 17794, "ĠGuess": 17795, "Ġcycles": 17796, "ä¾ĭ": 17797, "給": 17798, "rock": 17799, "umi": 17800, "Ġelite": 17801, "Ġquè": 17802, "åł±": 17803, 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"Ġdesse": 17864, "Ġgri": 17865, "ĠChairman": 17866, "çħ": 17867, "Ġcuenta": 17868, "anim": 17869, "Ġcrab": 17870, "Ġescal": 17871, "Ġpremière": 17872, "ĠGef": 17873, "Ġdining": 17874, "Ġseventh": 17875, "Ġchasing": 17876, "ĠTower": 17877, "Ġbrutal": 17878, "Ġfundamentally": 17879, "ãģ¨ãģĨ": 17880, "лениÑı": 17881, "stage": 17882, "Ġacquis": 17883, "Ġcylinder": 17884, "Ġcommander": 17885, "mem": 17886, "ĠUV": 17887, "happy": 17888, "Ġepsilon": 17889, "Ġinvitation": 17890, "Ġfarmer": 17891, "chair": 17892, "Ġdestiny": 17893, "Ġsovere": 17894, "ĠHebrew": 17895, "Ġservant": 17896, "Ġbew": 17897, "Ġgast": 17898, "uties": 17899, "Ġadministrative": 17900, "ĠCommand": 17901, "éta": 17902, "Ġnitrogen": 17903, "ê·¼": 17904, "Ġabi": 17905, "Ġvillain": 17906, "Ġblanket": 17907, "ĠSend": 17908, "Ġbeaten": 17909, "²Ħ": 17910, "Ġvolunt": 17911, "Ġscholar": 17912, "ĠEmperor": 17913, "Ġ43": 17914, "vable": 17915, "ĠDus": 17916, "ĠGU": 17917, "Ġtargeting": 17918, "www": 17919, "Ġamendment": 17920, 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"code": 22332, "Ġhierarchy": 22333, "netes": 22334, "Ġcounterpart": 22335, "Ġtôi": 22336, "Ġted": 22337, "ĠBart": 22338, "ĠëĿ¼": 22339, "ĠGenau": 22340, "Ġincoming": 22341, "ĠABC": 22342, "rique": 22343, "ĠоÑĤп": 22344, "qual": 22345, "Ġincentive": 22346, "Ġihren": 22347, "׳×Ļ": 22348, "loe": 22349, "Ġ1930": 22350, "Ġbarg": 22351, "Ġdiction": 22352, "Ġönce": 22353, "INS": 22354, "Ġreh": 22355, "isiaj": 22356, "mouth": 22357, "Ġscoring": 22358, "lık": 22359, "ĠìķĦ주": 22360, "ORIA": 22361, "ĠEstados": 22362, "Ġcompanion": 22363, "Ġassemble": 22364, "Ġpunished": 22365, "Ġital": 22366, "Ġprevents": 22367, "istes": 22368, "ĠKentucky": 22369, "Ġlocate": 22370, "Ġfasting": 22371, "ãģ¨æĢĿ": 22372, "ĥĢ": 22373, "ĠSeb": 22374, "ĠCrown": 22375, "opia": 22376, "Ġwhip": 22377, "usz": 22378, "ками": 22379, "Ġdatabases": 22380, "åŃĹ": 22381, "Ġprosec": 22382, "Ġ1997": 22383, "ĠìĤ´ì§Ŀ": 22384, "ĠSolar": 22385, "ĠPues": 22386, "ĠZen": 22387, "ollo": 22388, "ĠGuru": 22389, "Ġsqueez": 22390, "ĠÐĹа": 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"ĠëIJ©ëĭĪëĭ¤": 23630, "Ġzeg": 23631, "åĪĩ": 23632, "ĠManhattan": 23633, "Ġsujet": 23634, "uelle": 23635, "stoff": 23636, "Ġdür": 23637, "Ġsubmar": 23638, "eses": 23639, "Ġaquele": 23640, "Ġnou": 23641, "ĠFaith": 23642, "tz": 23643, "ĠÑĤомÑĥ": 23644, "aceut": 23645, "liers": 23646, "Ġbandwidth": 23647, "Æ°á»Ŀ": 23648, "Ġrespective": 23649, "ĠAve": 23650, "Ġspreadshe": 23651, "ĠSent": 23652, "icamente": 23653, "Ġinfra": 23654, "Ġlearners": 23655, "Ġà®ī": 23656, "aiah": 23657, "renal": 23658, "Ġmustard": 23659, "Ġhabt": 23660, "çĥ": 23661, "ĠQué": 23662, "Ġanalyzing": 23663, "æ¯ı": 23664, "Ġsolic": 23665, "Ġ×Ķ×ķ×IJ": 23666, "Ġcausa": 23667, "Ġwelcomed": 23668, "ĠSuccess": 23669, "Ġfacile": 23670, "ĠÐŁÐ¾ÑĤомÑĥ": 23671, "schein": 23672, "Ġfetch": 23673, "Ġstrat": 23674, "ĠÑģÑĤоиÑĤ": 23675, "ìĹIJìĦľëĬĶ": 23676, "ĠÑģпоÑģоб": 23677, "mam": 23678, "ĠserÃŃa": 23679, "naments": 23680, "writer": 23681, "Ġconsulting": 23682, "íĺĢ": 23683, "ĠBerkeley": 23684, "eu": 23685, "asive": 23686, "UU": 23687, "ĠAnalyt": 23688, "Ġsubmission": 23689, "Ġmagnificent": 23690, "enza": 23691, "Ġecon": 23692, "Ġprofiles": 23693, "Ġincar": 23694, "Ab": 23695, "ĠNun": 23696, "Ġhic": 23697, "screaming": 23698, "Ġresilient": 23699, "åĪ©": 23700, "grund": 23701, "Ġconcur": 23702, "Ġbereits": 23703, "LD": 23704, "Ġnurt": 23705, "ìī": 23706, "Ġfeast": 23707, "Ġencuent": 23708, "ĠMichel": 23709, "Ġsuprem": 23710, "\"]": 23711, "Ġfeeds": 23712, "ĠKollegen": 23713, "isser": 23714, "ĠFeng": 23715, "ĠWen": 23716, "mun": 23717, "ĠtenÃŃa": 23718, "ĠWrest": 23719, "Ġìĺ¤ëĬĺìĿĢ": 23720, "Ġstead": 23721, "Ġrestoration": 23722, "Ġdonated": 23723, "Ġdels": 23724, "Ġcensus": 23725, "Ġdesperately": 23726, "worthy": 23727, "HE": 23728, "ĠSpa": 23729, "ĠBryan": 23730, "Ġhj": 23731, "ĠRaw": 23732, "ìķĦë": 23733, "ĠCamera": 23734, "Ġzien": 23735, "Ġstyl": 23736, "ĠTW": 23737, "ĠCheese": 23738, "borne": 23739, "Ġobl": 23740, "ĠAlready": 23741, "Ġunstable": 23742, "Ġflames": 23743, "post": 23744, "Ha": 23745, "romagn": 23746, "ĠìĹĦë§Ī": 23747, "dest": 23748, "Ġkolej": 23749, "Ġtemporarily": 23750, "Ġdetermining": 23751, "ĠGlass": 23752, "ÑĢон": 23753, "olan": 23754, "Ġdominated": 23755, "åĮĸ": 23756, "____": 23757, "ĠÙĩذا": 23758, "ĠDana": 23759, "Ġdinheiro": 23760, "aqu": 23761, "민": 23762, "ĠÃłs": 23763, "ĠJoey": 23764, "ĠGriff": 23765, "Ġattain": 23766, "Ġtransitions": 23767, "ĠLiterally": 23768, "енд": 23769, "ĠHaven": 23770, "Ġgrabbing": 23771, "Ġcrystals": 23772, "ĠFourth": 23773, "Ġcandles": 23774, "ĠÑģлÑĥÑĩа": 23775, "rico": 23776, "Ġ5000": 23777, "etto": 23778, "Ġundo": 23779, "Ġkto": 23780, "Ġdivert": 23781, "Ġchir": 23782, "Ġpersec": 23783, "Ġhiking": 23784, "Ġannouncements": 23785, "çĶ±": 23786, "зÑĭ": 23787, "Ġauc": 23788, "Ġsystemic": 23789, "ĠRM": 23790, "Ïĥα": 23791, "ĠÐĶж": 23792, "Ġyar": 23793, "ĠWard": 23794, "Ġpissed": 23795, "Ġcarn": 23796, "Ġautonomous": 23797, "ãħİãħİ": 23798, "sover": 23799, "æ²ĴéĮ¯": 23800, "å¾Ī好": 23801, "Ġreflex": 23802, "Ġgardens": 23803, "Ġdated": 23804, "ì±": 23805, "amiÄĻ": 23806, "Ġcontinuity": 23807, "Ġcitizenship": 23808, "Ġschwer": 23809, "Ġzak": 23810, "table": 23811, "ĠÑģÑĩ": 23812, "è§ģ": 23813, "ĠÏĥε": 23814, "Ġgenerates": 23815, "구ëĤĺ": 23816, "öh": 23817, "óm": 23818, "alam": 23819, "ĠJUDY": 23820, "ĠBug": 23821, "Ġãģ¦": 23822, "Ġdrones": 23823, "Ġágua": 23824, "acaks": 23825, "æļ": 23826, "ĠÐļон": 23827, "×ĸ×Ķ": 23828, "Ġstrive": 23829, "ĠAltern": 23830, "Ġnearest": 23831, "Ġproyect": 23832, "tera": 23833, "ĠASHLEY": 23834, "Ġworm": 23835, "Ġreplay": 23836, "Ġtara": 23837, "ĠIndians": 23838, "ãĤ°": 23839, "icaid": 23840, "ĠìĪľ": 23841, "Ġappealing": 23842, "ĠWes": 23843, "Ġmentions": 23844, "Ġделе": 23845, "Ġkw": 23846, "Ġfragile": 23847, "isz": 23848, "ków": 23849, "hang": 23850, "color": 23851, "Ġpresidente": 23852, "87": 23853, "еÑĦ": 23854, "çĪ¸": 23855, "Ġдобав": 23856, "ĠNelson": 23857, "áfic": 23858, "ĠMICHAEL": 23859, "Ġmechanic": 23860, "Ġmetres": 23861, "ĠoczywiÅĽcie": 23862, "ĠCind": 23863, "ĠogsÃ¥": 23864, "Ġlandsca": 23865, "ACE": 23866, "Ġheadlines": 23867, "Ġcatalyst": 23868, "ĠCatch": 23869, "inkles": 23870, "Ġpills": 23871, "ordo": 23872, "Ġimmigrant": 23873, "Ġexamination": 23874, "Ġaccidents": 23875, "zÄħd": 23876, "Ġquiere": 23877, "Ġnella": 23878, "Ġ67": 23879, "Ġpassa": 23880, "Ġsuperfic": 23881, "istor": 23882, "Ġnov": 23883, "ëĭµ": 23884, "Ġmandate": 23885, "isons": 23886, "ĠVirtual": 23887, "Ġselber": 23888, "Ġcounseling": 23889, "ĠNBA": 23890, "Ġsept": 23891, "Ġbeliever": 23892, "Ġmarvel": 23893, "ĠIntegr": 23894, "ĠмÑĸ": 23895, "Ġorph": 23896, "Ġbackward": 23897, "ĠGeneration": 23898, "ĠPict": 23899, "ĠÑĤоÑĤ": 23900, "Ġtapi": 23901, "prochen": 23902, "Ġhallway": 23903, "hte": 23904, "ĠÛģÛĴ": 23905, "ĠZum": 23906, "èĢģ師": 23907, "achment": 23908, "iquer": 23909, "folg": 23910, "ĠEddie": 23911, "ĠKil": 23912, "Ġwellness": 23913, "stock": 23914, "è¼ĥ": 23915, "Ġkaç": 23916, "Ġterrorism": 23917, "Ġpointer": 23918, "Of": 23919, "heric": 23920, "ĠUltimately": 23921, "Ġmeses": 23922, "ĠTrade": 23923, "Ġpint": 23924, "Ġtuition": 23925, "Ġdisagre": 23926, "Ġê²ĮìŀĦ": 23927, "Ġmanuscript": 23928, "Ġroomm": 23929, "Ġoutputs": 23930, "еÑĨи": 23931, "Ġries": 23932, "Ġsalud": 23933, "otzdem": 23934, "Ġmasses": 23935, "ĠbyÅĤa": 23936, "Ġclearing": 23937, "Ġdiscourse": 23938, "atson": 23939, "Ġfolded": 23940, "ĠJar": 23941, "ÙĦÙī": 23942, "900": 23943, "ĠÑĥÑģп": 23944, "Ġprophecy": 23945, "Ġinterfere": 23946, "иÑħод": 23947, "à¹Į": 23948, "Ġthri": 23949, "Ġ×ŀש": 23950, "Ġlazım": 23951, "Ġ1992": 23952, "Ġfuturo": 23953, "Ġlocking": 23954, "Ġembargo": 23955, "ĠNeither": 23956, "ivamente": 23957, "ĠmÃ¥ste": 23958, "Ġmik": 23959, "Ġcollector": 23960, "екоÑĤоÑĢ": 23961, "ĠGand": 23962, "Ġsentir": 23963, "ĠMight": 23964, "å¡Ķ": 23965, "Ġganzen": 23966, "UC": 23967, "Ġrelating": 23968, "SD": 23969, "Ġmosquito": 23970, "GR": 23971, "Ġhollow": 23972, "âĺħ": 23973, "ĠWalker": 23974, "Ġaffiliate": 23975, "Ġduplicate": 23976, "нем": 23977, "Ġgrape": 23978, "ĠOrganization": 23979, "Ġsynt": 23980, "Joe": 23981, "Ġgeg": 23982, "Ġrevealing": 23983, "ĠEthan": 23984, "outer": 23985, "Ġyay": 23986, "é«Ķ": 23987, "лаÑĢ": 23988, "Ġreportedly": 23989, "Ġihrer": 23990, "Ġrecognise": 23991, "Ġbumper": 23992, "ĠRandy": 23993, "ĠVenus": 23994, "tles": 23995, "Ġappetite": 23996, "Ġglucose": 23997, "Ġchodzi": 23998, "ĠFurthermore": 23999, "tir": 24000, "Ġconta": 24001, "Ġintuition": 24002, "Ġaltitude": 24003, "Ġchunks": 24004, "ĠJoshua": 24005, "ıģım": 24006, "rylic": 24007, "leans": 24008, "ĠíĶ¼ë": 24009, "LL": 24010, "Que": 24011, "Ġgor": 24012, "ĠзнаÑĩиÑĤ": 24013, "Ġpoems": 24014, "Ġexcel": 24015, "Ġexplored": 24016, "Ġpopul": 24017, "Ġincluso": 24018, "stä": 24019, "ĠGavin": 24020, "alling": 24021, "ĠÏĦον": 24022, "é©": 24023, "arbeit": 24024, "ĠGas": 24025, "Ġglorious": 24026, "rieben": 24027, "Ġspam": 24028, "Ġindoor": 24029, "Ġthrust": 24030, "ĠAld": 24031, "ĠPrior": 24032, "Ġonboard": 24033, "ãģłãģķãģĦ": 24034, "oca": 24035, "ASH": 24036, "£ł": 24037, "ĠChristine": 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27008, "Ġamendments": 27009, "Ġöz": 27010, "Ġdigits": 27011, "urai": 27012, "Ġchaotic": 27013, "ĠMasters": 27014, "eon": 27015, "ĠCash": 27016, "ĠCuz": 27017, "Ġbedeutet": 27018, "Ġscanning": 27019, "Ġжд": 27020, "неÑĤ": 27021, "Ġcertainty": 27022, "jek": 27023, "Ġdijo": 27024, "ĠClimate": 27025, "Ġrinse": 27026, "Ġkrij": 27027, "veland": 27028, "Ġsoundtrack": 27029, "ĠSafe": 27030, "ĠNova": 27031, "94": 27032, "Ġathe": 27033, "ĠVerb": 27034, "oler": 27035, "ìĿ´ì£ł": 27036, "Ġvin": 27037, "Ġrespiratory": 27038, "ĠStudy": 27039, "ĠCAM": 27040, "Ġavocado": 27041, "ĠZhen": 27042, "Ġlatency": 27043, "Ġfeathers": 27044, "Ġcontar": 27045, "ĠвеÑī": 27046, "Ġfark": 27047, "Ġblended": 27048, "Ġexploded": 27049, "ĠXX": 27050, "ĠBenim": 27051, "Ġalguém": 27052, "istoire": 27053, "Ġconfidential": 27054, "Ġmast": 27055, "Ġì¿": 27056, "geh": 27057, "Ġdisrespect": 27058, "ĠSystems": 27059, "Æ°a": 27060, "Ed": 27061, "Ġwys": 27062, "Ġexotic": 27063, "Ġglowing": 27064, "ùng": 27065, "ounge": 27066, 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27930, "Ġfreel": 27931, "ĠAlzheimer": 27932, "ĠÅļ": 27933, "Her": 27934, "Ġtornado": 27935, "agens": 27936, "ĠìŀĪìĸ´ìĦľ": 27937, "ĠTransform": 27938, "Ġprocesso": 27939, "Ġmillise": 27940, "Ġprofessionally": 27941, "Ġmemb": 27942, "ocation": 27943, "Ġstyling": 27944, "ĠобÑıз": 27945, "ĠOperation": 27946, "Ġwygl": 27947, "ĠRan": 27948, "ĠçļĦ": 27949, "ĠKin": 27950, "á»±c": 27951, "ĠBAR": 27952, "Ġpaperwork": 27953, "Ġtule": 27954, "Ġqueria": 27955, "Ġcomply": 27956, "ĠHair": 27957, "×Ļ׼": 27958, "ĠпÑĢоÑģÑĤ": 27959, "Ġmutation": 27960, "Ġreprés": 27961, "Ġoctopus": 27962, "Ġimportantes": 27963, "Ġdeserved": 27964, "etr": 27965, "Ġdisasters": 27966, "lında": 27967, "iqué": 27968, "ĠDeshalb": 27969, "soo": 27970, "ossip": 27971, "Ġrelieved": 27972, "ĠCollins": 27973, "Ġwaterproof": 27974, "ĠYuk": 27975, "Ġcopying": 27976, "Ġbütün": 27977, "ĠHeute": 27978, "ĠEntre": 27979, "Ġresidual": 27980, "Ġcolonies": 27981, "Ġénorm": 27982, "ĠErin": 27983, "Ġstan": 27984, "Ġtremendously": 27985, "Ġcaptures": 27986, "ĠSai": 27987, "âce": 27988, "ĠmiaÅĤ": 27989, "Ġ87": 27990, "Ġlogging": 27991, "Ġinserted": 27992, "Ġinherently": 27993, "ìĿij": 27994, "lave": 27995, "ниÑĩ": 27996, "Ġfemmes": 27997, "Ġdép": 27998, "uks": 27999, "acia": 28000, "ĠWade": 28001, "Ġjij": 28002, "ĠVincent": 28003, "ĠIceland": 28004, "hem": 28005, "Ġapology": 28006, "ĠPeg": 28007, "Ġglued": 28008, "Ġcompanions": 28009, "ĠLiver": 28010, "Ġcriticized": 28011, "leading": 28012, "Ġsäga": 28013, "æ¼Ĥ": 28014, "Ġsquid": 28015, "Ġnarratives": 28016, "Ġtaka": 28017, "nez": 28018, "weit": 28019, "Ġtripod": 28020, "Ġexplic": 28021, "Ġspinal": 28022, "Ġapproximation": 28023, "Ġpagar": 28024, "ĠCalvin": 28025, "ĠведÑĮ": 28026, "Ġlac": 28027, "Ġproactive": 28028, "ĠTrain": 28029, "orf": 28030, "Ġsten": 28031, "Ġgrapes": 28032, "Ġmeus": 28033, "Ġautomat": 28034, "Ġbiased": 28035, "Ġchaîne": 28036, "coal": 28037, "Ġrencont": 28038, "ĠKum": 28039, "Ġfestivals": 28040, "Ġstartups": 28041, "Ġaka": 28042, "ãģ¹": 28043, "Ġcylind": 28044, "sna": 28045, "CRI": 28046, "Ġresultado": 28047, "Ġmilestone": 28048, "ĠÏħ": 28049, "Ġteleport": 28050, "zych": 28051, "62": 28052, "åħ³": 28053, "ĠFear": 28054, "Ġnucleus": 28055, "Ġshines": 28056, "hov": 28057, "ĠPartners": 28058, "ĠKas": 28059, "Ġnadie": 28060, "Ġalerts": 28061, "ĠBILL": 28062, "strong": 28063, "ĠNate": 28064, "ĠDenmark": 28065, "ĠCav": 28066, "OST": 28067, "hält": 28068, "ĠìķĦëĭĮ": 28069, "anyon": 28070, "Ġencourages": 28071, "ĠпоÑģÑĤав": 28072, "ĠHuang": 28073, "ãģĬé¡ĺãģĦ": 28074, "STA": 28075, "Ġpaints": 28076, "ãģĻãģĶ": 28077, "Ġschedules": 28078, "Ġcheated": 28079, "Ġapprox": 28080, "Ġï·": 28081, "Ġ».": 28082, "Ġsmiles": 28083, "isure": 28084, "Ġnered": 28085, "arden": 28086, "Ġcurt": 28087, "ĠëĮ": 28088, "ĠRoth": 28089, "Ġpuisque": 28090, "ĠGET": 28091, "ĠVeget": 28092, "Ġproduz": 28093, "ĠBelgium": 28094, "ĠCampus": 28095, "ר×Ļ×Ŀ": 28096, "icut": 28097, "ĠÑģним": 28098, "Ġréuss": 28099, "Ġslippery": 28100, "ĠEw": 28101, "ų": 28102, "ĠLegends": 28103, "ĠTiffany": 28104, "ализ": 28105, "ĠпеÑĢев": 28106, "ĠогÑĢом": 28107, "Ġcros": 28108, "ĠCE": 28109, "Bu": 28110, "Ġensures": 28111, "Ġgrandchildren": 28112, "Ġacuerdo": 28113, "Ġprisoner": 28114, "Ġthirsty": 28115, "bane": 28116, "Ġë¹ł": 28117, "Ġúltima": 28118, "ĠLaunch": 28119, "nity": 28120, "Ġcombustion": 28121, "Ġunicorn": 28122, "Ġfamille": 28123, "Ġlowering": 28124, "ĠYing": 28125, "building": 28126, "Ġduo": 28127, "ĠMéxico": 28128, "astian": 28129, "Ġ먹ìĿĦ": 28130, "ĠRalph": 28131, "Ġrewrite": 28132, "Ġglam": 28133, "ifique": 28134, "Er": 28135, "ĠRunning": 28136, "онов": 28137, "Ġmeanings": 28138, "Ġchewy": 28139, "ĠLeslie": 28140, "Ġfinest": 28141, "Ġhahaha": 28142, "ĠSTEP": 28143, "Ġloneliness": 28144, "rians": 28145, "Ġquestioned": 28146, "Ġesque": 28147, "Ġsinking": 28148, "Ġpeso": 28149, "ĠWrong": 28150, "asmine": 28151, "Ġdefinitive": 28152, "Ġbuys": 28153, "Ġcruc": 28154, "cool": 28155, "ĠëłĪ": 28156, "Ġpó": 28157, "Ġutilized": 28158, "Ġworthwhile": 28159, "ĠDylan": 28160, "ESE": 28161, "Ġvertex": 28162, "tı": 28163, "ĠFir": 28164, "Ġzaw": 28165, "ĠGed": 28166, "ĠÐĿап": 28167, "dz": 28168, "Ġcursor": 28169, "Ġswipe": 28170, "Ġinevitably": 28171, "Ġposters": 28172, "Ġinclined": 28173, "Ġgreeting": 28174, "Ġdisappointment": 28175, "ãģ¾ãģ§": 28176, "Ġrelação": 28177, "TT": 28178, "Ġrabb": 28179, "ĠMaine": 28180, "Ġanalyzed": 28181, "FE": 28182, "ĠÐŁÐ¾Ð»": 28183, "ĠSandra": 28184, "Ġplague": 28185, "ARE": 28186, "Ġvär": 28187, "ĠViv": 28188, "umed": 28189, "hando": 28190, "houette": 28191, "ĠBailey": 28192, "ä¸įéģİ": 28193, "yson": 28194, "Ġsemua": 28195, "Ġhardcore": 28196, "âĤ¬": 28197, "Ñĸм": 28198, "éra": 28199, "OTH": 28200, "Ġforeigners": 28201, "ĠPalestinian": 28202, "Ġproprio": 28203, "аний": 28204, "Ġmyths": 28205, "WH": 28206, "Ġninth": 28207, "ĠCreator": 28208, "лом": 28209, "ĠFlip": 28210, "Ġeman": 28211, "ĠkiÅŁ": 28212, "zieh": 28213, "ĠEarnest": 28214, "system": 28215, "ĸìĹIJ": 28216, "Ġarmies": 28217, "ĠOutside": 28218, "Ġharus": 28219, "æºĸ": 28220, "одаÑĢ": 28221, "Ġvisitor": 28222, "çŃĶ": 28223, "Ġstrengthening": 28224, "Ġ92": 28225, "vio": 28226, "Ġ리": 28227, "Ġgreedy": 28228, "Ġpoquito": 28229, "uder": 28230, "ĠKopf": 28231, "Ġëĭ¤ìĿĮìĹIJ": 28232, "Ġseis": 28233, "ático": 28234, "Ġtrusting": 28235, "ÃŃp": 28236, "ĠEmm": 28237, "leen": 28238, "ĠاÙĦÙĨ": 28239, "Ġrecruitment": 28240, "ĠFilip": 28241, "ĠÙĥÙĦ": 28242, "Clint": 28243, "ĠвеÑģ": 28244, "auft": 28245, "Ġdominate": 28246, "Ġresto": 28247, "Ġkra": 28248, "ái": 28249, "ĠCait": 28250, "rows": 28251, "Ġcountryside": 28252, "Ġ1945": 28253, "аÑĨиÑİ": 28254, "Ġди": 28255, "Ġkernel": 28256, "lov": 28257, "Ġcalculating": 28258, "دا": 28259, "ĠWalt": 28260, "Ġempowering": 28261, "Ġchassis": 28262, "linear": 28263, "гÑĥ": 28264, "Ġnova": 28265, "Ġuy": 28266, "Ġ69": 28267, "Ġencompass": 28268, "trl": 28269, "Ġcomputational": 28270, "Ġworms": 28271, "Ġnhiá»ģu": 28272, "Ġastronauts": 28273, "Ġves": 28274, "Ġsytu": 28275, "Ġdemanded": 28276, "Ġcs": 28277, "ĠMol": 28278, "Ġ`": 28279, "Ġchant": 28280, "Ġthereby": 28281, "Ġpenis": 28282, "Ġemoc": 28283, "wyn": 28284, "Ñĥже": 28285, "Ġtread": 28286, "óle": 28287, "Ġdeepest": 28288, "Ġmache": 28289, "ĠVent": 28290, "ĠAmsterdam": 28291, "ãĥĽ": 28292, "Ġrebel": 28293, "Ġ61": 28294, "ĠвкÑĥÑģ": 28295, "uffs": 28296, "ĠdoÄŁru": 28297, "ĠNapole": 28298, "ήÏĥ": 28299, "Ġworkouts": 28300, "ĠGlad": 28301, "неÑģ": 28302, "Ġtensions": 28303, "ĠShift": 28304, "ĠGuer": 28305, "íĮIJ": 28306, "Ġì¹ľêµ¬": 28307, "Ðĸ": 28308, "Ġimplant": 28309, "êu": 28310, "ê¸Ģ": 28311, "Ġauthorized": 28312, "CER": 28313, "ĠRV": 28314, "Ġhil": 28315, "lev": 28316, "cimento": 28317, "ĠUFO": 28318, "ìĥĪ": 28319, "è¨Ĥ": 28320, "wor": 28321, "Ġdances": 28322, "ĠPixel": 28323, "çľĭä¸Ģä¸ĭ": 28324, "Ġtrotzdem": 28325, "Ġobten": 28326, "ĠAlfred": 28327, "Ġcostly": 28328, "ĠStanley": 28329, "Ġterrorists": 28330, "ĠWid": 28331, "ħëĭĪëĭ¤": 28332, "Ġleicht": 28333, "ìĿ´ìĬ¤": 28334, "Ġdobrze": 28335, "Ġhesit": 28336, "Ġerzäh": 28337, "Ġeinige": 28338, "Ġhebt": 28339, "Ñģе": 28340, "Ġunpredict": 28341, "Cómo": 28342, "remos": 28343, "ĠThankfully": 28344, "Ġpurse": 28345, "chs": 28346, "ancer": 28347, "ulos": 28348, "stud": 28349, "æľīæ²Ĵæľī": 28350, "Ġneurolog": 28351, "ĠAncient": 28352, "Out": 28353, "awsze": 28354, "Ġoppose": 28355, "Ġantibodies": 28356, "ĠSomehow": 28357, "ropolitan": 28358, "ktor": 28359, "ĠÑģÑĤоÑĢонÑĭ": 28360, "Ġrockets": 28361, "Ġdisable": 28362, "Ġcatastroph": 28363, "´ìŀ": 28364, "Ġcyn": 28365, "ĠдÑĢÑĥзÑĮÑı": 28366, "Ġinstructors": 28367, "emaal": 28368, "Ġetwa": 28369, "Ġyuan": 28370, "ĠGround": 28371, "Ġpremiere": 28372, "Ñĩив": 28373, "Ġsaint": 28374, "yba": 28375, "Ġkok": 28376, "Ġcontractors": 28377, "Ġê°ģ": 28378, "Ġ×IJ׾": 28379, "Ġheadline": 28380, "Ġcompletamente": 28381, "Ġinexpensive": 28382, "Ġviu": 28383, "ĠGrande": 28384, "Ġbleed": 28385, "물": 28386, "Ġ73": 28387, "ĠtodavÃŃa": 28388, "ĠRush": 28389, "ĠElder": 28390, "ê°ĢëĬĶ": 28391, "ĠRou": 28392, "ĠженÑī": 28393, "ĠMira": 28394, "Ġdeine": 28395, "Ġkarma": 28396, "Ġumm": 28397, "Ġentsche": 28398, "ĠHolocaust": 28399, "Ġdiscoveries": 28400, "aments": 28401, "Ġraison": 28402, "Ġburgers": 28403, "Back": 28404, "Ġgdy": 28405, "ĠAG": 28406, "ĠDaw": 28407, "ìķł": 28408, "headed": 28409, "ĠClar": 28410, "Inst": 28411, "ĠLieutenant": 28412, "ĠAfD": 28413, "ĠCes": 28414, "Ġpersonalized": 28415, "Ġinterfaces": 28416, "à¸Īะ": 28417, "ĠÑĢеж": 28418, "Ġsuic": 28419, "Ġstarving": 28420, "Ġoxide": 28421, "Ġdecorated": 28422, "ĠDU": 28423, "ĠìĺĪìģĺ": 28424, "Ġquo": 28425, "Ġdistortion": 28426, "段": 28427, "Ġ먹ìĸ´ë": 28428, "Ġstakes": 28429, "æĺİçĻ½": 28430, "Ġsyntax": 28431, "Ġbiết": 28432, "thy": 28433, "icie": 28434, "Ġbrasile": 28435, "isis": 28436, "RC": 28437, "Ġshook": 28438, "Ġdepths": 28439, "ĠCosta": 28440, "Ġvocals": 28441, "Ġcoaster": 28442, "Ġfalou": 28443, "ettle": 28444, "Ġkennen": 28445, "Ġderive": 28446, "Ġaids": 28447, "ĠÐĿик": 28448, "Ġentwic": 28449, "Ġvertically": 28450, "ĠÍ": 28451, "ĠSUV": 28452, "Ġfireworks": 28453, "Ġspecifics": 28454, "交": 28455, "Ġinsisted": 28456, "Ġdeshalb": 28457, "ĠGonz": 28458, "love": 28459, "ĠMilitary": 28460, "ĠPierre": 28461, "ĠâĪ": 28462, "ĠWhose": 28463, "Ġperfume": 28464, "ĠÏĢε": 28465, "Ġlowered": 28466, "Ġcrosses": 28467, "Ġtranslates": 28468, "Ġarriba": 28469, "ÃŃdo": 28470, "ĠLev": 28471, "åħ§": 28472, "ĠCiao": 28473, "Ġscholarships": 28474, "Ġgestures": 28475, "ĠÑĢезÑĥлÑĮÑĤаÑĤ": 28476, "Ġquestão": 28477, "ĠColonel": 28478, "ĠBott": 28479, "رÙģ": 28480, "NING": 28481, "ĠWatching": 28482, "ĠPurple": 28483, "ÑģÑĤÑĢан": 28484, "Ġexecutives": 28485, "ĠKris": 28486, "orneys": 28487, "еннÑĭй": 28488, "Ġcoated": 28489, "Ä©": 28490, "Ġparked": 28491, "ĠÑģвеÑĤ": 28492, "!!!!!": 28493, "ĠFloyd": 28494, "ısı": 28495, "ziÄĩ": 28496, "Ġmotivate": 28497, "ĠElon": 28498, "lean": 28499, "Ĩĵ": 28500, "Ġip": 28501, "Ġniż": 28502, "ĠExperience": 28503, "ĠTina": 28504, "ĠKollege": 28505, "ĠAmbassador": 28506, "inya": 28507, "Ġtheft": 28508, "Ġheures": 28509, "ĠMyst": 28510, "Ġmaison": 28511, "leb": 28512, "Ġbowls": 28513, "ĠBürger": 28514, "ĠRoosevelt": 28515, "RP": 28516, "ê°ĢìļĶ": 28517, "ĠDelicious": 28518, "erdings": 28519, "ĠAssociate": 28520, "ousse": 28521, "ĠCort": 28522, "ĠRepeat": 28523, "ĠGlory": 28524, "Ġcontag": 28525, "à¹Ģล": 28526, "ĠParad": 28527, "ĠKerry": 28528, "Ġê¿": 28529, "ĠWave": 28530, "å¿ħ": 28531, "Ġgateway": 28532, "çIJĥ": 28533, "!ãĢį": 28534, "Ġtranscend": 28535, "Ġdamages": 28536, "Ġtails": 28537, "Ġgravitational": 28538, "ĠShield": 28539, "Ġprimitive": 28540, "Ġcarriers": 28541, "ĠHuawei": 28542, "ÙĤد": 28543, "Ġfeliz": 28544, "ĠMia": 28545, "åĥķ": 28546, "ĠпÑĢÑıмо": 28547, "ĠпÑĢоиÑģÑħодиÑĤ": 28548, "ĠMurphy": 28549, "ĠActiv": 28550, "ãĥĥãĤ¯": 28551, "Ġdiscomfort": 28552, "×ij×Ķ": 28553, "ĠKell": 28554, "ĠCentury": 28555, "Ġspaghetti": 28556, "ĠDurch": 28557, "Ġcierto": 28558, "ĠEmpress": 28559, "Ġguts": 28560, "neg": 28561, "ĠдоÑģÑĤаÑĤоÑĩно": 28562, "Ġvoluntary": 28563, "失": 28564, "Ġsquirrel": 28565, "欢": 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29381, "Ġrainfall": 29382, "ĠMouse": 29383, "Ġrefund": 29384, "ĠZw": 29385, "assemb": 29386, "Ġ220": 29387, "ĠOrd": 29388, "è§Ĵ": 29389, "Ġveins": 29390, "ĠGiant": 29391, "Ġmãe": 29392, "Ġvap": 29393, "Ġmisses": 29394, "οÏħÏĤ": 29395, "Mo": 29396, "ĠEntwick": 29397, "INT": 29398, "ÙĨت": 29399, "Ġtheoretically": 29400, "Ġtearing": 29401, "Ġtroubled": 29402, "prem": 29403, "Ġrepetitive": 29404, "Ġâĸ": 29405, "Ġheavenly": 29406, "ĠAmber": 29407, "Ġполож": 29408, "Ġíķ´ì¤": 29409, "Ġvowel": 29410, "anking": 29411, "ĠWirtschaft": 29412, "Ġirr": 29413, "Ġcozy": 29414, "Ġunfamiliar": 29415, "ĠPors": 29416, "Ġë§ŀìķĦ": 29417, "ĠTimothy": 29418, "ÑģолÑİÑĤ": 29419, "pex": 29420, "ĠVIS": 29421, ")(": 29422, "Ġsuperst": 29423, "Ġimprov": 29424, "ĠBeng": 29425, "Ġdisconnected": 29426, "Ġapt": 29427, "ÑĢен": 29428, "ĠExtra": 29429, "Ġбел": 29430, "shop": 29431, "dings": 29432, "ĠConnecticut": 29433, "ì°¬": 29434, "ĠGC": 29435, "åıĸ": 29436, "beh": 29437, "Jeremy": 29438, "ĠBatt": 29439, "ãģ¸": 29440, 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29554, "Ġkara": 29555, "าà¸Ļ": 29556, "Ġfavorable": 29557, "ĠìķĬìķĺ": 29558, "ĠHispanic": 29559, "à¸ľ": 29560, "Ġ×Ķ׼": 29561, "Ġvalidate": 29562, "ĠResident": 29563, "Ġcomenz": 29564, "beiter": 29565, "erer": 29566, "ä¸Ģèµ·": 29567, "Ġdado": 29568, "atching": 29569, "metros": 29570, "ĠHin": 29571, "ĠDum": 29572, "Ġhazır": 29573, "ĠNatalie": 29574, "Ġencryption": 29575, "оÑĩка": 29576, "mma": 29577, "houses": 29578, "Ġanalytical": 29579, "ĠDang": 29580, "first": 29581, "æŃĮ": 29582, "çºĮ": 29583, "ĠEnc": 29584, "cando": 29585, "Ġludzi": 29586, "wart": 29587, "Ġstatistic": 29588, "ĠìĤ°": 29589, "Ġcommenting": 29590, "Ġcoordinated": 29591, "ĠHyper": 29592, "åļ": 29593, "ĠBert": 29594, "çľ¾": 29595, "ĠHip": 29596, "kem": 29597, "ünü": 29598, "Ġzal": 29599, "ĠíķĺëĬĶëį°": 29600, "ĠRobot": 29601, "éĸ±": 29602, "rawn": 29603, "Ġrhetoric": 29604, "ullah": 29605, "ĠDiet": 29606, "Ġtakich": 29607, "Ġpossessed": 29608, "ĵľëĬĶ": 29609, "Ġwakes": 29610, "ĠRaf": 29611, "Mart": 29612, "Ġecc": 29613, 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"ĠSlowly": 29674, "ĠBrig": 29675, "ĠTorah": 29676, "Ġache": 29677, "Ġ???": 29678, "ĠDob": 29679, "kannt": 29680, "Mary": 29681, "Ġstam": 29682, "ĠDemon": 29683, "pla": 29684, "ĠFreund": 29685, "ĠBenn": 29686, "Ġhighs": 29687, "Ġکر": 29688, "ĠPrepare": 29689, "Ġproxy": 29690, "Ġcampo": 29691, "ĠAugen": 29692, "£¨ë": 29693, "ĠChloe": 29694, "icularly": 29695, "young": 29696, "ĠãģĮ": 29697, "©Ķë": 29698, "Ġscratching": 29699, "Ġglac": 29700, "Ġgemeinsam": 29701, "anal": 29702, "acaksın": 29703, "ĠForum": 29704, "ennial": 29705, "ĠResources": 29706, "ãģ¨æĢĿãģĦãģ¾ãģĻ": 29707, "Ġmeisten": 29708, "ĠFell": 29709, "Ġunanim": 29710, "ĠTB": 29711, "ĠSelbst": 29712, "æĨ": 29713, "Ġintimidating": 29714, "ĠGefühl": 29715, "Ġì½Ķë¡ľ": 29716, "æĭī": 29717, "idor": 29718, "iciones": 29719, "arsa": 29720, "]..": 29721, "azo": 29722, "Ġkendi": 29723, "ĠTage": 29724, "termin": 29725, "ĠProzent": 29726, "Maybe": 29727, "lé": 29728, "Ġquesti": 29729, "Ġmemes": 29730, "Ġcorre": 29731, "ĠVIP": 29732, "ĠGallery": 29733, "Ġurgency": 29734, "Ġnoche": 29735, "Ġkindly": 29736, "ĠMeredith": 29737, "ĠváºŃy": 29738, "ĠاÙĦب": 29739, "ĠEstado": 29740, "åĩºä¾Ĩ": 29741, "zug": 29742, "oque": 29743, "Ġobesity": 29744, "Off": 29745, "ĠEuropeans": 29746, "öd": 29747, "ì¹´ë": 29748, "Ġhoop": 29749, "Ġenjoys": 29750, "ĠChip": 29751, "patient": 29752, "Ġmicroscope": 29753, "Ġlegitim": 29754, "ĠÑıвлÑıеÑĤÑģÑı": 29755, "Ïĥι": 29756, "argent": 29757, "Ġsham": 29758, "Ġlicensing": 29759, "olia": 29760, "Sorry": 29761, "rama": 29762, "Ġaccelerated": 29763, "Ġwym": 29764, "Ġfairness": 29765, "ĠReading": 29766, "Ġslack": 29767, "ĠDok": 29768, "ziÄĻkujÄĻ": 29769, "Ġrubbing": 29770, "аÑĤÑĥ": 29771, "Ġallocated": 29772, "jung": 29773, "Ġpains": 29774, "Ġwinding": 29775, "Ġgeliyor": 29776, "ĠCU": 29777, "mot": 29778, "cock": 29779, "ĠPosition": 29780, "bros": 29781, "Ġlivestream": 29782, "ĠBrain": 29783, "ì°©": 29784, "Ġprzek": 29785, "ĠEi": 29786, "ĠCoco": 29787, "ба": 29788, "Ġshovel": 29789, "ãĥıãĥı": 29790, "ea": 29791, "Ġchocol": 29792, "Ġrebellion": 29793, "Ġshowc": 29794, "ĠHalo": 29795, "Ġdividend": 29796, "mission": 29797, "Ġusando": 29798, "Ġ[\"": 29799, "Ġfalei": 29800, "æĽ¸": 29801, "Black": 29802, "ĠSurely": 29803, "ĠÅ»": 29804, "Ġphilosopher": 29805, "ä½łä»¬": 29806, "Ġoverhe": 29807, "ĠBorn": 29808, "Ġobjetivo": 29809, "Ġ128": 29810, "scheid": 29811, "ĠNazis": 29812, "Ġsolche": 29813, "lift": 29814, "cede": 29815, "adors": 29816, "Ġmarshm": 29817, "ĠLORD": 29818, "ĶìĿ´íģ¬": 29819, "Ġowning": 29820, "Cont": 29821, "Ġlandscapes": 29822, "Ġlending": 29823, "ĠAuthority": 29824, "овой": 29825, "oqu": 29826, "ĠSes": 29827, "ĠFerrari": 29828, "Ġresponsabil": 29829, "Ġvários": 29830, "Ġdelic": 29831, "Ġembark": 29832, "Ġembroider": 29833, "Ġframeworks": 29834, "Ġsimmer": 29835, "Ġnacional": 29836, "Ġremainder": 29837, "ĠVielleicht": 29838, "Ġquieres": 29839, "ìĹĶ": 29840, "Ġtestoster": 29841, "ihen": 29842, "ĠOz": 29843, "èle": 29844, "Ġportrayed": 29845, "κε": 29846, "ĠPolitik": 29847, "Ġaperture": 29848, "Ġbland": 29849, "indust": 29850, "ĠобÑĢаÑĤ": 29851, "ĠThous": 29852, "Bay": 29853, "Ġdando": 29854, "Ġsher": 29855, "Ġadmissions": 29856, "ĠCrew": 29857, "ĠÑĸн": 29858, "SINGING": 29859, "Ġounce": 29860, "Ġiy": 29861, "Ġbasil": 29862, "Ġovertime": 29863, "Ġthreaten": 29864, "Ġpartnered": 29865, "ĠCann": 29866, "avana": 29867, "ĠзнаеÑĤе": 29868, "éĢĻäºĽ": 29869, "ĠоÑĤÑģ": 29870, "ĠTudo": 29871, "ì½Ķ": 29872, "ĠëĨĢë": 29873, "fel": 29874, "Ġrearr": 29875, "Ġinward": 29876, "ĠRogers": 29877, "à¹ĥห": 29878, "Ġtweak": 29879, "Ġdryer": 29880, "cession": 29881, "Ġrigorous": 29882, "ĠDaar": 29883, "omics": 29884, "Ġfats": 29885, "vad": 29886, "Ġzipper": 29887, "acceptable": 29888, "Ġdemonstrating": 29889, "ĠYum": 29890, "Ġbeau": 29891, "Ġroster": 29892, "Ġpredominantly": 29893, "еÑĢÑĥ": 29894, "ningar": 29895, "Ġtriangles": 29896, "Ġtexting": 29897, "Ġberries": 29898, "ĠìĤ¬ì§Ħ": 29899, "éĶĻ": 29900, "adder": 29901, "Ġfaites": 29902, "ĠImage": 29903, "lere": 29904, "Ġbounds": 29905, "ĠLaur": 29906, "ĠìķĦ무ë": 29907, "Ġmio": 29908, "Ġusa": 29909, "ĠØ°": 29910, "Ġtoen": 29911, "ĠJang": 29912, "že": 29913, "chod": 29914, "anan": 29915, "ĠобÑĢазом": 29916, "Ġpersever": 29917, "ĠSwe": 29918, "Ġaugment": 29919, "ä¸ĥ": 29920, "uggling": 29921, "ièrement": 29922, "istles": 29923, "acjÄĻ": 29924, "91": 29925, "Ġmah": 29926, "ĠKIR": 29927, "Die": 29928, "Ġdownhill": 29929, "Ġ1968": 29930, "оÑĢоÑĪо": 29931, "å¹¹": 29932, "ographics": 29933, "Ġtässä": 29934, "ê²łì£ł": 29935, "ĠлиÑĩ": 29936, "AUDIO": 29937, "ĠплоÑħ": 29938, "Ġproposing": 29939, "éł»": 29940, "Ġtempted": 29941, "Ġconverting": 29942, "ĠLehr": 29943, "Ġpersone": 29944, "ĠFeeling": 29945, "ìĸ´ì£¼": 29946, "ombres": 29947, "Ġ׾×Ļ": 29948, "Ġguru": 29949, "Ġdement": 29950, "низ": 29951, "иÑĤелей": 29952, "Ġcompañ": 29953, "æľª": 29954, "å¸ĮæľĽ": 29955, "Ġredo": 29956, "Ġconductor": 29957, "mia": 29958, "Ġidols": 29959, "ĠMul": 29960, "Ġinex": 29961, "Ġtämä": 29962, "Ġimpacting": 29963, "Ġdaylight": 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"ãħĭãħĭãħĭ": 30021, "ĠOriginal": 30022, "×Ļ×Ĺ": 30023, "Ġbroadcasting": 30024, "ĠпеÑĢвÑĭй": 30025, "uchi": 30026, "Ġheure": 30027, "Ġgrabs": 30028, "ĠWOR": 30029, "ĠPlaid": 30030, "Min": 30031, "Ġpaz": 30032, "ĠPuis": 30033, "umu": 30034, "itates": 30035, "Ġcoats": 30036, "Ġbuen": 30037, "Ġheir": 30038, "Ġpneum": 30039, "שר": 30040, "enser": 30041, "ĠJUDGE": 30042, "Ġblonde": 30043, "á¹Ľ": 30044, "Ġgak": 30045, "Ġsık": 30046, "Ġquoted": 30047, "Ġequipo": 30048, "Ġwishing": 30049, "ÃŃcia": 30050, "Ġverbs": 30051, "çµĦ": 30052, "ĠCanadians": 30053, "Ġgoverning": 30054, "ĠEvans": 30055, "Euro": 30056, "Ġgenres": 30057, "Ġunterschied": 30058, "ĠBecky": 30059, "³¼ê²ĮìļĶ": 30060, "Ġeinge": 30061, "ĠRaise": 30062, "oland": 30063, "ĠStrateg": 30064, "Ġeres": 30065, "ĠVeterans": 30066, "Ġbreakout": 30067, "Ġsanté": 30068, "Ġadel": 30069, "Ġinvestigated": 30070, "Ġpeur": 30071, "Ġagile": 30072, "Ġrailroad": 30073, "anska": 30074, "Ġей": 30075, "Ġexpos": 30076, "atories": 30077, "ĠContent": 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30196, "Ġextraction": 30197, "ĠAV": 30198, "ĠGibbs": 30199, "Ġíľ": 30200, "Ġsı": 30201, "IAM": 30202, "View": 30203, "ĠGRANT": 30204, "Ġ몸": 30205, "Ġverification": 30206, "Ġdepicted": 30207, "ĠMoz": 30208, "oux": 30209, "Ġtul": 30210, "Ġscanner": 30211, "Ġcomedian": 30212, "ĠVolks": 30213, "ĠJEFF": 30214, "è¨Ĥéĸ±": 30215, "§Ħ": 30216, "Ġdistraction": 30217, "rá": 30218, "ĠINTER": 30219, "Ġsincer": 30220, "Ġ×ŀת": 30221, "Ġש׳": 30222, "Ġconstructive": 30223, "arf": 30224, "ĠëĪĦë": 30225, "Ġeco": 30226, "ramos": 30227, "Ġrenewed": 30228, "inement": 30229, "ĠUb": 30230, "ĠPepper": 30231, "ì§Ģê°Ģ": 30232, "ĠDarwin": 30233, "Ġmerchand": 30234, "Ġvárias": 30235, "èce": 30236, "NG": 30237, "ĠìľĦíķ´ìĦľ": 30238, "ĠакÑĤив": 30239, "ĠUnters": 30240, "عÙĦ": 30241, "Ġintric": 30242, "omma": 30243, "ieving": 30244, "ĠCaroline": 30245, "åĵģ": 30246, "ĠPRES": 30247, "Ġperformer": 30248, "Ġautour": 30249, "ãģ¾ãģĽãĤĵ": 30250, "Ġutterly": 30251, "Ġsynthesis": 30252, "Ġlesbian": 30253, "Ġretrieve": 30254, 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30313, "mesi": 30314, "Ġrelaxation": 30315, "ĠÑģвоиÑħ": 30316, "Ġmargins": 30317, "ĠsaÄŁ": 30318, "ĠPride": 30319, "ĠÏĦοÏħÏĤ": 30320, "иÑĨи": 30321, "enci": 30322, "Does": 30323, "Ġcorpse": 30324, "Ġendurance": 30325, "Ġíŀĺ": 30326, "ì¹´": 30327, "Ġhaircut": 30328, "Ġinterrupted": 30329, "Ġwindy": 30330, "ĠCaleb": 30331, "ÏģÏĩ": 30332, "ĠPourquoi": 30333, "Ġholistic": 30334, "uclear": 30335, "ĠWhole": 30336, "士": 30337, "Act": 30338, "Ġgallon": 30339, "cade": 30340, "ĠRegional": 30341, "roads": 30342, "ĠSchne": 30343, "áng": 30344, "Ġизмен": 30345, "ãĤĪãģŃ": 30346, "Ġmenus": 30347, "Ġsplitting": 30348, "Ġpriced": 30349, "ĠÎĵ": 30350, "Ġusername": 30351, "ĠÐŀÑĩ": 30352, "Ġcompressed": 30353, "yin": 30354, "Ġguardian": 30355, "Ġgoof": 30356, "Ġchecklist": 30357, "Ġinterchange": 30358, "Ġexpedition": 30359, "Ġextern": 30360, "Ġinfrared": 30361, "engo": 30362, "Ġdenying": 30363, "Ġpackets": 30364, "onent": 30365, "BB": 30366, "ĠIncre": 30367, "Ġsini": 30368, "ÃŁer": 30369, "èg": 30370, 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"selling": 30427, "ĠTask": 30428, "hoon": 30429, "ĠCoc": 30430, "ĠParks": 30431, "Ġrepetition": 30432, "ĠÑĤÑĥда": 30433, "Ġensl": 30434, "ĠdeÄŁiÅŁ": 30435, "ĠOrlando": 30436, "ĠMainten": 30437, "æŃ¢": 30438, "ocument": 30439, "ĠHC": 30440, "Ġscooter": 30441, "ĠнапиÑģ": 30442, "Ġtighter": 30443, "Ġtease": 30444, "Ġremoves": 30445, "Ġkijken": 30446, "ĠÑģÑĥÑīеÑģÑĤв": 30447, "Ġthé": 30448, "ĠвÑĭглÑıд": 30449, "Ġrelieve": 30450, "Ġmitä": 30451, "Ġstationary": 30452, "öff": 30453, "pable": 30454, "Ġarter": 30455, "Ġdéf": 30456, "rative": 30457, "Ġconect": 30458, "Ġsaddle": 30459, "ĠDiane": 30460, "Ġcommemor": 30461, "fendim": 30462, "SÃŃ": 30463, "Ġíģ´ë": 30464, "Ġmange": 30465, "atte": 30466, "Ġarrogant": 30467, "Ġrobotic": 30468, "ĠgiÃł": 30469, "æĺ¯çļĦ": 30470, "Ġneighbourhood": 30471, "isson": 30472, "Ġдвиж": 30473, "ĠRI": 30474, "ĠNorman": 30475, "brand": 30476, "amation": 30477, "Ġrazor": 30478, "Ġmurders": 30479, "ĠÑĤÑĥ": 30480, "Ġwszystkim": 30481, "Ġutilities": 30482, "Ġmicroscop": 30483, "ê¿": 30484, "Ġdaqui": 30485, "ollar": 30486, "ĠÐĶавайÑĤе": 30487, "Ġannée": 30488, "Ġkilometres": 30489, "Ġhomosexual": 30490, "Ġarchitects": 30491, "ãģ¡ãģ¯": 30492, "Ġniye": 30493, "LER": 30494, "Ġmicrophones": 30495, "ĠStunden": 30496, "Ġconsecutive": 30497, "ienda": 30498, "vänd": 30499, "DER": 30500, "Ġlifts": 30501, "ĠMeat": 30502, "Ġsavez": 30503, "íĸĪëįĺ": 30504, "Men": 30505, "Ġdismant": 30506, "거를": 30507, "Ġinsulation": 30508, "Ġscall": 30509, "Ġspooky": 30510, "Ġparc": 30511, "Ġballet": 30512, "ĠWhatsApp": 30513, "Ġfranc": 30514, "Ġdeliberate": 30515, "ĠíħĮ": 30516, "Ġmars": 30517, "ĠZur": 30518, "Pr": 30519, "disciplinary": 30520, "Ġobsession": 30521, "ме": 30522, "Ġmarching": 30523, "ĠEmergency": 30524, "iguous": 30525, "Ġszy": 30526, "ĠLands": 30527, "Ġboarding": 30528, "ĠпоÑĩÑĤи": 30529, "Ġenvy": 30530, "Ġcompassionate": 30531, "Ġmerci": 30532, "Ġdesirable": 30533, "dale": 30534, "Ġcanım": 30535, "ĠAntar": 30536, "temps": 30537, "Ġconfigured": 30538, "ĠCompared": 30539, "neh": 30540, "icating": 30541, "Ġnickel": 30542, "ÙĪÙĤ": 30543, "ÙĥÙĪÙĨ": 30544, "opes": 30545, "Ġformulas": 30546, "ĠÐķÑģÑĤÑĮ": 30547, "Ġpobl": 30548, "ĠPJ": 30549, "ĠLud": 30550, "ä»ĬåĽŀ": 30551, "ĠBrid": 30552, "ĠHog": 30553, "ĠBris": 30554, "Jen": 30555, "Ġshading": 30556, "ĠYas": 30557, "Ġdisturbed": 30558, "Ġrecommending": 30559, "Ġcé": 30560, "ĠHOW": 30561, "ìĹĪìĸ´": 30562, "Ġreversed": 30563, "ĠInterestingly": 30564, "ioxid": 30565, "åħŃ": 30566, "Ġìĺ¤ì¼ĢìĿ´": 30567, "ếu": 30568, "xx": 30569, "Ġouais": 30570, "ĠYouTubers": 30571, "ĠRosa": 30572, "ĠHaupt": 30573, "jadi": 30574, "Ġvlogs": 30575, "Ġcultura": 30576, "ĠLeadership": 30577, "ĠHep": 30578, "Ġillum": 30579, "´ëıĻ": 30580, "Ġcustomized": 30581, "Ġmarca": 30582, "Ġquatro": 30583, "Ġнаг": 30584, "ĠSpaceX": 30585, "ĠEigen": 30586, "asting": 30587, "ĠolduÄŁu": 30588, "Ġforts": 30589, "ãģī": 30590, "riment": 30591, "iencia": 30592, "Ġtenir": 30593, "roffen": 30594, "Ġ1979": 30595, "Ġcie": 30596, "ĠëIJĺê³ł": 30597, "Ġescri": 30598, "ÏĮÏĤ": 30599, "íı¬": 30600, "uzzy": 30601, "Cong": 30602, "ìĿ¸ìĿ´": 30603, "Great": 30604, "sil": 30605, "éch": 30606, "ãģ¨ãģĭ": 30607, "Ġmultic": 30608, "ĠDisk": 30609, "²ķ": 30610, "Ġfazla": 30611, "Ġlevant": 30612, "Ġabajo": 30613, "urry": 30614, "stru": 30615, "Ġ먹ëĬĶ": 30616, "Ġaccessory": 30617, "Ġдвиг": 30618, "ĠRid": 30619, "2019": 30620, "Ġdownstream": 30621, "æķ¸": 30622, "Ġkaz": 30623, "utan": 30624, "Ġcharcoal": 30625, "Ġafect": 30626, "wu": 30627, "Ġcontexts": 30628, "Ġfeared": 30629, "ĠìĦ¤": 30630, "Ġhistories": 30631, "Ġfas": 30632, "ensible": 30633, "Ġcocoa": 30634, "illar": 30635, "geons": 30636, "Ġspirituality": 30637, "ĠPew": 30638, "Ġpharmacy": 30639, "Ġpassions": 30640, "Ġbos": 30641, "Ġallá": 30642, "Ġthriving": 30643, "ĠReact": 30644, "Ġoccupy": 30645, "Ġwithdrawal": 30646, "Ġallowance": 30647, "ĠFraktion": 30648, "Ġbuddies": 30649, "Ġidle": 30650, "Ġdissolved": 30651, "Ġprevalent": 30652, "Ġmilitar": 30653, "Ġsensing": 30654, "Ġpojaw": 30655, "Ġancora": 30656, "Ġabundant": 30657, "Ġhairst": 30658, "ãģĤãĤĮ": 30659, "Ġtwee": 30660, "Ġnächste": 30661, "ĠMöglichkeit": 30662, "Ġhoo": 30663, "ufficient": 30664, "Ġfantast": 30665, "Ġedible": 30666, "Ġëĸ¨ìĸ´ì": 30667, "ìĽĥ": 30668, "Ġvein": 30669, "ucci": 30670, "Ġdevotion": 30671, "Ġconcealer": 30672, "income": 30673, "Ġrecycled": 30674, "ĠìĬ¤íĥĢ": 30675, "Ġpontos": 30676, "Ġdessus": 30677, "Ġvérit": 30678, "Ġreflections": 30679, "ĠAA": 30680, "Ġtakeaway": 30681, "bare": 30682, "ĠContact": 30683, "eil": 30684, "ĠHear": 30685, "Ġmirac": 30686, "ĠGerilim": 30687, "ĠÑģамÑĭй": 30688, "Ġvivo": 30689, "Ġkilograms": 30690, "ĠCrim": 30691, "ût": 30692, "78": 30693, "Ġsincerely": 30694, "raz": 30695, "Ġë³µ": 30696, "Ġarriv": 30697, "Ġconception": 30698, "ĠPersian": 30699, "Ġsjäl": 30700, "Ġstarring": 30701, "ĠìķĦ무": 30702, "ĠForever": 30703, "еÑģÑĤÑĮ": 30704, "Ġveil": 30705, "Ġsubtit": 30706, "odka": 30707, "ĠоÑĤноÑĪ": 30708, "Ġcooks": 30709, "енÑı": 30710, "Kay": 30711, "Ġniños": 30712, "ĠPhone": 30713, "Ġstitching": 30714, "Ġfingerprint": 30715, "é¢ĺ": 30716, "λά": 30717, "Ġdedicate": 30718, "ĠLob": 30719, "Ġblacks": 30720, "ĠBle": 30721, "bout": 30722, "ĠÄijang": 30723, "Ġeks": 30724, "Ġsquash": 30725, "ĠKü": 30726, "odi": 30727, "ĠnÆ°á»Ľc": 30728, "Ġvoyage": 30729, "Ġplayful": 30730, "ĠØ¥ÙĦÙī": 30731, "anic": 30732, "Ġcondemn": 30733, "ĠBöyle": 30734, "ĠPolize": 30735, "ãĤ¿ãĥ¼": 30736, "Ġayuda": 30737, "Ġpam": 30738, "à¹Ħà¸Ľ": 30739, "ĠKathy": 30740, "един": 30741, "нова": 30742, "Ġbrig": 30743, "eger": 30744, "Ġeagle": 30745, "Ġvisions": 30746, "ĠíķŃìĥģ": 30747, "Ġshitty": 30748, "Ġhott": 30749, "ĠBritt": 30750, "utors": 30751, "ENTE": 30752, "æĽ²": 30753, "Ġphon": 30754, "ĠBing": 30755, "ĠподдеÑĢж": 30756, "spring": 30757, "æĸ¯": 30758, "etten": 30759, "Ġpilgr": 30760, "Ġediyor": 30761, "енÑĤÑĭ": 30762, "aggio": 30763, "Ġjul": 30764, "Ġcomprend": 30765, "teil": 30766, "Ġز": 30767, "Ġperformers": 30768, "Ġinfamous": 30769, "ĠMK": 30770, "çª": 30771, "æ³ģ": 30772, "otle": 30773, "eff": 30774, "ĠHash": 30775, "Ġcoward": 30776, "ĠBRA": 30777, "ĠDD": 30778, "Ġcomida": 30779, "Ġplata": 30780, "Ġflap": 30781, "ĠMehr": 30782, "ribution": 30783, "ĠYemen": 30784, "Ġmysteries": 30785, "ĠÄ°yi": 30786, "Ġstell": 30787, "Ġeyeliner": 30788, "Ġdeles": 30789, "Ġnailed": 30790, "Ġillnesses": 30791, "Ġstacks": 30792, "Ġtrabajar": 30793, "flower": 30794, "ciu": 30795, "Ġcrude": 30796, "Ġsubstantially": 30797, "Ġhomem": 30798, "Ġnephew": 30799, "Ġstamps": 30800, "Ġcarbs": 30801, "ÑĮÑĤе": 30802, "mooth": 30803, "Ġtunnels": 30804, "acie": 30805, "æ³¢": 30806, "ĠSeñ": 30807, "ĠHera": 30808, "ĠìķĦëĭĪìĹIJìļĶ": 30809, "ĠWyoming": 30810, "ĠHDMI": 30811, "ĠLis": 30812, "ución": 30813, "Ġsteer": 30814, "оÑİ": 30815, "иÑĤа": 30816, "NT": 30817, "Ġìĸ¼êµ´": 30818, "Ġpalms": 30819, "Ġneon": 30820, "ованиÑı": 30821, "Ġfiltering": 30822, "Ġjouer": 30823, "ĠHö": 30824, "ĠнеÑģ": 30825, "ê²łìĸ´ìļĶ": 30826, "Ġ81": 30827, "Ġstoryline": 30828, "Ġprzep": 30829, "Ġthanking": 30830, "ĠBoeing": 30831, "Ġsoftly": 30832, "jem": 30833, "алÑĮнÑĭÑħ": 30834, "Ġflashlight": 30835, "ĠпÑĥ": 30836, "ĠWOMAN": 30837, "ắc": 30838, "ÃŃch": 30839, "Ġluxurious": 30840, "Ġwün": 30841, "Ġimpactful": 30842, "Ġconson": 30843, "reu": 30844, "irring": 30845, "ifter": 30846, "Ġconstituents": 30847, "èIJ½": 30848, "Ġ94": 30849, "ĠTou": 30850, "gom": 30851, "ĠìĥĿê°ģìĿĦ": 30852, "Ġstereotypes": 30853, "Ġmożli": 30854, "åĪĨ享": 30855, "Ĥ¨": 30856, "Ġpencils": 30857, "ĠÑģлож": 30858, "Ġihrem": 30859, "ĠBesch": 30860, "ĠKoh": 30861, "ĠEntscheid": 30862, "Ġlek": 30863, "Ġförs": 30864, "Ġtotalmente": 30865, "Ġlively": 30866, "Ġentropy": 30867, "Ġdiscern": 30868, "ĠÐĹна": 30869, "Ġdov": 30870, "Ġmythology": 30871, "è¨ĺå¾Ĺ": 30872, "apanese": 30873, "Ġapproximate": 30874, "аÑĤив": 30875, "ifiable": 30876, "ĠSeo": 30877, "åĢĴ": 30878, "´ìĭ¬íŀĪ": 30879, "Ġìĺ·": 30880, "Ġtemporal": 30881, "ĠiT": 30882, "Ġestat": 30883, "ким": 30884, "Ġsprink": 30885, "Ġgrund": 30886, "Ġinfantry": 30887, "Ġschaffen": 30888, "ç´Ħ": 30889, "Ġank": 30890, "riages": 30891, "ĠYeon": 30892, "ĠMoroc": 30893, "Ġinvasive": 30894, "ģĶ": 30895, "Ġparenting": 30896, "ĠRis": 30897, "ibile": 30898, "Ġmods": 30899, "å½¢": 30900, "ĠпÑĢовеÑĢ": 30901, "ĠThing": 30902, "ĠWherever": 30903, "Ġacknowledging": 30904, "Ġpawn": 30905, "ummer": 30906, "orb": 30907, "69": 30908, "Ġretrouve": 30909, "Ġrelies": 30910, "ĠHighway": 30911, "Ġawe": 30912, "ãģ§ãģĻãģĭ": 30913, "itaire": 30914, "Ġapplicant": 30915, "Ġaisle": 30916, "worm": 30917, "Ġpayload": 30918, "Ġcarre": 30919, "ĠBach": 30920, "æł¼": 30921, "Ġì¹ľêµ¬ë": 30922, "ние": 30923, "ĠitÃŃs": 30924, "onnaise": 30925, "sol": 30926, "èı¯": 30927, "algia": 30928, "Ġrocking": 30929, "Ġbesten": 30930, "rites": 30931, "^^": 30932, "иной": 30933, "Ġbaixo": 30934, "Ġ기ìĸµ": 30935, "оÑĤÑĢи": 30936, "sim": 30937, "Ġincarn": 30938, "ëĭ¤ìĿĮ": 30939, "Ġlick": 30940, "sided": 30941, "Ġ71": 30942, "forder": 30943, "Ġresonance": 30944, "Ġtegen": 30945, "Ġmetaph": 30946, "owser": 30947, "Ġ×IJ׳×Ĺ׳×ķ": 30948, "?ãĢį": 30949, "Ġspielen": 30950, "Ġvolley": 30951, "ĶìĿ´íģ¬ìĹħ": 30952, "looked": 30953, "Ġsentenced": 30954, "Ġmultiplying": 30955, "Ġideals": 30956, "Ġwahrscheinlich": 30957, "Ġdeposits": 30958, "bilir": 30959, "Ġeffet": 30960, "illon": 30961, "Īë§Į": 30962, "Ġtestimon": 30963, "Ġzawsze": 30964, "ĠпÑĢоÑĨеÑģÑģ": 30965, "ĠLav": 30966, "ä¸įéĮ¯": 30967, "Ġtravailler": 30968, "Ġlaisse": 30969, "ĠMountains": 30970, "ĠÑĢоб": 30971, "Ġexamined": 30972, "itus": 30973, "Was": 30974, "лÑĭ": 30975, "Ġattributed": 30976, "ĠìĬ¹": 30977, "ĠBaron": 30978, "Ġgep": 30979, "Ġattent": 30980, "ĠCollection": 30981, "Ġtheat": 30982, "ĠCai": 30983, "Ġwells": 30984, "Ġhumano": 30985, "çĹħ": 30986, "ĠHast": 30987, "ĠÑħоÑĤÑı": 30988, "czas": 30989, "Ġpermits": 30990, "Ġlegg": 30991, "Ġepo": 30992, "ĠFen": 30993, "Ġthi": 30994, "ĠFoi": 30995, "Ġélect": 30996, "Ġ83": 30997, "Ġoverth": 30998, "Ġè¬Ŀè¬Ŀ": 30999, "Ġtenant": 31000, "è²·": 31001, "Next": 31002, "Ġpraised": 31003, "security": 31004, "ĠImpact": 31005, "为ä»Ģä¹Ī": 31006, "Ġvouch": 31007, "Ġnegó": 31008, "Ġunve": 31009, "Ġcriticize": 31010, "ĠKenya": 31011, "Ġtactic": 31012, "Ġlogr": 31013, "Ġpois": 31014, "Ġpapa": 31015, "speaks": 31016, "ðŁij": 31017, "ispers": 31018, "Ġsurplus": 31019, "Ġcolder": 31020, "åįĹ": 31021, "åIJ¬": 31022, "plets": 31023, "ĠVienna": 31024, "ĠLead": 31025, "Ġaerial": 31026, "ĠTah": 31027, "енÑĤов": 31028, "ĠGreeks": 31029, "Cam": 31030, "Ġmáxim": 31031, "Ġkuin": 31032, "chio": 31033, "Ġdemonstrates": 31034, "anos": 31035, "ĠCert": 31036, "ĠÑįн": 31037, "Ġblogs": 31038, "ĠìĦľìļ¸": 31039, "Ġbeams": 31040, "иков": 31041, "Ġprompted": 31042, "Ġfrightening": 31043, "ĠPorsche": 31044, "ãģĪãģ¦": 31045, "larını": 31046, "Ġchilling": 31047, "isphere": 31048, "Ġflashing": 31049, "ĠKard": 31050, "bread": 31051, "Ġexh": 31052, "Ġtycker": 31053, "Ġecological": 31054, "ĠMae": 31055, "Ġ×ŀ×IJ×ķ×ĵ": 31056, "ĠëĤĺëıĦ": 31057, "лон": 31058, "yss": 31059, "Ġpergunt": 31060, "Ġprix": 31061, "izzard": 31062, "Ġcancers": 31063, "Ġ91": 31064, "susp": 31065, "ĠItem": 31066, "ÅŁa": 31067, "Ġpest": 31068, "ĠtakÄħ": 31069, "Ġlymph": 31070, "ĠPatri": 31071, "fill": 31072, "Ġreconna": 31073, "Ġoptimism": 31074, "Ġmimic": 31075, "Ġì²ľ": 31076, "ĠMadame": 31077, "ocy": 31078, "lining": 31079, "åijĬ訴": 31080, "erme": 31081, "Ġfolders": 31082, "ĠczÅĤ": 31083, "uchar": 31084, "Ġcurso": 31085, "Ġbreach": 31086, "ниÑĤÑĮ": 31087, "ĠpamiÄĻ": 31088, "Ġelig": 31089, "Ġautop": 31090, "Flow": 31091, "Ġprogrammed": 31092, "ĠProcess": 31093, "Ġfigur": 31094, "ĠSF": 31095, "ĠEles": 31096, "Ġprogrammes": 31097, "Ġdizzy": 31098, "ìĭľê°Ħ": 31099, "Ġлибо": 31100, "Ġsniff": 31101, "ĠSebastian": 31102, "ĠHye": 31103, "Ġ4000": 31104, "Ġpermite": 31105, "æ¢Ŀ": 31106, "ĠзаÑī": 31107, "Ġguit": 31108, "ĠDais": 31109, "Ġaccordance": 31110, "Ġmodular": 31111, "ogeneous": 31112, "æĭį": 31113, "Ġpouquinho": 31114, "Ġartillery": 31115, "Ġlubric": 31116, "Ġvolcan": 31117, "ĠNH": 31118, "ðŁ¤": 31119, "Ġdean": 31120, "Rh": 31121, "Ġministre": 31122, "åĿIJ": 31123, "ĠInv": 31124, "ĠBulgar": 31125, "ĠDaten": 31126, "èİ": 31127, "Im": 31128, "Ġoriginated": 31129, "ĠNixon": 31130, "integr": 31131, "Ġlacks": 31132, "ĠNacht": 31133, "ìĸ´ëĤĺ": 31134, "camera": 31135, "Ġradish": 31136, "kiye": 31137, "Ġanges": 31138, "Ġpréf": 31139, "juk": 31140, "ĠBee": 31141, "ĠBU": 31142, "ĠвоÑģп": 31143, "ĠBT": 31144, "êmes": 31145, "ĠStück": 31146, "ĠInk": 31147, "æĪĸèĢħ": 31148, "ĠSergeant": 31149, "ĠMultip": 31150, "Ġhiçbir": 31151, "ĠСам": 31152, "ĠDé": 31153, "olph": 31154, "ìĸ¸": 31155, "Ġimpat": 31156, "ĠìķĬê³ł": 31157, "ĠÑĤакого": 31158, "ĠнавеÑĢное": 31159, "Ġunpredictable": 31160, "Ġmend": 31161, "ĠìĹĨìĸ´ìļĶ": 31162, "ĠjakieÅĽ": 31163, "Ġanni": 31164, "Ġdonné": 31165, "ĠKirsty": 31166, "Ġrectangular": 31167, "Ġempezar": 31168, "ĠExchange": 31169, "ê°Ķ": 31170, "Ġéconom": 31171, "ãģĵãĤĵ": 31172, "elin": 31173, "reibt": 31174, "Ġ×Ķפ": 31175, "Ġcemetery": 31176, "Ġespañol": 31177, "olin": 31178, "лÑİд": 31179, "Ġgrâce": 31180, "allen": 31181, "ĠPhilos": 31182, "ĠErst": 31183, "ĠìĥĪ": 31184, "ĠVid": 31185, "Give": 31186, "OH": 31187, "μο": 31188, "ĠPare": 31189, "Ġmetabolism": 31190, "Ġmaple": 31191, "Ġaxle": 31192, "ĠDy": 31193, "Ġkomme": 31194, "Ïİν": 31195, "Ġgreatness": 31196, "Ġverified": 31197, "Ġspé": 31198, "ĠFahrenheit": 31199, "ĠBren": 31200, "ĠConfeder": 31201, "Ġhistoire": 31202, "Ġeliminating": 31203, "ĠAdding": 31204, "ĠAbi": 31205, "æĿİ": 31206, "Ġhospitality": 31207, "tim": 31208, "Ġbonito": 31209, "Ġpartes": 31210, "ĠдÑĢÑĥгиÑħ": 31211, "ĠShay": 31212, "ĠSed": 31213, "Ġregrets": 31214, "Ñıми": 31215, "Ġtenants": 31216, "éĢŁ": 31217, "ĠPTS": 31218, "Ġdevi": 31219, "ĠLate": 31220, "uez": 31221, "Ġsöyl": 31222, "ãĤ»": 31223, "Ġìŀ¬ë°Į": 31224, "Ġtoggle": 31225, "Ġmasking": 31226, "алÑĮного": 31227, "Ġpersön": 31228, "Ġamerican": 31229, "fik": 31230, "ĠRGB": 31231, "enson": 31232, "ĠKA": 31233, "wwww": 31234, "ĠÑĢег": 31235, "metics": 31236, "Ġeducator": 31237, "ãĤ·ãĥ«ãĤ¯": 31238, "park": 31239, "елÑĮзÑı": 31240, "arus": 31241, "ÑĢеÑĤ": 31242, "Ġfeito": 31243, "Ġchoir": 31244, "Ġlargo": 31245, "Ġeens": 31246, "Ġwatts": 31247, "ĠSingle": 31248, "Ġsusceptible": 31249, "icer": 31250, "ĠвклÑİÑĩ": 31251, "Ġpus": 31252, "íĻĺ": 31253, "Eng": 31254, "Ġfantas": 31255, "Ġspecification": 31256, "Ġconfronted": 31257, "ĠColumbus": 31258, "ивеÑĤ": 31259, "arım": 31260, "Ġcaffeine": 31261, "munition": 31262, "Ġmigrants": 31263, "lide": 31264, "itations": 31265, "ĠGeme": 31266, "ẫ": 31267, "Ġplanner": 31268, "Ġstimulate": 31269, "Ġaproxim": 31270, "ceu": 31271, "ĠNom": 31272, "Ġvog": 31273, "ĠÑĢаÑģÑĤ": 31274, "Ġenseñ": 31275, "Ġsellers": 31276, "Ġguten": 31277, "zd": 31278, "Cal": 31279, "Ġdescript": 31280, "Ġreconciliation": 31281, "zinho": 31282, "á¹ĩa": 31283, "ãģĺãĤĥãģĤ": 31284, "acyj": 31285, "ĠCOL": 31286, "saw": 31287, "ĠíĻķìĿ¸": 31288, "Ġvarit": 31289, "Ġpartnering": 31290, "Ġdetention": 31291, "Ġbombing": 31292, "clapping": 31293, "iencies": 31294, "ondu": 31295, "AME": 31296, "Ġê°ĻìĬµëĭĪëĭ¤": 31297, "cÃŃa": 31298, "ĠпоÑģÑĤо": 31299, "ĠASMR": 31300, "Ġhomepage": 31301, "Ġsiè": 31302, "antha": 31303, "ĠPoll": 31304, "Ġigen": 31305, "cych": 31306, "Ġê°ijìŀIJ기": 31307, "Ġconsiderably": 31308, "ä»ĸçļĦ": 31309, "ĠArist": 31310, "Ġwithstand": 31311, "Ġqualitative": 31312, "ĠKraft": 31313, "ĠÑįлекÑĤ": 31314, "ĠBead": 31315, "екÑĤив": 31316, "Ġcrushing": 31317, "ì³IJ": 31318, "Ġnavy": 31319, "ÙĪÚº": 31320, "sho": 31321, "Ġoak": 31322, "ippers": 31323, "Ġsoils": 31324, "Ġpigment": 31325, "Ġevitar": 31326, "ãĥĩ": 31327, "Ġfuse": 31328, "ĠDale": 31329, ":\"": 31330, "Ġcomplètement": 31331, "Ġkel": 31332, "à¹Ĩ": 31333, "Ġquatre": 31334, "ĠUM": 31335, "Ġë§IJë": 31336, "æł¹": 31337, "ÃŃr": 31338, "Ġleisure": 31339, "ĠHousing": 31340, "Ġfolds": 31341, "estion": 31342, "ARS": 31343, "Ġmash": 31344, "urpose": 31345, "Ġaccumulated": 31346, "ĠStuff": 31347, "èªŀ": 31348, "Ġtapes": 31349, "ĠÑģилÑĮно": 31350, "ĠLOVE": 31351, "Ġ1982": 31352, "Ġscars": 31353, "Ġcapitalist": 31354, "ĠNed": 31355, "Ġsoften": 31356, "Ġnotably": 31357, "Ġforcément": 31358, "ĠRaum": 31359, "ĠнеобÑħод": 31360, "Ġtrademark": 31361, "Ġfertig": 31362, "Ġ?!": 31363, "æĹł": 31364, "Ġreinforced": 31365, "Ġrecharge": 31366, "ĠPutting": 31367, "Ġvillains": 31368, "Ġhandic": 31369, "Ġadvertisement": 31370, "تÙĬ": 31371, "ĠÑģÑĥм": 31372, "ĠRiley": 31373, "×ķ×ij×": 31374, "京": 31375, "Os": 31376, "از": 31377, "Boy": 31378, "Ġsquish": 31379, "ocket": 31380, "Ġtestify": 31381, "æ¼Ķ": 31382, "Ġ׾×ŀ×": 31383, "ĠмаÑģÑģ": 31384, "manuel": 31385, "ĠArkansas": 31386, "iffe": 31387, "Ġanalysts": 31388, "ĠDeaf": 31389, "Ġjó": 31390, "Ġgroceries": 31391, "ĠWheel": 31392, "ĠÑĢиÑģ": 31393, "Ġcòn": 31394, "ĠCob": 31395, "Ġprisons": 31396, "ève": 31397, "ĠCabinet": 31398, "Ġposed": 31399, "Ġguerre": 31400, "ĠLloyd": 31401, "Ġclerk": 31402, "Ġcrises": 31403, "ĠSho": 31404, "ĠOre": 31405, "ĠFootball": 31406, "ĠAdvis": 31407, "ĠZheng": 31408, "èį": 31409, "ĠAMY": 31410, "Ġunfor": 31411, "Ġmonaster": 31412, "Ġcompile": 31413, "Ġimmortal": 31414, "atable": 31415, "Ġparano": 31416, "Ġtiver": 31417, "ĠSteph": 31418, "ĠFuÃŁ": 31419, "Ġdiscontin": 31420, "Ġripe": 31421, "Ġhacking": 31422, "Ġsiendo": 31423, "Ġseguro": 31424, "altres": 31425, "Ġanderes": 31426, "Ġ리ë": 31427, "Ġexports": 31428, "æŃ¥": 31429, "Ġtabii": 31430, "Ġ기ëĭ¤ë": 31431, "Ġbothering": 31432, "Ġpickle": 31433, "ĠBRIAN": 31434, "Ġaltar": 31435, "ĠпÑĢиб": 31436, "Ġtransferring": 31437, "ĠVors": 31438, "ĠÙĩÙĪ": 31439, "ĠZa": 31440, "ĠFrances": 31441, "Ġbrowse": 31442, "emit": 31443, "Ġchewing": 31444, "ĠFreddy": 31445, "Ġeditors": 31446, "älle": 31447, "ĠíĮĢ": 31448, "ĠSque": 31449, "ĠCultural": 31450, "awk": 31451, "ĠSache": 31452, "ĠCarbon": 31453, "ắt": 31454, "FL": 31455, "ĠNGO": 31456, "peÅĤ": 31457, "ĠSou": 31458, "Ġhvor": 31459, "unintelligible": 31460, "Ġë²ķ": 31461, "Ġ°": 31462, "iin": 31463, "Ġ×¢×Ŀ": 31464, "Ġderrière": 31465, "Ġczym": 31466, "ĠApost": 31467, "Ġregarder": 31468, "Ġagrade": 31469, "ĠCandy": 31470, "Ġmare": 31471, "Ġintroduces": 31472, "birds": 31473, "Ġuniquely": 31474, "Ġmuk": 31475, "Ġcooker": 31476, "Ġcrews": 31477, "Ġjeito": 31478, "ERT": 31479, "¶Ħë": 31480, "nisse": 31481, "Ġef": 31482, "Ġcarte": 31483, "ĠYak": 31484, "ĠPAT": 31485, "ино": 31486, "bokki": 31487, "Ġmates": 31488, "Ġdistint": 31489, "Ġì½Ķë¡ľëĤĺ": 31490, "Ġyıl": 31491, "Ġκάν": 31492, "Ġconfigurations": 31493, "enga": 31494, "recht": 31495, "Happy": 31496, "ãĤĦãģ£ãģ¦": 31497, "invest": 31498, "Ġreconstruct": 31499, "ĠÑįÑĤомÑĥ": 31500, "Ġmosque": 31501, "raum": 31502, "Ġvoyez": 31503, "ĠNBC": 31504, "ĠìŀIJìĭł": 31505, "Ġsturdy": 31506, "Ġкап": 31507, "Ġansch": 31508, "alid": 31509, "Ġmasih": 31510, "ĠREP": 31511, "Ġì½Ķë": 31512, "Ġdeduct": 31513, "Ġsalir": 31514, "wurf": 31515, "ilot": 31516, "ĠMutter": 31517, "olds": 31518, "ĠFEMA": 31519, "ĠBib": 31520, "Ġneighboring": 31521, "Ġbliss": 31522, "Ġíĺ¼": 31523, "лиÑģÑĮ": 31524, "ĠÑĤÑĢеб": 31525, "Ġå°±æĺ¯": 31526, "Ġgrenade": 31527, "Ġegal": 31528, "Ġfinely": 31529, "Ġpetals": 31530, "Ġkeer": 31531, "Ġchyba": 31532, "Ġskipping": 31533, "Ġthirteen": 31534, "Ġgravy": 31535, "ĠSAT": 31536, "61": 31537, "Ġног": 31538, "Ġmins": 31539, "ITE": 31540, "Ġsozial": 31541, "íķĺë©´ìĦľ": 31542, "ruktur": 31543, "Ġвозмож": 31544, "ĠопÑıÑĤÑĮ": 31545, "Ġarth": 31546, "ĠCuban": 31547, "Ġtreasures": 31548, "Ġfertilizer": 31549, "Ġawakening": 31550, "Ġë°±ìĭł": 31551, "Ġrall": 31552, "Ġdepict": 31553, "ĠPablo": 31554, "Ġnineteen": 31555, "Ġwatt": 31556, "Ġentirety": 31557, "KS": 31558, "ĠWoods": 31559, "Sch": 31560, "ĠÚ©ÙĪ": 31561, "ĠDry": 31562, "ãģŀ": 31563, "uve": 31564, "Ġreconstruction": 31565, "Ġanatomy": 31566, "Ī를": 31567, "Ġbaba": 31568, "Ġlistener": 31569, "Ġsharpen": 31570, "ĠPeru": 31571, "ĠвÑĭз": 31572, "Ġrecreation": 31573, "Ġinitiate": 31574, "Ġcalor": 31575, "ĠNaj": 31576, "gee": 31577, "ĠFeels": 31578, "ĠSnapchat": 31579, "ĠTet": 31580, "ĠNest": 31581, "ĠDaf": 31582, "ĠFinish": 31583, "ĠÑĤаким": 31584, "úc": 31585, "izens": 31586, "Ġspins": 31587, "Ġembry": 31588, "Ġpassages": 31589, "Ġcient": 31590, "Ġjustification": 31591, "ä»ĸ說": 31592, "Ġolmaz": 31593, "Ġflooded": 31594, "Ġemoji": 31595, "Ġembracing": 31596, "Ġdiscard": 31597, "ĠBasic": 31598, "agog": 31599, "ĠìľĦíķ´": 31600, "Ġasylum": 31601, "erin": 31602, "Ġfim": 31603, "Ġninja": 31604, "Ġautomate": 31605, "Ġallergic": 31606, "ÿÿÿÿ": 31607, "amam": 31608, "ĠмаÑĢ": 31609, "ĠOi": 31610, "äus": 31611, "Ġinduct": 31612, "ĠBEN": 31613, "ĠzÅĤ": 31614, "Ġkażdy": 31615, "ĠAMP": 31616, "nÄĽ": 31617, "Sure": 31618, "Ġquil": 31619, "Ġespec": 31620, "rok": 31621, "BSCRI": 31622, "Ġliebe": 31623, "pus": 31624, "achsen": 31625, "Ġcricket": 31626, "ëĬIJ": 31627, "ĠFrame": 31628, "ekkür": 31629, "arb": 31630, "ĠpÅĻ": 31631, "иÑģÑģ": 31632, "Ġzeggen": 31633, "Ġdoubles": 31634, "ĠDre": 31635, "test": 31636, "insp": 31637, "boys": 31638, "Ġmão": 31639, "ĠVerse": 31640, "Ġmuscular": 31641, "ĠMALE": 31642, "Ġdulu": 31643, "Ġoccasional": 31644, "Lo": 31645, "conomic": 31646, "Ġvak": 31647, "Ġremedy": 31648, "å¤ł": 31649, "ĠâĻªâĻªâĻª": 31650, "vem": 31651, "Ġönem": 31652, "ĠkarÅŁÄ±": 31653, "ĠSharp": 31654, "hur": 31655, "Ġë°©ë²ķ": 31656, "Ġgrandson": 31657, "Ġaktiv": 31658, "ĠThrones": 31659, "ĠìķĪìĹIJ": 31660, "Ġtots": 31661, "Ġsubd": 31662, "ĠPaula": 31663, "Ġgraves": 31664, "ĠBrent": 31665, "ĠникÑĤо": 31666, "Ġsöz": 31667, "Ġcrec": 31668, "ĠVladimir": 31669, "çĸ«": 31670, "Ġпой": 31671, "Ġ\"-": 31672, "Ġpsy": 31673, "atri": 31674, "idan": 31675, "Ġaún": 31676, "Ġstandardized": 31677, "ì¹ĺë": 31678, "ĠкÑĢов": 31679, "ĠZhu": 31680, "something": 31681, "Ġ750": 31682, "Ġmujeres": 31683, "Ġait": 31684, "éĹ´": 31685, "agu": 31686, "Ġcorrected": 31687, "ikka": 31688, "eled": 31689, "ĠCareer": 31690, "owym": 31691, "Ġroommate": 31692, "Ġdescendants": 31693, "ĠNapoleon": 31694, "ĠÐĶо": 31695, "íĸĪìĸ´ìļĶ": 31696, "Ġbunun": 31697, "ĠMicha": 31698, "ç·ļ": 31699, "Ġdescob": 31700, "PI": 31701, "Ġpalabra": 31702, "Ġtracked": 31703, "Ġdependence": 31704, "ĠBarack": 31705, "åģĩ": 31706, "Ġfertility": 31707, "ĠSouthwest": 31708, "Ġincomplete": 31709, "Ġcomunic": 31710, "Ġcompris": 31711, "ĠRestaur": 31712, "Ġacron": 31713, "κα": 31714, "Ġapprentices": 31715, "Ġmusst": 31716, "ĠAbr": 31717, "Ġpentru": 31718, "ĠConsort": 31719, "ĠAvec": 31720, "Ġdumplings": 31721, "LR": 31722, "Ġwszystkie": 31723, "Ġswamp": 31724, "нев": 31725, "uggle": 31726, "Ġwatercolor": 31727, "Ġproton": 31728, "ĠEspaña": 31729, "ocking": 31730, "овал": 31731, "Ġtakim": 31732, "Very": 31733, "Ġdementia": 31734, "ĠÅŁeyi": 31735, "Jac": 31736, "ĠMacBook": 31737, "ĠLiv": 31738, "fficients": 31739, "ĠHunt": 31740, "Ġoverlay": 31741, "æĦŁè¦º": 31742, "ĠSkype": 31743, "punkt": 31744, "Ġconfined": 31745, "ĠAdrian": 31746, "رÙĥ": 31747, "ĠJeep": 31748, "Ġenquanto": 31749, "Ġanest": 31750, "оÑĤвеÑĤ": 31751, "ĠменÑĮ": 31752, "Ġirrigation": 31753, "á»ijn": 31754, "Ġeighteen": 31755, "ĠPon": 31756, "Ġrescued": 31757, "Ġ1983": 31758, "rü": 31759, "jae": 31760, "ĠJeong": 31761, "Ġamazingly": 31762, "ĠFDP": 31763, "Ġbackstage": 31764, "cue": 31765, "ĠÏĥÏĦην": 31766, "ĠاÙĦص": 31767, "Ġlivestock": 31768, "ĠWarner": 31769, "Ġmajors": 31770, "ãĥģãĥ£": 31771, "Ġcooperative": 31772, "ĠBrady": 31773, "rained": 31774, "rieb": 31775, "Ġ×ij×ŀ×": 31776, "ĠдоволÑĮно": 31777, "ĠFE": 31778, "Ġleaked": 31779, "ĠMercury": 31780, "Ġpersuade": 31781, "Ġtransformer": 31782, "ĠNorweg": 31783, "ĠìŬ룬": 31784, "ĠzrobiÄĩ": 31785, "Ġcardiovascular": 31786, "ĠCrash": 31787, "Ġgossip": 31788, "аÑģÑĤÑĮ": 31789, "Ġ쪽": 31790, "Ġswept": 31791, "ĠHorn": 31792, "ĠAté": 31793, "Ġbukan": 31794, "ĠKaw": 31795, "KY": 31796, "ĠStories": 31797, "Gary": 31798, "Ġgardening": 31799, "ĠQuickly": 31800, "ĠFalcon": 31801, "Ġovat": 31802, "cı": 31803, "ĠComplet": 31804, "ĠDate": 31805, "ĠпÑĢим": 31806, "Ġläuft": 31807, "ĠAudrey": 31808, "ĠWent": 31809, "ĠpelÃŃcul": 31810, "Ġcarriage": 31811, "Ġunacceptable": 31812, "nymi": 31813, "ĠÑģлÑĭÑĪ": 31814, "Ġterre": 31815, "uellement": 31816, "EEEE": 31817, "Ġpharmac": 31818, "hões": 31819, "Ġzich": 31820, "Ġmigrate": 31821, "ĠFry": 31822, "ñana": 31823, "ĠMuito": 31824, "EOVER": 31825, "Ġfortress": 31826, "ĠCompan": 31827, "ĠJSON": 31828, "ordnung": 31829, "Ġwarto": 31830, "Ġungef": 31831, "ìħĶìĦľ": 31832, "ĠÑĢок": 31833, "Ġpaddle": 31834, "Jared": 31835, "Ġsubmitting": 31836, "Ġlatch": 31837, "Ġfug": 31838, "ĠкоÑģ": 31839, "ĠEf": 31840, "Ġlaunches": 31841, "Ġft": 31842, "otechn": 31843, "Ġtravelled": 31844, "اÙģ": 31845, "éģķ": 31846, "Ġproch": 31847, "Ġdedim": 31848, "83": 31849, "Ġrebound": 31850, "ĠLU": 31851, "path": 31852, "ĠÑģпÑĢав": 31853, "Ġöl": 31854, "ĠíĤ¤": 31855, "Ġprivat": 31856, "Ġtractor": 31857, "ĠAttention": 31858, "Ser": 31859, "Ġcoses": 31860, "ária": 31861, "pal": 31862, "ĠìĿĢ": 31863, "Ġsuccessor": 31864, "Ġconnectors": 31865, "ĠÑĥÑģÑĤанов": 31866, "Ġgenocide": 31867, "Ġsufficiently": 31868, "ĠAixò": 31869, "Ġstabilize": 31870, "Ġcongest": 31871, "Ġcarving": 31872, "Ġzost": 31873, "ĠбÑĭÑģÑĤÑĢо": 31874, "Ġshortest": 31875, "Ġlivel": 31876, "Ġ89": 31877, "éģĬ": 31878, "Ġerk": 31879, "Ġportraits": 31880, "à¥Ģ": 31881, "èĺ": 31882, "boat": 31883, "llah": 31884, "ANC": 31885, "Ġempirical": 31886, "ĠEcho": 31887, "ĠNederland": 31888, "è¿Ļä¹Ī": 31889, "Net": 31890, "Ġcuidado": 31891, "ĠRoma": 31892, "Ġcalf": 31893, "Ġgiants": 31894, "ĠExplorer": 31895, "ĠCollect": 31896, "alition": 31897, "ĠDestiny": 31898, "Ġausge": 31899, "ĠEdu": 31900, "ĠClo": 31901, "Ġearrings": 31902, "ĠTrack": 31903, "ĠROS": 31904, "ĠBelle": 31905, "çĻ¾": 31906, "Ġpueda": 31907, "Ġdaytime": 31908, "Ġsupplier": 31909, "ĠSV": 31910, "ĠExhale": 31911, "Ġgalera": 31912, "course": 31913, "Ġcentimeter": 31914, "ĠBast": 31915, "mud": 31916, "Ġsangat": 31917, "ĠPhysical": 31918, "Ġprivately": 31919, "Ġtrata": 31920, "lynn": 31921, "illi": 31922, "Ġë©ĶìĿ´íģ¬ìĹħ": 31923, "Ġcrystall": 31924, "Ġpods": 31925, "ản": 31926, "inator": 31927, "ĠRecords": 31928, "å®ĺ": 31929, "ÄŁimiz": 31930, "issement": 31931, "hare": 31932, "hadow": 31933, "ĠDK": 31934, "ĠìķĮê³ł": 31935, "Ġwyn": 31936, "Ġrequesting": 31937, "ĠDonna": 31938, "ĠìĹ´ìĭ¬íŀĪ": 31939, "inea": 31940, "Ġexert": 31941, "ĠDuncan": 31942, "ĠвеÑĩ": 31943, "ĠHah": 31944, "à¤Ĥ": 31945, "ĠLif": 31946, "ĠFinding": 31947, "ĠNov": 31948, "Ġзнак": 31949, "ĠоÑĦ": 31950, "ĠQuè": 31951, "Ġquarterback": 31952, "ĠÑĦак": 31953, "Ġbipartisan": 31954, "ÄŁin": 31955, "Ġnécess": 31956, "Ġreferendum": 31957, "Ġcompiler": 31958, "Ġprobabil": 31959, "еди": 31960, "Ġtrader": 31961, "æĺĵ": 31962, "ĠRum": 31963, "geme": 31964, "Ġdio": 31965, "ĠbÄĻdziemy": 31966, "ĠÏĢά": 31967, "꾸": 31968, "×ķ×ĺ": 31969, "Ġà¤ķ": 31970, "Ġблаг": 31971, "Ġscalp": 31972, "ĠPause": 31973, "Ġcaption": 31974, "Ġendanger": 31975, "Ġenlar": 31976, "Ġrotten": 31977, "ãĥĥãĥĪ": 31978, "Ġwah": 31979, "èĤī": 31980, "Ġdzi": 31981, "ĠInstall": 31982, "Ay": 31983, "Ġcrear": 31984, "енÑĤа": 31985, "Ġweighing": 31986, "Ġbutterflies": 31987, "ĠGast": 31988, "äºķ": 31989, "horn": 31990, "warz": 31991, "ICEOVER": 31992, "ĠнайÑĤи": 31993, "Ġcoefficients": 31994, "ç°¡åĸ®": 31995, "ĠSpencer": 31996, "ĠHigher": 31997, "Ġcowork": 31998, "å¨ĺ": 31999, "ĠкоÑĤоÑĢое": 32000, "Ġmonit": 32001, "Ġdysfunction": 32002, "ĠÑģÑĤанов": 32003, "Ġtournaments": 32004, "Ġoyster": 32005, "BN": 32006, "Ġtrud": 32007, "slow": 32008, "ĠPenny": 32009, "ĠOdys": 32010, "ær": 32011, "Ġfou": 32012, "Ġenjoyment": 32013, "аÑĤÑĭ": 32014, "ĠwyglÄħda": 32015, "алÑĮнаÑı": 32016, "ĠProtect": 32017, "Ġmoy": 32018, "Ġclaw": 32019, "Ġsuspicion": 32020, "Ġsacrificed": 32021, "Ġgosto": 32022, "Big": 32023, "Ġaggressively": 32024, "Ġvorne": 32025, "ãĥł": 32026, "Ġblamed": 32027, "ĠSehr": 32028, "פר": 32029, "cito": 32030, "Ġseals": 32031, "Ġmujer": 32032, "ĠWeird": 32033, "Ġforens": 32034, "Ġcontributes": 32035, "estra": 32036, "Ġpog": 32037, "LOL": 32038, "Ġhacerlo": 32039, "оÑĤÑĮ": 32040, "fiction": 32041, "79": 32042, "λο": 32043, "大æ¦Ĥ": 32044, "声": 32045, "ĠÑĤоб": 32046, "ĠGS": 32047, "ĠClara": 32048, "itez": 32049, "Ġadvocating": 32050, "ĠíĶĦë": 32051, "sung": 32052, "Ġvertices": 32053, "Ġnavigating": 32054, "Ġeuropé": 32055, "çļĨ": 32056, "Ġslowed": 32057, "Ġforeground": 32058, "ĠIndustrial": 32059, "Ġadore": 32060, "ìĭŃ": 32061, "Ġcréer": 32062, "æŀĹ": 32063, "chnitt": 32064, "Ġunaware": 32065, "Ġcurly": 32066, "entar": 32067, "Ġler": 32068, "Ġprohibited": 32069, "ĠHeroes": 32070, "ĠReed": 32071, "uca": 32072, "Ġsmok": 32073, "Ġkunna": 32074, "zeitig": 32075, "immen": 32076, "ĠLun": 32077, "ĠабÑģолÑİÑĤ": 32078, "Ġdegli": 32079, "Ġvillagers": 32080, "Ġpreset": 32081, "zept": 32082, "uds": 32083, "Ġemit": 32084, "ä½łè¦ģ": 32085, "Ġëī": 32086, "ëĬĶì§Ģ": 32087, "нако": 32088, "Ġosób": 32089, "Ġ1969": 32090, "ĠÐIJÑĢ": 32091, "Ġmanchmal": 32092, "ĠBrock": 32093, "Ġmantra": 32094, "ĠWIL": 32095, "bach": 32096, "inä": 32097, "elas": 32098, "keln": 32099, "Ġdisciple": 32100, "Ġqualc": 32101, "Ġdehyd": 32102, "ìĿ´ëĿ¼ëĬĶ": 32103, "Af": 32104, "ìĦ±ìĿ´": 32105, "Ryan": 32106, "Ġpuppet": 32107, "ĠдÑĢÑĥгие": 32108, "Ġrud": 32109, "Ġpending": 32110, "Plus": 32111, "ĠìķĬìĿĦ": 32112, "Ġbá»ĭ": 32113, "ĠSega": 32114, "çe": 32115, "Ġprogrammer": 32116, "bli": 32117, "Ġunl": 32118, "Ġenslaved": 32119, "Ġsociété": 32120, "Äģh": 32121, "Ġinheritance": 32122, "ĠBangl": 32123, "ermaid": 32124, "Ġpractitioner": 32125, "ĠStalin": 32126, "ĠUser": 32127, "cible": 32128, "Ġcardiac": 32129, "ĠKoreans": 32130, "Ġdumped": 32131, "Ġ×Ķ×Ļ×Ķ": 32132, "áis": 32133, "Ġhydraulic": 32134, "oubtedly": 32135, "ĠPit": 32136, "Ġpicnic": 32137, "Ġbehöver": 32138, "ĠÑģмог": 32139, "Ġbraking": 32140, "é»ij": 32141, "utar": 32142, "ĠìĦ¸ë": 32143, "ubl": 32144, "Ġüz": 32145, "Ġmajesty": 32146, "Ġbers": 32147, "utable": 32148, "Ġhotter": 32149, "çħ§": 32150, "ÛĮÙĨ": 32151, "Ġbiases": 32152, "Ġsubjected": 32153, "Ġnaughty": 32154, "Ġcircus": 32155, "ãģĹãģĭ": 32156, "ĠImmedi": 32157, "ĠStefan": 32158, "ĠTriple": 32159, "enk": 32160, "Ġwit": 32161, "Ġrecycle": 32162, "emie": 32163, "dated": 32164, "Ġunload": 32165, "Ġpopula": 32166, "chin": 32167, "Ġyields": 32168, "Ġenglish": 32169, "ĠBonnie": 32170, "Ġspiders": 32171, "Ãģ": 32172, "Ġerosion": 32173, "éĥ¨åĪĨ": 32174, "ĠNICK": 32175, "иÑıÑħ": 32176, "Ġimpart": 32177, "Ġкни": 32178, "Ġresolutions": 32179, "Ġlithium": 32180, "Ġconvergence": 32181, "ĠTara": 32182, "Ġдве": 32183, "ths": 32184, "ĠCindy": 32185, "æĪijè¦ģ": 32186, "幫": 32187, "ĠDIE": 32188, "Ġassurance": 32189, "ĠопиÑģ": 32190, "Ġbuckets": 32191, "Ġcues": 32192, "ĠQuiet": 32193, "Ġsimilarity": 32194, "Ġfoundational": 32195, "ĠMinist": 32196, "滿": 32197, "Ġpian": 32198, "Ġcentr": 32199, "Ġnumb": 32200, "Ġmonks": 32201, "ujourd": 32202, "enzie": 32203, "Ġskateboard": 32204, "Ġdlatego": 32205, "ĠÑģоÑĤ": 32206, "ĠAE": 32207, "Ġmasterpiece": 32208, "ĠSolomon": 32209, "ĠReddit": 32210, "Ġriot": 32211, "abl": 32212, "ĠJazz": 32213, "Ġelectromagnetic": 32214, "Ġinsecure": 32215, "ĠCompet": 32216, "geries": 32217, "обод": 32218, "ł×ķ": 32219, "ðŁĴ": 32220, "Ġsenators": 32221, "ĠBrisbane": 32222, "ĠAlb": 32223, "uttering": 32224, "ĠAllow": 32225, "zero": 32226, "Ġpai": 32227, "ĠÐIJлекÑģ": 32228, "ĠDisplay": 32229, "ĠBlade": 32230, "ĠApps": 32231, "Ġpä": 32232, "ĠдеÑģÑı": 32233, "Ġquella": 32234, "ĠGao": 32235, "еннÑĭÑħ": 32236, "Ġspoilers": 32237, "Ġgallons": 32238, "ĠÙĦÙĬ": 32239, "ĠZion": 32240, "æľīä¸Ģ": 32241, "onie": 32242, "ragt": 32243, "ĠChand": 32244, "Ġë³ij": 32245, "Ġblunt": 32246, "Ġusu": 32247, "ĠKad": 32248, "rakt": 32249, "Ġcinematic": 32250, "Ġammunition": 32251, "rene": 32252, "Ġfourteen": 32253, "ĠCarn": 32254, "crit": 32255, "Ġtenure": 32256, "vu": 32257, "Ġprincipalmente": 32258, "Ġalleen": 32259, "éĢĻä¸Ģ": 32260, "Ġkomplett": 32261, "Ġdüny": 32262, "James": 32263, "Ġreceptor": 32264, "Ġoneself": 32265, "guru": 32266, "Ġmerchant": 32267, "liness": 32268, "Ġoverlooked": 32269, "Ġharmonic": 32270, "éķ¿": 32271, "ieso": 32272, "×ķ×ŀ": 32273, "colm": 32274, "ĠпÑĢоекÑĤ": 32275, "ĠAda": 32276, "اس": 32277, "Tim": 32278, "Ġrecurring": 32279, "Ġproceeds": 32280, "ĠParticularly": 32281, "ĠDownload": 32282, "etrical": 32283, "Ġmatrices": 32284, "Ġproyecto": 32285, "ancies": 32286, "ĠUhm": 32287, "Ġcaves": 32288, "Ġìĸ´ëł¤": 32289, "ĠLeaf": 32290, "ĠобÑĭÑĩ": 32291, "ĠìĿ´ìľł": 32292, "Europe": 32293, "ĠtÄħ": 32294, "Ġpuls": 32295, "Ġtakiego": 32296, "ÐĿе": 32297, "GU": 32298, "Ġfors": 32299, "Ïģγ": 32300, "Ġfotos": 32301, "Ġ))": 32302, "Ġ멤ë": 32303, "Ġaquilo": 32304, "ĠKurd": 32305, "ï¸ı": 32306, "ptic": 32307, "ĠDort": 32308, "Ġmisery": 32309, "auso": 32310, "åĬŁ": 32311, "chuckling": 32312, "ĠRidge": 32313, "ĠíĸĪìĬµëĭĪëĭ¤": 32314, "Ġ***": 32315, "客": 32316, "ĠHmmm": 32317, "Ġgeographic": 32318, "Ġanys": 32319, "Ġtalvez": 32320, "Ġskelet": 32321, "Ġsignatures": 32322, "Ġliters": 32323, "IJë©´": 32324, "ĠÑģвоего": 32325, "Ġskiing": 32326, "ĠÐľÐ¾Ñģ": 32327, "Ġadopting": 32328, "Ġhaft": 32329, "Ġsymmetric": 32330, "ĠLiqu": 32331, "Ġthyroid": 32332, "Ġmisin": 32333, "lude": 32334, "Ġhull": 32335, "ĠXD": 32336, "ĠGust": 32337, "zeich": 32338, "Ġvibrations": 32339, "Ġesemp": 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33211, "ĠпоÑģÑĤоÑıн": 33212, "ĠHood": 33213, "Huh": 33214, "Ġë¶ģ": 33215, "Ġhyung": 33216, "ĠMeeting": 33217, "Ġimporta": 33218, "Ġì°¾ìķĦ": 33219, "ĠVern": 33220, "Ġstripped": 33221, "Ġrefuses": 33222, "Ġqualifications": 33223, "opl": 33224, "ĢëıĦ": 33225, "ixÃŃ": 33226, "Ġdiab": 33227, "itime": 33228, "flows": 33229, "Ġinac": 33230, "ĠGong": 33231, "Ġmeaningless": 33232, "Ġcourageous": 33233, "Ġmicrobi": 33234, "azy": 33235, "hist": 33236, "Ġvolunteering": 33237, "VIE": 33238, "Ġviolated": 33239, "Ġsympathy": 33240, "ĠEdit": 33241, "好åĥı": 33242, "electric": 33243, "product": 33244, "Ġpandemia": 33245, "Ġgeometric": 33246, "ĠConvers": 33247, "gre": 33248, "Ġglut": 33249, "isted": 33250, "ĠاÙĦÙĥ": 33251, "ĠChain": 33252, "ĠPresent": 33253, "ĠYin": 33254, "ĠÑģог": 33255, "ĠVlog": 33256, "Ġìĸ´ë¨¸": 33257, "Ġdonn": 33258, "Ġhitch": 33259, "ucking": 33260, "ãģĬãģĦ": 33261, "wald": 33262, "risk": 33263, "Ġhari": 33264, "ĠKens": 33265, "ĠIdol": 33266, "Ġвнимание": 33267, "Ġtodd": 33268, 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33328, "หà¸Ļ": 33329, "Ġappli": 33330, "ா?": 33331, "üyorum": 33332, "ĠинÑĤеÑĢеÑģно": 33333, "Ġdisinfect": 33334, ">-": 33335, "Ġchampagne": 33336, "Ġkla": 33337, "opers": 33338, "Trans": 33339, "ĠDesert": 33340, "Ġcultivate": 33341, "ĠFucking": 33342, "idelity": 33343, "ĠÑĤан": 33344, "Ġincub": 33345, "Ġtemu": 33346, "Ġlearner": 33347, "founder": 33348, "ĠSyl": 33349, "ãĤĢ": 33350, "Ġfato": 33351, "zier": 33352, "ĠìĹĨìĿ´": 33353, "ĠìĪ¨": 33354, "Ġpsycho": 33355, "ĠÑĤелеÑĦ": 33356, "Ġregarde": 33357, "Ġrepresentations": 33358, "Ġlitigation": 33359, "Ġspann": 33360, "ults": 33361, "bior": 33362, "è¦ĭãģ¦": 33363, "ä¸įå¤ļ": 33364, "ĠSurvey": 33365, "ĠLEDs": 33366, "Ġträ": 33367, "Ġlên": 33368, "Ġantioxid": 33369, "еÑĢом": 33370, "Ġinduction": 33371, "Ġfooled": 33372, "ätzlich": 33373, "ĠговоÑĢÑıÑĤ": 33374, "ĠFact": 33375, "umbai": 33376, "Ġwiggle": 33377, "NOUN": 33378, "Ġdévelopp": 33379, "ĠClaro": 33380, "Ġì¸": 33381, "ë¬": 33382, "ãģªãĤĵãģł": 33383, "Ġaccumulate": 33384, "Ġmaintains": 33385, "ëĦ": 33386, "ĠFighter": 33387, "íĨł": 33388, "Ġmatin": 33389, "Ġcoupon": 33390, "Ġstunt": 33391, "Ġdebuted": 33392, "å¾ħãģ£ãģ¦": 33393, "Ġprag": 33394, "иваем": 33395, "73": 33396, "Ġexpres": 33397, "Ġìĺ¤ë¹ł": 33398, "ĠпеÑĢÑģон": 33399, "Ġcalculus": 33400, "Ġabrupt": 33401, "ĠInspector": 33402, "ourt": 33403, "æĸĻ": 33404, "źniej": 33405, "intense": 33406, "Ba": 33407, "Ġlounge": 33408, "Ġasthma": 33409, "ĠHiç": 33410, "ª»": 33411, "Ġeditorial": 33412, "Ġseize": 33413, "Ġkır": 33414, "Ġmouve": 33415, "Ġtierra": 33416, "Ġtestosterone": 33417, "Ġrh": 33418, "ĠKingston": 33419, "ELLE": 33420, "ĠRepresentative": 33421, "Ġ1974": 33422, "Ġiba": 33423, "Ts": 33424, "Ġsorta": 33425, "Ġ(?)": 33426, "ĠتÙĪ": 33427, "ĠëĤ´ëł¤": 33428, "Ġbekommt": 33429, "Ġspiritually": 33430, "Ġdistorted": 33431, "Mad": 33432, "Ġreim": 33433, "ánh": 33434, "ĠOttoman": 33435, "ĠRelig": 33436, "ĠEls": 33437, "Ġretained": 33438, "ĠLaughs": 33439, "æĢ»": 33440, "ĠSAS": 33441, "ĠколиÑĩеÑģÑĤво": 33442, "×ķתר": 33443, "Ġinnovate": 33444, "Ġkork": 33445, "ĠÑĢаÑģÑģказÑĭв": 33446, "ondere": 33447, "ivi": 33448, "aye": 33449, "ounty": 33450, "ĠполÑĥÑĩаеÑĤÑģÑı": 33451, "Ġbuns": 33452, "åħ«": 33453, "Ġyüzden": 33454, "Ġsurgeries": 33455, "Ø£ÙĨ": 33456, "Ġbankruptcy": 33457, "welt": 33458, "Ġsiamo": 33459, "Ġdarkest": 33460, "ĠHann": 33461, "gga": 33462, "Ġformas": 33463, "ĠDj": 33464, "named": 33465, "Ġshields": 33466, "ueller": 33467, "ĠFew": 33468, "Ġlace": 33469, "Ġfurious": 33470, "ĠYU": 33471, "Ġsocietal": 33472, "Ġjudgement": 33473, "ĠDos": 33474, "Ġjab": 33475, "laws": 33476, "Ġreinvent": 33477, "ĠKatherine": 33478, "ĠChoi": 33479, "adows": 33480, "Ġrans": 33481, "oden": 33482, "ĠMidwest": 33483, "nın": 33484, "Ġdeport": 33485, "ĠDip": 33486, "ç´ħ": 33487, "Ġatención": 33488, "ĠCourtney": 33489, "ividad": 33490, "ĠÚ©Ûģ": 33491, "Ġefficacy": 33492, "ĠBrooks": 33493, "Ġreferral": 33494, "ĠконÑĨ": 33495, "Ġmalicious": 33496, "Ġkir": 33497, "ĠGoddess": 33498, "Ġfunky": 33499, "Ġinterim": 33500, "ĠKörper": 33501, "Ġìĸ¼ë§": 33502, "kur": 33503, "Ġкли": 33504, "Ġtrucs": 33505, "gesetz": 33506, "Ġzug": 33507, "ĠGlück": 33508, "ĠMinute": 33509, "Ġprestigious": 33510, "Ġniez": 33511, "Ġconcentrations": 33512, "лаÑģÑĤи": 33513, "ĠSis": 33514, "ĠVitamin": 33515, "kov": 33516, "ĠPBS": 33517, "Ġнее": 33518, "Ġretailers": 33519, "Ġconventions": 33520, "ĠSamantha": 33521, "Ġproudly": 33522, "Jordan": 33523, "ĠJASON": 33524, "atk": 33525, "Ġtriste": 33526, "Ġstär": 33527, "Ġreiterate": 33528, "Ġposterior": 33529, "Ġ1973": 33530, "ĠPine": 33531, "ĠJuliet": 33532, "Ġpedir": 33533, "kil": 33534, "Ġoverlapping": 33535, "Ġexclude": 33536, "Ġeconóm": 33537, "Ġaccepts": 33538, "ĠSter": 33539, "決": 33540, "Ġìļ´ëıĻ": 33541, "estab": 33542, "Ġtug": 33543, "arg": 33544, "Ġlivro": 33545, "اص": 33546, "Ġseams": 33547, "Ġburaya": 33548, "Ġello": 33549, "ĠTM": 33550, "ĠPaw": 33551, "ĠIndex": 33552, "Exc": 33553, "Ġinspirational": 33554, "Ġdunk": 33555, "è°ģ": 33556, "akter": 33557, "Ġconditioner": 33558, "ĠSalut": 33559, "ÅĤec": 33560, "Ġìī½": 33561, "ĠÑĥзна": 33562, "ĠRomeo": 33563, "fruit": 33564, "ĠYO": 33565, "Ġchá»ī": 33566, "бÑĥ": 33567, "bons": 33568, "Ġreproductive": 33569, "Ġorada": 33570, "Ġíļ¨": 33571, "Ġtentar": 33572, "Ġmañana": 33573, "ãĤ¬": 33574, "Ġsolvent": 33575, "Jessica": 33576, "ĠLegal": 33577, "Ġtua": 33578, "Ġsic": 33579, "ĠEQ": 33580, "aukee": 33581, "ìĭľëĭ¤": 33582, "ĠÅŀu": 33583, "Ġadhere": 33584, "ĠTul": 33585, "Ġà®Ĩ": 33586, "Ġtextbooks": 33587, "ĠFifth": 33588, "Ġexperi": 33589, "Ġchic": 33590, "Ġheap": 33591, "inely": 33592, "atra": 33593, "Two": 33594, "Ġhelemaal": 33595, "Ġfren": 33596, "æݨ": 33597, "Ġbisher": 33598, "اش": 33599, "ĠìĦłìĥĿ": 33600, "ĠTages": 33601, "Ġsá»±": 33602, "Ġbullied": 33603, "ؤ": 33604, "Ġbenefited": 33605, "ĠPreviously": 33606, "ĠÑįÑĦÑĦ": 33607, "Ùį": 33608, "Ġsenate": 33609, "ĠMorm": 33610, "ijke": 33611, "ĠFlu": 33612, "Ġincorporating": 33613, "jack": 33614, "ĠпиÑĤ": 33615, "Ġimply": 33616, "Ġhacks": 33617, "ĠRICH": 33618, "ĠкваÑĢ": 33619, "ĠпÑĢекÑĢаÑģ": 33620, "Ġdependency": 33621, "Ġìļ©": 33622, "Ġì±ħ": 33623, "Ġwährend": 33624, "Ġsulla": 33625, "ĠPittsburgh": 33626, "Ġesempio": 33627, "¼ë¡ľ": 33628, "prot": 33629, "ĠRosen": 33630, "ĠIndependence": 33631, "Ġparsley": 33632, "iegen": 33633, "Ġhaw": 33634, "Ġaquell": 33635, "ĠCAP": 33636, "ĠÑĢабоÑĤаÑĤÑĮ": 33637, "ĠCliff": 33638, "ionar": 33639, "Ġsecuring": 33640, "æĪijåĢijçļĦ": 33641, "νε": 33642, "Ġutilis": 33643, "Ġcoule": 33644, "ĠPing": 33645, "Ġtrek": 33646, "Ġfak": 33647, "Ġenorme": 33648, "Ġìĭ«": 33649, "让": 33650, "Ġdoubling": 33651, "ĠнÑĢавиÑĤÑģÑı": 33652, "Ġhed": 33653, "hoven": 33654, "ĠStanding": 33655, "ĠmÃŃn": 33656, "ĠJimin": 33657, "Ġmonarch": 33658, "Ġcoke": 33659, "Ġmr": 33660, "Ġclic": 33661, "Ãį": 33662, "Ġimpeachment": 33663, "Ġdurability": 33664, "Ġvarios": 33665, "Ġcommercials": 33666, "Ġgreetings": 33667, "ĠRi": 33668, "ĠAppreci": 33669, "ìŀĪëĬĶ": 33670, "Ġrésult": 33671, "ért": 33672, "Ġsalute": 33673, "Ġpoderia": 33674, "Ġsunrise": 33675, "veck": 33676, "Ġreluctant": 33677, "Ġcommissioner": 33678, "念": 33679, "âte": 33680, "ĠKenny": 33681, "ĠSiri": 33682, "ãĥĥãĥĹ": 33683, "ĠëĬĺ": 33684, "ĠEE": 33685, "Ġunch": 33686, "кон": 33687, "ĠاÙĦØ¥": 33688, "Ġbelts": 33689, "Ġhass": 33690, "ĠмоÑı": 33691, "Ġdisplaced": 33692, "Ġabra": 33693, "ÎŃλ": 33694, "Ġscratches": 33695, "Ġcomet": 33696, "Ġauthorization": 33697, "ĠLLC": 33698, "Ġproduk": 33699, "Ġrehabilitation": 33700, "åŀ": 33701, "ÑĸÑĩ": 33702, "uding": 33703, "olit": 33704, "Ġ105": 33705, "Ġexpands": 33706, "Ġaltri": 33707, "ĠKomment": 33708, "Ġanf": 33709, "Pl": 33710, "ĠMana": 33711, "fed": 33712, "Ġbri": 33713, "Ġora": 33714, "Gs": 33715, "ĠGur": 33716, "uckland": 33717, "Ġjunction": 33718, "Ġironic": 33719, "ĠFeed": 33720, "Ġprakt": 33721, "ĠHammer": 33722, "ĮëıĦ": 33723, "ĠTracy": 33724, "çµ±": 33725, "ĠAside": 33726, "него": 33727, "ĠиÑģполÑĮзоваÑĤÑĮ": 33728, "Ġzaj": 33729, "Ġequitable": 33730, "Ġcurb": 33731, "ĠãģĵãĤĮ": 33732, "Ġderivatives": 33733, "Ġpuppies": 33734, "ĠKenneth": 33735, "ĠCompl": 33736, "igram": 33737, "ĠGarcia": 33738, ")\"": 33739, "ĠHarbor": 33740, "estial": 33741, "Ġä¾Ĩ": 33742, "Ġers": 33743, "æ¹": 33744, "Ġunwanted": 33745, "Ġbelang": 33746, "аго": 33747, "emb": 33748, "dos": 33749, "ĠìĻľë": 33750, "ĠBudget": 33751, "Ġbattling": 33752, "ØŃت": 33753, "kok": 33754, "наÑĩала": 33755, "Ġplag": 33756, "Ġcantidad": 33757, "Ġgrupos": 33758, "Ġplugins": 33759, "lerini": 33760, "ĠимееÑĤ": 33761, "Ġsozusagen": 33762, "olics": 33763, "Ġpueblo": 33764, "Ġreminis": 33765, "rän": 33766, "ĠMorrison": 33767, "Ġlinha": 33768, "Ġbreaths": 33769, "ĠTaste": 33770, "Ġenfrent": 33771, "ĠDocker": 33772, "Ġден": 33773, "Ġethnicity": 33774, "Ġwob": 33775, "Ġsuffers": 33776, "Ġtransitioning": 33777, "ĠRange": 33778, "ÄĻdzy": 33779, "ĠкаÑĤ": 33780, "Ġsyner": 33781, "Ġdonut": 33782, "Ġprobabilities": 33783, "ĠOmar": 33784, "Which": 33785, "uish": 33786, "isin": 33787, "Ġdemos": 33788, "ĠìłĢ기": 33789, "Ġëĺijê°Ļ": 33790, "Ġедин": 33791, "Ġcerve": 33792, "Ġjoka": 33793, "IAN": 33794, "Ġkilometer": 33795, "Ġhorizontally": 33796, "ĠBhag": 33797, "Ġ->": 33798, "ĠMonitor": 33799, "Ġknowledgeable": 33800, "Ġfav": 33801, "Ġpinned": 33802, "ĠeBay": 33803, "icker": 33804, "Ġìŀłê¹IJë§Į": 33805, "ĠXiaomi": 33806, "Ġcapit": 33807, "Ġnp": 33808, "Ġ1965": 33809, "hoe": 33810, "Ġnok": 33811, "ĠSage": 33812, "ĠнелÑĮзÑı": 33813, "ĠTow": 33814, "gam": 33815, "Ġdicen": 33816, "ĠSUBSCRIBE": 33817, "Ġreboot": 33818, "Ġpaj": 33819, "Ġë³´ìŬë": 33820, "Ġthicken": 33821, "ĠReality": 33822, "idän": 33823, "Na": 33824, "Ġê²ĥìĿĢ": 33825, "!!)": 33826, "Ġroutines": 33827, "Ġодного": 33828, "Ġexting": 33829, "Ġì¦Ŀ": 33830, "Ġsulfur": 33831, "Ġcarve": 33832, "Ġasteroid": 33833, "ĠWarrior": 33834, "Ġphotographers": 33835, "Ġpell": 33836, "Ġcrossover": 33837, "æĪijçŁ¥éģĵ": 33838, "Ġhacemos": 33839, "ĠNej": 33840, "Ġsettling": 33841, "Ġirm": 33842, "ĠBooks": 33843, "ientôt": 33844, "Ġespacio": 33845, "ĠScholars": 33846, "Ġdoomed": 33847, "ĠIRS": 33848, "wohl": 33849, "Ġsegue": 33850, "ĠëĪĦê°Ģ": 33851, "Ġpratic": 33852, "BT": 33853, "ĠConsidering": 33854, "ĠBuffalo": 33855, "Ġtrainings": 33856, "Ġgebru": 33857, "ĠGleich": 33858, "Ġpirates": 33859, "Ġenvelop": 33860, "Ġreopen": 33861, "imat": 33862, "Ġtee": 33863, "Ġsued": 33864, "feh": 33865, "Ġ×Ķק": 33866, "Ġdiets": 33867, "Ġjuntos": 33868, "asto": 33869, "Ġmisunderstood": 33870, "Ġruim": 33871, "Ġclassify": 33872, "ĠпÑĢодÑĥк": 33873, "Ġinse": 33874, "Ġillustrated": 33875, "Ġcorrosion": 33876, "Ġaccred": 33877, "ĠAuntie": 33878, "ĠпÑĢивеÑĤ": 33879, "ĠLIVE": 33880, "Ġrek": 33881, "Ġreceipt": 33882, "åĪ°åºķ": 33883, "ĠBarbie": 33884, "ĠSnake": 33885, "turn": 33886, "Jeff": 33887, "ãģĬãģĬ": 33888, "ķĦ": 33889, "VOICEOVER": 33890, "coll": 33891, "Ġrunners": 33892, "ìłľë": 33893, "osos": 33894, "moon": 33895, "Ġkeynote": 33896, "ĠInstit": 33897, "SPEAK": 33898, "Ġplugs": 33899, "Ġcurv": 33900, "ĠYuri": 33901, "ĠTheres": 33902, "ĠPs": 33903, "ĠμÏĢο": 33904, "Ġconverter": 33905, "Ġrefine": 33906, "Ġbadass": 33907, "Ġοι": 33908, "Ġregen": 33909, "azzi": 33910, "ÙĬÙģ": 33911, "Ġseized": 33912, "Ġiçer": 33913, "ilee": 33914, "Ġupstream": 33915, "Ġbuds": 33916, "Ġpim": 33917, "Ġíķĺ루": 33918, "Ġalluded": 33919, "Ġthemed": 33920, "Ġconsisting": 33921, "Ġbons": 33922, "unuz": 33923, "ĠпÑĢовод": 33924, "ĠLovely": 33925, "à¥ĭ": 33926, "Ġparach": 33927, "ĠStaats": 33928, "éļĬ": 33929, "Ġselective": 33930, "Ġfase": 33931, "ĠGeorget": 33932, "Ġcocaine": 33933, "Ġreproduction": 33934, "ĠLara": 33935, "ĠLD": 33936, "Ġgh": 33937, "Jon": 33938, "ĠlÃ¥": 33939, "ĠëijIJë": 33940, "Ġtyped": 33941, "ĠBana": 33942, "ëĵľë": 33943, "Ġsavory": 33944, "ĠZomb": 33945, "standen": 33946, "Ġpedestrian": 33947, "Ġdifférents": 33948, "Ġìĭ¸": 33949, "èī¯": 33950, "Ġcomplained": 33951, "ç¦ı": 33952, "ĠÐļÑĤо": 33953, "Ġ׾פ": 33954, "aliÅĽmy": 33955, "Ġmortar": 33956, "Ġverdict": 33957, "Ġsuficiente": 33958, "ĠMillion": 33959, "mittel": 33960, "inals": 33961, "ĠاÙĦØ®": 33962, "аÑİÑģÑĮ": 33963, "ĠmiÄĻdzy": 33964, "ĠOle": 33965, "Ġinvert": 33966, "czyÄĩ": 33967, "озможно": 33968, "starter": 33969, "Ġauditor": 33970, "ĠScout": 33971, "chien": 33972, "ĠSverige": 33973, "uffled": 33974, "Ġzehn": 33975, "ĠAuckland": 33976, "Ġargent": 33977, "Ġ1976": 33978, "ĠHoe": 33979, "Ġbothers": 33980, "Ġsocialist": 33981, "Ġpliers": 33982, "Ġemergen": 33983, "ĠXP": 33984, "еÑĢов": 33985, "More": 33986, "ĠLevi": 33987, "ĠAnders": 33988, "ibilidad": 33989, "ĠParents": 33990, "Ġinduced": 33991, "ìĸ´ì¤": 33992, "Ġbalances": 33993, "ĠвÑĭÑĪ": 33994, "Ġsubmarine": 33995, "Start": 33996, "Ġdries": 33997, "Ġvolver": 33998, "Ġticking": 33999, "cott": 34000, "Ġfaj": 34001, "prés": 34002, "ĠSabb": 34003, "ĠзаÑĩ": 34004, "ĠпокÑĥп": 34005, "Ġbaptized": 34006, "ĠBrilliant": 34007, "ĠÐijог": 34008, "Ġmots": 34009, "bits": 34010, "Ġlattice": 34011, "æĪijè·Łä½ł": 34012, "Ġcoriander": 34013, "Ġresidency": 34014, "ync": 34015, "Ġpierwszy": 34016, "ĠKnock": 34017, "ĠZap": 34018, "ĠÐķв": 34019, "견": 34020, "å°ıå¿ĥ": 34021, "Ġuneven": 34022, "ĠJas": 34023, "odor": 34024, "ç¿Ĵ": 34025, "74": 34026, "ĠSite": 34027, "Ġaconteceu": 34028, "ympt": 34029, "Ġtrilogy": 34030, "Ġlantern": 34031, "ĠZucker": 34032, "vari": 34033, "welling": 34034, "ĠPotato": 34035, "gomery": 34036, "Ġreacted": 34037, "ĠChron": 34038, "Ġjede": 34039, "beeld": 34040, "Ġtwent": 34041, "Ġlact": 34042, "æ¨Ĥ": 34043, "Ġrése": 34044, "Ġrelent": 34045, "Ġfurnace": 34046, "Ġwidget": 34047, "Ġearthquakes": 34048, "ĠAdjust": 34049, "ilit": 34050, "ĠØ£ÙĪ": 34051, "Ġhearings": 34052, "Ġdefendant": 34053, "irsiniz": 34054, "Ġbask": 34055, "cja": 34056, "ľ¨": 34057, "Ġrifles": 34058, "Ġinstal": 34059, "ĠForgive": 34060, "pical": 34061, "ĠÐŀÑĩенÑĮ": 34062, "Ġpetites": 34063, "Ġhp": 34064, "Ġrenowned": 34065, "ĠInn": 34066, "Ġ주ìĦ¸ìļĶ": 34067, "Ġemphasized": 34068, "éĹ®é¢ĺ": 34069, "ĠìŀĪì£ł": 34070, "Ġê²ĥìľ¼ë¡ľ": 34071, "ãĤĨ": 34072, "Åĵ": 34073, "gili": 34074, "Dave": 34075, "Ġexhausting": 34076, "ÅĤug": 34077, "Ġschema": 34078, "μά": 34079, "cycl": 34080, "Ġautant": 34081, "Ġparcel": 34082, "Ġmateria": 34083, "ĠBerry": 34084, "ĠÑģами": 34085, "Ġextracted": 34086, "ĠSaying": 34087, "ismatic": 34088, "ĠпопÑĢоб": 34089, "Ġneuron": 34090, "graph": 34091, "ľë©´": 34092, "Ġenclosure": 34093, "ĠJohann": 34094, "Ġaftermath": 34095, "ÑĤоб": 34096, "Ġuży": 34097, "Ġsamp": 34098, "360": 34099, "ĠMei": 34100, "Ġtaco": 34101, "Ġreceptors": 34102, "Ġpunches": 34103, "ĠHoje": 34104, "ĠÙĩÙĨا": 34105, "=\"#": 34106, "ĠAngular": 34107, "Ġmusique": 34108, "Ġrol": 34109, "Ġñ": 34110, "sterreich": 34111, "Ġclam": 34112, "ĠTreasury": 34113, "chemical": 34114, "Ġapar": 34115, "Ġappend": 34116, "Ġforbid": 34117, "ĠHamburg": 34118, "аков": 34119, "Ġê¸Ī": 34120, "ilda": 34121, "Ġpreparations": 34122, "ĠmogÄħ": 34123, "Ġcamino": 34124, "Eric": 34125, "ĠBlind": 34126, "èĪĩ": 34127, "å¹´çļĦ": 34128, "ĠDiscovery": 34129, "ì¸ł": 34130, "çĪ¶": 34131, "Ġinterpreter": 34132, "Ġbred": 34133, "ĠPsalm": 34134, "Ġdefended": 34135, "ìī¬": 34136, "ĠErfahr": 34137, "ĠPeach": 34138, "Ġmoons": 34139, "ĠOst": 34140, "Ġspécial": 34141, "Ġarriver": 34142, "ĠWis": 34143, "uci": 34144, "Ġrobotics": 34145, "IVE": 34146, "Ġsiege": 34147, "arla": 34148, "Ġseparates": 34149, "ĠTC": 34150, "íı°": 34151, "quisite": 34152, "Ġparentheses": 34153, "ике": 34154, "ç«Ļ": 34155, "Ġtrous": 34156, "建": 34157, "ĠÑģилÑĮ": 34158, "Ġbeers": 34159, "ĠплаÑĤ": 34160, "ãģĻãģĶãģĦ": 34161, "Ġsola": 34162, "Ġdès": 34163, "mingham": 34164, "ikte": 34165, "Ġoops": 34166, "Ġtwitch": 34167, "å°ĩ": 34168, "ÏĪ": 34169, "ĠShouldn": 34170, "uvre": 34171, "Ġleer": 34172, "criptions": 34173, "Ġeyeshadow": 34174, "ĠGuo": 34175, "ĠPowell": 34176, "Ġsupuesto": 34177, "Ġana": 34178, "rals": 34179, "ĠMontreal": 34180, "Ġsurfing": 34181, "ĠÐŁÐµÑĢв": 34182, "×ŀ×ķ": 34183, "Ġmilliseconds": 34184, "Ġsuburbs": 34185, "Ġplaneta": 34186, "ÑĥÑĪка": 34187, "hrlich": 34188, "ĠHY": 34189, "ĠسÛĴ": 34190, "ĠMM": 34191, "ĠEff": 34192, "åı¯æĦĽ": 34193, "ĠHS": 34194, "anson": 34195, "Ġì§ģìłij": 34196, "Ġsuo": 34197, "Ġdeploying": 34198, "Ġkunt": 34199, "tering": 34200, "Ġerect": 34201, "ìŀ¥ìĿ´": 34202, "ĠìĿĮìĭĿ": 34203, "Ġspecimen": 34204, "!...": 34205, "æĪij說": 34206, "Ġligne": 34207, "Ġkonst": 34208, "adequ": 34209, "Ġìĥģíĥľ": 34210, "Ġaccessed": 34211, "ĠPole": 34212, "kill": 34213, "Ġë²Ħë": 34214, "Ġauthenticity": 34215, "Ġappelle": 34216, "ulle": 34217, "Ġrevision": 34218, "Ġgoats": 34219, "гли": 34220, "Ġpau": 34221, "ĠRanger": 34222, "ĠImag": 34223, "author": 34224, "Ġeve": 34225, "ĠMessenger": 34226, "Ġnay": 34227, "Ġwholes": 34228, "ätte": 34229, "Ġonwards": 34230, "ĠDepois": 34231, "ĠíijľíĺĦ": 34232, "ĠSARS": 34233, "Ġwszystkich": 34234, "Ġdestru": 34235, "umbing": 34236, "Ġcompatibility": 34237, "Ġmisinformation": 34238, "odore": 34239, "ĠFavor": 34240, "eko": 34241, "ıĮ": 34242, "waukee": 34243, "ĠTeaching": 34244, "ĠKO": 34245, "Ġbetting": 34246, "Ġquests": 34247, "Ġvivre": 34248, "ĠмÑĥзÑĭ": 34249, "Ġsaga": 34250, "Ġswell": 34251, "Ġgehe": 34252, "æĢİ麼樣": 34253, "ĠоÑĢганиз": 34254, "Ġgide": 34255, "ĠGross": 34256, "Ġdalej": 34257, "Ġclaws": 34258, "á»Ļc": 34259, "Ġprejudice": 34260, "Ġinsign": 34261, "ihood": 34262, "Ġpled": 34263, "Ġdónde": 34264, "ĠPolitical": 34265, "Ġpremises": 34266, "undert": 34267, "عت": 34268, "onnen": 34269, "Ġespaço": 34270, "Ġfé": 34271, "ĠHarrison": 34272, "ĠCensus": 34273, "Ġcardio": 34274, "Ġdiy": 34275, "Ġmilieu": 34276, "Ġjournée": 34277, "ĠRelease": 34278, "NIE": 34279, "ĠMuk": 34280, "idée": 34281, "á»įi": 34282, "Ġiçinde": 34283, "ŀĻ": 34284, "Ġresonate": 34285, "Ġmoles": 34286, "ĠFlying": 34287, "ĠGloria": 34288, "ĠPastor": 34289, "ĠArena": 34290, "好ä¸į好": 34291, "NON": 34292, "олов": 34293, "ĠallÃŃ": 34294, "omat": 34295, "ìĸ´ëıĦ": 34296, "ĠcaracterÃŃst": 34297, "Ġdeclining": 34298, "ÑĸÑı": 34299, "anco": 34300, "ĠInform": 34301, "Ġbargain": 34302, "Ġbushes": 34303, "ĠNaturally": 34304, "Ġrechts": 34305, "ĠTensor": 34306, "ĠPatricia": 34307, "Ġprincipio": 34308, "ĠMumbai": 34309, "Ġwomb": 34310, "Ġnostra": 34311, "Ġdilemma": 34312, "Ġirgendwann": 34313, "Ġ1964": 34314, "ĠenergÃŃa": 34315, "ĠнаÑĢ": 34316, "Ġsegregation": 34317, "ĠAthlet": 34318, "Ġ»,": 34319, "Ġyeni": 34320, "ĠSeit": 34321, "Ġvenom": 34322, "Ġdakika": 34323, "ĠëıĮë": 34324, "ĠÃīl": 34325, "Ġfus": 34326, "ĠMog": 34327, "¦½ëĭĪëĭ¤": 34328, "Ġremar": 34329, "ĠTeddy": 34330, "Ġbreasts": 34331, "icans": 34332, "æĶ¶çľĭ": 34333, "kap": 34334, "ĠhÆ¡n": 34335, "ĠJP": 34336, "ãĥ³ãĤ¿": 34337, "Ġresurrect": 34338, "ĠìĿ¸ë": 34339, "herical": 34340, "Ġfotograf": 34341, "ĠJosé": 34342, "Ġlivelihood": 34343, "Ġbibli": 34344, "teri": 34345, "Ġvorstellen": 34346, "ĠAAA": 34347, "Ġassessing": 34348, "YA": 34349, "Ġsplend": 34350, "Ġexcav": 34351, "Ġbaptism": 34352, "yll": 34353, "wow": 34354, "Mac": 34355, "Ġplastics": 34356, "teokbokki": 34357, "Ġintéressant": 34358, "Ġcommanded": 34359, "Ġfamously": 34360, "ĠÐĺли": 34361, "ĠManuel": 34362, "Ġsouthwest": 34363, "Ġdeformation": 34364, "ÃŃculo": 34365, "ĠнаÑħодиÑĤÑģÑı": 34366, "ĠPatter": 34367, "degree": 34368, "ĠczÄĻsto": 34369, "\"-": 34370, "Ġìħĭ": 34371, "Ġmanger": 34372, "ĠTrustee": 34373, "Ģ리": 34374, "Ġpuntos": 34375, "ivable": 34376, "Ġvolatile": 34377, "ĠëĬIJ": 34378, "Ġinstability": 34379, "Ġciel": 34380, "ciÄħ": 34381, "Ġpurity": 34382, "ноÑģÑĤ": 34383, "Sil": 34384, "edar": 34385, "åĻ¨": 34386, "NOUNCER": 34387, "Ġspelled": 34388, "GER": 34389, "Ġsanctuary": 34390, "Ġaccelerating": 34391, "Ġscout": 34392, "ĠпÑĢев": 34393, "fahren": 34394, "ãģĵãģ¡ãĤī": 34395, "ĠëĤĺìĺ¨": 34396, "ĠpoczÄħt": 34397, "ĠMeu": 34398, "kaar": 34399, "³´ê³ł": 34400, "akra": 34401, "Down": 34402, "ĠÃĦr": 34403, "ĠElite": 34404, "Ġallons": 34405, "Ġmayonnaise": 34406, "ĠSustain": 34407, "prisingly": 34408, "Ġsupervis": 34409, "Ġê·¸ëłĩì£ł": 34410, "Ġunemployed": 34411, "Ġfreshly": 34412, "Ġ×ŀ×¢": 34413, "ĠDh": 34414, "Ġtackling": 34415, "Ġogr": 34416, "Ġì´Īë": 34417, "ãĤĪãĤį": 34418, "Ġloft": 34419, "arah": 34420, "ĠAirl": 34421, "ĠDir": 34422, "ĠÐľÐ¾Ð¶Ð½Ð¾": 34423, "Ġbooking": 34424, "ĠCRA": 34425, "Ġhttps": 34426, "Ġchoke": 34427, "Ġgown": 34428, "Ġnoite": 34429, "Ġzac": 34430, "istol": 34431, "Ġsecre": 34432, "Ġresembles": 34433, "Ġcuad": 34434, "ìĤ¬ê°Ģ": 34435, "show": 34436, "Ġblanc": 34437, "Ġagu": 34438, "ĠPrint": 34439, "asted": 34440, "ĠWeather": 34441, "ipl": 34442, "Ġobscure": 34443, "Ġconte": 34444, "oughs": 34445, ");": 34446, "ĠDame": 34447, "ä¸Ģ缴": 34448, "Ġclarification": 34449, "Ġintimacy": 34450, "Ġuphold": 34451, "ĠMirror": 34452, "Ġwagon": 34453, "xide": 34454, "Ġclog": 34455, "apper": 34456, "ĠImmediately": 34457, "úde": 34458, "Ġtouchdown": 34459, "Ġrooft": 34460, "аÑĪа": 34461, "Ġçıkt": 34462, "Ġlaisser": 34463, "ĠUnreal": 34464, "ensitive": 34465, "Ġ123": 34466, "Ġplaster": 34467, "Ġducks": 34468, "Ġetme": 34469, "Ġbishop": 34470, "brevi": 34471, "Ġbic": 34472, "ä¸ĭåİ»": 34473, "Ġruntime": 34474, "Ġambitions": 34475, "маÑĤ": 34476, "ĠWein": 34477, "ĠMari": 34478, "ĠíĬ¸ë": 34479, "Ġresolver": 34480, "ĠngÃły": 34481, "ĠRise": 34482, "ãĤĪãģĨãģ«": 34483, "ĠCrus": 34484, "Ġmerchandise": 34485, "Ġeli": 34486, "Ġstatewide": 34487, "Ġowl": 34488, "éģł": 34489, "æĶ¹": 34490, "Ġtwisting": 34491, "Ġcontaminated": 34492, "ĠCommerce": 34493, "hythm": 34494, "ĠÃĪ": 34495, "Ġìĭ¤ë": 34496, "Ġmusste": 34497, "uir": 34498, "Ġsums": 34499, "ĠSomewhere": 34500, "ãĥİ": 34501, "Ġkami": 34502, "Ġaired": 34503, "ĠANDREW": 34504, "Ġêº": 34505, "Ġviendo": 34506, "Ġantibody": 34507, "Ġabsolument": 34508, "Ġprotesters": 34509, "ĠQuébec": 34510, "stadt": 34511, "Shaun": 34512, "Ġchambers": 34513, "ĠWear": 34514, "ĠEffects": 34515, "Ġhazards": 34516, "Ġnei": 34517, "Ġcorazón": 34518, "Ġá¼": 34519, "ĠSG": 34520, "Ķ©": 34521, "ĠìĹŃìĭľ": 34522, "Ġcomfy": 34523, "ĠCody": 34524, "Ġpensando": 34525, "Ġganska": 34526, "ĠAcross": 34527, "öllig": 34528, "abyte": 34529, "Ġwedge": 34530, "Ġkalian": 34531, "Ġsigue": 34532, "endes": 34533, "ĠGroÃŁ": 34534, "Ġutiliser": 34535, "Ġflown": 34536, "аниÑİ": 34537, "Ġlevar": 34538, "restrial": 34539, "Ġillustrations": 34540, "Ġaslında": 34541, "BLEEP": 34542, "ĠдоÑģÑĤ": 34543, "Ġturret": 34544, "Ġsuitcase": 34545, "ziÄĻki": 34546, "Ġsketches": 34547, "Ġacred": 34548, "ĠRei": 34549, "Ġtsun": 34550, "ĠSag": 34551, "Ġthirds": 34552, "ĠKIRBY": 34553, "rai": 34554, "Ġhumanos": 34555, "Ġrecommends": 34556, "Ġextraordinarily": 34557, "Ġcommencement": 34558, "KN": 34559, "opez": 34560, "Ġ×ijש": 34561, "Ġlethal": 34562, "ĠEstamos": 34563, "Ġinspector": 34564, "ĠSeok": 34565, "eun": 34566, "Ġoffshore": 34567, "Ġgettin": 34568, "years": 34569, "ĠSilence": 34570, "ĠNatur": 34571, "upun": 34572, "Ġtrzy": 34573, "Ġnoget": 34574, "Ġhamburger": 34575, "ĠPraise": 34576, "énd": 34577, "Ġ1971": 34578, "ylie": 34579, "krit": 34580, "ĠìĥĿê°ģìĿ´": 34581, "çļ®": 34582, "Ġmomentos": 34583, "Ġesté": 34584, "Ġdissemin": 34585, "Ġgigs": 34586, "Ġdesaf": 34587, "Ġavis": 34588, "ĠZoo": 34589, "ĠìķĬìĿĢ": 34590, "häng": 34591, "åı¥": 34592, "hake": 34593, "ĠBism": 34594, "Ġrethink": 34595, "ĠMalcolm": 34596, "Ġidentifies": 34597, "lower": 34598, "ixel": 34599, "ĠtvÃ¥": 34600, "ked": 34601, "ierz": 34602, "Ġöffentlich": 34603, "Ġproclaim": 34604, "soon": 34605, "lol": 34606, "Ġloi": 34607, "Ġbitten": 34608, "rollo": 34609, "Ġsermon": 34610, "Ġesqu": 34611, "Ġjackets": 34612, "Ġgráfic": 34613, "ĠпоказÑĭв": 34614, "Ġcabeza": 34615, "chodzi": 34616, "Ġpelvis": 34617, "Ġnostalgia": 34618, "Ġbrew": 34619, "Ġshortcuts": 34620, "ĠAdemás": 34621, "Ġsuperficial": 34622, "åħ©åĢĭ": 34623, "Ġboca": 34624, "ĠæĪijæĺ¯": 34625, "imentos": 34626, "åĽłä¸º": 34627, "Ġsprouts": 34628, "é£Ľ": 34629, "ĠJonas": 34630, "ĠFlorence": 34631, "static": 34632, "daughter": 34633, "*)": 34634, "ÅĤby": 34635, "fashion": 34636, "ĠGinger": 34637, "Ġ매ë": 34638, "Ġhustle": 34639, "utos": 34640, "ĠÑĤÑıж": 34641, "ĠLös": 34642, "ש×Ļ×Ŀ": 34643, "anych": 34644, "tuber": 34645, "Ġtidy": 34646, "Ġfrontal": 34647, "Ġwhiskey": 34648, "Ġhumid": 34649, "ĠÎŁ": 34650, "Ġridge": 34651, "Ġmarin": 34652, "Ġbientôt": 34653, "ĠCarrie": 34654, "chw": 34655, "Ġtahun": 34656, "ĠErgeb": 34657, "FR": 34658, "Ġìłķë¶Ģ": 34659, "ĠSoldier": 34660, "Ġenlightenment": 34661, "Ġexamining": 34662, "ĠNotre": 34663, "Ġeram": 34664, "ĠSunny": 34665, "Ġlayered": 34666, "ĠDazu": 34667, "rades": 34668, "好åIJĥ": 34669, "ĠнаÑĪей": 34670, "Ġtimber": 34671, "Ġmanners": 34672, "ĠBirmingham": 34673, "Ġminiature": 34674, "ometers": 34675, "Ġfiller": 34676, "ĠRip": 34677, "ĠKomb": 34678, "owner": 34679, "ì¿": 34680, "idian": 34681, "Ġdemás": 34682, "ĠÙĪت": 34683, "Ġprecautions": 34684, "Ġgoverno": 34685, "zelf": 34686, "ĠComplete": 34687, "å¸ĥ": 34688, "ĠPhantom": 34689, "ãģ¾ãģļ": 34690, "Ġнез": 34691, "ĠкаÑĢÑĤ": 34692, "ĠAntwort": 34693, "ĠPfizer": 34694, "ĠFranco": 34695, "ĠwÅĤ": 34696, "Ġfrig": 34697, "esper": 34698, "Ġkale": 34699, "Ġfilmmaker": 34700, "Ġkurt": 34701, "Ġinvalid": 34702, "å±Ģ": 34703, "arella": 34704, "Äĥng": 34705, "ramento": 34706, "Ġnutritional": 34707, "Ġdictators": 34708, "Ġafin": 34709, "Ġfuzzy": 34710, "ĠGina": 34711, "ót": 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36666, "à±": 36667, "Ġstumbled": 36668, "Ġaider": 36669, "Ġsaben": 36670, "xter": 36671, "ĠCities": 36672, "ĠTürk": 36673, "ëĭ¥": 36674, "chine": 36675, "Ġtopping": 36676, "Ġpoisoned": 36677, "ĠRomania": 36678, "×ĵ×Ļ": 36679, "Ģë¡ľ": 36680, "ĠпоÑĢÑıд": 36681, "Ġchirping": 36682, "ĠìĻĦë": 36683, "×ij×¢": 36684, "Ġcuanto": 36685, "Ġdonating": 36686, "ĠRegent": 36687, "ĠBeruf": 36688, "Ġdistracting": 36689, "Ġstamina": 36690, "ĠDarren": 36691, "Ġì¶ķ": 36692, "lists": 36693, "dal": 36694, "chuss": 36695, "Ġeconomist": 36696, "ãģĪãĥ¼": 36697, "orgt": 36698, "Ġistiyorum": 36699, "è¿Ľ": 36700, "ĠSurprise": 36701, "ĠHao": 36702, "Ġìµľê³ł": 36703, "ĠGW": 36704, "ĠInner": 36705, "Ġquieren": 36706, "Ġminded": 36707, "Ġsupercomputer": 36708, "Ġdiagrams": 36709, "íĬľë": 36710, "ê²łìĸ´": 36711, "ĠобÑĬÑıÑģ": 36712, "Ġestaban": 36713, "Ġdestroys": 36714, "ĠBreaking": 36715, "ĠkarÄ±ÅŁ": 36716, "Ġrebuilding": 36717, "ľëĮĢ": 36718, "ливо": 36719, "ĠSauce": 36720, "ĠFusion": 36721, "×ķ×ŀ×": 36722, 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36781, "òng": 36782, "çŁ³": 36783, "«": 36784, "ĠClaudia": 36785, "ĠCharacter": 36786, "ниÑĨ": 36787, "coat": 36788, "Ġprogressed": 36789, "ĠFergus": 36790, "Ġìĺ¤ëĬ": 36791, "Ġoat": 36792, "ordable": 36793, "ĠLey": 36794, "ĠHeraus": 36795, "Ġresultados": 36796, "ĠKayla": 36797, "Ġriff": 36798, "Ġchegou": 36799, "Ġxi": 36800, "Ġspacious": 36801, "Ġrecognised": 36802, "Ġech": 36803, "ĠTie": 36804, "Ġlauncher": 36805, "Jim": 36806, "Ġsuppression": 36807, "ĠImpossible": 36808, "Ġguitars": 36809, "ĠFourier": 36810, "иÑĩеÑģкий": 36811, "ĠTherap": 36812, "ĠKaf": 36813, "centered": 36814, "ĠÑģооÑĤвеÑĤ": 36815, "Ġklim": 36816, "Ġcarbohydrates": 36817, "ignant": 36818, "ĠAstron": 36819, "Ġemple": 36820, "Ġdrastic": 36821, "ĠмиÑĢе": 36822, "вин": 36823, "uw": 36824, "Ġprettier": 36825, "Ġdonuts": 36826, "ĠAthena": 36827, "Ġdissert": 36828, "Ġplante": 36829, "Ġuranium": 36830, "ìĿĮë": 36831, "aré": 36832, "Ġrzecz": 36833, "Ġdisplaying": 36834, "æĪ²": 36835, "Ġsarc": 36836, "rão": 36837, 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"Ġforgiving": 37701, "Ġconcurrent": 37702, "ĠVu": 37703, "ĠíĬ¹íŀĪ": 37704, "ĠBROWN": 37705, "ounded": 37706, "\";": 37707, "Ġtremb": 37708, "Ġtiet": 37709, "ĠÑĢежим": 37710, "Ġnutshell": 37711, "елиÑĩ": 37712, "Ġlosers": 37713, "ricting": 37714, "Ġredeem": 37715, "defined": 37716, "Nice": 37717, "Ġbroadband": 37718, "KO": 37719, "Ġteasing": 37720, "Ġpartisan": 37721, "ıma": 37722, "Ġìŀ¬ë¯¸": 37723, "ĠJourney": 37724, "Ġslopes": 37725, "uning": 37726, "grunts": 37727, "Ġtäll": 37728, "Ġuncovered": 37729, "ĠmyÅĽlÄĻ": 37730, "ĠEsther": 37731, "äºİ": 37732, "ĠHealthy": 37733, "Ġë°ij": 37734, "rée": 37735, "Ġpolarization": 37736, "Ġflav": 37737, "Ġcambiar": 37738, "Ġyr": 37739, "ĠRanch": 37740, "Ġsplits": 37741, "Ġtrouvé": 37742, "åľĭ家": 37743, "Ġrecorder": 37744, "Ġdépart": 37745, "ÙĪب": 37746, "ĠKry": 37747, "Ġinteressant": 37748, "Ġederim": 37749, "ÅĽwiad": 37750, "ilateral": 37751, "wright": 37752, "Ġpourra": 37753, "êter": 37754, "Ġcamel": 37755, "áŀ": 37756, "Ġrapidement": 37757, "Ġmej": 37758, "Ġstiffness": 37759, "ADAS": 37760, "Ġdiffers": 37761, "Ġalot": 37762, "ĠSig": 37763, "ÑıÑĤелÑĮ": 37764, "Ġabstraction": 37765, "åľĺ": 37766, "Ġkeiner": 37767, "grupp": 37768, "ĠSherlock": 37769, "íĺĶ": 37770, "Ġcite": 37771, "Ġoverflow": 37772, "Ġtại": 37773, "úcar": 37774, "bula": 37775, "Ġconjunto": 37776, "ĠCI": 37777, "Ġmoderator": 37778, "Ġindirectly": 37779, "Ġalleine": 37780, "âĤ": 37781, "ÑĪиб": 37782, "Ġбаб": 37783, "Ġdanach": 37784, "Ġ1939": 37785, "Ġpromet": 37786, "Ġdestinations": 37787, "ĠIllust": 37788, "ικÏĮ": 37789, "Ġsabes": 37790, "Ġheh": 37791, "ĠGesetzent": 37792, "ĠMiz": 37793, "енко": 37794, "ĠMys": 37795, "Ь": 37796, "ĠJudaism": 37797, "Ġmustache": 37798, "Ġstimmt": 37799, "ĠGaza": 37800, "Ġvolte": 37801, "Ġnuo": 37802, "Ġmón": 37803, "ĠComput": 37804, "ูà¹Ī": 37805, "ĠRadi": 37806, "Ġexceptionally": 37807, "Ġassumes": 37808, "éĸĭå¿ĥ": 37809, "ãģĪãģ°": 37810, "inform": 37811, "Ġshrine": 37812, "æĵĬ": 37813, "Ġimplication": 37814, "ĠFitz": 37815, "æ²ĴéĹľä¿Ĥ": 37816, "!.": 37817, "Ġlt": 37818, "Ġalloy": 37819, "Ġethic": 37820, "Ġmonastery": 37821, "ìĭľì£ł": 37822, "icação": 37823, "Ġcoordinating": 37824, "ĠMoto": 37825, "Ġoverlook": 37826, "Ġchois": 37827, "Ġantibiotic": 37828, "ĠMinne": 37829, "ĠBJ": 37830, "ĠApa": 37831, "orian": 37832, "Ġspilled": 37833, "Jam": 37834, "Ġhusbands": 37835, "Ġcreations": 37836, "Ġañ": 37837, "üssel": 37838, "ĠìĿ´ìļ©": 37839, "Ġanalyse": 37840, "rose": 37841, "Ġpunched": 37842, "Ġpresque": 37843, "Ġastronomy": 37844, "Ġschwierig": 37845, "ĠEbola": 37846, "Ġcis": 37847, "Ġacet": 37848, "ĠFX": 37849, "endre": 37850, "ĠìĿĮìķħ": 37851, "Ġwebpage": 37852, "Ġfreaked": 37853, "Ġlatte": 37854, "Ġì¿ł": 37855, "Ġ머ë": 37856, "Never": 37857, "Gra": 37858, "íĻĶ를": 37859, "eyed": 37860, "Ġë°ľëĿ¼": 37861, "Ġespera": 37862, "Ġaparece": 37863, "ração": 37864, "Ġdisruptive": 37865, "ĠJoint": 37866, "urous": 37867, "reas": 37868, "ĠquerÃŃa": 37869, "Ġdistributions": 37870, "Ġexponent": 37871, "ì¹ĺ를": 37872, "Ġdl": 37873, "zhou": 37874, "ĠHearing": 37875, "å·®ä¸įå¤ļ": 37876, "ĠCraw": 37877, "Ġfloats": 37878, "ounced": 37879, "Lab": 37880, "World": 37881, "Ġburdens": 37882, "Ġauthoritarian": 37883, "ĠBolt": 37884, "ĠоднÑĥ": 37885, "Ġpigeon": 37886, "Ġdistractions": 37887, "ĠHerausforder": 37888, "Ġzest": 37889, "esc": 37890, "Ġshakes": 37891, "atas": 37892, "ĠÙħØ´": 37893, "holes": 37894, "Ġthinkers": 37895, "alta": 37896, "Ġarche": 37897, "ĠSuk": 37898, "anha": 37899, "Ġtempting": 37900, "Ġyoutuber": 37901, "Ġvì": 37902, "ĠdziaÅĤa": 37903, "ĠVatican": 37904, "Park": 37905, "Ġsupers": 37906, "ĠNikki": 37907, "ëĬIJë": 37908, "orang": 37909, "ramient": 37910, "鬼": 37911, "Ġê°ĸê³ł": 37912, "Ġdesserts": 37913, "Ġavere": 37914, "ĠGregory": 37915, "Ġëĵ¤ìĸ´ìĺ": 37916, "Ġcosting": 37917, "ĠClinic": 37918, "Ġrebels": 37919, "ĠMob": 37920, "Ġbunlar": 37921, "ĠYours": 37922, "ertime": 37923, "Ġretali": 37924, "mara": 37925, "atus": 37926, "alles": 37927, "ĠдÑĢ": 37928, "ĠдиÑģ": 37929, "Ġdiscounts": 37930, "ĠGUY": 37931, "Ġкакое": 37932, "ĠExperiment": 37933, "rement": 37934, "ĠXiang": 37935, "Ġbate": 37936, "WE": 37937, "Ġspecialize": 37938, "Ġdeity": 37939, "ĠLoki": 37940, "mag": 37941, "ĠNit": 37942, "West": 37943, "Ġmaternal": 37944, "Ġquis": 37945, "åŁºæľ¬": 37946, "broken": 37947, "Ġlasers": 37948, "Ġhakk": 37949, "ĠAngels": 37950, "Ġmastery": 37951, "antis": 37952, "Tiffany": 37953, "eee": 37954, "çij": 37955, "orem": 37956, "Ġinacc": 37957, "Ġjurisdictions": 37958, "ĠKardash": 37959, "æľº": 37960, "Il": 37961, "ĠSinn": 37962, "åĭķçĶ»": 37963, "Ġathletics": 37964, "cÄĻ": 37965, "Ġloosely": 37966, "Ġdieta": 37967, "Ag": 37968, "Ġ??": 37969, "ĠëĮĢíijľ": 37970, "Ġsuperv": 37971, "Ġnutrit": 37972, "Ġdrifting": 37973, "ĠìĦłìĥĿëĭĺ": 37974, "ĠпонÑıл": 37975, "ĠVictory": 37976, "ÙĦØ©": 37977, "×ķ׳×Ķ": 37978, "ĠпиÑĪ": 37979, "Ġshaved": 37980, "Ġmesure": 37981, "onden": 37982, "Ùĥر": 37983, "Ġexile": 37984, "ĠDesde": 37985, "ĠPinterest": 37986, "Ġattachments": 37987, "Ġhombres": 37988, "Ġfines": 37989, "ĠìĦ¸ìĥģ": 37990, "Ġsleeps": 37991, "ĠTaco": 37992, "ĠIRA": 37993, "rios": 37994, "Ġoll": 37995, "etes": 37996, "Ġunut": 37997, "fashioned": 37998, "Ġtreball": 37999, "ĠNearly": 38000, "ĠÑĢеалÑĮно": 38001, "Ġchil": 38002, "éĢ±": 38003, "ÄŁa": 38004, "ĠMEL": 38005, "roscop": 38006, "ĠCG": 38007, "Ġvenge": 38008, "Ġdishwasher": 38009, "algic": 38010, "Ġmodifier": 38011, "Ġembassy": 38012, "timer": 38013, "emics": 38014, "Ġintricate": 38015, "Ġevet": 38016, "ĠëĮĢë°ķ": 38017, "Ġisot": 38018, "ĠнаÑĥÑĩ": 38019, "ĠQuiz": 38020, "reso": 38021, "δÏİ": 38022, "Ġyelled": 38023, "Ġfeder": 38024, "ELLER": 38025, "Ġexceeded": 38026, "onas": 38027, "icano": 38028, "ĠживоÑĤ": 38029, "ĠMao": 38030, "ĠKazuto": 38031, "Ġãħĭãħĭãħĭãħĭ": 38032, "Ġfrontline": 38033, "ĠHungarian": 38034, "Ġüberall": 38035, "awat": 38036, "Ġgrips": 38037, "ições": 38038, "arnya": 38039, "ĠÍ¡": 38040, "Ġseid": 38041, "Ġanak": 38042, "Ġacabou": 38043, "íķij": 38044, "Ġnotorious": 38045, "ĠGodzilla": 38046, "Ġovercoming": 38047, "ĠPend": 38048, "Ġolabilir": 38049, "ülme": 38050, "Ġerhalten": 38051, "ãĤīãģĦ": 38052, "ê·¹": 38053, "ĠMeter": 38054, "Ġstaan": 38055, "Ol": 38056, "Ġchats": 38057, "ĠBuenos": 38058, "ÃŃve": 38059, "aluable": 38060, "Ġstrategically": 38061, "Ġcomprised": 38062, "ĠпеÑĢÑģонаж": 38063, "Ġwann": 38064, "ĠCen": 38065, "ниÑĤе": 38066, "Łģ": 38067, "ĠÑĤобой": 38068, "iad": 38069, "ĠkardeÅŁim": 38070, "ĠCongressman": 38071, "reaming": 38072, "homme": 38073, "Ġcommunaut": 38074, "Ġalcoholic": 38075, "Ġpickled": 38076, "Ġacord": 38077, "position": 38078, "egól": 38079, "Ġtroubling": 38080, "ĠMarcheg": 38081, "Ġzumindest": 38082, "Ġseamlessly": 38083, "Ġolun": 38084, "ĠTVs": 38085, "ĠпÑĢакÑĤиÑĩеÑģки": 38086, "Ġbackend": 38087, "ãģĵãĤĵãģ«ãģ¡ãģ¯": 38088, "idable": 38089, "Ġgadget": 38090, "Ġfaço": 38091, "ĠMarchegiani": 38092, "Ġë°¤": 38093, "Ġaccidental": 38094, "ĠLP": 38095, "Ġeldest": 38096, "ĠAdmiral": 38097, "ĠnÄĥm": 38098, "lever": 38099, "Ġpastel": 38100, "Ġfondo": 38101, "Connie": 38102, "Ġtercer": 38103, "Ġpact": 38104, "ĠMonte": 38105, "Ġmeats": 38106, "ĠSMS": 38107, "ĠAustralians": 38108, "ç¼": 38109, "Rhett": 38110, "Ġexactement": 38111, "Ġë¹¼": 38112, "ĠMOD": 38113, "ç¡": 38114, "ĠRapt": 38115, "ĠNoch": 38116, "Ġabort": 38117, "ĠNaval": 38118, "ĠFuji": 38119, "INTER": 38120, "ĠновÑĭй": 38121, "Ġmiejsce": 38122, "ĠICU": 38123, "ĠGraduate": 38124, "ĠGlen": 38125, "ardi": 38126, "ĠÈĺ": 38127, "Ġsolder": 38128, "Ġprofessions": 38129, "Ġorthog": 38130, "omn": 38131, "introdu": 38132, "ĠDenise": 38133, "ìŀIJ를": 38134, "Ġcorrespondence": 38135, "AMA": 38136, "Ġinflict": 38137, "Ġfand": 38138, "ĠGü": 38139, "ĠÑĩеÑĤ": 38140, "Ġtraced": 38141, "Ġpatents": 38142, "Ġambush": 38143, "Ġlotta": 38144, "ffer": 38145, "ĠWagner": 38146, "Ġimperson": 38147, "Ġextrêmement": 38148, "ÙĤت": 38149, "conduct": 38150, "Att": 38151, "ĠMueller": 38152, "ĠAlicia": 38153, "Ġcyc": 38154, "Ġhacker": 38155, "Ġtys": 38156, "Ġhail": 38157, "ĠзаÑıв": 38158, "Ġpasso": 38159, "Ġì¶Ķê°Ģ": 38160, "ĠÎĪ": 38161, "Ġpackaged": 38162, "ĠCynthia": 38163, "heet": 38164, "ä¸ŃåĽ½": 38165, "ĠNissan": 38166, "ĠQuesto": 38167, "é¨": 38168, "did": 38169, "Ġμια": 38170, "ĠEllis": 38171, "ĠAnalysis": 38172, "cemos": 38173, "Ġaseg": 38174, "ĠMyster": 38175, "ĠCao": 38176, "Ġtuv": 38177, "ĠIndustry": 38178, "ì£¼ê³ł": 38179, "otal": 38180, "Ġpequeño": 38181, "bras": 38182, "Ġcomprehend": 38183, "ĠSimpson": 38184, "ÑģÑĤвие": 38185, "ocracy": 38186, "иÑĩеÑģки": 38187, "ĠMush": 38188, "ĠLaurie": 38189, "Ġtriangular": 38190, "ĠPresents": 38191, "ĠKunden": 38192, "ç´¹": 38193, "æѦ": 38194, "ĠIss": 38195, "ĠDeck": 38196, "á»ĥn": 38197, "ĠDarkness": 38198, "Ġinflammatory": 38199, "eremiah": 38200, "Ġwarmed": 38201, "veyard": 38202, "ĠMemory": 38203, "etty": 38204, "Ġtaxpayers": 38205, "à¸ĵ": 38206, "Ø¡": 38207, "Ġpractise": 38208, "ëĭ¬ë": 38209, "Ġdrilled": 38210, "mÃ¼ÅŁ": 38211, "logo": 38212, "ĠFach": 38213, "¤ë¡ľ": 38214, "Ġübrigens": 38215, "Ġkonnten": 38216, "Ġnormalmente": 38217, "Ġargues": 38218, "ilingual": 38219, "°ë¥¼": 38220, "egal": 38221, "Ġtravaill": 38222, "ovy": 38223, "аÑĤо": 38224, "Ġruth": 38225, "ĠLights": 38226, "Ġconsisted": 38227, "×ijר×Ļ×Ŀ": 38228, "Ġstereotype": 38229, "Ġpayer": 38230, "ĠRee": 38231, "ĠAirbnb": 38232, "Ġdrowned": 38233, "ĠZoe": 38234, "Ġcanopy": 38235, "Ġbarr": 38236, "ĠноÑĩ": 38237, "Ġpagan": 38238, "Ġjars": 38239, "Ġrê": 38240, "erver": 38241, "æĪ¿": 38242, "ieben": 38243, "Ġespect": 38244, "ĠFi": 38245, "Ġunwilling": 38246, "Ġtechnician": 38247, "ặt": 38248, "member": 38249, "ĠCanal": 38250, "سÙħ": 38251, "Ġlieber": 38252, "Ġinference": 38253, "Ġhonoring": 38254, "åijµ": 38255, "ĠCampaign": 38256, "Ġlineage": 38257, "ĠStress": 38258, "Ġvictories": 38259, "Ġdeja": 38260, "×£": 38261, "êtes": 38262, "blick": 38263, "Ġменее": 38264, "oths": 38265, "ĠCouple": 38266, "Jason": 38267, "ĠNicolas": 38268, "екÑģ": 38269, "lib": 38270, "Ġherramient": 38271, "Ġ×IJ×ķ×ŀר": 38272, "Ġвидим": 38273, "millimeter": 38274, "Ġsilhouette": 38275, "Ġdriveway": 38276, "Ġcherish": 38277, "ãħłãħł": 38278, "Ġransom": 38279, "Ġinterdisciplinary": 38280, "ĠPortal": 38281, "Ġtrag": 38282, "thood": 38283, "Ġtedious": 38284, "Ġglossy": 38285, "Ġprépar": 38286, "ĠCay": 38287, "ĠTook": 38288, "ĠBottom": 38289, "Ġzig": 38290, "å«": 38291, "åį±": 38292, "represented": 38293, "à¹Ģลย": 38294, "Ġdesarrollo": 38295, "ìĦľë": 38296, "Ġviscos": 38297, "Ġmilligram": 38298, "ĠGund": 38299, "Ġferment": 38300, "drum": 38301, "Ġdrawers": 38302, "Laugh": 38303, "Ġpelos": 38304, "Ġpavement": 38305, "Ġmemoir": 38306, "avait": 38307, "Ġ2050": 38308, "¤ë¥¼": 38309, "Ġrazón": 38310, "Ġflourish": 38311, "Ġstern": 38312, "ä¸Ī": 38313, "ĠChung": 38314, "Ġserpent": 38315, "ĠGentlemen": 38316, "羣çļĦå¾Ī": 38317, "kook": 38318, "Ġlut": 38319, "importe": 38320, "parent": 38321, "Ġwsz": 38322, "Ġscree": 38323, "ĠMitarbeiter": 38324, "å·´": 38325, "mut": 38326, "Ġìĸĺ기를": 38327, "Ġsemble": 38328, "ĠOW": 38329, "Ġinvestigator": 38330, "ĠCheryl": 38331, "ĠGerald": 38332, "Ġprere": 38333, "Ġcompares": 38334, "nyt": 38335, "Ġdiferença": 38336, "?-": 38337, "Ġquá": 38338, "ר×Ļ": 38339, "Sen": 38340, "Ġheps": 38341, "Ġgratuit": 38342, "Ġconsort": 38343, "ĠSTOP": 38344, "ĠProtestant": 38345, "Ġelectrode": 38346, "âĹ": 38347, "Ġsecurely": 38348, "иÑĩеÑģкой": 38349, "Ġtää": 38350, "Ġregisters": 38351, "ĠHeavenly": 38352, "ogly": 38353, "issä": 38354, "ĠPhysics": 38355, "ĠMerkel": 38356, "Ġrév": 38357, "éĻ¢": 38358, "Ġerased": 38359, "ĠSacramento": 38360, "Ġcoffin": 38361, "Ġexacer": 38362, "Ġlanz": 38363, "Ġpoets": 38364, "ulif": 38365, "Ġì¹ĺë": 38366, "ĠNerd": 38367, "ĠNCT": 38368, "ĠHour": 38369, "nehmer": 38370, "ŀĺëıĦ": 38371, "ĠPrinci": 38372, "Sw": 38373, "mies": 38374, "armed": 38375, "ĠBeatles": 38376, "Ġpropagation": 38377, "Ġexchanged": 38378, "Ġcumulative": 38379, "Ġì§ijìĹIJ": 38380, "Ġdefeating": 38381, "æĬ±": 38382, "bels": 38383, "Ġwes": 38384, "ĠOdyssey": 38385, "ä½łæĥ³": 38386, "avior": 38387, "ĠìľĦìĹIJ": 38388, "Ġbrit": 38389, "Ġhijo": 38390, "DAY": 38391, "ĠاÙĦتÙĬ": 38392, "ĠСеÑĢг": 38393, "Ñĥка": 38394, "edsiÄĻ": 38395, "Ġimpos": 38396, "Ġellas": 38397, "Ġfirearms": 38398, "ĠNR": 38399, "Ġ×ij×IJ": 38400, "ĠÐŁÐ¾ÐºÐ°": 38401, "awi": 38402, "ĠìĦ±ê³µ": 38403, "Ġpupils": 38404, "ĠTack": 38405, "Ġfrase": 38406, "ĠShip": 38407, "Ġstad": 38408, "举": 38409, "ĠGreater": 38410, "unun": 38411, "immung": 38412, "grown": 38413, "ĠNXT": 38414, "ĠAmericas": 38415, "fox": 38416, "Ġmanten": 38417, "éłIJåĤĻ": 38418, "ĠÑģок": 38419, "Ġrikt": 38420, "lectric": 38421, "deep": 38422, "ĠзнаеÑĪÑĮ": 38423, "Ġbenut": 38424, "ĠInfrast": 38425, "ĠEmir": 38426, "ĠоÑĤпÑĢав": 38427, "ĠKimchi": 38428, "ĠFinnish": 38429, "´ìłģ": 38430, "inaire": 38431, "Ġoike": 38432, "æ¸ħæ¥ļ": 38433, "Ġhostage": 38434, "ĠButton": 38435, "ÙĤÙĬ": 38436, "eking": 38437, "ĠKazakh": 38438, "Ġcomforting": 38439, "Ġsog": 38440, "Ġgreeted": 38441, "guitar": 38442, "payer": 38443, "Ġrelational": 38444, "Ġconstruir": 38445, "çī¹åĪ¥": 38446, "opian": 38447, "ĠVolume": 38448, "ieth": 38449, "ÑģÑĤвом": 38450, "urrection": 38451, "liÅĽmy": 38452, "Ġhemisphere": 38453, "ĠBean": 38454, "IGN": 38455, "Ġkötü": 38456, "ĠFallout": 38457, "Ġbrace": 38458, "ç¹¼çºĮ": 38459, "ÏĢά": 38460, "ĠHAS": 38461, "Ġgé": 38462, "Ġcharacterize": 38463, "ặc": 38464, "ĠMilky": 38465, "Ġtumors": 38466, "Ġnuit": 38467, "ĠGaz": 38468, "ĠìŀĪëĭ¤ëĬĶ": 38469, "ĠгаÑĢ": 38470, "essment": 38471, "ĠAbe": 38472, "Ġë½ij": 38473, "ĠEinsatz": 38474, "JIN": 38475, "jä": 38476, "Cry": 38477, "ĠPromised": 38478, "ĠÑģеÑĢд": 38479, "okus": 38480, "Ġscalable": 38481, "ĠпоÑģмоÑĤÑĢеÑĤÑĮ": 38482, "ücklich": 38483, "Ġrealism": 38484, "Ġmayo": 38485, "Ġjuvenile": 38486, "Ġheadlights": 38487, "ĠgörÃ¼ÅŁ": 38488, "ĠReform": 38489, "Ġhalves": 38490, "czne": 38491, "Ġbreakup": 38492, "żej": 38493, "Ġrätt": 38494, "Day": 38495, "ĠìĿ¼ë³¸": 38496, "Ġmuerte": 38497, "Ġtunes": 38498, "ĠSmile": 38499, "record": 38500, "Ġrecherche": 38501, "atisfied": 38502, "Ġpozi": 38503, "Ġcelebrations": 38504, "isexual": 38505, "ĠROB": 38506, "thirds": 38507, "ĠFortune": 38508, "ĠÑĤой": 38509, "Ġbranded": 38510, "loo": 38511, "Ġdud": 38512, "Ġrandomized": 38513, "Ġcombin": 38514, "ä¸ĢäºĽ": 38515, "ieran": 38516, "czenia": 38517, "įãĥ«": 38518, "Ġcurator": 38519, "Ġartery": 38520, "ĠÑĥÑĪ": 38521, "ĠÑĩиÑĤ": 38522, "Ġsubsidies": 38523, "Ġblossom": 38524, "ĠTwilight": 38525, "Ġhyvä": 38526, "ĠPompe": 38527, "ĠCisco": 38528, "ĠÐŁÑĢо": 38529, "Ġbiri": 38530, "Ġgern": 38531, "Ġrebuilt": 38532, "Ġwcze": 38533, "Ġbenefici": 38534, "Ġdrummer": 38535, "Ġsolids": 38536, "Ġdiyorsun": 38537, "ãģĤãĤĬãģĮãģ¨ãģĨãģĶãģĸãģĦãģ¾ãģĹãģŁ": 38538, "lated": 38539, "Ġmuddy": 38540, "Ġholog": 38541, "Ġclaps": 38542, "ĠRings": 38543, "ĠOkey": 38544, "ĠBrave": 38545, "Ġvaluation": 38546, "Ġmigrant": 38547, "Ġintermitt": 38548, "Ġeigene": 38549, "iliary": 38550, "ãĥ¼ãĥĪ": 38551, "markt": 38552, "kr": 38553, "ĠRib": 38554, "á»Ļi": 38555, "Ġaccusations": 38556, "Ġarab": 38557, "wash": 38558, "ĠBardzo": 38559, "Ġugh": 38560, "esters": 38561, "ophren": 38562, "Ġalimentos": 38563, "ĠUz": 38564, "ÖĤ": 38565, "Ġ650": 38566, "ĠпÑĢиеÑħ": 38567, "FI": 38568, "Ġsampai": 38569, "Ġparlé": 38570, "hesion": 38571, "Ġsır": 38572, "Ġapparatus": 38573, "Ġcorrelated": 38574, "ĠPrincipal": 38575, "Ġcorr": 38576, "ĠOfficial": 38577, "иÑĩеÑģкие": 38578, "Ġterminals": 38579, "Should": 38580, "Ġvacun": 38581, "Ġstellt": 38582, "Ġmooi": 38583, "etzung": 38584, "ĠкÑĢа": 38585, "Ġdai": 38586, "Ġпож": 38587, "Team": 38588, "ĠPPE": 38589, "ĠÐŀÑģ": 38590, "ĠLeah": 38591, "ĠIvy": 38592, "yst": 38593, "Ġuhhh": 38594, "Ġnighttime": 38595, "Ġtrendy": 38596, "Ġsecurities": 38597, "Ġcontinents": 38598, "Ġfirsthand": 38599, "ĠVeron": 38600, "ĠëĤ®": 38601, "Ġbrowsing": 38602, "ĠCada": 38603, "tro": 38604, "Ġtramp": 38605, "reib": 38606, "Ġerstmal": 38607, "irler": 38608, "Ġpsic": 38609, "Ġgetir": 38610, "ĠNP": 38611, "Ġdzieci": 38612, "обÑĢаз": 38613, "Ġmagician": 38614, "Ġscrutiny": 38615, "Ġslab": 38616, "ĠOT": 38617, "isty": 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"Ġappreh": 38675, "ìłķìĿ´": 38676, "Pop": 38677, "Ġjong": 38678, "ĠGiul": 38679, "ĠADHD": 38680, "Ġhören": 38681, "Ġelo": 38682, "ivent": 38683, "Ġrus": 38684, "Ġoutrageous": 38685, "Ġmastered": 38686, "Ġ커": 38687, "ÙĪÙģ": 38688, "ipes": 38689, "ĠRudy": 38690, "Jacob": 38691, "Ġbullish": 38692, "Ġtapped": 38693, "Ġfaud": 38694, "izophren": 38695, "ĠÑģоÑħ": 38696, "ĠDarling": 38697, "Ġ1963": 38698, "ĠPrevention": 38699, "²Ķ": 38700, "Ġabdominal": 38701, "stones": 38702, "Ġavaient": 38703, "á»ķi": 38704, "make": 38705, "Ġsare": 38706, "ĠInstant": 38707, "кам": 38708, "Ġkeeper": 38709, "Ġblankets": 38710, "ãģ§ãģĹãĤĩãģĨ": 38711, "Ġsweats": 38712, "ĠMinneapolis": 38713, "åħ¨éĥ¨": 38714, "Ġgenommen": 38715, "Ġfasten": 38716, "ĠBrussels": 38717, "åij¼": 38718, "Ġcafeter": 38719, "Ġabsorbing": 38720, "Ġhago": 38721, "ĠElmo": 38722, "Ġgusto": 38723, "ĠYap": 38724, "Música": 38725, "Ġtert": 38726, "Ġbanda": 38727, "Ġmily": 38728, "Ġthereafter": 38729, "ĠStockholm": 38730, "ĠCarson": 38731, "Ġcalibration": 38732, "avaÅŁ": 38733, "ansa": 38734, "ikke": 38735, "Ġforesee": 38736, "Ġqualche": 38737, "Ġdeste": 38738, "æ¤": 38739, "ünüz": 38740, "Ġforge": 38741, "Dis": 38742, "esten": 38743, "Ġδια": 38744, "Ġencaps": 38745, "ĠGespr": 38746, "Ġchercher": 38747, "ickets": 38748, "ÑĤоÑĢÑĭ": 38749, "Cr": 38750, "ĠТакже": 38751, "Ġrabbits": 38752, "ĠDot": 38753, "heiten": 38754, "Ġcausal": 38755, "ĠFoster": 38756, "ajÄħc": 38757, "Ġbereit": 38758, "Ġayudar": 38759, "é«Ļ": 38760, "ãģ³": 38761, "song": 38762, "comb": 38763, "Ġfringe": 38764, "Ġcybersecurity": 38765, "Ġ뾨": 38766, "Ġkier": 38767, "Ġbeschäft": 38768, "ĠконÑĨе": 38769, "Ġfacilit": 38770, "ĠNamen": 38771, "Ġbilateral": 38772, "tx": 38773, "ĠWissenschaft": 38774, "Ġnuances": 38775, "Ġripping": 38776, "Ġfy": 38777, "ĠSicherheit": 38778, "ĠGhana": 38779, "olon": 38780, "Ġtopped": 38781, "ĠMorocco": 38782, "Ġradial": 38783, "ĠLEE": 38784, "ĠAndreas": 38785, "edd": 38786, "ĠìĹ´ë": 38787, "ĠAirlines": 38788, "ãģĵãĤį": 38789, 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"Ġliquids": 38960, "Ġcapacit": 38961, "arken": 38962, "Ġvagina": 38963, "Ġmashed": 38964, "Ġemerges": 38965, "yscy": 38966, "Ġunrelated": 38967, "ĠGuild": 38968, "Ġinverted": 38969, "itives": 38970, "Tra": 38971, "Ġbegr": 38972, "Ġalte": 38973, "ì§ķ": 38974, "ãĤģãģ¦": 38975, "ĠÑĢазÑĢабоÑĤ": 38976, "finder": 38977, "Ġдалее": 38978, "ĠблагодаÑĢ": 38979, "walker": 38980, "Ġcrater": 38981, "assadors": 38982, "rences": 38983, "inski": 38984, "ĠKIM": 38985, "ĠElliot": 38986, "2017": 38987, "ĠSr": 38988, "inka": 38989, "anov": 38990, "Ġìŀĺ못": 38991, "Ġproprietary": 38992, "displaystyle": 38993, "ĠÑģим": 38994, "Ġизб": 38995, "ĠPanel": 38996, "Ġinstincts": 38997, "ĠCommunications": 38998, "麻": 38999, "midt": 39000, "Ġë§Įëĵ¤ìĸ´": 39001, "ĠÑģлова": 39002, "ĠGilbert": 39003, "缮åīį": 39004, "Так": 39005, "voorbeeld": 39006, "еÑİÑģÑĮ": 39007, "aryn": 39008, "quez": 39009, "Ġdart": 39010, "ÑĸÑĪ": 39011, "ĠHut": 39012, "Sal": 39013, "Ġsoutheast": 39014, "Ġpesticides": 39015, "Ġhelicopters": 39016, 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"ĠìŀĪëĬĶëį°ìļĶ": 43550, "zew": 43551, "baren": 43552, "Ġstol": 43553, "Ġbelongings": 43554, "ĠResearchers": 43555, "Ġefendim": 43556, "ÏħÏĩ": 43557, "ÅĤÄħcz": 43558, "ĠUng": 43559, "ĠJub": 43560, "Ġcerebral": 43561, "á»ĩu": 43562, "Ġצר": 43563, "ĠподаÑĢ": 43564, "Ġmarched": 43565, "Ġawaken": 43566, "Ġako": 43567, "Ġacept": 43568, "Ġinitiation": 43569, "è¯ī": 43570, "lot": 43571, "ĠwÅĤas": 43572, "ĠMongol": 43573, "utral": 43574, "Ġtentang": 43575, "Ġinversion": 43576, "ĠìĿ´íĽĦ": 43577, "Ġlok": 43578, "ÅĤbym": 43579, "RS": 43580, "Ġstos": 43581, "Ġinteracts": 43582, "ĠCalendar": 43583, "Ġvanish": 43584, "Ġphysiology": 43585, "Ġlinearly": 43586, "ĠJY": 43587, "ÄŁan": 43588, "funded": 43589, "iziert": 43590, "Ġzmian": 43591, "ĠGrill": 43592, "Ġunbelievably": 43593, "otechnology": 43594, "ĠCars": 43595, "ĠÙĨÛģ": 43596, "ĠFolge": 43597, "ĠBeverly": 43598, "äischen": 43599, "Ġaumento": 43600, "ìĽĮìĦľ": 43601, "Ġmailbox": 43602, "Ġsteeds": 43603, "ĠPeak": 43604, "å·§": 43605, "Ġwykor": 43606, "Ġprawda": 43607, "иÑĤÑĭ": 43608, "Ġdiscours": 43609, "Ġaccuse": 43610, "cesso": 43611, "uire": 43612, "Ġпопад": 43613, "Ġtha": 43614, "Ġmeasurable": 43615, "beeping": 43616, "ĠInnen": 43617, "ĠпÑıÑĤÑĮ": 43618, "Ġcompeted": 43619, "ĠItalians": 43620, "Ġencontra": 43621, "Ġniew": 43622, "Ġfiltration": 43623, "ĠпÑĢоÑĦеÑģÑģ": 43624, "Ġpajamas": 43625, "Ġcilantro": 43626, "ĠSoc": 43627, "Luc": 43628, "Ġê¹Ģë": 43629, "ĠOdd": 43630, "Ġhydration": 43631, "мов": 43632, "Ġplywood": 43633, "ĠCompetition": 43634, "изнеÑģ": 43635, "flight": 43636, "ĠBeit": 43637, "bourg": 43638, "Ġcoils": 43639, "Ġcâmera": 43640, "Ġamended": 43641, "Äģm": 43642, "Angel": 43643, "ĠStacy": 43644, "flo": 43645, "Ġnormale": 43646, "Ġconsonant": 43647, "Ġaccompanying": 43648, "кÑĸ": 43649, "Ġirritated": 43650, "ĠfÃ¥tt": 43651, "Ġcrocodile": 43652, "IJĺëĬĶ": 43653, "Ġalbeit": 43654, "ĠPhilosophy": 43655, "ç´¯": 43656, "ÅĨ": 43657, "ytic": 43658, "Ġrèg": 43659, "Ġfrança": 43660, "Ġattentive": 43661, "Ham": 43662, "Ġalrededor": 43663, "æĿ¿": 43664, "sei": 43665, "ĠÑģвид": 43666, "Ġgimbal": 43667, "Ġchina": 43668, "ĠðŁİ¶": 43669, "ĠÐĴам": 43670, "Ġstimulating": 43671, "ĠOra": 43672, "ytes": 43673, "Ġheft": 43674, "Ġhaters": 43675, "Ġcomplexes": 43676, "Ġ03": 43677, "ród": 43678, "clear": 43679, "Ġbesteht": 43680, "çķĻè¨Ģ": 43681, "wny": 43682, "moil": 43683, "Ġsloppy": 43684, "Ġinsignificant": 43685, "Ġdubbed": 43686, "Ġëĸł": 43687, "Ġconsigo": 43688, "лÑĥÑĪай": 43689, "Sn": 43690, "Ġ×Ķצ": 43691, "ĠÎĮ": 43692, "Ġnadzie": 43693, "Ġfreshmen": 43694, "taa": 43695, "ĠuwagÄĻ": 43696, "ĠFavorite": 43697, "ĠCriminal": 43698, "Ġeviden": 43699, "Ġsymb": 43700, "Les": 43701, "ĠBeau": 43702, "uned": 43703, "plement": 43704, "Ac": 43705, "Ġdermat": 43706, "ĠNolan": 43707, "Ñĭп": 43708, "Ġsitt": 43709, "Ġeverlasting": 43710, "Ġestavam": 43711, "Ġмик": 43712, "Ġkhác": 43713, "Ġinvit": 43714, "Ġtreble": 43715, "Ġjig": 43716, "mani": 43717, "Ġtuvo": 43718, "ĠRUS": 43719, "ĠErde": 43720, "ĠDziÄĻkujÄĻ": 43721, "Ġblueberries": 43722, "kell": 43723, "acions": 43724, "çĪ·": 43725, "ви": 43726, "LET": 43727, "Ġsprout": 43728, "Ġspor": 43729, "Ġbên": 43730, "ĠMona": 43731, "ĠContain": 43732, "ĠKeys": 43733, "озÑı": 43734, "Ġfunción": 43735, "Ġrappelle": 43736, "Ġevolves": 43737, "Ġscraping": 43738, "Ġcomentários": 43739, "Ġpratique": 43740, "Ġauxiliary": 43741, "ĠSponge": 43742, "Ñģким": 43743, "uvo": 43744, "ĠÑģамо": 43745, "Ġsank": 43746, "Ġhighways": 43747, "Ġinventions": 43748, "Ġиногда": 43749, "Ġcreatively": 43750, "Ġbenchmarks": 43751, "oncé": 43752, "alal": 43753, "Ġsotto": 43754, "Ġcalves": 43755, "ĠMov": 43756, "Ġlavender": 43757, "Ġeyeballs": 43758, "Ġawaiting": 43759, "ĠPaty": 43760, "ÙĦÙĩ": 43761, "Ġembroidery": 43762, "Ġduh": 43763, "Ġcamar": 43764, "ĠBOB": 43765, "Ġspaced": 43766, "ĠgÅĤos": 43767, "аемÑģÑı": 43768, "Ġescapes": 43769, "ĠRogue": 43770, "zcz": 43771, "èŀ": 43772, "¬ë¥¼": 43773, "ĠMoże": 43774, "ĠеÑģÑĤе": 43775, "ĠBurada": 43776, "éĮ²": 43777, "wd": 43778, "uuuu": 43779, "Ġsash": 43780, "ĠLub": 43781, "Ġnotebooks": 43782, "Ġmae": 43783, "Ġconflicting": 43784, "Ġsummertime": 43785, "acas": 43786, "Ġbauen": 43787, "blowing": 43788, "ạo": 43789, "Ġìĸ¸ìłľ": 43790, "ä»ĬæĹ¥ãģ¯": 43791, "ĠSenhor": 43792, "ĠiPhones": 43793, "ĠQuarter": 43794, "ĠìłľëĮĢë¡ľ": 43795, "uÃŁ": 43796, "Ġë§Ī무ë": 43797, "Ġsettlers": 43798, "Ġcrest": 43799, "Ġtransc": 43800, "æĽ¾": 43801, "Ġriots": 43802, "Ġclones": 43803, "ĠOprah": 43804, "ίζ": 43805, "Ġpals": 43806, ".......": 43807, "ãģĶãģĸãģĦãģ¾ãģĻ": 43808, "ĠÑĢоÑģÑģ": 43809, "ĠLaser": 43810, "Ġzaczy": 43811, "Ġsevi": 43812, "Ġregeneration": 43813, "ìĹ¼": 43814, "would": 43815, "Ġüzerine": 43816, "ĠStraÃŁe": 43817, "Ġvengeance": 43818, "Ġrer": 43819, "ĠSafari": 43820, "ĠHEY": 43821, "çķ«": 43822, "Ġsacar": 43823, "Ġimagem": 43824, "ĠBundest": 43825, "mesan": 43826, "ĠPaste": 43827, "Ġsizz": 43828, "ĠпоÑģÑĤÑĥп": 43829, "×Ķ×ķ": 43830, "trad": 43831, "Ġfrançaise": 43832, "ĠBou": 43833, "Ġbarre": 43834, "ĠZhi": 43835, "ĠGeez": 43836, "ihad": 43837, "Ġreconoc": 43838, "Ġpelig": 43839, "Ġindices": 43840, "Ġë°ĶëĢ": 43841, "Ġconduction": 43842, "Ġìķħ": 43843, "Ġzeker": 43844, "Ġfum": 43845, "ĠWür": 43846, "breaker": 43847, "Ġsprite": 43848, "Crowd": 43849, "Ġopener": 43850, "Ġolv": 43851, "Ġbuenas": 43852, "ĠSilk": 43853, "ĠHIM": 43854, "kop": 43855, "compl": 43856, "Ġpossono": 43857, "³Ģ": 43858, "Ġoscillator": 43859, "ĠSith": 43860, "èĥ¡": 43861, "ажи": 43862, "Ġraft": 43863, "hall": 43864, "Ġschneller": 43865, "Ġimporting": 43866, "Ġassembling": 43867, "Ġubiqu": 43868, "Ġactivates": 43869, "acci": 43870, "ĵľë¥¼": 43871, "Ġcomposers": 43872, "ĠACL": 43873, "Conf": 43874, "Ġì½ĺ": 43875, "ĠнекоÑĤоÑĢÑĭе": 43876, "Ġcandies": 43877, "åĬłåħ¥": 43878, "ĠMuss": 43879, "à¹ĥà¸Ĭ": 43880, "Ġduda": 43881, "ником": 43882, "meden": 43883, "Ġìĸ´ëķĮ": 43884, "ĠYeshua": 43885, "zag": 43886, "hodou": 43887, "Ġaloud": 43888, "ĠPalmer": 43889, "imize": 43890, "ãĤ·ãĥ§": 43891, "Ġmaritime": 43892, "Ġcommunal": 43893, "Ġbadges": 43894, "Ġrugby": 43895, "Ġmarshmallow": 43896, "Ġfiery": 43897, "Ġaccountant": 43898, "Ġabla": 43899, "ĠMonroe": 43900, "ĠFont": 43901, "ĠBoost": 43902, "ĠBarnes": 43903, "answer": 43904, "ĠBurning": 43905, "Ġä¸įæĺ¯": 43906, "Ġangef": 43907, "ĠWesley": 43908, "lls": 43909, "ìµ": 43910, "ש׾": 43911, "iliÅĽmy": 43912, "×IJף": 43913, "amura": 43914, "ĠFuj": 43915, "Ġpani": 43916, "ĠTrop": 43917, "arbeiten": 43918, "Ġrue": 43919, "ĠRare": 43920, "ängen": 43921, "ĠÑģмоÑĤÑĢеÑĤÑĮ": 43922, "ĠÐļаÑĢ": 43923, "ĠMTV": 43924, "boarding": 43925, "][": 43926, "ĠëłĪë": 43927, "stanbul": 43928, "pielt": 43929, "ĠHardy": 43930, "ĠEngagement": 43931, "ĠDienst": 43932, "Ġwären": 43933, "Ġfuego": 43934, "Ġestruct": 43935, "Ġcalam": 43936, "ĠResponse": 43937, "ĠãĤĦ": 43938, "ĠMohammad": 43939, "Ġresisting": 43940, "Ġdurant": 43941, "èģ¯": 43942, "åĨµ": 43943, "ĠOLED": 43944, "Ġverz": 43945, "män": 43946, "ĠÙĨÛĴ": 43947, "Ġparanoid": 43948, "ĠAware": 43949, "ĠEngineers": 43950, "Ġprocedural": 43951, "Ġpersonnage": 43952, "Ġfarklı": 43953, "é¡Ĩ": 43954, "flowing": 43955, "ĠмеÑģÑĤа": 43956, "ĠBare": 43957, "istem": 43958, "ĠpoczÄħtku": 43959, "Ġpersonajes": 43960, "Ġìĸ´ëłµ": 43961, "Ńī": 43962, "ĠХоÑĤÑı": 43963, "Ġunsett": 43964, "ĠAbsol": 43965, "Ġấy": 43966, "ĠMAYOR": 43967, "полне": 43968, "Ġinforming": 43969, "Ġamps": 43970, "ÐŁÑĢ": 43971, "ĠëŃĶ": 43972, "aeda": 43973, "Ġ×Ķ×ij×": 43974, "ấn": 43975, "kelijk": 43976, "Ġatheist": 43977, "Ġtrout": 43978, "Ġneues": 43979, "ĠNokia": 43980, "machen": 43981, "Ġwholesale": 43982, "ırd": 43983, "Ins": 43984, "ĠÑįп": 43985, "Ġprick": 43986, "ĠKindern": 43987, "à¸Ĺำ": 43988, "Ġclassy": 43989, "Ġînt": 43990, "ĠShopify": 43991, "ĠÑģоÑĢ": 43992, "ĠзакÑĢÑĭ": 43993, "zuk": 43994, "Ġuniversally": 43995, "Ġteaspoons": 43996, "Ġrecount": 43997, "ĠnÃ¥gonting": 43998, "ĠXue": 43999, "isième": 44000, "Ġweakest": 44001, "ĠteÅŁekkür": 44002, "Ġmathematically": 44003, "ĠHos": 44004, "Ġíķľëĭ¤": 44005, "Ġpartager": 44006, "ĠDarr": 44007, "êº": 44008, "Ġεκ": 44009, "Ġgerms": 44010, "Ġgelir": 44011, "Ġdul": 44012, ",-": 44013, "Ġìĸ¸ë": 44014, "Ġ×ŀצ": 44015, "ĠÑıÑĢ": 44016, "Ġquotid": 44017, "Ġprzysz": 44018, "Ġhardness": 44019, "Ġaquatic": 44020, "ĠJungle": 44021, "ĠPCR": 44022, "ĠEliot": 44023, "Ġostr": 44024, "Ġmapa": 44025, "essä": 44026, "ĠGIR": 44027, "ĠDriving": 44028, "ĠSami": 44029, "ĠMedien": 44030, "ĠCompanies": 44031, "ĠPharm": 44032, "seits": 44033, "ĠRim": 44034, "ĠοÏĢο": 44035, "Ġweiteren": 44036, "Ġpizzas": 44037, "ĠLydia": 44038, "ĠHeights": 44039, "Ġsincerity": 44040, "Ġnossas": 44041, "ĠdÅĤ": 44042, "Ġalarming": 44043, "ĠCauc": 44044, "ĠÑģмÑĭÑģ": 44045, "facing": 44046, "bags": 44047, "WW": 44048, "ĠØ´ÙĬ": 44049, "Ġcourtroom": 44050, "ĠPhillip": 44051, "Ġê²ĥì²ĺëŁ¼": 44052, "ĠSpieler": 44053, "ãĤıãģĭ": 44054, "Ġkant": 44055, "Ġadmitting": 44056, "ãĥģãĥ£ãĥ³ãĥįãĥ«": 44057, "Ġcontainment": 44058, "å¼ł": 44059, "Ġremovable": 44060, "Ġjumper": 44061, "focused": 44062, "ĠиÑĤоге": 44063, "ĠТем": 44064, "Ġvase": 44065, "ĠUSC": 44066, "ĠMonate": 44067, "ĠJacobs": 44068, "ĠHOL": 44069, "iked": 44070, "erweise": 44071, "Ġgoodies": 44072, "Ġhomage": 44073, "׼ש×Ļ×ķ": 44074, "Ġquais": 44075, "Ġinicial": 44076, "Ġguarding": 44077, "Ġdazz": 44078, "Ġcombos": 44079, "ĠÑĥпÑĢав": 44080, "ĠTalent": 44081, "å¥ĩæĢª": 44082, "Ġór": 44083, "Ġintermittent": 44084, "ĠMcCarthy": 44085, "Ġspans": 44086, "Ġtyre": 44087, "Ġquy": 44088, "èĪĪ": 44089, "jut": 44090, "ĠZent": 44091, "Ġgat": 44092, "大åĵ¥": 44093, "Ġscaffold": 44094, "Ġnecesario": 44095, "ĠZahlen": 44096, "ĠSAND": 44097, "ĠPU": 44098, "Everything": 44099, "----------------": 44100, "ĠвзÑıÑĤÑĮ": 44101, "Ġsparks": 44102, "Ġpendulum": 44103, "×ŀף": 44104, "Ġìĥīê¹": 44105, "Ġmultiplier": 44106, "Ġладно": 44107, "urat": 44108, "Ġupsetting": 44109, "è¡Ģ": 44110, "bak": 44111, "ĠìµľëĮĢ": 44112, "Ġanál": 44113, "ĠJOE": 44114, "Ġkosten": 44115, "ĠPatty": 44116, "ĠGuin": 44117, "cked": 44118, "ĠEgyptians": 44119, "ĠCitizens": 44120, "ר׼": 44121, "ĠÐķÑīе": 44122, "Ġйого": 44123, "Ġsnowfl": 44124, "Ġlekker": 44125, "Ġacost": 44126, "ĠBabe": 44127, "Ġgamble": 44128, "Ġadjective": 44129, "кими": 44130, "oys": 44131, "Ġmontre": 44132, "ĠHyundai": 44133, "Ġmoisturizing": 44134, "Ġmozzarella": 44135, "OOO": 44136, "Ġfacult": 44137, "Ġdoet": 44138, "Ġfearless": 44139, "Ġespresso": 44140, "Ġallora": 44141, "ĠCinc": 44142, "ãĥ¼ãĤ¸": 44143, "Ġconteúdo": 44144, "ĠPelosi": 44145, "Ġminder": 44146, "root": 44147, "Ġíķłë": 44148, "Ġпад": 44149, "ĠCalling": 44150, "ĠConfig": 44151, "ĠConsole": 44152, "insky": 44153, "énergie": 44154, "Ġsolitary": 44155, "оде": 44156, "Ġguarded": 44157, "160": 44158, "ĠпÑģиÑħ": 44159, "ĠShap": 44160, "Ġtitre": 44161, "ologne": 44162, "ĠпаÑĢÑĥ": 44163, "ĠPRE": 44164, "ãĥ¼ãĥī": 44165, "Ġln": 44166, "ĠMitgl": 44167, "ĠCarry": 44168, "Ġspind": 44169, "ĠCanton": 44170, "Ġkingdoms": 44171, "remo": 44172, "Ġraging": 44173, "Ġincapable": 44174, "ĠWR": 44175, "åĨįè§ģ": 44176, "ĠÑģобÑģÑĤвен": 44177, "ĠкакиÑħ": 44178, "ĠSHE": 44179, "ëĭ¹íŀĪ": 44180, "Ġscarcity": 44181, "Ġperde": 44182, "Ġexits": 44183, "ĠSinger": 44184, "Ġsupper": 44185, "Ġmunicipality": 44186, "ĠDiversity": 44187, "Ġtiro": 44188, "iels": 44189, "ĠlÃŃder": 44190, "Ġbluff": 44191, "Ġatra": 44192, "lys": 44193, "Ġmahd": 44194, "Ġcódigo": 44195, "ĠHarlem": 44196, "rule": 44197, "icity": 44198, "Ġsimplistic": 44199, "ĠKonst": 44200, "åģ¥": 44201, "ELLI": 44202, "Ġförsta": 44203, "Ġconstitutes": 44204, "ĠÑģÑĤоÑĢонÑĥ": 44205, "Ġurged": 44206, "ĠPanda": 44207, "ì°¨ë": 44208, "rece": 44209, "Ġpatriot": 44210, "ĠCrush": 44211, "Ġwink": 44212, "ойÑĤи": 44213, "urança": 44214, "Ġseizures": 44215, "Ġelectrod": 44216, "ĠDonkey": 44217, "ĠIU": 44218, "ĠMOS": 44219, "Ġalkal": 44220, "ì´ī": 44221, "besondere": 44222, "Ġparallels": 44223, "Ġbitterness": 44224, "ättre": 44225, "essional": 44226, "Ġsoybean": 44227, "Ġcollab": 44228, "ĠReporting": 44229, "å§Ķ": 44230, "Ġкомпании": 44231, "Ġwszyscy": 44232, "ĠCrunch": 44233, "iseen": 44234, "Ġambassadors": 44235, "ĠChev": 44236, "åįĪ": 44237, "овÑĭе": 44238, "sca": 44239, "ĠÑĢеÑĪил": 44240, "оÑĤо": 44241, "Ġgleichzeitig": 44242, "mern": 44243, "üst": 44244, "ĠHae": 44245, "³´ê²łìĬµëĭĪëĭ¤": 44246, "Ġshores": 44247, "Ġdepress": 44248, "Ġahor": 44249, "ĠSteuer": 44250, "ahh": 44251, "Ġrevise": 44252, "ĠÑģамÑĭе": 44253, "jat": 44254, "Ġherbal": 44255, "Ġcuánt": 44256, "Ġbuna": 44257, "niejsze": 44258, "Finally": 44259, "×ķ×ĸ": 44260, "cje": 44261, "ĠìŀĪê±°ëĵłìļĶ": 44262, "ĠëĤĺëĪ": 44263, "Ġprzest": 44264, "ãĥ¼ãĥł": 44265, "lica": 44266, "ĠDuch": 44267, "å°įå°į": 44268, "ÑĸйÑģÑĮ": 44269, "passen": 44270, "Ġsatisfies": 44271, "ĠAdditional": 44272, "Ġcámara": 44273, "еÑĩение": 44274, "Ġpomp": 44275, "Ġë§IJìĿ´": 44276, "ĠMills": 44277, "евид": 44278, "Ġrespectable": 44279, "Ġfilament": 44280, "Ġvender": 44281, "Ġmattered": 44282, "oure": 44283, "층": 44284, "Korean": 44285, "Ġestudio": 44286, "Ġcactus": 44287, "ĠVive": 44288, "ĠRag": 44289, "Ġcompliqué": 44290, "ĠÙĪÛģ": 44291, "Ġtao": 44292, "¦¿": 44293, "Since": 44294, "Ġjeopard": 44295, "ĠSell": 44296, "åºĶ": 44297, "ĠìĺĽ": 44298, "Ġketo": 44299, "Ġintelig": 44300, "ĠAngeb": 44301, "Ġtiden": 44302, "Ġsocio": 44303, "Ġreminiscent": 44304, "Ġcaregiver": 44305, "Space": 44306, "ĠExercise": 44307, "ĠBecome": 44308, "êts": 44309, "akk": 44310, "!..": 44311, "ĠÑģпÑĢоÑģ": 44312, "ĠαÏĢο": 44313, "Ġshootings": 44314, "Ġape": 44315, "ĠSammy": 44316, "ĠKung": 44317, "Ġcuál": 44318, "ĠLup": 44319, "æĿŁ": 44320, "ä¾ĨåĪ°": 44321, "ĠÑģÑĤÑĥд": 44322, "Ġsweeter": 44323, "Ġcomum": 44324, "ĠAds": 44325, "hyung": 44326, "ĠбÑĥдÑĥÑī": 44327, "Ġwaffle": 44328, "ĠOrb": 44329, "Ġlaut": 44330, "Ġforecasting": 44331, "åª": 44332, "Ġrapping": 44333, "Ġprefers": 44334, "Ġbenz": 44335, "Ġnik": 44336, "ĠBahn": 44337, "Ġsanding": 44338, "Ġimminent": 44339, "ĠпÑĢоблемÑĭ": 44340, "Ġdoivent": 44341, "ола": 44342, "Ġżycia": 44343, "ihu": 44344, "Ġexistem": 44345, "ĠInterior": 44346, "ĠTakes": 44347, "Ġtoddler": 44348, "Ġdictatorship": 44349, "ĠSmithson": 44350, 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"Ġsurrendered": 48802, "Ġlibrarians": 48803, "125": 48804, "ĠÑģиг": 48805, "Ġuniformly": 48806, "ĠEagles": 48807, "ìķĻ": 48808, "иÑĤан": 48809, "andid": 48810, "ĠìłĪëĮĢ": 48811, "Ġض": 48812, "Ġarrests": 48813, "ĠCSV": 48814, "ĠAzerbaijan": 48815, "ortic": 48816, "ĠDX": 48817, "ĠAdventures": 48818, "Ġabus": 48819, "ĠFau": 48820, "Ġschlimm": 48821, "Ġrattling": 48822, "Ġconsumes": 48823, "ĠTolkien": 48824, "Ġresurrected": 48825, "ĠXY": 48826, "íĬ¸ê°Ģ": 48827, "ĠвÑĭÑģÑĤÑĥп": 48828, "ĠAngie": 48829, "żenia": 48830, "Mic": 48831, "ĠSheila": 48832, "achtet": 48833, "Ġoverst": 48834, "Ġlâ": 48835, "Ġineffective": 48836, "æĿ¡": 48837, "æĢİä¹ĪäºĨ": 48838, "å¿Ļ": 48839, "Ġwichtiger": 48840, "Ġvino": 48841, "Ġpum": 48842, "Ġangled": 48843, "ĠPione": 48844, "ĠMỹ": 48845, "ãģĿãĤĮãģ¯": 48846, "woÅĽÄĩ": 48847, "draw": 48848, "ัà¹Ī": 48849, "markets": 48850, "Ġcafes": 48851, "ĠCem": 48852, "âĿ¤": 48853, "ĠSuit": 48854, "MK": 48855, "Ġemphasizes": 48856, "Ġtortilla": 48857, "Ġmejorar": 48858, "ĠSurviv": 48859, "casting": 48860, "Ġeducación": 48861, "ĠGum": 48862, "uely": 48863, "ĠìĹ¬ê¸°ëĬĶ": 48864, "Ġstretchy": 48865, "ença": 48866, "Ġwithhold": 48867, "Ġexiting": 48868, "Ġenthalpy": 48869, "ĠTransit": 48870, "ılmÄ±ÅŁ": 48871, "alies": 48872, "Ġsalvar": 48873, "Ġleaned": 48874, "ĠgroÃŁes": 48875, "Ġfitt": 48876, "аки": 48877, "Sarah": 48878, "Ġhostel": 48879, "Ġfingerna": 48880, "ĠnadziejÄĻ": 48881, "wives": 48882, "Rec": 48883, "Ġspool": 48884, "аÑĤов": 48885, "ĠEnemy": 48886, "Ġfury": 48887, "Ġdetta": 48888, "ĠFay": 48889, "éļ¨": 48890, "ÑıÑİÑĤ": 48891, "Ġaproximadamente": 48892, "Ġsilos": 48893, "Ġmagist": 48894, "Ġcree": 48895, "ĠKrank": 48896, "ĠDOWN": 48897, "Ġstartled": 48898, "Ġreborn": 48899, "ĠUmwelt": 48900, "ĠSuzanne": 48901, "ниÑĨÑĭ": 48902, "outez": 48903, "ĠJAC": 48904, "yards": 48905, "radas": 48906, "rau": 48907, "ipts": 48908, "hail": 48909, "Ġparagraphs": 48910, "Ġmeglio": 48911, "Ġisolating": 48912, "Ġaceite": 48913, "ĠHarsh": 48914, "Ġcyst": 48915, "ĠBlockchain": 48916, "ĠÑħоÑĢоÑĪий": 48917, "Ġvirtuous": 48918, "Ġinvestigación": 48919, "Ġdevoir": 48920, "Ġmasturb": 48921, "ĠSale": 48922, "ÙĬرة": 48923, "ĠΧ": 48924, "ĠStraÃŁen": 48925, "Ġdikk": 48926, "Ġafore": 48927, "ĠJungkook": 48928, "Ġchociaż": 48929, "ĠDebatte": 48930, "Ġweirdly": 48931, "Ġviaje": 48932, "regist": 48933, "Help": 48934, "Ġkinderen": 48935, "Ġformulated": 48936, "Ġenfim": 48937, "ĠTowards": 48938, "коÑĹ": 48939, "ivering": 48940, "ĠдеÑĤи": 48941, "charger": 48942, "Ġpurl": 48943, "Ġacademically": 48944, "ĠNurse": 48945, "Ġdeleting": 48946, "ayo": 48947, "Ġrefusal": 48948, "Ġdepicts": 48949, "ĠDracula": 48950, "Ġtoasted": 48951, "ĠZombie": 48952, "ĠSuperior": 48953, "ĠBold": 48954, "Ġquizzes": 48955, "Ġgle": 48956, "450": 48957, "Ġcomeço": 48958, "ynn": 48959, "Ġverst": 48960, "ĠOlaf": 48961, "Ġpomoc": 48962, "ĠSask": 48963, "ëĺ": 48964, "ĠTCP": 48965, "ĠProperty": 48966, "íķĺì£ł": 48967, "à¸ľà¸¡": 48968, "boom": 48969, "aros": 48970, "ĠÑĢоÑģÑģий": 48971, "ĠбÑĭваеÑĤ": 48972, "åĩºåİ»": 48973, "ĠìĿ´ìķ¼ê¸°ë¥¼": 48974, "Ġcombien": 48975, "vacc": 48976, "Ġebenfalls": 48977, "para": 48978, "Ġзм": 48979, "Ġdesperation": 48980, "ordre": 48981, "Ġש׾×Ļ": 48982, "Ġgenerously": 48983, "ĠÐŀк": 48984, "Ġorbiting": 48985, "></": 48986, "ĠespÃŃ": 48987, "ĠCOP": 48988, "åŃ©åŃIJ": 48989, "visible": 48990, "ĠпÑĢеÑģÑĤÑĥп": 48991, "Ġstitched": 48992, "à¯Ī.": 48993, "Ġlatent": 48994, "ĠPrab": 48995, "ĠMcN": 48996, "ĠHealing": 48997, "ĠCuriosity": 48998, "cert": 48999, "Ġ민주": 49000, "Ġpatiently": 49001, "ĠYT": 49002, "foreign": 49003, "Ġvẫn": 49004, "Ġindustri": 49005, "Ġcocktails": 49006, "Ġbrighten": 49007, "Ġconsolidated": 49008, "аÑĢд": 49009, "ltry": 49010, "Ġgrille": 49011, "Ġbona": 49012, "Ġdiligently": 49013, "ĠWrestleMania": 49014, "erkt": 49015, "energy": 49016, "999": 49017, "à®ķவ": 49018, "Ġtote": 49019, "iono": 49020, "DIO": 49021, "Ġschizophrenia": 49022, "Ġpostponed": 49023, "ĠQiu": 49024, "ĠÏĥÏħν": 49025, "ĠzdjÄĻ": 49026, "Ġspannend": 49027, "ĠDIS": 49028, "Rel": 49029, "Ġrhin": 49030, "immune": 49031, "Old": 49032, "Ġplötzlich": 49033, "Ġmound": 49034, "Ġastronomical": 49035, "ĠGuid": 49036, "ĠCul": 49037, "HI": 49038, "ĠÅł": 49039, "Ġrepo": 49040, "ĠMaurice": 49041, "ä¸ĢçĤ¹": 49042, "Ġbandits": 49043, "ĠDesktop": 49044, "äss": 49045, "fta": 49046, "Ġlicence": 49047, "Ġimaginar": 49048, "ĠEntreprene": 49049, "xo": 49050, "Ġ맼ìŀĪëĬĶ": 49051, "Ġ×Ķ×ij": 49052, "Ġpumpkins": 49053, "Ġkanssa": 49054, "ĠjÄĻzy": 49055, "Ġcommunauté": 49056, "bür": 49057, "Ġerhö": 49058, "ĠWolver": 49059, "ĠSharing": 49060, "令": 49061, "Ġpakai": 49062, "Ġinsulted": 49063, "ÐľÑĭ": 49064, "оÑĹ": 49065, "Ġconsiste": 49066, "æĮij": 49067, "Ġyoungsters": 49068, "Ġgleichen": 49069, "weder": 49070, "Ġmote": 49071, "Ġclauses": 49072, "état": 49073, "prus": 49074, "Ġwast": 49075, "ç»ĻæĪij": 49076, "ĠCrisp": 49077, "ĠçĦ¶å¾Į": 49078, "Ġoffenders": 49079, "Ġconvection": 49080, "Ġconfian": 49081, "ollow": 49082, "amet": 49083, "ĠÑĹÑħ": 49084, "第äºĮåĢĭ": 49085, "fficiency": 49086, "Ġunglaub": 49087, "igans": 49088, "Ġmarketed": 49089, "ĠVAN": 49090, "Ġproclaimed": 49091, "Ġcélulas": 49092, "Ġcollide": 49093, "ĠOculus": 49094, "adore": 49095, "Ji": 49096, "Ġsustaining": 49097, "ĠFasc": 49098, "Ġsetzt": 49099, "Ġnosaltres": 49100, "Most": 49101, "ĠвÑĩ": 49102, "Ġnauc": 49103, "ĠBhar": 49104, "çĪ¸çĪ¸": 49105, "æĪijè·Łä½łè¬Ľ": 49106, "Ġyêu": 49107, "Ġtimest": 49108, "Ġpertama": 49109, "irmi": 49110, "Ġzwr": 49111, "Ġverbess": 49112, "Ġvortex": 49113, "ĠSTACK": 49114, "ثر": 49115, "¹Ħë": 49116, "ĶĶìĺ¤": 49117, "Ġlinkage": 49118, "ĠFraser": 49119, "enario": 49120, "ĠëĿ¼ëĬĶ": 49121, "ĠìĦłë°°": 49122, "hthal": 49123, "Ġê¹Į": 49124, "ĠKhông": 49125, "Ãĥ": 49126, "Ġscrambled": 49127, "ĠEink": 49128, "Ġmicroorgan": 49129, "Ġnarcissist": 49130, "ĠKombat": 49131, "Ġ맡": 49132, "ĠAGA": 49133, "Ġperfekt": 49134, "ĠSerie": 49135, "determ": 49136, "-'": 49137, "Ġponytail": 49138, "Ġkoska": 49139, "ìĵ": 49140, "Ġobec": 49141, "Ġchests": 49142, "veer": 49143, "Ġuprising": 49144, "Ġstoked": 49145, "associ": 49146, "Ġprodução": 49147, "ĠShape": 49148, "ìłľê°Ģ": 49149, "ĠëĶ°": 49150, "Ġjon": 49151, "Ġinadvert": 49152, "antas": 49153, "ĠнаконеÑĨ": 49154, "Ġå°įåķĬ": 49155, "ĠArsenal": 49156, "Ġproteg": 49157, "Ġliberté": 49158, "Ġglare": 49159, "åĪļ": 49160, "å·²ç»ı": 49161, "Ġverein": 49162, "Ġinserts": 49163, "ĠJana": 49164, "Ġwydaje": 49165, "ÅĤum": 49166, "Ġ%.": 49167, "origine": 49168, "Ġsynagogue": 49169, "Ġfallait": 49170, "Ġdisobed": 49171, "Ġantic": 49172, "ĠCycl": 49173, "Ġasynchronous": 49174, "Ġë²Įìį¨": 49175, "Ġgesund": 49176, "Ġgagn": 49177, "Ġpea": 49178, "Ġgrin": 49179, "ést": 49180, "Ġsauc": 49181, "ĠMäd": 49182, "íķ´ëıĦ": 49183, "pps": 49184, "ĠεÏĢι": 49185, "Ġpeuple": 49186, "Ġdeben": 49187, "ĠBree": 49188, "ĠÑĢолÑĮ": 49189, "Ġкаким": 49190, "Ġútil": 49191, "Ġdistributor": 49192, "алÑĭ": 49193, "ĠswojÄħ": 49194, "Ġfolklore": 49195, "Ġreceivers": 49196, "ĠMOO": 49197, "bins": 49198, "astre": 49199, "ìķĪë": 49200, "ĠëĦ£ê³ł": 49201, "Ġmultimedia": 49202, "Ġgebaut": 49203, "овÑĭÑħ": 49204, "ãy": 49205, "Ġdane": 49206, "okol": 49207, "emitism": 49208, "ONEY": 49209, "ĠyaÄŁ": 49210, "Ġchauff": 49211, "容æĺĵ": 49212, "Ġesfuer": 49213, "Äĥn": 49214, "ertas": 49215, "Ġfonctionne": 49216, "omina": 49217, "Ġivory": 49218, "ĠYoutuber": 49219, "ĠSkywalker": 49220, "иÑĩеÑģкаÑı": 49221, "toi": 49222, "Ġveya": 49223, "Ġgelernt": 49224, "Ġchancellor": 49225, "ĠStatistics": 49226, "Ġwelded": 49227, "Ġondan": 49228, "ĠSei": 49229, "Ġmedically": 49230, "Ġenergized": 49231, "ĠVia": 49232, "Ġвик": 49233, "Ġuninter": 49234, "Ġhighness": 49235, "ĠíĮĶë": 49236, "Ġamplified": 49237, "ĠSergey": 49238, "ĠMins": 49239, "warm": 49240, "pell": 49241, "ophile": 49242, "Ġhè": 49243, "ĠBelo": 49244, "ĠSketch": 49245, "Ġcharacterization": 49246, "ansen": 49247, "ĠÑĤÑĥÑĢ": 49248, "Ġãħĭãħĭãħĭ": 49249, "Note": 49250, "ĠkoÅŁ": 49251, "Ġciert": 49252, "flu": 49253, "Ġbaht": 49254, "ĠDowntown": 49255, "ĠCRIS": 49256, "odie": 49257, "140": 49258, "Ġlitres": 49259, "Ġgriev": 49260, "æ§ĺ": 49261, "ĠìĶ¨ê°Ģ": 49262, "Ġsucceeds": 49263, "Ġ__": 49264, "enting": 49265, "Ġvimos": 49266, "Ġsì": 49267, "defense": 49268, "ĠMcD": 49269, "ĠMarion": 49270, "ĠDont": 49271, "ĠDDR": 49272, "ĠLazar": 49273, "ĠDAR": 49274, "Ġkuv": 49275, "Kn": 49276, "Ġsembla": 49277, "Ġairborne": 49278, "ĠViolence": 49279, "ëIJIJ": 49280, "Ġrestraint": 49281, "Ġwhistles": 49282, "Ġscolded": 49283, "Ġacceso": 49284, "Ġabsolutamente": 49285, "ĠTyl": 49286, "ĠSap": 49287, "¶Ģë¶Ħ": 49288, "itäten": 49289, "adem": 49290, "Ġý": 49291, "Ġprescribe": 49292, "ĠMage": 49293, "ĠHelena": 49294, "å¾Īæľī": 49295, "亲": 49296, "vt": 49297, "Ġvienen": 49298, "Ġsneez": 49299, "Ġmolé": 49300, "Æ°á»Łng": 49301, "Ġtransporting": 49302, "ĠLean": 49303, "Ġkung": 49304, "ÑĥÑĢа": 49305, "ÏĦÎŃ": 49306, "utches": 49307, "onders": 49308, "liyor": 49309, "Nat": 49310, "Ġzij": 49311, "Ġmammal": 49312, "Ġkäyt": 49313, "ĠJoanna": 49314, "sent": 49315, "Ġस": 49316, "Ġvested": 49317, "ĠErfahrung": 49318, "okee": 49319, "Ġclipping": 49320, "ĠListening": 49321, "Ġ(#": 49322, "fö": 49323, "Ġvidare": 49324, "Ġbrittle": 49325, "ĠSTART": 49326, "ĠDamas": 49327, "ĠYog": 49328, "ãĤĵãģ¨": 49329, "gart": 49330, "Ġverlier": 49331, "Ġheartfelt": 49332, "ĠdoÅĽÄĩ": 49333, "ì¹ĺê°Ģ": 49334, ".»": 49335, "Ġmaximal": 49336, "Ġdistintos": 49337, "ĠìĻľëĥIJíķĺë©´": 49338, "Ġsailed": 49339, "Ġconveyed": 49340, "ĠTinder": 49341, "ĠSUPER": 49342, "ниÑĨÑĥ": 49343, "controlled": 49344, "Ġfunz": 49345, "Ġbastards": 49346, "ĠGinsburg": 49347, "Ġnuovo": 49348, "ĠPere": 49349, "ĠJES": 49350, "ĠDingen": 49351, "ĠBets": 49352, "umba": 49353, "acción": 49354, "ĠìŀĪì§Ģë§Į": 49355, "Ġretra": 49356, "ĠLaurent": 49357, "Ġpozy": 49358, "Ġgrooves": 49359, "Ġmáquina": 49360, "Ġminion": 49361, "Ġdeinen": 49362, "ĠShaun": 49363, "×Ļ×Ļ": 49364, "Ġhonorary": 49365, "osaurus": 49366, "Ġzeit": 49367, "Ġespecie": 49368, "ĠBCE": 49369, "аÑĤе": 49370, "Justin": 49371, "ĠWheels": 49372, "ĠìĿ´íķ´": 49373, "ĠبÙĬÙĨ": 49374, "Ġpropulsion": 49375, "Ġperceber": 49376, "ĠNewman": 49377, "å´": 49378, "culosis": 49379, "Mi": 49380, "ĠаккÑĥ": 49381, "Ġmastering": 49382, "Ġläh": 49383, "Ġfists": 49384, "ä»Ķ": 49385, "Ġmarinade": 49386, "Lilly": 49387, "Ġëħ¸ëł¥": 49388, "ĠYH": 49389, "Ġurgently": 49390, "Ġinformational": 49391, "Ġacordo": 49392, "izzy": 49393, "ãģĦãģı": 49394, "ìĿ´ìĸ´": 49395, "imar": 49396, "ĠëĤĺìĺ¤ë": 49397, "Ġtwenties": 49398, "Ġrasp": 49399, "Ġbumpy": 49400, "بة": 49401, "worker": 49402, "Ġquickest": 49403, "Ġattaches": 49404, "виг": 49405, "ĠëĤĺíĥĢë": 49406, "Ġpuree": 49407, "Ġoversized": 49408, "Ġstirred": 49409, "Ġjakim": 49410, "Ġhomicide": 49411, "ãĤĤãģĹ": 49412, "iscilla": 49413, "Ġì±Ļ": 49414, "Ġspeculative": 49415, "Ġassists": 49416, "main": 49417, "jähr": 49418, "indet": 49419, "ĠÅŁur": 49420, "Ġforecasts": 49421, "Ġdiversion": 49422, "Ġtare": 49423, "Ġogl": 49424, "ĠOrganisation": 49425, "ĠChevy": 49426, "Ġbaja": 49427, "andır": 49428, "ĠÙĪÙĦا": 49429, "Ġradiant": 49430, "Ġliaison": 49431, "Ġdemokrat": 49432, "ĠMARC": 49433, "ÏĢοÏħ": 49434, "Ġrunt": 49435, "Ġprécis": 49436, "Ġgeven": 49437, "Ġvéhic": 49438, "ĠJESS": 49439, "STR": 49440, "Ġìĸĺë": 49441, "Ġvisionary": 49442, "Ġburadan": 49443, "ĠãģĤãĤĬ": 49444, "Ġrebirth": 49445, "Ġexhibited": 49446, "ĠMetall": 49447, "olie": 49448, "elyn": 49449, "Ġflavours": 49450, "Ġescrito": 49451, "ĠDelete": 49452, "ĠìķĮìķĺìĸ´": 49453, "ĠÑĥкÑĢаÑĹн": 49454, "Ġinterrupting": 49455, "Ġidentific": 49456, "ĠSuzuki": 49457, "ĠLanding": 49458, "件äºĭæĥħ": 49459, "andi": 49460, "Ġestran": 49461, "Ġcouleur": 49462, "Ġagrad": 49463, "ĠSny": 49464, "Ġà®ĩல": 49465, "Ġander": 49466, "Ġrua": 49467, "Ġprise": 49468, "Ġlaure": 49469, "ĠíĬĢ": 49470, "Ġmoderation": 49471, "Ġerfahren": 49472, "Ġdeconst": 49473, "ĠReese": 49474, "ĠPK": 49475, "etos": 49476, "ãģĵãĤĮãģ§": 49477, "ĠGravity": 49478, "ĠEren": 49479, "Ġoverboard": 49480, "Ġmüsst": 49481, "ĠEmail": 49482, "еÑĢм": 49483, "ydi": 49484, "iÄĻdzy": 49485, "ĠLOU": 49486, "ĠFuÃŁball": 49487, "ĠRD": 49488, "alts": 49489, "ĠìĬ¤íĬ¸ë": 49490, "ĠÐļÑĢаÑģ": 49491, "Ġtelev": 49492, "ĠÑĢо": 49493, "Ġresignation": 49494, "Ġjingle": 49495, "ĠStudien": 49496, "ĠIX": 49497, "ĠSentinel": 49498, "ĠPang": 49499, "éĦ": 49500, "Jake": 49501, "Ġpersonagem": 49502, "Ġmédia": 49503, "ĠChern": 49504, "antically": 49505, "Ġthá»Ŀi": 49506, "Ġparalysis": 49507, "Ġjapanese": 49508, "Ġconex": 49509, "Ġefic": 49510, "Ġunderside": 49511, "Ġneol": 49512, "Ġfian": 49513, "имоÑģÑĤÑĮ": 49514, "Ġquirky": 49515, "Ġpista": 49516, "ĠClement": 49517, "nothing": 49518, "ĠпоеÑħ": 49519, "Ġhorrend": 49520, "Ġconsolidate": 49521, "ploys": 49522, "emaker": 49523, "Jennifer": 49524, "Ġnuméro": 49525, "Ġfamoso": 49526, "ĠNeptune": 49527, "ĠíĸĪìĸ´": 49528, "ĠпÑĢезид": 49529, "Ġsitcom": 49530, "Ġserio": 49531, "Ġmue": 49532, "Ġglands": 49533, "Ġbörjar": 49534, "ĠYJ": 49535, "ĠRiot": 49536, "paragus": 49537, "Ġsegurança": 49538, "Ġimmature": 49539, "ĠMadonna": 49540, "à¸į": 49541, "Ġlingering": 49542, "Ġacesso": 49543, "ĠOrient": 49544, "ĠRecomm": 49545, "Ġcomplac": 49546, "founded": 49547, "attend": 49548, "Ġcielo": 49549, "ĠZhan": 49550, "naires": 49551, "cco": 49552, "Ġ×IJ׳": 49553, "Ġstata": 49554, "Ġcontradictory": 49555, "ĠSé": 49556, "ĠSAN": 49557, "ĠConnie": 49558, "Ġëĭ¹ìĭľ": 49559, "ĠÑģамой": 49560, "Ġmajestic": 49561, "ĠPenguin": 49562, "ĠCOME": 49563, "ÃŃcios": 49564, "pero": 49565, "Ġmg": 49566, "Ġfauc": 49567, "Ġcorrer": 49568, "ĠGottes": 49569, "ĠAnglo": 49570, "Har": 49571, "á»Ĺi": 49572, "Ġvitesse": 49573, "Ġannouncer": 49574, "ĠOmaha": 49575, "kum": 49576, "Ġspared": 49577, "ĠÑĢаза": 49578, "ĠполÑĥÑĩиÑĤÑģÑı": 49579, "Ġtähän": 49580, "Ġпонад": 49581, "Ġpertaining": 49582, "ĠRate": 49583, "iern": 49584, "Gold": 49585, "Ġteste": 49586, "ĠdeÄŁild": 49587, "Ġdamping": 49588, "ĠPartnership": 49589, "zysta": 49590, "geld": 49591, "Ġsmokes": 49592, "ĠMarriage": 49593, "쪽ìĹIJ": 49594, "èħ³": 49595, "isce": 49596, "Ġtryna": 49597, "ĠDirectory": 49598, "ĠëĤĺìĺ¬": 49599, "Ġshameful": 49600, "Ġmentre": 49601, "Ġassigning": 49602, "æĺ¯éĢĻ樣": 49603, "Ġrepertoire": 49604, "Ġobjetos": 49605, "稱": 49606, "Ġunderworld": 49607, "Ġendeavors": 49608, "Ġignite": 49609, "ĠÙĪج": 49610, "Ġexperient": 49611, "ĠÐĹап": 49612, "ĠзаклÑİÑĩ": 49613, "Ġvoltages": 49614, "Ġniego": 49615, "Ġdeficits": 49616, "Ġbuenos": 49617, "ĠSleeping": 49618, "ĠSalem": 49619, "Ġunlocking": 49620, "Ġinteracted": 49621, "Ġentendeu": 49622, "ĠSuperintendent": 49623, "Ġszczegól": 49624, "Ġquas": 49625, "Ġpaling": 49626, "Ġkho": 49627, "بØŃ": 49628, "Ġcolabor": 49629, "ĠпÑĢигоÑĤов": 49630, "Ġmauv": 49631, "ĠJudas": 49632, "ĠAssist": 49633, "ĠÑĤеÑĢÑĢи": 49634, "ĠнаÑģколÑĮко": 49635, "Ġsubsidy": 49636, "ĠEmbassy": 49637, "Ġdagen": 49638, "ĠSanto": 49639, "èĪ¬": 49640, "ש×ķ×ij": 49641, "Ġabruptly": 49642, "ĠAdapt": 49643, "Ġvaak": 49644, "Ġpostal": 49645, "Ġinvestir": 49646, "Ġfiquei": 49647, "Ġdowntime": 49648, "ĠWebb": 49649, "ĠNCAA": 49650, "ĠEstoy": 49651, "олоÑĤ": 49652, "ĠìĤ¬ê±´": 49653, "Ġnationalist": 49654, "ĠKathryn": 49655, "ĠKop": 49656, "éª": 49657, "Sean": 49658, "ONA": 49659, "ĠBj": 49660, "×¢×Ŀ": 49661, "ÃŃb": 49662, "idamente": 49663, "Ġглаза": 49664, "Ġunnie": 49665, "Ġgemaakt": 49666, "ĠINTERVIEWER": 49667, "ĠHaut": 49668, "ίο": 49669, "geois": 49670, "wydd": 49671, "Ġколи": 49672, "Ġtightened": 49673, "Ġplanners": 49674, "Ġherum": 49675, "Ġgörün": 49676, "Ġelectronically": 49677, "Ġceram": 49678, "Ġëĭ¤ìĸijíķľ": 49679, "Ġepilepsy": 49680, "ĠeÄŁ": 49681, "lins": 49682, "ĠShiny": 49683, "æł¡": 49684, "ĠÑģолн": 49685, "Ġmacaron": 49686, "Ġimpacto": 49687, "ĠVegan": 49688, "zeÅĦ": 49689, "ĠRapha": 49690, "ĠPars": 49691, "ĠLEO": 49692, "ãģĬãģ£": 49693, "cü": 49694, "Ġ׾×Ķ×Ļ×ķת": 49695, "Ġähnlich": 49696, "Ġfloss": 49697, "ĠAZ": 49698, "Ġmöchten": 49699, "Ġgrooming": 49700, "Ġgrasses": 49701, "ranch": 49702, "Ġrecibir": 49703, "Ġbouncy": 49704, "ĠHobby": 49705, "Ġviktig": 49706, "Ġbegitu": 49707, "ĠPicasso": 49708, "ĠKush": 49709, "모": 49710, "Ġobstruction": 49711, "Ġë¶ĦìľĦ": 49712, "Ġmicrob": 49713, "ĠWestminster": 49714, "rops": 49715, "dul": 49716, "Ġdevo": 49717, "ĠLehrer": 49718, "ĠAdvisor": 49719, "ucken": 49720, "ĠбÑĥм": 49721, "Ġflattering": 49722, "ĠTruman": 49723, "ĠSempre": 49724, "ĠMcCain": 49725, "ĠHindus": 49726, "Julia": 49727, "Ġwatershed": 49728, "Ġlush": 49729, "ìłĦë": 49730, "Before": 49731, "ĠÐĴÑĤоÑĢ": 49732, "ĠSaaS": 49733, "Ġsitzt": 49734, "Ġbeetle": 49735, "ĠEssential": 49736, "enko": 49737, "ĠëķĮëıĦ": 49738, "Ġrevving": 49739, "Ġpoorer": 49740, "Ġcoerc": 49741, "Ġidee": 49742, "Ġcoû": 49743, "alet": 49744, "Ġzdrow": 49745, "Ġfender": 49746, "growth": 49747, "DING": 49748, "Ġzde": 49749, "ä¸ĬéĿ¢": 49750, "ENTS": 49751, "Ġfacets": 49752, "éļª": 49753, "ushima": 49754, "ĠÅŁeh": 49755, "Ġparasite": 49756, "Ġlapse": 49757, "ĠMeer": 49758, "ĠKund": 49759, "Ġslog": 49760, "Ġbrunch": 49761, "ĠChart": 49762, "arz": 49763, "ĠMUS": 49764, "Ġoffenses": 49765, "Ġinglés": 49766, "Ġfoliage": 49767, "oplan": 49768, "Aut": 49769, 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"Ġfamiliarity": 49828, "Ġдив": 49829, "Ġprojets": 49830, "Ġaqueles": 49831, "ĠGlue": 49832, "those": 49833, "Ġinception": 49834, "Ġaquellos": 49835, "Ġillusions": 49836, "Ġattends": 49837, "rese": 49838, "Ġswarm": 49839, "Ġswab": 49840, "Ġregardez": 49841, "Ġposição": 49842, "Ġakhir": 49843, "Ġextracting": 49844, "Ġanecdote": 49845, "ĠTale": 49846, "Ġвин": 49847, "Ġabges": 49848, "ĠoluÅŁ": 49849, "Ġcomplicado": 49850, "Ġcovari": 49851, "ÑĸÑĤÑĮ": 49852, "Der": 49853, "Ġ×Ļ×Ķ": 49854, "Form": 49855, "Ġìĸ´ì¨Įëĵł": 49856, "Ġreadable": 49857, "Ġinhibit": 49858, "Ġdecipher": 49859, "ĠAngry": 49860, "pg": 49861, "வத": 49862, "ĠÑģобÑģÑĤвенно": 49863, "Ġsamh": 49864, "Ġescr": 49865, "Ġencompasses": 49866, "Ġauster": 49867, "Ġconfisc": 49868, "ĠMandal": 49869, "Ġ}": 49870, "atcher": 49871, "=#": 49872, "çļĦæŶåĢĻ": 49873, "Ġкино": 49874, "Ġstal": 49875, "lungs": 49876, "Ġvole": 49877, "Ġrequis": 49878, "ĠãĤĪ": 49879, "Ġpén": 49880, "Ġlecturer": 49881, "Ġinscription": 49882, "Ġcervical": 49883, 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50228, "Ġmalfunction": 50229, "Ġasteroids": 50230, "irez": 50231, "amorph": 50232, "ĠÑģоÑĤÑĢÑĥд": 50233, "Ġfreshwater": 50234, "Ġarran": 50235, "ĠпÑĢÑĭ": 50236, "ног": 50237, "Ġdiabetic": 50238, "ĠÙĤاÙĦ": 50239, "Ġoppress": 50240, "Ġcapacitance": 50241, "performance": 50242, "crates": 50243, "Ġapostle": 50244, "ĠJEN": 50245, "OULD": 50246, "Intro": 50247, "Ġstalls": 50248, "ĠABOUT": 50249, "cticamente": 50250, "Ġdiligent": 50251, "Ġmanifests": 50252, "ĠPakistani": 50253, "Ġ('": 50254, "åľº": 50255, "": 50256}
\ No newline at end of file
diff --git a/musetalk/whisper/whisper/audio.py b/musetalk/whisper/whisper/audio.py
new file mode 100644
index 0000000..6a1986d
--- /dev/null
+++ b/musetalk/whisper/whisper/audio.py
@@ -0,0 +1,125 @@
+import os
+from functools import lru_cache
+from typing import Union
+
+import ffmpeg
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+from .utils import exact_div
+
+# hard-coded audio hyperparameters
+SAMPLE_RATE = 16000
+N_FFT = 400
+N_MELS = 80
+HOP_LENGTH = 160
+CHUNK_LENGTH = 30
+N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE  # 480000: number of samples in a chunk
+N_FRAMES = exact_div(N_SAMPLES, HOP_LENGTH)  # 3000: number of frames in a mel spectrogram input
+
+
+def load_audio(file: str, sr: int = SAMPLE_RATE):
+    """
+    Open an audio file and read as mono waveform, resampling as necessary
+
+    Parameters
+    ----------
+    file: str
+        The audio file to open
+
+    sr: int
+        The sample rate to resample the audio if necessary
+
+    Returns
+    -------
+    A NumPy array containing the audio waveform, in float32 dtype.
+    """
+    try:
+        # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
+        # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
+        out, _ = (
+            ffmpeg.input(file, threads=0)
+            .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sr)
+            .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
+        )
+    except ffmpeg.Error as e:
+        raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
+
+    return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
+
+
+def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1):
+    """
+    Pad or trim the audio array to N_SAMPLES, as expected by the encoder.
+    """
+    if torch.is_tensor(array):
+        if array.shape[axis] > length:
+            array = array.index_select(dim=axis, index=torch.arange(length))
+
+        if array.shape[axis] < length:
+            pad_widths = [(0, 0)] * array.ndim
+            pad_widths[axis] = (0, length - array.shape[axis])
+            array = F.pad(array, [pad for sizes in pad_widths[::-1] for pad in sizes])
+    else:
+        if array.shape[axis] > length:
+            array = array.take(indices=range(length), axis=axis)
+
+        if array.shape[axis] < length:
+            pad_widths = [(0, 0)] * array.ndim
+            pad_widths[axis] = (0, length - array.shape[axis])
+            array = np.pad(array, pad_widths)
+
+    return array
+
+
+@lru_cache(maxsize=None)
+def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor:
+    """
+    load the mel filterbank matrix for projecting STFT into a Mel spectrogram.
+    Allows decoupling librosa dependency; saved using:
+
+        np.savez_compressed(
+            "mel_filters.npz",
+            mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80),
+        )
+    """
+    assert n_mels == 80, f"Unsupported n_mels: {n_mels}"
+    with np.load(os.path.join(os.path.dirname(__file__), "assets", "mel_filters.npz")) as f:
+        return torch.from_numpy(f[f"mel_{n_mels}"]).to(device)
+
+
+def log_mel_spectrogram(audio: Union[str, np.ndarray, torch.Tensor], n_mels: int = N_MELS):
+    """
+    Compute the log-Mel spectrogram of
+
+    Parameters
+    ----------
+    audio: Union[str, np.ndarray, torch.Tensor], shape = (*)
+        The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz
+
+    n_mels: int
+        The number of Mel-frequency filters, only 80 is supported
+
+    Returns
+    -------
+    torch.Tensor, shape = (80, n_frames)
+        A Tensor that contains the Mel spectrogram
+    """
+    if not torch.is_tensor(audio):
+        if isinstance(audio, str):
+            audio = load_audio(audio)
+        audio = torch.from_numpy(audio)
+        
+    window = torch.hann_window(N_FFT).to(audio.device)
+    stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True)
+    
+    magnitudes = stft[:, :-1].abs() ** 2
+
+    filters = mel_filters(audio.device, n_mels)
+    mel_spec = filters @ magnitudes
+
+    log_spec = torch.clamp(mel_spec, min=1e-10).log10()
+    log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
+    log_spec = (log_spec + 4.0) / 4.0
+    return log_spec
diff --git a/musetalk/whisper/whisper/decoding.py b/musetalk/whisper/whisper/decoding.py
new file mode 100644
index 0000000..1835054
--- /dev/null
+++ b/musetalk/whisper/whisper/decoding.py
@@ -0,0 +1,729 @@
+from dataclasses import dataclass, field
+from typing import Dict, List, Tuple, Iterable, Optional, Sequence, Union, TYPE_CHECKING
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import Tensor
+from torch.distributions import Categorical
+
+from .audio import CHUNK_LENGTH
+from .tokenizer import Tokenizer, get_tokenizer
+from .utils import compression_ratio
+
+if TYPE_CHECKING:
+    from .model import Whisper
+
+
+@torch.no_grad()
+def detect_language(model: "Whisper", mel: Tensor, tokenizer: Tokenizer = None) -> Tuple[Tensor, List[dict]]:
+    """
+    Detect the spoken language in the audio, and return them as list of strings, along with the ids
+    of the most probable language tokens and the probability distribution over all language tokens.
+    This is performed outside the main decode loop in order to not interfere with kv-caching.
+
+    Returns
+    -------
+    language_tokens : Tensor, shape = (n_audio,)
+        ids of the most probable language tokens, which appears after the startoftranscript token.
+    language_probs : List[Dict[str, float]], length = n_audio
+        list of dictionaries containing the probability distribution over all languages.
+    """
+    if tokenizer is None:
+        tokenizer = get_tokenizer(model.is_multilingual)
+    if tokenizer.language is None or tokenizer.language_token not in tokenizer.sot_sequence:
+        raise ValueError(f"This model doesn't have language tokens so it can't perform lang id")
+
+    single = mel.ndim == 2
+    if single:
+        mel = mel.unsqueeze(0)
+
+    # skip encoder forward pass if already-encoded audio features were given
+    if mel.shape[-2:] != (model.dims.n_audio_ctx, model.dims.n_audio_state):
+        mel = model.encoder(mel)
+
+    # forward pass using a single token, startoftranscript
+    n_audio = mel.shape[0]
+    x = torch.tensor([[tokenizer.sot]] * n_audio).to(mel.device)  # [n_audio, 1]
+    logits = model.logits(x, mel)[:, 0]
+
+    # collect detected languages; suppress all non-language tokens
+    mask = torch.ones(logits.shape[-1], dtype=torch.bool)
+    mask[list(tokenizer.all_language_tokens)] = False
+    logits[:, mask] = -np.inf
+    language_tokens = logits.argmax(dim=-1)
+    language_token_probs = logits.softmax(dim=-1).cpu()
+    language_probs = [
+        {
+            c: language_token_probs[i, j].item()
+            for j, c in zip(tokenizer.all_language_tokens, tokenizer.all_language_codes)
+        }
+        for i in range(n_audio)
+    ]
+
+    if single:
+        language_tokens = language_tokens[0]
+        language_probs = language_probs[0]
+
+    return language_tokens, language_probs
+
+
+@dataclass(frozen=True)
+class DecodingOptions:
+    task: str = "transcribe"  # whether to perform X->X "transcribe" or X->English "translate"
+    language: Optional[str] = None  # language that the audio is in; uses detected language if None
+
+    # sampling-related options
+    temperature: float = 0.0
+    sample_len: Optional[int] = None  # maximum number of tokens to sample
+    best_of: Optional[int] = None     # number of independent samples to collect, when t > 0
+    beam_size: Optional[int] = None   # number of beams in beam search, when t == 0
+    patience: Optional[float] = None  # patience in beam search (https://arxiv.org/abs/2204.05424)
+
+    # options for ranking generations (either beams or best-of-N samples)
+    length_penalty: Optional[float] = None   # "alpha" in Google NMT, None defaults to length norm
+
+    # prompt, prefix, and token suppression
+    prompt: Optional[Union[str, List[int]]] = None   # text or tokens for the previous context
+    prefix: Optional[Union[str, List[int]]] = None   # text or tokens to prefix the current context
+    suppress_blank: bool = True                      # this will suppress blank outputs
+
+    # list of tokens ids (or comma-separated token ids) to suppress
+    # "-1" will suppress a set of symbols as defined in `tokenizer.non_speech_tokens()`
+    suppress_tokens: Optional[Union[str, Iterable[int]]] = "-1"
+
+    # timestamp sampling options
+    without_timestamps: bool = False              # use <|notimestamps|> to sample text tokens only
+    max_initial_timestamp: Optional[float] = 1.0  # the initial timestamp cannot be later than this
+
+    # implementation details
+    fp16: bool = True  # use fp16 for most of the calculation
+
+
+@dataclass(frozen=True)
+class DecodingResult:
+    audio_features: Tensor
+    language: str
+    encoder_embeddings: np.ndarray
+    decoder_embeddings: np.ndarray
+    language_probs: Optional[Dict[str, float]] = None
+    tokens: List[int] = field(default_factory=list)
+    text: str = ""
+    avg_logprob: float = np.nan
+    no_speech_prob: float = np.nan
+    temperature: float = np.nan
+    compression_ratio: float = np.nan
+
+
+class Inference:
+    def logits(self, tokens: Tensor, audio_features: Tensor) -> Tensor:
+        """Perform a forward pass on the decoder and return per-token logits"""
+        raise NotImplementedError
+
+    def rearrange_kv_cache(self, source_indices) -> None:
+        """Update the key-value cache according to the updated beams"""
+        raise NotImplementedError
+
+    def cleanup_caching(self) -> None:
+        """Clean up any resources or hooks after decoding is finished"""
+        pass
+
+
+class PyTorchInference(Inference):
+    def __init__(self, model: "Whisper", initial_token_length: int):
+        self.model: "Whisper" = model
+        self.initial_token_length = initial_token_length
+        self.kv_cache = {}
+        self.hooks = []
+
+    def logits(self, tokens: Tensor, audio_features: Tensor, include_embeddings=False) -> Tensor:
+        if not self.kv_cache:
+            self.kv_cache, self.hooks = self.model.install_kv_cache_hooks()
+
+        if tokens.shape[-1] > self.initial_token_length:
+            # only need to use the last token except in the first forward pass
+            tokens = tokens[:, -1:]
+
+        return_val = self.model.decoder(tokens, audio_features,
+                                        kv_cache=self.kv_cache, include_embeddings=include_embeddings)
+        return return_val
+
+    def cleanup_caching(self):
+        for hook in self.hooks:
+            hook.remove()
+
+        self.kv_cache = {}
+        self.hooks = []
+
+    def rearrange_kv_cache(self, source_indices):
+        for module, tensor in self.kv_cache.items():
+            # update the key/value cache to contain the selected sequences
+            self.kv_cache[module] = tensor[source_indices].detach()
+
+
+class SequenceRanker:
+    def rank(self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]]) -> List[int]:
+        """
+        Given a list of groups of samples and their cumulative log probabilities,
+        return the indices of the samples in each group to select as the final result
+        """
+        raise NotImplementedError
+
+
+class MaximumLikelihoodRanker(SequenceRanker):
+    """
+    Select the sample with the highest log probabilities, penalized using either
+    a simple length normalization or Google NMT paper's length penalty
+    """
+
+    def __init__(self, length_penalty: Optional[float]):
+        self.length_penalty = length_penalty
+
+    def rank(self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]]):
+        def scores(logprobs, lengths):
+            result = []
+            for logprob, length in zip(logprobs, lengths):
+                if self.length_penalty is None:
+                    penalty = length
+                else:
+                    # from the Google NMT paper
+                    penalty = ((5 + length) / 6) ** self.length_penalty
+                result.append(logprob / penalty)
+            return result
+
+        # get the sequence with the highest score
+        lengths = [[len(t) for t in s] for s in tokens]
+        return [np.argmax(scores(p, l)) for p, l in zip(sum_logprobs, lengths)]
+
+
+class TokenDecoder:
+    def reset(self):
+        """Initialize any stateful variables for decoding a new sequence"""
+
+    def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]:
+        """Specify how to select the next token, based on the current trace and logits
+
+        Parameters
+        ----------
+        tokens : Tensor, shape = (n_batch, current_sequence_length)
+            all tokens in the context so far, including the prefix and sot_sequence tokens
+
+        logits : Tensor, shape = (n_batch, vocab_size)
+            per-token logits of the probability distribution at the current step
+
+        sum_logprobs : Tensor, shape = (n_batch)
+            cumulative log probabilities for each sequence
+
+        Returns
+        -------
+        tokens : Tensor, shape = (n_batch, current_sequence_length + 1)
+            the tokens, appended with the selected next token
+
+        completed : bool
+            True if all sequences has reached the end of text
+
+        """
+        raise NotImplementedError
+
+    def finalize(
+        self, tokens: Tensor, sum_logprobs: Tensor
+    ) -> Tuple[Sequence[Sequence[Tensor]], List[List[float]]]:
+        """Finalize search and return the final candidate sequences
+
+        Parameters
+        ----------
+        tokens : Tensor, shape = (n_audio, n_group, current_sequence_length)
+            all tokens in the context so far, including the prefix and sot_sequence
+
+        sum_logprobs : Tensor, shape = (n_audio, n_group)
+            cumulative log probabilities for each sequence
+
+        Returns
+        -------
+        tokens : Sequence[Sequence[Tensor]], length = n_audio
+            sequence of Tensors containing candidate token sequences, for each audio input
+
+        sum_logprobs : List[List[float]], length = n_audio
+            sequence of cumulative log probabilities corresponding to the above
+
+        """
+        raise NotImplementedError
+
+
+class GreedyDecoder(TokenDecoder):
+    def __init__(self, temperature: float, eot: int):
+        self.temperature = temperature
+        self.eot = eot
+
+    def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]:
+        temperature = self.temperature
+        if temperature == 0:
+            next_tokens = logits.argmax(dim=-1)
+        else:
+            next_tokens = Categorical(logits=logits / temperature).sample()
+
+        logprobs = F.log_softmax(logits.float(), dim=-1)
+        current_logprobs = logprobs[torch.arange(logprobs.shape[0]), next_tokens]
+        sum_logprobs += current_logprobs * (tokens[:, -1] != self.eot)
+
+        next_tokens[tokens[:, -1] == self.eot] = self.eot
+        tokens = torch.cat([tokens, next_tokens[:, None]], dim=-1)
+
+        completed = (tokens[:, -1] == self.eot).all()
+        return tokens, completed
+
+    def finalize(self, tokens: Tensor, sum_logprobs: Tensor):
+        # make sure each sequence has at least one EOT token at the end
+        tokens = F.pad(tokens, (0, 1), value=self.eot)
+        return tokens, sum_logprobs.tolist()
+
+
+class BeamSearchDecoder(TokenDecoder):
+    def __init__(self, beam_size: int, eot: int, inference: Inference, patience: Optional[float] = None):
+        self.beam_size = beam_size
+        self.eot = eot
+        self.inference = inference
+        self.patience = patience or 1.0
+        self.max_candidates: int = round(beam_size * self.patience)
+        self.finished_sequences = None
+
+        assert self.max_candidates > 0, f"Invalid beam size ({beam_size}) or patience ({patience})"
+
+    def reset(self):
+        self.finished_sequences = None
+
+    def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]:
+        if tokens.shape[0] % self.beam_size != 0:
+            raise ValueError(f"{tokens.shape}[0] % {self.beam_size} != 0")
+
+        n_audio = tokens.shape[0] // self.beam_size
+        if self.finished_sequences is None:  # for the first update
+            self.finished_sequences = [{} for _ in range(n_audio)]
+
+        logprobs = F.log_softmax(logits.float(), dim=-1)
+        next_tokens, source_indices, finished_sequences = [], [], []
+        for i in range(n_audio):
+            scores, sources, finished = {}, {}, {}
+
+            # STEP 1: calculate the cumulative log probabilities for possible candidates
+            for j in range(self.beam_size):
+                idx = i * self.beam_size + j
+                prefix = tokens[idx].tolist()
+                for logprob, token in zip(*logprobs[idx].topk(self.beam_size + 1)):
+                    new_logprob = (sum_logprobs[idx] + logprob).item()
+                    sequence = tuple(prefix + [token.item()])
+                    scores[sequence] = new_logprob
+                    sources[sequence] = idx
+
+            # STEP 2: rank the candidates and keep the top beam_size sequences for each audio
+            saved = 0
+            for sequence in sorted(scores, key=scores.get, reverse=True):
+                if sequence[-1] == self.eot:
+                    finished[sequence] = scores[sequence]
+                else:
+                    sum_logprobs[len(next_tokens)] = scores[sequence]
+                    next_tokens.append(sequence)
+                    source_indices.append(sources[sequence])
+
+                    saved += 1
+                    if saved == self.beam_size:
+                        break
+
+            finished_sequences.append(finished)
+
+        tokens = torch.tensor(next_tokens, device=tokens.device)
+        self.inference.rearrange_kv_cache(source_indices)
+
+        # add newly finished sequences to self.finished_sequences
+        assert len(self.finished_sequences) == len(finished_sequences)
+        for previously_finished, newly_finished in zip(self.finished_sequences, finished_sequences):
+            for seq in sorted(newly_finished, key=newly_finished.get, reverse=True):
+                if len(previously_finished) >= self.max_candidates:
+                    break  # the candidate list is full
+                previously_finished[seq] = newly_finished[seq]
+
+        # mark as completed if all audio has enough number of samples
+        completed = all(
+            len(sequences) >= self.max_candidates for sequences in self.finished_sequences
+        )
+        return tokens, completed
+
+    def finalize(self, preceding_tokens: Tensor, sum_logprobs: Tensor):
+        # collect all finished sequences, including patience, and add unfinished ones if not enough
+        sum_logprobs = sum_logprobs.cpu()
+        for i, sequences in enumerate(self.finished_sequences):
+            if len(sequences) < self.beam_size:  # when not enough sequences are finished
+                for j in list(np.argsort(sum_logprobs[i]))[::-1]:
+                    sequence = preceding_tokens[i, j].tolist() + [self.eot]
+                    sequences[tuple(sequence)] = sum_logprobs[i][j].item()
+                    if len(sequences) >= self.beam_size:
+                        break
+
+        tokens: List[List[Tensor]] = [
+            [torch.tensor(seq) for seq in sequences.keys()] for sequences in self.finished_sequences
+        ]
+        sum_logprobs: List[List[float]] = [
+            list(sequences.values()) for sequences in self.finished_sequences
+        ]
+        return tokens, sum_logprobs
+
+
+class LogitFilter:
+    def apply(self, logits: Tensor, tokens: Tensor) -> None:
+        """Apply any filtering or masking to logits in-place
+
+        Parameters
+        ----------
+        logits : Tensor, shape = (n_batch, vocab_size)
+            per-token logits of the probability distribution at the current step
+
+        tokens : Tensor, shape = (n_batch, current_sequence_length)
+            all tokens in the context so far, including the prefix and sot_sequence tokens
+
+        """
+        raise NotImplementedError
+
+
+class SuppressBlank(LogitFilter):
+    def __init__(self, tokenizer: Tokenizer, sample_begin: int):
+        self.tokenizer = tokenizer
+        self.sample_begin = sample_begin
+
+    def apply(self, logits: Tensor, tokens: Tensor):
+        if tokens.shape[1] == self.sample_begin:
+            logits[:, self.tokenizer.encode(" ") + [self.tokenizer.eot]] = -np.inf
+
+
+class SuppressTokens(LogitFilter):
+    def __init__(self, suppress_tokens: Sequence[int]):
+        self.suppress_tokens = list(suppress_tokens)
+
+    def apply(self, logits: Tensor, tokens: Tensor):
+        logits[:, self.suppress_tokens] = -np.inf
+
+
+class ApplyTimestampRules(LogitFilter):
+    def __init__(
+        self, tokenizer: Tokenizer, sample_begin: int, max_initial_timestamp_index: Optional[int]
+    ):
+        self.tokenizer = tokenizer
+        self.sample_begin = sample_begin
+        self.max_initial_timestamp_index = max_initial_timestamp_index
+
+    def apply(self, logits: Tensor, tokens: Tensor):
+        # suppress <|notimestamps|> which is handled by without_timestamps
+        if self.tokenizer.no_timestamps is not None:
+            logits[:, self.tokenizer.no_timestamps] = -np.inf
+
+        # timestamps have to appear in pairs, except directly before EOT; mask logits accordingly
+        for k in range(tokens.shape[0]):
+            seq = [t for t in tokens[k, self.sample_begin :].tolist()]
+            last_was_timestamp = len(seq) >= 1 and seq[-1] >= self.tokenizer.timestamp_begin
+            penultimate_was_timestamp = len(seq) < 2 or seq[-2] >= self.tokenizer.timestamp_begin
+
+            if last_was_timestamp:
+                if penultimate_was_timestamp:  # has to be non-timestamp
+                    logits[k, self.tokenizer.timestamp_begin :] = -np.inf
+                else:  # cannot be normal text tokens
+                    logits[k, : self.tokenizer.eot] = -np.inf
+
+        # apply the `max_initial_timestamp` option
+        if tokens.shape[1] == self.sample_begin and self.max_initial_timestamp_index is not None:
+            last_allowed = self.tokenizer.timestamp_begin + self.max_initial_timestamp_index
+            logits[:, last_allowed + 1 :] = -np.inf
+
+        # if sum of probability over timestamps is above any other token, sample timestamp
+        logprobs = F.log_softmax(logits.float(), dim=-1)
+        for k in range(tokens.shape[0]):
+            timestamp_logprob = logprobs[k, self.tokenizer.timestamp_begin :].logsumexp(dim=-1)
+            max_text_token_logprob = logprobs[k, : self.tokenizer.timestamp_begin].max()
+            if timestamp_logprob > max_text_token_logprob:
+                logits[k, : self.tokenizer.timestamp_begin] = -np.inf
+
+
+class DecodingTask:
+    inference: Inference
+    sequence_ranker: SequenceRanker
+    decoder: TokenDecoder
+    logit_filters: List[LogitFilter]
+
+    def __init__(self, model: "Whisper", options: DecodingOptions):
+        self.model = model
+
+        language = options.language or "en"
+        tokenizer = get_tokenizer(model.is_multilingual, language=language, task=options.task)
+        self.tokenizer: Tokenizer = tokenizer
+        self.options: DecodingOptions = self._verify_options(options)
+
+        self.n_group: int = options.beam_size or options.best_of or 1
+        self.n_ctx: int = model.dims.n_text_ctx
+        self.sample_len: int = options.sample_len or model.dims.n_text_ctx // 2
+
+        self.sot_sequence: Tuple[int] = tokenizer.sot_sequence
+        if self.options.without_timestamps:
+            self.sot_sequence = tokenizer.sot_sequence_including_notimestamps
+
+        self.initial_tokens: Tuple[int] = self._get_initial_tokens()
+        self.sample_begin: int = len(self.initial_tokens)
+        self.sot_index: int = self.initial_tokens.index(tokenizer.sot)
+
+        # inference: implements the forward pass through the decoder, including kv caching
+        self.inference = PyTorchInference(model, len(self.initial_tokens))
+
+        # sequence ranker: implements how to rank a group of sampled sequences
+        self.sequence_ranker = MaximumLikelihoodRanker(options.length_penalty)
+
+        # decoder: implements how to select the next tokens, given the autoregressive distribution
+        if options.beam_size is not None:
+            self.decoder = BeamSearchDecoder(
+                options.beam_size, tokenizer.eot, self.inference, options.patience
+            )
+        else:
+            self.decoder = GreedyDecoder(options.temperature, tokenizer.eot)
+
+        # logit filters: applies various rules to suppress or penalize certain tokens
+        self.logit_filters = []
+        if self.options.suppress_blank:
+            self.logit_filters.append(SuppressBlank(self.tokenizer, self.sample_begin))
+        if self.options.suppress_tokens:
+            self.logit_filters.append(SuppressTokens(self._get_suppress_tokens()))
+        if not options.without_timestamps:
+            precision = CHUNK_LENGTH / model.dims.n_audio_ctx  # usually 0.02 seconds
+            max_initial_timestamp_index = None
+            if options.max_initial_timestamp:
+                max_initial_timestamp_index = round(self.options.max_initial_timestamp / precision)
+            self.logit_filters.append(
+                ApplyTimestampRules(tokenizer, self.sample_begin, max_initial_timestamp_index)
+            )
+
+    def _verify_options(self, options: DecodingOptions) -> DecodingOptions:
+        if options.beam_size is not None and options.best_of is not None:
+            raise ValueError("beam_size and best_of can't be given together")
+        if options.temperature == 0:
+            if options.best_of is not None:
+                raise ValueError("best_of with greedy sampling (T=0) is not compatible")
+        if options.patience is not None and options.beam_size is None:
+            raise ValueError("patience requires beam_size to be given")
+        if options.length_penalty is not None and not (0 <= options.length_penalty <= 1):
+            raise ValueError("length_penalty (alpha) should be a value between 0 and 1")
+
+        return options
+
+    def _get_initial_tokens(self) -> Tuple[int]:
+        tokens = list(self.sot_sequence)
+        prefix = self.options.prefix
+        prompt = self.options.prompt
+
+        if prefix:
+            prefix_tokens = (
+                self.tokenizer.encode(" " + prefix.strip()) if isinstance(prefix, str) else prefix
+            )
+            if self.sample_len is not None:
+                max_prefix_len = self.n_ctx // 2 - self.sample_len
+                prefix_tokens = prefix_tokens[-max_prefix_len:]
+            tokens = tokens + prefix_tokens
+
+        if prompt:
+            prompt_tokens = (
+                self.tokenizer.encode(" " + prompt.strip()) if isinstance(prompt, str) else prompt
+            )
+            tokens = [self.tokenizer.sot_prev] + prompt_tokens[-(self.n_ctx // 2 - 1) :] + tokens
+
+        return tuple(tokens)
+
+    def _get_suppress_tokens(self) -> Tuple[int]:
+        suppress_tokens = self.options.suppress_tokens
+
+        if isinstance(suppress_tokens, str):
+            suppress_tokens = [int(t) for t in suppress_tokens.split(",")]
+
+        if -1 in suppress_tokens:
+            suppress_tokens = [t for t in suppress_tokens if t >= 0]
+            suppress_tokens.extend(self.tokenizer.non_speech_tokens)
+        elif suppress_tokens is None or len(suppress_tokens) == 0:
+            suppress_tokens = []  # interpret empty string as an empty list
+        else:
+            assert isinstance(suppress_tokens, list), "suppress_tokens must be a list"
+
+        suppress_tokens.extend(
+            [self.tokenizer.sot, self.tokenizer.sot_prev, self.tokenizer.sot_lm]
+        )
+        if self.tokenizer.no_speech is not None:
+            # no-speech probability is collected separately
+            suppress_tokens.append(self.tokenizer.no_speech)
+
+        return tuple(sorted(set(suppress_tokens)))
+
+    def _get_audio_features(self, mel: Tensor, include_embeddings: bool = False):
+        if self.options.fp16:
+            mel = mel.half()
+
+        if mel.shape[-2:] == (self.model.dims.n_audio_ctx, self.model.dims.n_audio_state):
+            # encoded audio features are given; skip audio encoding
+            audio_features = mel
+        else:
+            result = self.model.encoder(mel, include_embeddings)
+            if include_embeddings:
+                audio_features, embeddings = result
+            else:
+                audio_features = result
+
+        if audio_features.dtype != (torch.float16 if self.options.fp16 else torch.float32):
+            return TypeError(f"audio_features has an incorrect dtype: {audio_features.dtype}")
+
+        if include_embeddings:
+            return audio_features, embeddings
+        else:
+            return audio_features
+
+    def _detect_language(self, audio_features: Tensor, tokens: Tensor):
+        languages = [self.options.language] * audio_features.shape[0]
+        lang_probs = None
+
+        if self.options.language is None or self.options.task == "lang_id":
+            lang_tokens, lang_probs = self.model.detect_language(audio_features, self.tokenizer)
+            languages = [max(probs, key=probs.get) for probs in lang_probs]
+            if self.options.language is None:
+                tokens[:, self.sot_index + 1] = lang_tokens  # write language tokens
+
+        return languages, lang_probs
+
+    def _main_loop(self, audio_features: Tensor, tokens: Tensor):
+        assert audio_features.shape[0] == tokens.shape[0]
+        n_batch = tokens.shape[0]
+        sum_logprobs: Tensor = torch.zeros(n_batch, device=audio_features.device)
+        no_speech_probs = [np.nan] * n_batch
+
+        try:
+            embeddings = []
+            for i in range(self.sample_len):
+                logits, token_embeddings = self.inference.logits(tokens, audio_features, include_embeddings=True)
+
+                if i == 0 and self.tokenizer.no_speech is not None:  # save no_speech_probs
+                    probs_at_sot = logits[:, self.sot_index].float().softmax(dim=-1)
+                    no_speech_probs = probs_at_sot[:, self.tokenizer.no_speech].tolist()
+
+                # now we need to consider the logits at the last token only
+                logits = logits[:, -1]
+                token_embeddings = token_embeddings[:, :, -1]
+
+                # Append embeddings together
+                embeddings.append(token_embeddings)
+
+                # apply the logit filters, e.g. for suppressing or applying penalty to
+                for logit_filter in self.logit_filters:
+                    logit_filter.apply(logits, tokens)
+
+                # expand the tokens tensor with the selected next tokens
+                tokens, completed = self.decoder.update(tokens, logits, sum_logprobs)
+
+                if completed or tokens.shape[-1] > self.n_ctx:
+                    break
+        finally:
+            if completed:
+                embeddings = embeddings[:-1]
+            embeddings = np.stack(embeddings, 2)
+            self.inference.cleanup_caching()
+
+        return tokens, sum_logprobs, no_speech_probs, embeddings
+
+    @torch.no_grad()
+    def run(self, mel: Tensor) -> List[DecodingResult]:
+        self.decoder.reset()
+        tokenizer: Tokenizer = self.tokenizer
+        n_audio: int = mel.shape[0]
+
+        # encoder forward pass
+        forward_pass: Tuple[Tensor, np.ndarray] = self._get_audio_features(mel, include_embeddings=True)
+        audio_features, encoder_embeddings = forward_pass
+        tokens: Tensor = torch.tensor([self.initial_tokens]).repeat(n_audio, 1)
+
+        # detect language if requested, overwriting the language token
+        languages, language_probs = self._detect_language(audio_features, tokens)
+        if self.options.task == "lang_id":
+            return [
+                DecodingResult(audio_features=features, language=language, language_probs=probs)
+                for features, language, probs in zip(audio_features, languages, language_probs)
+            ]
+
+        # repeat the audio & text tensors by the group size, for beam search or best-of-n sampling
+        audio_features = audio_features.repeat_interleave(self.n_group, dim=0)
+        tokens = tokens.repeat_interleave(self.n_group, dim=0).to(audio_features.device)
+
+        # call the main sampling loop
+        tokens, sum_logprobs, no_speech_probs, decoder_embeddings = self._main_loop(audio_features, tokens)
+
+        # reshape the tensors to have (n_audio, n_group) as the first two dimensions
+        audio_features = audio_features[:: self.n_group]
+        no_speech_probs = no_speech_probs[:: self.n_group]
+        assert audio_features.shape[0] == len(no_speech_probs) == n_audio
+
+        tokens = tokens.reshape(n_audio, self.n_group, -1)
+        sum_logprobs = sum_logprobs.reshape(n_audio, self.n_group)
+
+        # get the final candidates for each group, and slice between the first sampled token and EOT
+        tokens, sum_logprobs = self.decoder.finalize(tokens, sum_logprobs)
+        tokens: List[List[Tensor]] = [
+            [t[self.sample_begin : (t == tokenizer.eot).nonzero()[0, 0]] for t in s] for s in tokens
+        ]
+
+        # select the top-ranked sample in each group
+        selected = self.sequence_ranker.rank(tokens, sum_logprobs)
+        tokens: List[List[int]] = [t[i].tolist() for i, t in zip(selected, tokens)]
+        texts: List[str] = [tokenizer.decode(t).strip() for t in tokens]
+
+        sum_logprobs: List[float] = [lp[i] for i, lp in zip(selected, sum_logprobs)]
+        avg_logprobs: List[float] = [lp / (len(t) + 1) for t, lp in zip(tokens, sum_logprobs)]
+
+        fields = (texts, languages, tokens, audio_features, avg_logprobs, no_speech_probs)
+        if len(set(map(len, fields))) != 1:
+            raise RuntimeError(f"inconsistent result lengths: {list(map(len, fields))}")
+
+        return [
+            DecodingResult(
+                audio_features=features,
+                language=language,
+                tokens=tokens,
+                text=text,
+                avg_logprob=avg_logprob,
+                no_speech_prob=no_speech_prob,
+                temperature=self.options.temperature,
+                compression_ratio=compression_ratio(text),
+                encoder_embeddings=encoder_embeddings,
+                decoder_embeddings=decoder_embeddings
+            )
+            for text, language, tokens, features, avg_logprob, no_speech_prob in zip(*fields)
+        ]
+
+
+@torch.no_grad()
+def decode(model: "Whisper", mel: Tensor, options: DecodingOptions = DecodingOptions()) -> Union[DecodingResult, List[DecodingResult]]:
+    """
+    Performs decoding of 30-second audio segment(s), provided as Mel spectrogram(s).
+
+    Parameters
+    ----------
+    model: Whisper
+        the Whisper model instance
+
+    mel: torch.Tensor, shape = (80, 3000) or (*, 80, 3000)
+        A tensor containing the Mel spectrogram(s)
+
+    options: DecodingOptions
+        A dataclass that contains all necessary options for decoding 30-second segments
+
+    Returns
+    -------
+    result: Union[DecodingResult, List[DecodingResult]]
+        The result(s) of decoding contained in `DecodingResult` dataclass instance(s)
+    """
+    single = mel.ndim == 2
+    if single:
+        mel = mel.unsqueeze(0)
+
+    result = DecodingTask(model, options).run(mel)
+    
+    if single:
+        result = result[0]
+
+    return result
diff --git a/musetalk/whisper/whisper/model.py b/musetalk/whisper/whisper/model.py
new file mode 100644
index 0000000..acb6a0b
--- /dev/null
+++ b/musetalk/whisper/whisper/model.py
@@ -0,0 +1,290 @@
+from dataclasses import dataclass
+from typing import Dict
+from typing import Iterable, Optional
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import Tensor
+from torch import nn
+
+from .transcribe import transcribe as transcribe_function
+from .decoding import detect_language as detect_language_function, decode as decode_function
+
+
+@dataclass
+class ModelDimensions:
+    n_mels: int
+    n_audio_ctx: int
+    n_audio_state: int
+    n_audio_head: int
+    n_audio_layer: int
+    n_vocab: int
+    n_text_ctx: int
+    n_text_state: int
+    n_text_head: int
+    n_text_layer: int
+
+
+class LayerNorm(nn.LayerNorm):
+    def forward(self, x: Tensor) -> Tensor:
+        return super().forward(x.float()).type(x.dtype)
+
+
+class Linear(nn.Linear):
+    def forward(self, x: Tensor) -> Tensor:
+        return F.linear(
+            x, self.weight.to(x.dtype), None if self.bias is None else self.bias.to(x.dtype)
+        )
+
+
+class Conv1d(nn.Conv1d):
+    def _conv_forward(self, x: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor:
+        return super()._conv_forward(
+            x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype)
+        )
+
+
+def sinusoids(length, channels, max_timescale=10000):
+    """Returns sinusoids for positional embedding"""
+    assert channels % 2 == 0
+    log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
+    inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2))
+    scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
+    return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1)
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(self, n_state: int, n_head: int):
+        super().__init__()
+        self.n_head = n_head
+        self.query = Linear(n_state, n_state)
+        self.key = Linear(n_state, n_state, bias=False)
+        self.value = Linear(n_state, n_state)
+        self.out = Linear(n_state, n_state)
+
+    def forward(
+        self,
+        x: Tensor,
+        xa: Optional[Tensor] = None,
+        mask: Optional[Tensor] = None,
+        kv_cache: Optional[dict] = None,
+    ):
+        q = self.query(x)
+
+        if kv_cache is None or xa is None:
+            # hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors;
+            # otherwise, perform key/value projections for self- or cross-attention as usual.
+            k = self.key(x if xa is None else xa)
+            v = self.value(x if xa is None else xa)
+        else:
+            # for cross-attention, calculate keys and values once and reuse in subsequent calls.
+            k = kv_cache.get(self.key, self.key(xa))
+            v = kv_cache.get(self.value, self.value(xa))
+
+        wv = self.qkv_attention(q, k, v, mask)
+        return self.out(wv)
+
+    def qkv_attention(self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None):
+        n_batch, n_ctx, n_state = q.shape
+        scale = (n_state // self.n_head) ** -0.25
+        q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale
+        k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale
+        v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
+
+        qk = q @ k
+        if mask is not None:
+            qk = qk + mask[:n_ctx, :n_ctx]
+
+        w = F.softmax(qk.float(), dim=-1).to(q.dtype)
+        return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2)
+
+
+class ResidualAttentionBlock(nn.Module):
+    def __init__(self, n_state: int, n_head: int, cross_attention: bool = False):
+        super().__init__()
+
+        self.attn = MultiHeadAttention(n_state, n_head)
+        self.attn_ln = LayerNorm(n_state)
+
+        self.cross_attn = MultiHeadAttention(n_state, n_head) if cross_attention else None
+        self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None
+
+        n_mlp = n_state * 4
+        self.mlp = nn.Sequential(Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state))
+        self.mlp_ln = LayerNorm(n_state)
+
+    def forward(
+        self,
+        x: Tensor,
+        xa: Optional[Tensor] = None,
+        mask: Optional[Tensor] = None,
+        kv_cache: Optional[dict] = None,
+    ):
+        x = x + self.attn(self.attn_ln(x), mask=mask, kv_cache=kv_cache)
+        if self.cross_attn:
+            x = x + self.cross_attn(self.cross_attn_ln(x), xa, kv_cache=kv_cache)
+        x = x + self.mlp(self.mlp_ln(x))
+        return x
+
+
+class AudioEncoder(nn.Module):
+    def __init__(self, n_mels: int, n_ctx: int, n_state: int, n_head: int, n_layer: int):
+        super().__init__()
+        self.conv1 = Conv1d(n_mels, n_state, kernel_size=3, padding=1)
+        self.conv2 = Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1)
+        self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state))
+
+        self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList(
+            [ResidualAttentionBlock(n_state, n_head) for _ in range(n_layer)]
+        )
+        self.ln_post = LayerNorm(n_state)
+
+    def forward(self, x: Tensor, include_embeddings: bool = False):
+        """
+        x : torch.Tensor, shape = (batch_size, n_mels, n_ctx)
+            the mel spectrogram of the audio
+        include_embeddings: bool
+            whether to include intermediate steps in the output
+        """
+        x = F.gelu(self.conv1(x))
+        x = F.gelu(self.conv2(x))
+        x = x.permute(0, 2, 1)
+
+        assert x.shape[1:] == self.positional_embedding.shape, "incorrect audio shape"
+        x = (x + self.positional_embedding).to(x.dtype)
+
+        if include_embeddings:
+            embeddings = [x.cpu().detach().numpy()]
+
+        for block in self.blocks:
+            x = block(x)
+            if include_embeddings:
+                embeddings.append(x.cpu().detach().numpy())
+
+        x = self.ln_post(x)
+
+        if include_embeddings:
+            embeddings = np.stack(embeddings, axis=1)
+            return x, embeddings
+        else:
+            return x
+
+
+class TextDecoder(nn.Module):
+    def __init__(self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int):
+        super().__init__()
+
+        self.token_embedding = nn.Embedding(n_vocab, n_state)
+        self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state))
+
+        self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList(
+            [ResidualAttentionBlock(n_state, n_head, cross_attention=True) for _ in range(n_layer)]
+        )
+        self.ln = LayerNorm(n_state)
+
+        mask = torch.empty(n_ctx, n_ctx).fill_(-np.inf).triu_(1)
+        self.register_buffer("mask", mask, persistent=False)
+
+    def forward(self, x: Tensor, xa: Tensor, kv_cache: Optional[dict] = None, include_embeddings: bool = False):
+        """
+        x : torch.LongTensor, shape = (batch_size, <= n_ctx)
+            the text tokens
+        xa : torch.Tensor, shape = (batch_size, n_mels, n_audio_ctx)
+            the encoded audio features to be attended on
+        include_embeddings : bool
+            Whether to include intermediate values in the output to this function
+        """
+        offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0
+        x = self.token_embedding(x) + self.positional_embedding[offset : offset + x.shape[-1]]
+        x = x.to(xa.dtype)
+
+        if include_embeddings:
+            embeddings = [x.cpu().detach().numpy()]
+
+        for block in self.blocks:
+            x = block(x, xa, mask=self.mask, kv_cache=kv_cache)
+            if include_embeddings:
+                embeddings.append(x.cpu().detach().numpy())
+
+        x = self.ln(x)
+        logits = (x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)).float()
+
+        if include_embeddings:
+            embeddings = np.stack(embeddings, axis=1)
+            return logits, embeddings
+        else:
+            return logits
+
+
+class Whisper(nn.Module):
+    def __init__(self, dims: ModelDimensions):
+        super().__init__()
+        self.dims = dims
+        self.encoder = AudioEncoder(
+            self.dims.n_mels,
+            self.dims.n_audio_ctx,
+            self.dims.n_audio_state,
+            self.dims.n_audio_head,
+            self.dims.n_audio_layer,
+        )
+        self.decoder = TextDecoder(
+            self.dims.n_vocab,
+            self.dims.n_text_ctx,
+            self.dims.n_text_state,
+            self.dims.n_text_head,
+            self.dims.n_text_layer,
+        )
+
+    def embed_audio(self, mel: torch.Tensor):
+        return self.encoder.forward(mel)
+
+    def logits(self, tokens: torch.Tensor, audio_features: torch.Tensor):
+        return self.decoder.forward(tokens, audio_features)
+
+    def forward(self, mel: torch.Tensor, tokens: torch.Tensor) -> Dict[str, torch.Tensor]:
+        return self.decoder(tokens, self.encoder(mel))
+
+    @property
+    def device(self):
+        return next(self.parameters()).device
+
+    @property
+    def is_multilingual(self):
+        return self.dims.n_vocab == 51865
+
+    def install_kv_cache_hooks(self, cache: Optional[dict] = None):
+        """
+        The `MultiHeadAttention` module optionally accepts `kv_cache` which stores the key and value
+        tensors calculated for the previous positions. This method returns a dictionary that stores
+        all caches, and the necessary hooks for the key and value projection modules that save the
+        intermediate tensors to be reused during later calculations.
+
+        Returns
+        -------
+        cache : Dict[nn.Module, torch.Tensor]
+            A dictionary object mapping the key/value projection modules to its cache
+        hooks : List[RemovableHandle]
+            List of PyTorch RemovableHandle objects to stop the hooks to be called
+        """
+        cache = {**cache} if cache is not None else {}
+        hooks = []
+
+        def save_to_cache(module, _, output):
+            if module not in cache or output.shape[1] > self.decoder.positional_embedding.shape[0]:
+                cache[module] = output  # save as-is, for the first token or cross attention
+            else:
+                cache[module] = torch.cat([cache[module], output], dim=1).detach()
+            return cache[module]
+
+        def install_hooks(layer: nn.Module):
+            if isinstance(layer, MultiHeadAttention):
+                hooks.append(layer.key.register_forward_hook(save_to_cache))
+                hooks.append(layer.value.register_forward_hook(save_to_cache))
+
+        self.decoder.apply(install_hooks)
+        return cache, hooks
+
+    detect_language = detect_language_function
+    transcribe = transcribe_function
+    decode = decode_function
diff --git a/musetalk/whisper/whisper/normalizers/__init__.py b/musetalk/whisper/whisper/normalizers/__init__.py
new file mode 100644
index 0000000..0b10d5a
--- /dev/null
+++ b/musetalk/whisper/whisper/normalizers/__init__.py
@@ -0,0 +1,2 @@
+from .basic import BasicTextNormalizer
+from .english import EnglishTextNormalizer
diff --git a/musetalk/whisper/whisper/normalizers/basic.py b/musetalk/whisper/whisper/normalizers/basic.py
new file mode 100644
index 0000000..ef8d249
--- /dev/null
+++ b/musetalk/whisper/whisper/normalizers/basic.py
@@ -0,0 +1,71 @@
+import re
+import unicodedata
+
+import regex
+
+# non-ASCII letters that are not separated by "NFKD" normalization
+ADDITIONAL_DIACRITICS = {
+    "œ": "oe",
+    "Œ": "OE",
+    "ø": "o",
+    "Ø": "O",
+    "æ": "ae",
+    "Æ": "AE",
+    "ß": "ss",
+    "ẞ": "SS",
+    "đ": "d",
+    "Đ": "D",
+    "ð": "d",
+    "Ð": "D",
+    "þ": "th",
+    "Þ": "th",
+    "ł": "l",
+    "Ł": "L",
+}
+
+
+def remove_symbols_and_diacritics(s: str, keep=""):
+    """
+    Replace any other markers, symbols, and punctuations with a space,
+    and drop any diacritics (category 'Mn' and some manual mappings)
+    """
+    return "".join(
+        c
+        if c in keep
+        else ADDITIONAL_DIACRITICS[c]
+        if c in ADDITIONAL_DIACRITICS
+        else ""
+        if unicodedata.category(c) == "Mn"
+        else " "
+        if unicodedata.category(c)[0] in "MSP"
+        else c
+        for c in unicodedata.normalize("NFKD", s)
+    )
+
+
+def remove_symbols(s: str):
+    """
+    Replace any other markers, symbols, punctuations with a space, keeping diacritics
+    """
+    return "".join(
+        " " if unicodedata.category(c)[0] in "MSP" else c for c in unicodedata.normalize("NFKC", s)
+    )
+
+
+class BasicTextNormalizer:
+    def __init__(self, remove_diacritics: bool = False, split_letters: bool = False):
+        self.clean = remove_symbols_and_diacritics if remove_diacritics else remove_symbols
+        self.split_letters = split_letters
+
+    def __call__(self, s: str):
+        s = s.lower()
+        s = re.sub(r"[<\[][^>\]]*[>\]]", "", s)  # remove words between brackets
+        s = re.sub(r"\(([^)]+?)\)", "", s)  # remove words between parenthesis
+        s = self.clean(s).lower()
+
+        if self.split_letters:
+            s = " ".join(regex.findall(r"\X", s, regex.U))
+
+        s = re.sub(r"\s+", " ", s)  # replace any successive whitespace characters with a space
+
+        return s
diff --git a/musetalk/whisper/whisper/normalizers/english.json b/musetalk/whisper/whisper/normalizers/english.json
new file mode 100644
index 0000000..bd84ae7
--- /dev/null
+++ b/musetalk/whisper/whisper/normalizers/english.json
@@ -0,0 +1,1742 @@
+{
+    "accessorise": "accessorize",
+    "accessorised": "accessorized",
+    "accessorises": "accessorizes",
+    "accessorising": "accessorizing",
+    "acclimatisation": "acclimatization",
+    "acclimatise": "acclimatize",
+    "acclimatised": "acclimatized",
+    "acclimatises": "acclimatizes",
+    "acclimatising": "acclimatizing",
+    "accoutrements": "accouterments",
+    "aeon": "eon",
+    "aeons": "eons",
+    "aerogramme": "aerogram",
+    "aerogrammes": "aerograms",
+    "aeroplane": "airplane",
+    "aeroplanes": "airplanes",
+    "aesthete": "esthete",
+    "aesthetes": "esthetes",
+    "aesthetic": "esthetic",
+    "aesthetically": "esthetically",
+    "aesthetics": "esthetics",
+    "aetiology": "etiology",
+    "ageing": "aging",
+    "aggrandisement": "aggrandizement",
+    "agonise": "agonize",
+    "agonised": "agonized",
+    "agonises": "agonizes",
+    "agonising": "agonizing",
+    "agonisingly": "agonizingly",
+    "almanack": "almanac",
+    "almanacks": "almanacs",
+    "aluminium": "aluminum",
+    "amortisable": "amortizable",
+    "amortisation": "amortization",
+    "amortisations": "amortizations",
+    "amortise": "amortize",
+    "amortised": "amortized",
+    "amortises": "amortizes",
+    "amortising": "amortizing",
+    "amphitheatre": "amphitheater",
+    "amphitheatres": "amphitheaters",
+    "anaemia": "anemia",
+    "anaemic": "anemic",
+    "anaesthesia": "anesthesia",
+    "anaesthetic": "anesthetic",
+    "anaesthetics": "anesthetics",
+    "anaesthetise": "anesthetize",
+    "anaesthetised": "anesthetized",
+    "anaesthetises": "anesthetizes",
+    "anaesthetising": "anesthetizing",
+    "anaesthetist": "anesthetist",
+    "anaesthetists": "anesthetists",
+    "anaesthetize": "anesthetize",
+    "anaesthetized": "anesthetized",
+    "anaesthetizes": "anesthetizes",
+    "anaesthetizing": "anesthetizing",
+    "analogue": "analog",
+    "analogues": "analogs",
+    "analyse": "analyze",
+    "analysed": "analyzed",
+    "analyses": "analyzes",
+    "analysing": "analyzing",
+    "anglicise": "anglicize",
+    "anglicised": "anglicized",
+    "anglicises": "anglicizes",
+    "anglicising": "anglicizing",
+    "annualised": "annualized",
+    "antagonise": "antagonize",
+    "antagonised": "antagonized",
+    "antagonises": "antagonizes",
+    "antagonising": "antagonizing",
+    "apologise": "apologize",
+    "apologised": "apologized",
+    "apologises": "apologizes",
+    "apologising": "apologizing",
+    "appal": "appall",
+    "appals": "appalls",
+    "appetiser": "appetizer",
+    "appetisers": "appetizers",
+    "appetising": "appetizing",
+    "appetisingly": "appetizingly",
+    "arbour": "arbor",
+    "arbours": "arbors",
+    "archeological": "archaeological",
+    "archaeologically": "archeologically",
+    "archaeologist": "archeologist",
+    "archaeologists": "archeologists",
+    "archaeology": "archeology</span>",
+    "ardour": "ardor",
+    "armour": "armor",
+    "armoured": "armored",
+    "armourer": "armorer",
+    "armourers": "armorers",
+    "armouries": "armories",
+    "armoury": "armory",
+    "artefact": "artifact",
+    "artefacts": "artifacts",
+    "authorise": "authorize",
+    "authorised": "authorized",
+    "authorises": "authorizes",
+    "authorising": "authorizing",
+    "axe": "ax",
+    "backpedalled": "backpedaled",
+    "backpedalling": "backpedaling",
+    "bannister": "banister",
+    "bannisters": "banisters",
+    "baptise": "baptize",
+    "baptised": "baptized",
+    "baptises": "baptizes",
+    "baptising": "baptizing",
+    "bastardise": "bastardize",
+    "bastardised": "bastardized",
+    "bastardises": "bastardizes",
+    "bastardising": "bastardizing",
+    "battleax": "battleaxe",
+    "baulk": "balk",
+    "baulked": "balked",
+    "baulking": "balking",
+    "baulks": "balks",
+    "bedevilled": "bedeviled",
+    "bedevilling": "bedeviling",
+    "behaviour": "behavior",
+    "behavioural": "behavioral",
+    "behaviourism": "behaviorism",
+    "behaviourist": "behaviorist",
+    "behaviourists": "behaviorists",
+    "behaviours": "behaviors",
+    "behove": "behoove",
+    "behoved": "behooved",
+    "behoves": "behooves",
+    "bejewelled": "bejeweled",
+    "belabour": "belabor",
+    "belaboured": "belabored",
+    "belabouring": "belaboring",
+    "belabours": "belabors",
+    "bevelled": "beveled",
+    "bevvies": "bevies",
+    "bevvy": "bevy",
+    "biassed": "biased",
+    "biassing": "biasing",
+    "bingeing": "binging",
+    "bougainvillaea": "bougainvillea",
+    "bougainvillaeas": "bougainvilleas",
+    "bowdlerise": "bowdlerize",
+    "bowdlerised": "bowdlerized",
+    "bowdlerises": "bowdlerizes",
+    "bowdlerising": "bowdlerizing",
+    "breathalyse": "breathalyze",
+    "breathalysed": "breathalyzed",
+    "breathalyser": "breathalyzer",
+    "breathalysers": "breathalyzers",
+    "breathalyses": "breathalyzes",
+    "breathalysing": "breathalyzing",
+    "brutalise": "brutalize",
+    "brutalised": "brutalized",
+    "brutalises": "brutalizes",
+    "brutalising": "brutalizing",
+    "busses": "buses",
+    "bussing": "busing",
+    "caesarean": "cesarean",
+    "caesareans": "cesareans",
+    "calibre": "caliber",
+    "calibres": "calibers",
+    "calliper": "caliper",
+    "callipers": "calipers",
+    "callisthenics": "calisthenics",
+    "canalise": "canalize",
+    "canalised": "canalized",
+    "canalises": "canalizes",
+    "canalising": "canalizing",
+    "cancelation": "cancellation",
+    "cancelations": "cancellations",
+    "cancelled": "canceled",
+    "cancelling": "canceling",
+    "candour": "candor",
+    "cannibalise": "cannibalize",
+    "cannibalised": "cannibalized",
+    "cannibalises": "cannibalizes",
+    "cannibalising": "cannibalizing",
+    "canonise": "canonize",
+    "canonised": "canonized",
+    "canonises": "canonizes",
+    "canonising": "canonizing",
+    "capitalise": "capitalize",
+    "capitalised": "capitalized",
+    "capitalises": "capitalizes",
+    "capitalising": "capitalizing",
+    "caramelise": "caramelize",
+    "caramelised": "caramelized",
+    "caramelises": "caramelizes",
+    "caramelising": "caramelizing",
+    "carbonise": "carbonize",
+    "carbonised": "carbonized",
+    "carbonises": "carbonizes",
+    "carbonising": "carbonizing",
+    "carolled": "caroled",
+    "carolling": "caroling",
+    "catalogue": "catalog",
+    "catalogued": "cataloged",
+    "catalogues": "catalogs",
+    "cataloguing": "cataloging",
+    "catalyse": "catalyze",
+    "catalysed": "catalyzed",
+    "catalyses": "catalyzes",
+    "catalysing": "catalyzing",
+    "categorise": "categorize",
+    "categorised": "categorized",
+    "categorises": "categorizes",
+    "categorising": "categorizing",
+    "cauterise": "cauterize",
+    "cauterised": "cauterized",
+    "cauterises": "cauterizes",
+    "cauterising": "cauterizing",
+    "cavilled": "caviled",
+    "cavilling": "caviling",
+    "centigramme": "centigram",
+    "centigrammes": "centigrams",
+    "centilitre": "centiliter",
+    "centilitres": "centiliters",
+    "centimetre": "centimeter",
+    "centimetres": "centimeters",
+    "centralise": "centralize",
+    "centralised": "centralized",
+    "centralises": "centralizes",
+    "centralising": "centralizing",
+    "centre": "center",
+    "centred": "centered",
+    "centrefold": "centerfold",
+    "centrefolds": "centerfolds",
+    "centrepiece": "centerpiece",
+    "centrepieces": "centerpieces",
+    "centres": "centers",
+    "channelled": "channeled",
+    "channelling": "channeling",
+    "characterise": "characterize",
+    "characterised": "characterized",
+    "characterises": "characterizes",
+    "characterising": "characterizing",
+    "cheque": "check",
+    "chequebook": "checkbook",
+    "chequebooks": "checkbooks",
+    "chequered": "checkered",
+    "cheques": "checks",
+    "chilli": "chili",
+    "chimaera": "chimera",
+    "chimaeras": "chimeras",
+    "chiselled": "chiseled",
+    "chiselling": "chiseling",
+    "circularise": "circularize",
+    "circularised": "circularized",
+    "circularises": "circularizes",
+    "circularising": "circularizing",
+    "civilise": "civilize",
+    "civilised": "civilized",
+    "civilises": "civilizes",
+    "civilising": "civilizing",
+    "clamour": "clamor",
+    "clamoured": "clamored",
+    "clamouring": "clamoring",
+    "clamours": "clamors",
+    "clangour": "clangor",
+    "clarinettist": "clarinetist",
+    "clarinettists": "clarinetists",
+    "collectivise": "collectivize",
+    "collectivised": "collectivized",
+    "collectivises": "collectivizes",
+    "collectivising": "collectivizing",
+    "colonisation": "colonization",
+    "colonise": "colonize",
+    "colonised": "colonized",
+    "coloniser": "colonizer",
+    "colonisers": "colonizers",
+    "colonises": "colonizes",
+    "colonising": "colonizing",
+    "colour": "color",
+    "colourant": "colorant",
+    "colourants": "colorants",
+    "coloured": "colored",
+    "coloureds": "coloreds",
+    "colourful": "colorful",
+    "colourfully": "colorfully",
+    "colouring": "coloring",
+    "colourize": "colorize",
+    "colourized": "colorized",
+    "colourizes": "colorizes",
+    "colourizing": "colorizing",
+    "colourless": "colorless",
+    "colours": "colors",
+    "commercialise": "commercialize",
+    "commercialised": "commercialized",
+    "commercialises": "commercializes",
+    "commercialising": "commercializing",
+    "compartmentalise": "compartmentalize",
+    "compartmentalised": "compartmentalized",
+    "compartmentalises": "compartmentalizes",
+    "compartmentalising": "compartmentalizing",
+    "computerise": "computerize",
+    "computerised": "computerized",
+    "computerises": "computerizes",
+    "computerising": "computerizing",
+    "conceptualise": "conceptualize",
+    "conceptualised": "conceptualized",
+    "conceptualises": "conceptualizes",
+    "conceptualising": "conceptualizing",
+    "connexion": "connection",
+    "connexions": "connections",
+    "contextualise": "contextualize",
+    "contextualised": "contextualized",
+    "contextualises": "contextualizes",
+    "contextualising": "contextualizing",
+    "cosier": "cozier",
+    "cosies": "cozies",
+    "cosiest": "coziest",
+    "cosily": "cozily",
+    "cosiness": "coziness",
+    "cosy": "cozy",
+    "councillor": "councilor",
+    "councillors": "councilors",
+    "counselled": "counseled",
+    "counselling": "counseling",
+    "counsellor": "counselor",
+    "counsellors": "counselors",
+    "crenelated": "crenellated",
+    "criminalise": "criminalize",
+    "criminalised": "criminalized",
+    "criminalises": "criminalizes",
+    "criminalising": "criminalizing",
+    "criticise": "criticize",
+    "criticised": "criticized",
+    "criticises": "criticizes",
+    "criticising": "criticizing",
+    "crueller": "crueler",
+    "cruellest": "cruelest",
+    "crystallisation": "crystallization",
+    "crystallise": "crystallize",
+    "crystallised": "crystallized",
+    "crystallises": "crystallizes",
+    "crystallising": "crystallizing",
+    "cudgelled": "cudgeled",
+    "cudgelling": "cudgeling",
+    "customise": "customize",
+    "customised": "customized",
+    "customises": "customizes",
+    "customising": "customizing",
+    "cypher": "cipher",
+    "cyphers": "ciphers",
+    "decentralisation": "decentralization",
+    "decentralise": "decentralize",
+    "decentralised": "decentralized",
+    "decentralises": "decentralizes",
+    "decentralising": "decentralizing",
+    "decriminalisation": "decriminalization",
+    "decriminalise": "decriminalize",
+    "decriminalised": "decriminalized",
+    "decriminalises": "decriminalizes",
+    "decriminalising": "decriminalizing",
+    "defence": "defense",
+    "defenceless": "defenseless",
+    "defences": "defenses",
+    "dehumanisation": "dehumanization",
+    "dehumanise": "dehumanize",
+    "dehumanised": "dehumanized",
+    "dehumanises": "dehumanizes",
+    "dehumanising": "dehumanizing",
+    "demeanour": "demeanor",
+    "demilitarisation": "demilitarization",
+    "demilitarise": "demilitarize",
+    "demilitarised": "demilitarized",
+    "demilitarises": "demilitarizes",
+    "demilitarising": "demilitarizing",
+    "demobilisation": "demobilization",
+    "demobilise": "demobilize",
+    "demobilised": "demobilized",
+    "demobilises": "demobilizes",
+    "demobilising": "demobilizing",
+    "democratisation": "democratization",
+    "democratise": "democratize",
+    "democratised": "democratized",
+    "democratises": "democratizes",
+    "democratising": "democratizing",
+    "demonise": "demonize",
+    "demonised": "demonized",
+    "demonises": "demonizes",
+    "demonising": "demonizing",
+    "demoralisation": "demoralization",
+    "demoralise": "demoralize",
+    "demoralised": "demoralized",
+    "demoralises": "demoralizes",
+    "demoralising": "demoralizing",
+    "denationalisation": "denationalization",
+    "denationalise": "denationalize",
+    "denationalised": "denationalized",
+    "denationalises": "denationalizes",
+    "denationalising": "denationalizing",
+    "deodorise": "deodorize",
+    "deodorised": "deodorized",
+    "deodorises": "deodorizes",
+    "deodorising": "deodorizing",
+    "depersonalise": "depersonalize",
+    "depersonalised": "depersonalized",
+    "depersonalises": "depersonalizes",
+    "depersonalising": "depersonalizing",
+    "deputise": "deputize",
+    "deputised": "deputized",
+    "deputises": "deputizes",
+    "deputising": "deputizing",
+    "desensitisation": "desensitization",
+    "desensitise": "desensitize",
+    "desensitised": "desensitized",
+    "desensitises": "desensitizes",
+    "desensitising": "desensitizing",
+    "destabilisation": "destabilization",
+    "destabilise": "destabilize",
+    "destabilised": "destabilized",
+    "destabilises": "destabilizes",
+    "destabilising": "destabilizing",
+    "dialled": "dialed",
+    "dialling": "dialing",
+    "dialogue": "dialog",
+    "dialogues": "dialogs",
+    "diarrhoea": "diarrhea",
+    "digitise": "digitize",
+    "digitised": "digitized",
+    "digitises": "digitizes",
+    "digitising": "digitizing",
+    "disc": "disk",
+    "discolour": "discolor",
+    "discoloured": "discolored",
+    "discolouring": "discoloring",
+    "discolours": "discolors",
+    "discs": "disks",
+    "disembowelled": "disemboweled",
+    "disembowelling": "disemboweling",
+    "disfavour": "disfavor",
+    "dishevelled": "disheveled",
+    "dishonour": "dishonor",
+    "dishonourable": "dishonorable",
+    "dishonourably": "dishonorably",
+    "dishonoured": "dishonored",
+    "dishonouring": "dishonoring",
+    "dishonours": "dishonors",
+    "disorganisation": "disorganization",
+    "disorganised": "disorganized",
+    "distil": "distill",
+    "distils": "distills",
+    "dramatisation": "dramatization",
+    "dramatisations": "dramatizations",
+    "dramatise": "dramatize",
+    "dramatised": "dramatized",
+    "dramatises": "dramatizes",
+    "dramatising": "dramatizing",
+    "draught": "draft",
+    "draughtboard": "draftboard",
+    "draughtboards": "draftboards",
+    "draughtier": "draftier",
+    "draughtiest": "draftiest",
+    "draughts": "drafts",
+    "draughtsman": "draftsman",
+    "draughtsmanship": "draftsmanship",
+    "draughtsmen": "draftsmen",
+    "draughtswoman": "draftswoman",
+    "draughtswomen": "draftswomen",
+    "draughty": "drafty",
+    "drivelled": "driveled",
+    "drivelling": "driveling",
+    "duelled": "dueled",
+    "duelling": "dueling",
+    "economise": "economize",
+    "economised": "economized",
+    "economises": "economizes",
+    "economising": "economizing",
+    "edoema": "edema",
+    "editorialise": "editorialize",
+    "editorialised": "editorialized",
+    "editorialises": "editorializes",
+    "editorialising": "editorializing",
+    "empathise": "empathize",
+    "empathised": "empathized",
+    "empathises": "empathizes",
+    "empathising": "empathizing",
+    "emphasise": "emphasize",
+    "emphasised": "emphasized",
+    "emphasises": "emphasizes",
+    "emphasising": "emphasizing",
+    "enamelled": "enameled",
+    "enamelling": "enameling",
+    "enamoured": "enamored",
+    "encyclopaedia": "encyclopedia",
+    "encyclopaedias": "encyclopedias",
+    "encyclopaedic": "encyclopedic",
+    "endeavour": "endeavor",
+    "endeavoured": "endeavored",
+    "endeavouring": "endeavoring",
+    "endeavours": "endeavors",
+    "energise": "energize",
+    "energised": "energized",
+    "energises": "energizes",
+    "energising": "energizing",
+    "enrol": "enroll",
+    "enrols": "enrolls",
+    "enthral": "enthrall",
+    "enthrals": "enthralls",
+    "epaulette": "epaulet",
+    "epaulettes": "epaulets",
+    "epicentre": "epicenter",
+    "epicentres": "epicenters",
+    "epilogue": "epilog",
+    "epilogues": "epilogs",
+    "epitomise": "epitomize",
+    "epitomised": "epitomized",
+    "epitomises": "epitomizes",
+    "epitomising": "epitomizing",
+    "equalisation": "equalization",
+    "equalise": "equalize",
+    "equalised": "equalized",
+    "equaliser": "equalizer",
+    "equalisers": "equalizers",
+    "equalises": "equalizes",
+    "equalising": "equalizing",
+    "eulogise": "eulogize",
+    "eulogised": "eulogized",
+    "eulogises": "eulogizes",
+    "eulogising": "eulogizing",
+    "evangelise": "evangelize",
+    "evangelised": "evangelized",
+    "evangelises": "evangelizes",
+    "evangelising": "evangelizing",
+    "exorcise": "exorcize",
+    "exorcised": "exorcized",
+    "exorcises": "exorcizes",
+    "exorcising": "exorcizing",
+    "extemporisation": "extemporization",
+    "extemporise": "extemporize",
+    "extemporised": "extemporized",
+    "extemporises": "extemporizes",
+    "extemporising": "extemporizing",
+    "externalisation": "externalization",
+    "externalisations": "externalizations",
+    "externalise": "externalize",
+    "externalised": "externalized",
+    "externalises": "externalizes",
+    "externalising": "externalizing",
+    "factorise": "factorize",
+    "factorised": "factorized",
+    "factorises": "factorizes",
+    "factorising": "factorizing",
+    "faecal": "fecal",
+    "faeces": "feces",
+    "familiarisation": "familiarization",
+    "familiarise": "familiarize",
+    "familiarised": "familiarized",
+    "familiarises": "familiarizes",
+    "familiarising": "familiarizing",
+    "fantasise": "fantasize",
+    "fantasised": "fantasized",
+    "fantasises": "fantasizes",
+    "fantasising": "fantasizing",
+    "favour": "favor",
+    "favourable": "favorable",
+    "favourably": "favorably",
+    "favoured": "favored",
+    "favouring": "favoring",
+    "favourite": "favorite",
+    "favourites": "favorites",
+    "favouritism": "favoritism",
+    "favours": "favors",
+    "feminise": "feminize",
+    "feminised": "feminized",
+    "feminises": "feminizes",
+    "feminising": "feminizing",
+    "fertilisation": "fertilization",
+    "fertilise": "fertilize",
+    "fertilised": "fertilized",
+    "fertiliser": "fertilizer",
+    "fertilisers": "fertilizers",
+    "fertilises": "fertilizes",
+    "fertilising": "fertilizing",
+    "fervour": "fervor",
+    "fibre": "fiber",
+    "fibreglass": "fiberglass",
+    "fibres": "fibers",
+    "fictionalisation": "fictionalization",
+    "fictionalisations": "fictionalizations",
+    "fictionalise": "fictionalize",
+    "fictionalised": "fictionalized",
+    "fictionalises": "fictionalizes",
+    "fictionalising": "fictionalizing",
+    "fillet": "filet",
+    "filleted": "fileted",
+    "filleting": "fileting",
+    "fillets": "filets",
+    "finalisation": "finalization",
+    "finalise": "finalize",
+    "finalised": "finalized",
+    "finalises": "finalizes",
+    "finalising": "finalizing",
+    "flautist": "flutist",
+    "flautists": "flutists",
+    "flavour": "flavor",
+    "flavoured": "flavored",
+    "flavouring": "flavoring",
+    "flavourings": "flavorings",
+    "flavourless": "flavorless",
+    "flavours": "flavors",
+    "flavoursome": "flavorsome",
+    "flyer / flier": "flier / flyer",
+    "foetal": "fetal",
+    "foetid": "fetid",
+    "foetus": "fetus",
+    "foetuses": "fetuses",
+    "formalisation": "formalization",
+    "formalise": "formalize",
+    "formalised": "formalized",
+    "formalises": "formalizes",
+    "formalising": "formalizing",
+    "fossilisation": "fossilization",
+    "fossilise": "fossilize",
+    "fossilised": "fossilized",
+    "fossilises": "fossilizes",
+    "fossilising": "fossilizing",
+    "fraternisation": "fraternization",
+    "fraternise": "fraternize",
+    "fraternised": "fraternized",
+    "fraternises": "fraternizes",
+    "fraternising": "fraternizing",
+    "fulfil": "fulfill",
+    "fulfilment": "fulfillment",
+    "fulfils": "fulfills",
+    "funnelled": "funneled",
+    "funnelling": "funneling",
+    "galvanise": "galvanize",
+    "galvanised": "galvanized",
+    "galvanises": "galvanizes",
+    "galvanising": "galvanizing",
+    "gambolled": "gamboled",
+    "gambolling": "gamboling",
+    "gaol": "jail",
+    "gaolbird": "jailbird",
+    "gaolbirds": "jailbirds",
+    "gaolbreak": "jailbreak",
+    "gaolbreaks": "jailbreaks",
+    "gaoled": "jailed",
+    "gaoler": "jailer",
+    "gaolers": "jailers",
+    "gaoling": "jailing",
+    "gaols": "jails",
+    "gasses": "gases",
+    "gage": "gauge",
+    "gaged": "gauged",
+    "gages": "gauges",
+    "gaging": "gauging",
+    "generalisation": "generalization",
+    "generalisations": "generalizations",
+    "generalise": "generalize",
+    "generalised": "generalized",
+    "generalises": "generalizes",
+    "generalising": "generalizing",
+    "ghettoise": "ghettoize",
+    "ghettoised": "ghettoized",
+    "ghettoises": "ghettoizes",
+    "ghettoising": "ghettoizing",
+    "gipsies": "gypsies",
+    "glamorise": "glamorize",
+    "glamorised": "glamorized",
+    "glamorises": "glamorizes",
+    "glamorising": "glamorizing",
+    "glamor": "glamour",
+    "globalisation": "globalization",
+    "globalise": "globalize",
+    "globalised": "globalized",
+    "globalises": "globalizes",
+    "globalising": "globalizing",
+    "glueing": "gluing",
+    "goitre": "goiter",
+    "goitres": "goiters",
+    "gonorrhoea": "gonorrhea",
+    "gramme": "gram",
+    "grammes": "grams",
+    "gravelled": "graveled",
+    "grey": "gray",
+    "greyed": "grayed",
+    "greying": "graying",
+    "greyish": "grayish",
+    "greyness": "grayness",
+    "greys": "grays",
+    "grovelled": "groveled",
+    "grovelling": "groveling",
+    "groyne": "groin",
+    "groynes": "groins",
+    "gruelling": "grueling",
+    "gruellingly": "gruelingly",
+    "gryphon": "griffin",
+    "gryphons": "griffins",
+    "gynaecological": "gynecological",
+    "gynaecologist": "gynecologist",
+    "gynaecologists": "gynecologists",
+    "gynaecology": "gynecology",
+    "haematological": "hematological",
+    "haematologist": "hematologist",
+    "haematologists": "hematologists",
+    "haematology": "hematology",
+    "haemoglobin": "hemoglobin",
+    "haemophilia": "hemophilia",
+    "haemophiliac": "hemophiliac",
+    "haemophiliacs": "hemophiliacs",
+    "haemorrhage": "hemorrhage",
+    "haemorrhaged": "hemorrhaged",
+    "haemorrhages": "hemorrhages",
+    "haemorrhaging": "hemorrhaging",
+    "haemorrhoids": "hemorrhoids",
+    "harbour": "harbor",
+    "harboured": "harbored",
+    "harbouring": "harboring",
+    "harbours": "harbors",
+    "harmonisation": "harmonization",
+    "harmonise": "harmonize",
+    "harmonised": "harmonized",
+    "harmonises": "harmonizes",
+    "harmonising": "harmonizing",
+    "homoeopath": "homeopath",
+    "homoeopathic": "homeopathic",
+    "homoeopaths": "homeopaths",
+    "homoeopathy": "homeopathy",
+    "homogenise": "homogenize",
+    "homogenised": "homogenized",
+    "homogenises": "homogenizes",
+    "homogenising": "homogenizing",
+    "honour": "honor",
+    "honourable": "honorable",
+    "honourably": "honorably",
+    "honoured": "honored",
+    "honouring": "honoring",
+    "honours": "honors",
+    "hospitalisation": "hospitalization",
+    "hospitalise": "hospitalize",
+    "hospitalised": "hospitalized",
+    "hospitalises": "hospitalizes",
+    "hospitalising": "hospitalizing",
+    "humanise": "humanize",
+    "humanised": "humanized",
+    "humanises": "humanizes",
+    "humanising": "humanizing",
+    "humour": "humor",
+    "humoured": "humored",
+    "humouring": "humoring",
+    "humourless": "humorless",
+    "humours": "humors",
+    "hybridise": "hybridize",
+    "hybridised": "hybridized",
+    "hybridises": "hybridizes",
+    "hybridising": "hybridizing",
+    "hypnotise": "hypnotize",
+    "hypnotised": "hypnotized",
+    "hypnotises": "hypnotizes",
+    "hypnotising": "hypnotizing",
+    "hypothesise": "hypothesize",
+    "hypothesised": "hypothesized",
+    "hypothesises": "hypothesizes",
+    "hypothesising": "hypothesizing",
+    "idealisation": "idealization",
+    "idealise": "idealize",
+    "idealised": "idealized",
+    "idealises": "idealizes",
+    "idealising": "idealizing",
+    "idolise": "idolize",
+    "idolised": "idolized",
+    "idolises": "idolizes",
+    "idolising": "idolizing",
+    "immobilisation": "immobilization",
+    "immobilise": "immobilize",
+    "immobilised": "immobilized",
+    "immobiliser": "immobilizer",
+    "immobilisers": "immobilizers",
+    "immobilises": "immobilizes",
+    "immobilising": "immobilizing",
+    "immortalise": "immortalize",
+    "immortalised": "immortalized",
+    "immortalises": "immortalizes",
+    "immortalising": "immortalizing",
+    "immunisation": "immunization",
+    "immunise": "immunize",
+    "immunised": "immunized",
+    "immunises": "immunizes",
+    "immunising": "immunizing",
+    "impanelled": "impaneled",
+    "impanelling": "impaneling",
+    "imperilled": "imperiled",
+    "imperilling": "imperiling",
+    "individualise": "individualize",
+    "individualised": "individualized",
+    "individualises": "individualizes",
+    "individualising": "individualizing",
+    "industrialise": "industrialize",
+    "industrialised": "industrialized",
+    "industrialises": "industrializes",
+    "industrialising": "industrializing",
+    "inflexion": "inflection",
+    "inflexions": "inflections",
+    "initialise": "initialize",
+    "initialised": "initialized",
+    "initialises": "initializes",
+    "initialising": "initializing",
+    "initialled": "initialed",
+    "initialling": "initialing",
+    "instal": "install",
+    "instalment": "installment",
+    "instalments": "installments",
+    "instals": "installs",
+    "instil": "instill",
+    "instils": "instills",
+    "institutionalisation": "institutionalization",
+    "institutionalise": "institutionalize",
+    "institutionalised": "institutionalized",
+    "institutionalises": "institutionalizes",
+    "institutionalising": "institutionalizing",
+    "intellectualise": "intellectualize",
+    "intellectualised": "intellectualized",
+    "intellectualises": "intellectualizes",
+    "intellectualising": "intellectualizing",
+    "internalisation": "internalization",
+    "internalise": "internalize",
+    "internalised": "internalized",
+    "internalises": "internalizes",
+    "internalising": "internalizing",
+    "internationalisation": "internationalization",
+    "internationalise": "internationalize",
+    "internationalised": "internationalized",
+    "internationalises": "internationalizes",
+    "internationalising": "internationalizing",
+    "ionisation": "ionization",
+    "ionise": "ionize",
+    "ionised": "ionized",
+    "ioniser": "ionizer",
+    "ionisers": "ionizers",
+    "ionises": "ionizes",
+    "ionising": "ionizing",
+    "italicise": "italicize",
+    "italicised": "italicized",
+    "italicises": "italicizes",
+    "italicising": "italicizing",
+    "itemise": "itemize",
+    "itemised": "itemized",
+    "itemises": "itemizes",
+    "itemising": "itemizing",
+    "jeopardise": "jeopardize",
+    "jeopardised": "jeopardized",
+    "jeopardises": "jeopardizes",
+    "jeopardising": "jeopardizing",
+    "jewelled": "jeweled",
+    "jeweller": "jeweler",
+    "jewellers": "jewelers",
+    "jewellery": "jewelry",
+    "judgement": "judgment",
+    "kilogramme": "kilogram",
+    "kilogrammes": "kilograms",
+    "kilometre": "kilometer",
+    "kilometres": "kilometers",
+    "labelled": "labeled",
+    "labelling": "labeling",
+    "labour": "labor",
+    "laboured": "labored",
+    "labourer": "laborer",
+    "labourers": "laborers",
+    "labouring": "laboring",
+    "labours": "labors",
+    "lacklustre": "lackluster",
+    "legalisation": "legalization",
+    "legalise": "legalize",
+    "legalised": "legalized",
+    "legalises": "legalizes",
+    "legalising": "legalizing",
+    "legitimise": "legitimize",
+    "legitimised": "legitimized",
+    "legitimises": "legitimizes",
+    "legitimising": "legitimizing",
+    "leukaemia": "leukemia",
+    "levelled": "leveled",
+    "leveller": "leveler",
+    "levellers": "levelers",
+    "levelling": "leveling",
+    "libelled": "libeled",
+    "libelling": "libeling",
+    "libellous": "libelous",
+    "liberalisation": "liberalization",
+    "liberalise": "liberalize",
+    "liberalised": "liberalized",
+    "liberalises": "liberalizes",
+    "liberalising": "liberalizing",
+    "licence": "license",
+    "licenced": "licensed",
+    "licences": "licenses",
+    "licencing": "licensing",
+    "likeable": "likable",
+    "lionisation": "lionization",
+    "lionise": "lionize",
+    "lionised": "lionized",
+    "lionises": "lionizes",
+    "lionising": "lionizing",
+    "liquidise": "liquidize",
+    "liquidised": "liquidized",
+    "liquidiser": "liquidizer",
+    "liquidisers": "liquidizers",
+    "liquidises": "liquidizes",
+    "liquidising": "liquidizing",
+    "litre": "liter",
+    "litres": "liters",
+    "localise": "localize",
+    "localised": "localized",
+    "localises": "localizes",
+    "localising": "localizing",
+    "louvre": "louver",
+    "louvred": "louvered",
+    "louvres": "louvers",
+    "lustre": "luster",
+    "magnetise": "magnetize",
+    "magnetised": "magnetized",
+    "magnetises": "magnetizes",
+    "magnetising": "magnetizing",
+    "manoeuvrability": "maneuverability",
+    "manoeuvrable": "maneuverable",
+    "manoeuvre": "maneuver",
+    "manoeuvred": "maneuvered",
+    "manoeuvres": "maneuvers",
+    "manoeuvring": "maneuvering",
+    "manoeuvrings": "maneuverings",
+    "marginalisation": "marginalization",
+    "marginalise": "marginalize",
+    "marginalised": "marginalized",
+    "marginalises": "marginalizes",
+    "marginalising": "marginalizing",
+    "marshalled": "marshaled",
+    "marshalling": "marshaling",
+    "marvelled": "marveled",
+    "marvelling": "marveling",
+    "marvellous": "marvelous",
+    "marvellously": "marvelously",
+    "materialisation": "materialization",
+    "materialise": "materialize",
+    "materialised": "materialized",
+    "materialises": "materializes",
+    "materialising": "materializing",
+    "maximisation": "maximization",
+    "maximise": "maximize",
+    "maximised": "maximized",
+    "maximises": "maximizes",
+    "maximising": "maximizing",
+    "meagre": "meager",
+    "mechanisation": "mechanization",
+    "mechanise": "mechanize",
+    "mechanised": "mechanized",
+    "mechanises": "mechanizes",
+    "mechanising": "mechanizing",
+    "mediaeval": "medieval",
+    "memorialise": "memorialize",
+    "memorialised": "memorialized",
+    "memorialises": "memorializes",
+    "memorialising": "memorializing",
+    "memorise": "memorize",
+    "memorised": "memorized",
+    "memorises": "memorizes",
+    "memorising": "memorizing",
+    "mesmerise": "mesmerize",
+    "mesmerised": "mesmerized",
+    "mesmerises": "mesmerizes",
+    "mesmerising": "mesmerizing",
+    "metabolise": "metabolize",
+    "metabolised": "metabolized",
+    "metabolises": "metabolizes",
+    "metabolising": "metabolizing",
+    "metre": "meter",
+    "metres": "meters",
+    "micrometre": "micrometer",
+    "micrometres": "micrometers",
+    "militarise": "militarize",
+    "militarised": "militarized",
+    "militarises": "militarizes",
+    "militarising": "militarizing",
+    "milligramme": "milligram",
+    "milligrammes": "milligrams",
+    "millilitre": "milliliter",
+    "millilitres": "milliliters",
+    "millimetre": "millimeter",
+    "millimetres": "millimeters",
+    "miniaturisation": "miniaturization",
+    "miniaturise": "miniaturize",
+    "miniaturised": "miniaturized",
+    "miniaturises": "miniaturizes",
+    "miniaturising": "miniaturizing",
+    "minibusses": "minibuses",
+    "minimise": "minimize",
+    "minimised": "minimized",
+    "minimises": "minimizes",
+    "minimising": "minimizing",
+    "misbehaviour": "misbehavior",
+    "misdemeanour": "misdemeanor",
+    "misdemeanours": "misdemeanors",
+    "misspelt": "misspelled",
+    "mitre": "miter",
+    "mitres": "miters",
+    "mobilisation": "mobilization",
+    "mobilise": "mobilize",
+    "mobilised": "mobilized",
+    "mobilises": "mobilizes",
+    "mobilising": "mobilizing",
+    "modelled": "modeled",
+    "modeller": "modeler",
+    "modellers": "modelers",
+    "modelling": "modeling",
+    "modernise": "modernize",
+    "modernised": "modernized",
+    "modernises": "modernizes",
+    "modernising": "modernizing",
+    "moisturise": "moisturize",
+    "moisturised": "moisturized",
+    "moisturiser": "moisturizer",
+    "moisturisers": "moisturizers",
+    "moisturises": "moisturizes",
+    "moisturising": "moisturizing",
+    "monologue": "monolog",
+    "monologues": "monologs",
+    "monopolisation": "monopolization",
+    "monopolise": "monopolize",
+    "monopolised": "monopolized",
+    "monopolises": "monopolizes",
+    "monopolising": "monopolizing",
+    "moralise": "moralize",
+    "moralised": "moralized",
+    "moralises": "moralizes",
+    "moralising": "moralizing",
+    "motorised": "motorized",
+    "mould": "mold",
+    "moulded": "molded",
+    "moulder": "molder",
+    "mouldered": "moldered",
+    "mouldering": "moldering",
+    "moulders": "molders",
+    "mouldier": "moldier",
+    "mouldiest": "moldiest",
+    "moulding": "molding",
+    "mouldings": "moldings",
+    "moulds": "molds",
+    "mouldy": "moldy",
+    "moult": "molt",
+    "moulted": "molted",
+    "moulting": "molting",
+    "moults": "molts",
+    "moustache": "mustache",
+    "moustached": "mustached",
+    "moustaches": "mustaches",
+    "moustachioed": "mustachioed",
+    "multicoloured": "multicolored",
+    "nationalisation": "nationalization",
+    "nationalisations": "nationalizations",
+    "nationalise": "nationalize",
+    "nationalised": "nationalized",
+    "nationalises": "nationalizes",
+    "nationalising": "nationalizing",
+    "naturalisation": "naturalization",
+    "naturalise": "naturalize",
+    "naturalised": "naturalized",
+    "naturalises": "naturalizes",
+    "naturalising": "naturalizing",
+    "neighbour": "neighbor",
+    "neighbourhood": "neighborhood",
+    "neighbourhoods": "neighborhoods",
+    "neighbouring": "neighboring",
+    "neighbourliness": "neighborliness",
+    "neighbourly": "neighborly",
+    "neighbours": "neighbors",
+    "neutralisation": "neutralization",
+    "neutralise": "neutralize",
+    "neutralised": "neutralized",
+    "neutralises": "neutralizes",
+    "neutralising": "neutralizing",
+    "normalisation": "normalization",
+    "normalise": "normalize",
+    "normalised": "normalized",
+    "normalises": "normalizes",
+    "normalising": "normalizing",
+    "odour": "odor",
+    "odourless": "odorless",
+    "odours": "odors",
+    "oesophagus": "esophagus",
+    "oesophaguses": "esophaguses",
+    "oestrogen": "estrogen",
+    "offence": "offense",
+    "offences": "offenses",
+    "omelette": "omelet",
+    "omelettes": "omelets",
+    "optimise": "optimize",
+    "optimised": "optimized",
+    "optimises": "optimizes",
+    "optimising": "optimizing",
+    "organisation": "organization",
+    "organisational": "organizational",
+    "organisations": "organizations",
+    "organise": "organize",
+    "organised": "organized",
+    "organiser": "organizer",
+    "organisers": "organizers",
+    "organises": "organizes",
+    "organising": "organizing",
+    "orthopaedic": "orthopedic",
+    "orthopaedics": "orthopedics",
+    "ostracise": "ostracize",
+    "ostracised": "ostracized",
+    "ostracises": "ostracizes",
+    "ostracising": "ostracizing",
+    "outmanoeuvre": "outmaneuver",
+    "outmanoeuvred": "outmaneuvered",
+    "outmanoeuvres": "outmaneuvers",
+    "outmanoeuvring": "outmaneuvering",
+    "overemphasise": "overemphasize",
+    "overemphasised": "overemphasized",
+    "overemphasises": "overemphasizes",
+    "overemphasising": "overemphasizing",
+    "oxidisation": "oxidization",
+    "oxidise": "oxidize",
+    "oxidised": "oxidized",
+    "oxidises": "oxidizes",
+    "oxidising": "oxidizing",
+    "paederast": "pederast",
+    "paederasts": "pederasts",
+    "paediatric": "pediatric",
+    "paediatrician": "pediatrician",
+    "paediatricians": "pediatricians",
+    "paediatrics": "pediatrics",
+    "paedophile": "pedophile",
+    "paedophiles": "pedophiles",
+    "paedophilia": "pedophilia",
+    "palaeolithic": "paleolithic",
+    "palaeontologist": "paleontologist",
+    "palaeontologists": "paleontologists",
+    "palaeontology": "paleontology",
+    "panelled": "paneled",
+    "panelling": "paneling",
+    "panellist": "panelist",
+    "panellists": "panelists",
+    "paralyse": "paralyze",
+    "paralysed": "paralyzed",
+    "paralyses": "paralyzes",
+    "paralysing": "paralyzing",
+    "parcelled": "parceled",
+    "parcelling": "parceling",
+    "parlour": "parlor",
+    "parlours": "parlors",
+    "particularise": "particularize",
+    "particularised": "particularized",
+    "particularises": "particularizes",
+    "particularising": "particularizing",
+    "passivisation": "passivization",
+    "passivise": "passivize",
+    "passivised": "passivized",
+    "passivises": "passivizes",
+    "passivising": "passivizing",
+    "pasteurisation": "pasteurization",
+    "pasteurise": "pasteurize",
+    "pasteurised": "pasteurized",
+    "pasteurises": "pasteurizes",
+    "pasteurising": "pasteurizing",
+    "patronise": "patronize",
+    "patronised": "patronized",
+    "patronises": "patronizes",
+    "patronising": "patronizing",
+    "patronisingly": "patronizingly",
+    "pedalled": "pedaled",
+    "pedalling": "pedaling",
+    "pedestrianisation": "pedestrianization",
+    "pedestrianise": "pedestrianize",
+    "pedestrianised": "pedestrianized",
+    "pedestrianises": "pedestrianizes",
+    "pedestrianising": "pedestrianizing",
+    "penalise": "penalize",
+    "penalised": "penalized",
+    "penalises": "penalizes",
+    "penalising": "penalizing",
+    "pencilled": "penciled",
+    "pencilling": "penciling",
+    "personalise": "personalize",
+    "personalised": "personalized",
+    "personalises": "personalizes",
+    "personalising": "personalizing",
+    "pharmacopoeia": "pharmacopeia",
+    "pharmacopoeias": "pharmacopeias",
+    "philosophise": "philosophize",
+    "philosophised": "philosophized",
+    "philosophises": "philosophizes",
+    "philosophising": "philosophizing",
+    "philtre": "filter",
+    "philtres": "filters",
+    "phoney": "phony",
+    "plagiarise": "plagiarize",
+    "plagiarised": "plagiarized",
+    "plagiarises": "plagiarizes",
+    "plagiarising": "plagiarizing",
+    "plough": "plow",
+    "ploughed": "plowed",
+    "ploughing": "plowing",
+    "ploughman": "plowman",
+    "ploughmen": "plowmen",
+    "ploughs": "plows",
+    "ploughshare": "plowshare",
+    "ploughshares": "plowshares",
+    "polarisation": "polarization",
+    "polarise": "polarize",
+    "polarised": "polarized",
+    "polarises": "polarizes",
+    "polarising": "polarizing",
+    "politicisation": "politicization",
+    "politicise": "politicize",
+    "politicised": "politicized",
+    "politicises": "politicizes",
+    "politicising": "politicizing",
+    "popularisation": "popularization",
+    "popularise": "popularize",
+    "popularised": "popularized",
+    "popularises": "popularizes",
+    "popularising": "popularizing",
+    "pouffe": "pouf",
+    "pouffes": "poufs",
+    "practise": "practice",
+    "practised": "practiced",
+    "practises": "practices",
+    "practising": "practicing",
+    "praesidium": "presidium",
+    "praesidiums": "presidiums",
+    "pressurisation": "pressurization",
+    "pressurise": "pressurize",
+    "pressurised": "pressurized",
+    "pressurises": "pressurizes",
+    "pressurising": "pressurizing",
+    "pretence": "pretense",
+    "pretences": "pretenses",
+    "primaeval": "primeval",
+    "prioritisation": "prioritization",
+    "prioritise": "prioritize",
+    "prioritised": "prioritized",
+    "prioritises": "prioritizes",
+    "prioritising": "prioritizing",
+    "privatisation": "privatization",
+    "privatisations": "privatizations",
+    "privatise": "privatize",
+    "privatised": "privatized",
+    "privatises": "privatizes",
+    "privatising": "privatizing",
+    "professionalisation": "professionalization",
+    "professionalise": "professionalize",
+    "professionalised": "professionalized",
+    "professionalises": "professionalizes",
+    "professionalising": "professionalizing",
+    "programme": "program",
+    "programmes": "programs",
+    "prologue": "prolog",
+    "prologues": "prologs",
+    "propagandise": "propagandize",
+    "propagandised": "propagandized",
+    "propagandises": "propagandizes",
+    "propagandising": "propagandizing",
+    "proselytise": "proselytize",
+    "proselytised": "proselytized",
+    "proselytiser": "proselytizer",
+    "proselytisers": "proselytizers",
+    "proselytises": "proselytizes",
+    "proselytising": "proselytizing",
+    "psychoanalyse": "psychoanalyze",
+    "psychoanalysed": "psychoanalyzed",
+    "psychoanalyses": "psychoanalyzes",
+    "psychoanalysing": "psychoanalyzing",
+    "publicise": "publicize",
+    "publicised": "publicized",
+    "publicises": "publicizes",
+    "publicising": "publicizing",
+    "pulverisation": "pulverization",
+    "pulverise": "pulverize",
+    "pulverised": "pulverized",
+    "pulverises": "pulverizes",
+    "pulverising": "pulverizing",
+    "pummelled": "pummel",
+    "pummelling": "pummeled",
+    "pyjama": "pajama",
+    "pyjamas": "pajamas",
+    "pzazz": "pizzazz",
+    "quarrelled": "quarreled",
+    "quarrelling": "quarreling",
+    "radicalise": "radicalize",
+    "radicalised": "radicalized",
+    "radicalises": "radicalizes",
+    "radicalising": "radicalizing",
+    "rancour": "rancor",
+    "randomise": "randomize",
+    "randomised": "randomized",
+    "randomises": "randomizes",
+    "randomising": "randomizing",
+    "rationalisation": "rationalization",
+    "rationalisations": "rationalizations",
+    "rationalise": "rationalize",
+    "rationalised": "rationalized",
+    "rationalises": "rationalizes",
+    "rationalising": "rationalizing",
+    "ravelled": "raveled",
+    "ravelling": "raveling",
+    "realisable": "realizable",
+    "realisation": "realization",
+    "realisations": "realizations",
+    "realise": "realize",
+    "realised": "realized",
+    "realises": "realizes",
+    "realising": "realizing",
+    "recognisable": "recognizable",
+    "recognisably": "recognizably",
+    "recognisance": "recognizance",
+    "recognise": "recognize",
+    "recognised": "recognized",
+    "recognises": "recognizes",
+    "recognising": "recognizing",
+    "reconnoitre": "reconnoiter",
+    "reconnoitred": "reconnoitered",
+    "reconnoitres": "reconnoiters",
+    "reconnoitring": "reconnoitering",
+    "refuelled": "refueled",
+    "refuelling": "refueling",
+    "regularisation": "regularization",
+    "regularise": "regularize",
+    "regularised": "regularized",
+    "regularises": "regularizes",
+    "regularising": "regularizing",
+    "remodelled": "remodeled",
+    "remodelling": "remodeling",
+    "remould": "remold",
+    "remoulded": "remolded",
+    "remoulding": "remolding",
+    "remoulds": "remolds",
+    "reorganisation": "reorganization",
+    "reorganisations": "reorganizations",
+    "reorganise": "reorganize",
+    "reorganised": "reorganized",
+    "reorganises": "reorganizes",
+    "reorganising": "reorganizing",
+    "revelled": "reveled",
+    "reveller": "reveler",
+    "revellers": "revelers",
+    "revelling": "reveling",
+    "revitalise": "revitalize",
+    "revitalised": "revitalized",
+    "revitalises": "revitalizes",
+    "revitalising": "revitalizing",
+    "revolutionise": "revolutionize",
+    "revolutionised": "revolutionized",
+    "revolutionises": "revolutionizes",
+    "revolutionising": "revolutionizing",
+    "rhapsodise": "rhapsodize",
+    "rhapsodised": "rhapsodized",
+    "rhapsodises": "rhapsodizes",
+    "rhapsodising": "rhapsodizing",
+    "rigour": "rigor",
+    "rigours": "rigors",
+    "ritualised": "ritualized",
+    "rivalled": "rivaled",
+    "rivalling": "rivaling",
+    "romanticise": "romanticize",
+    "romanticised": "romanticized",
+    "romanticises": "romanticizes",
+    "romanticising": "romanticizing",
+    "rumour": "rumor",
+    "rumoured": "rumored",
+    "rumours": "rumors",
+    "sabre": "saber",
+    "sabres": "sabers",
+    "saltpetre": "saltpeter",
+    "sanitise": "sanitize",
+    "sanitised": "sanitized",
+    "sanitises": "sanitizes",
+    "sanitising": "sanitizing",
+    "satirise": "satirize",
+    "satirised": "satirized",
+    "satirises": "satirizes",
+    "satirising": "satirizing",
+    "saviour": "savior",
+    "saviours": "saviors",
+    "savour": "savor",
+    "savoured": "savored",
+    "savouries": "savories",
+    "savouring": "savoring",
+    "savours": "savors",
+    "savoury": "savory",
+    "scandalise": "scandalize",
+    "scandalised": "scandalized",
+    "scandalises": "scandalizes",
+    "scandalising": "scandalizing",
+    "sceptic": "skeptic",
+    "sceptical": "skeptical",
+    "sceptically": "skeptically",
+    "scepticism": "skepticism",
+    "sceptics": "skeptics",
+    "sceptre": "scepter",
+    "sceptres": "scepters",
+    "scrutinise": "scrutinize",
+    "scrutinised": "scrutinized",
+    "scrutinises": "scrutinizes",
+    "scrutinising": "scrutinizing",
+    "secularisation": "secularization",
+    "secularise": "secularize",
+    "secularised": "secularized",
+    "secularises": "secularizes",
+    "secularising": "secularizing",
+    "sensationalise": "sensationalize",
+    "sensationalised": "sensationalized",
+    "sensationalises": "sensationalizes",
+    "sensationalising": "sensationalizing",
+    "sensitise": "sensitize",
+    "sensitised": "sensitized",
+    "sensitises": "sensitizes",
+    "sensitising": "sensitizing",
+    "sentimentalise": "sentimentalize",
+    "sentimentalised": "sentimentalized",
+    "sentimentalises": "sentimentalizes",
+    "sentimentalising": "sentimentalizing",
+    "sepulchre": "sepulcher",
+    "sepulchres": "sepulchers",
+    "serialisation": "serialization",
+    "serialisations": "serializations",
+    "serialise": "serialize",
+    "serialised": "serialized",
+    "serialises": "serializes",
+    "serialising": "serializing",
+    "sermonise": "sermonize",
+    "sermonised": "sermonized",
+    "sermonises": "sermonizes",
+    "sermonising": "sermonizing",
+    "sheikh": "sheik",
+    "shovelled": "shoveled",
+    "shovelling": "shoveling",
+    "shrivelled": "shriveled",
+    "shrivelling": "shriveling",
+    "signalise": "signalize",
+    "signalised": "signalized",
+    "signalises": "signalizes",
+    "signalising": "signalizing",
+    "signalled": "signaled",
+    "signalling": "signaling",
+    "smoulder": "smolder",
+    "smouldered": "smoldered",
+    "smouldering": "smoldering",
+    "smoulders": "smolders",
+    "snivelled": "sniveled",
+    "snivelling": "sniveling",
+    "snorkelled": "snorkeled",
+    "snorkelling": "snorkeling",
+    "snowplough": "snowplow",
+    "snowploughs": "snowplow",
+    "socialisation": "socialization",
+    "socialise": "socialize",
+    "socialised": "socialized",
+    "socialises": "socializes",
+    "socialising": "socializing",
+    "sodomise": "sodomize",
+    "sodomised": "sodomized",
+    "sodomises": "sodomizes",
+    "sodomising": "sodomizing",
+    "solemnise": "solemnize",
+    "solemnised": "solemnized",
+    "solemnises": "solemnizes",
+    "solemnising": "solemnizing",
+    "sombre": "somber",
+    "specialisation": "specialization",
+    "specialisations": "specializations",
+    "specialise": "specialize",
+    "specialised": "specialized",
+    "specialises": "specializes",
+    "specialising": "specializing",
+    "spectre": "specter",
+    "spectres": "specters",
+    "spiralled": "spiraled",
+    "spiralling": "spiraling",
+    "splendour": "splendor",
+    "splendours": "splendors",
+    "squirrelled": "squirreled",
+    "squirrelling": "squirreling",
+    "stabilisation": "stabilization",
+    "stabilise": "stabilize",
+    "stabilised": "stabilized",
+    "stabiliser": "stabilizer",
+    "stabilisers": "stabilizers",
+    "stabilises": "stabilizes",
+    "stabilising": "stabilizing",
+    "standardisation": "standardization",
+    "standardise": "standardize",
+    "standardised": "standardized",
+    "standardises": "standardizes",
+    "standardising": "standardizing",
+    "stencilled": "stenciled",
+    "stencilling": "stenciling",
+    "sterilisation": "sterilization",
+    "sterilisations": "sterilizations",
+    "sterilise": "sterilize",
+    "sterilised": "sterilized",
+    "steriliser": "sterilizer",
+    "sterilisers": "sterilizers",
+    "sterilises": "sterilizes",
+    "sterilising": "sterilizing",
+    "stigmatisation": "stigmatization",
+    "stigmatise": "stigmatize",
+    "stigmatised": "stigmatized",
+    "stigmatises": "stigmatizes",
+    "stigmatising": "stigmatizing",
+    "storey": "story",
+    "storeys": "stories",
+    "subsidisation": "subsidization",
+    "subsidise": "subsidize",
+    "subsidised": "subsidized",
+    "subsidiser": "subsidizer",
+    "subsidisers": "subsidizers",
+    "subsidises": "subsidizes",
+    "subsidising": "subsidizing",
+    "succour": "succor",
+    "succoured": "succored",
+    "succouring": "succoring",
+    "succours": "succors",
+    "sulphate": "sulfate",
+    "sulphates": "sulfates",
+    "sulphide": "sulfide",
+    "sulphides": "sulfides",
+    "sulphur": "sulfur",
+    "sulphurous": "sulfurous",
+    "summarise": "summarize",
+    "summarised": "summarized",
+    "summarises": "summarizes",
+    "summarising": "summarizing",
+    "swivelled": "swiveled",
+    "swivelling": "swiveling",
+    "symbolise": "symbolize",
+    "symbolised": "symbolized",
+    "symbolises": "symbolizes",
+    "symbolising": "symbolizing",
+    "sympathise": "sympathize",
+    "sympathised": "sympathized",
+    "sympathiser": "sympathizer",
+    "sympathisers": "sympathizers",
+    "sympathises": "sympathizes",
+    "sympathising": "sympathizing",
+    "synchronisation": "synchronization",
+    "synchronise": "synchronize",
+    "synchronised": "synchronized",
+    "synchronises": "synchronizes",
+    "synchronising": "synchronizing",
+    "synthesise": "synthesize",
+    "synthesised": "synthesized",
+    "synthesiser": "synthesizer",
+    "synthesisers": "synthesizers",
+    "synthesises": "synthesizes",
+    "synthesising": "synthesizing",
+    "syphon": "siphon",
+    "syphoned": "siphoned",
+    "syphoning": "siphoning",
+    "syphons": "siphons",
+    "systematisation": "systematization",
+    "systematise": "systematize",
+    "systematised": "systematized",
+    "systematises": "systematizes",
+    "systematising": "systematizing",
+    "tantalise": "tantalize",
+    "tantalised": "tantalized",
+    "tantalises": "tantalizes",
+    "tantalising": "tantalizing",
+    "tantalisingly": "tantalizingly",
+    "tasselled": "tasseled",
+    "technicolour": "technicolor",
+    "temporise": "temporize",
+    "temporised": "temporized",
+    "temporises": "temporizes",
+    "temporising": "temporizing",
+    "tenderise": "tenderize",
+    "tenderised": "tenderized",
+    "tenderises": "tenderizes",
+    "tenderising": "tenderizing",
+    "terrorise": "terrorize",
+    "terrorised": "terrorized",
+    "terrorises": "terrorizes",
+    "terrorising": "terrorizing",
+    "theatre": "theater",
+    "theatregoer": "theatergoer",
+    "theatregoers": "theatergoers",
+    "theatres": "theaters",
+    "theorise": "theorize",
+    "theorised": "theorized",
+    "theorises": "theorizes",
+    "theorising": "theorizing",
+    "tonne": "ton",
+    "tonnes": "tons",
+    "towelled": "toweled",
+    "towelling": "toweling",
+    "toxaemia": "toxemia",
+    "tranquillise": "tranquilize",
+    "tranquillised": "tranquilized",
+    "tranquilliser": "tranquilizer",
+    "tranquillisers": "tranquilizers",
+    "tranquillises": "tranquilizes",
+    "tranquillising": "tranquilizing",
+    "tranquillity": "tranquility",
+    "tranquillize": "tranquilize",
+    "tranquillized": "tranquilized",
+    "tranquillizer": "tranquilizer",
+    "tranquillizers": "tranquilizers",
+    "tranquillizes": "tranquilizes",
+    "tranquillizing": "tranquilizing",
+    "tranquilly": "tranquility",
+    "transistorised": "transistorized",
+    "traumatise": "traumatize",
+    "traumatised": "traumatized",
+    "traumatises": "traumatizes",
+    "traumatising": "traumatizing",
+    "travelled": "traveled",
+    "traveller": "traveler",
+    "travellers": "travelers",
+    "travelling": "traveling",
+    "travelog": "travelogue",
+    "travelogs": "travelogues",
+    "trialled": "trialed",
+    "trialling": "trialing",
+    "tricolour": "tricolor",
+    "tricolours": "tricolors",
+    "trivialise": "trivialize",
+    "trivialised": "trivialized",
+    "trivialises": "trivializes",
+    "trivialising": "trivializing",
+    "tumour": "tumor",
+    "tumours": "tumors",
+    "tunnelled": "tunneled",
+    "tunnelling": "tunneling",
+    "tyrannise": "tyrannize",
+    "tyrannised": "tyrannized",
+    "tyrannises": "tyrannizes",
+    "tyrannising": "tyrannizing",
+    "tyre": "tire",
+    "tyres": "tires",
+    "unauthorised": "unauthorized",
+    "uncivilised": "uncivilized",
+    "underutilised": "underutilized",
+    "unequalled": "unequaled",
+    "unfavourable": "unfavorable",
+    "unfavourably": "unfavorably",
+    "unionisation": "unionization",
+    "unionise": "unionize",
+    "unionised": "unionized",
+    "unionises": "unionizes",
+    "unionising": "unionizing",
+    "unorganised": "unorganized",
+    "unravelled": "unraveled",
+    "unravelling": "unraveling",
+    "unrecognisable": "unrecognizable",
+    "unrecognised": "unrecognized",
+    "unrivalled": "unrivaled",
+    "unsavoury": "unsavory",
+    "untrammelled": "untrammeled",
+    "urbanisation": "urbanization",
+    "urbanise": "urbanize",
+    "urbanised": "urbanized",
+    "urbanises": "urbanizes",
+    "urbanising": "urbanizing",
+    "utilisable": "utilizable",
+    "utilisation": "utilization",
+    "utilise": "utilize",
+    "utilised": "utilized",
+    "utilises": "utilizes",
+    "utilising": "utilizing",
+    "valour": "valor",
+    "vandalise": "vandalize",
+    "vandalised": "vandalized",
+    "vandalises": "vandalizes",
+    "vandalising": "vandalizing",
+    "vaporisation": "vaporization",
+    "vaporise": "vaporize",
+    "vaporised": "vaporized",
+    "vaporises": "vaporizes",
+    "vaporising": "vaporizing",
+    "vapour": "vapor",
+    "vapours": "vapors",
+    "verbalise": "verbalize",
+    "verbalised": "verbalized",
+    "verbalises": "verbalizes",
+    "verbalising": "verbalizing",
+    "victimisation": "victimization",
+    "victimise": "victimize",
+    "victimised": "victimized",
+    "victimises": "victimizes",
+    "victimising": "victimizing",
+    "videodisc": "videodisk",
+    "videodiscs": "videodisks",
+    "vigour": "vigor",
+    "visualisation": "visualization",
+    "visualisations": "visualizations",
+    "visualise": "visualize",
+    "visualised": "visualized",
+    "visualises": "visualizes",
+    "visualising": "visualizing",
+    "vocalisation": "vocalization",
+    "vocalisations": "vocalizations",
+    "vocalise": "vocalize",
+    "vocalised": "vocalized",
+    "vocalises": "vocalizes",
+    "vocalising": "vocalizing",
+    "vulcanised": "vulcanized",
+    "vulgarisation": "vulgarization",
+    "vulgarise": "vulgarize",
+    "vulgarised": "vulgarized",
+    "vulgarises": "vulgarizes",
+    "vulgarising": "vulgarizing",
+    "waggon": "wagon",
+    "waggons": "wagons",
+    "watercolour": "watercolor",
+    "watercolours": "watercolors",
+    "weaselled": "weaseled",
+    "weaselling": "weaseling",
+    "westernisation": "westernization",
+    "westernise": "westernize",
+    "westernised": "westernized",
+    "westernises": "westernizes",
+    "westernising": "westernizing",
+    "womanise": "womanize",
+    "womanised": "womanized",
+    "womaniser": "womanizer",
+    "womanisers": "womanizers",
+    "womanises": "womanizes",
+    "womanising": "womanizing",
+    "woollen": "woolen",
+    "woollens": "woolens",
+    "woollies": "woolies",
+    "woolly": "wooly",
+    "worshipped": "worshiped",
+    "worshipping": "worshiping",
+    "worshipper": "worshiper",
+    "yodelled": "yodeled",
+    "yodelling": "yodeling",
+    "yoghourt": "yogurt",
+    "yoghourts": "yogurts",
+    "yoghurt": "yogurt",
+    "yoghurts": "yogurts",
+    "mhm": "hmm",
+    "mm": "hmm",
+    "mmm": "hmm"
+}
\ No newline at end of file
diff --git a/musetalk/whisper/whisper/normalizers/english.py b/musetalk/whisper/whisper/normalizers/english.py
new file mode 100644
index 0000000..d5c2bb4
--- /dev/null
+++ b/musetalk/whisper/whisper/normalizers/english.py
@@ -0,0 +1,543 @@
+import json
+import os
+import re
+from fractions import Fraction
+from typing import Iterator, List, Match, Optional, Union
+
+from more_itertools import windowed
+
+from .basic import remove_symbols_and_diacritics
+
+
+class EnglishNumberNormalizer:
+    """
+    Convert any spelled-out numbers into arabic numbers, while handling:
+
+    - remove any commas
+    - keep the suffixes such as: `1960s`, `274th`, `32nd`, etc.
+    - spell out currency symbols after the number. e.g. `$20 million` -> `20000000 dollars`
+    - spell out `one` and `ones`
+    - interpret successive single-digit numbers as nominal: `one oh one` -> `101`
+    """
+
+    def __init__(self):
+        super().__init__()
+
+        self.zeros = {"o", "oh", "zero"}
+        self.ones = {
+            name: i
+            for i, name in enumerate(
+                [
+                    "one",
+                    "two",
+                    "three",
+                    "four",
+                    "five",
+                    "six",
+                    "seven",
+                    "eight",
+                    "nine",
+                    "ten",
+                    "eleven",
+                    "twelve",
+                    "thirteen",
+                    "fourteen",
+                    "fifteen",
+                    "sixteen",
+                    "seventeen",
+                    "eighteen",
+                    "nineteen",
+                ],
+                start=1,
+            )
+        }
+        self.ones_plural = {
+            "sixes" if name == "six" else name + "s": (value, "s")
+            for name, value in self.ones.items()
+        }
+        self.ones_ordinal = {
+            "zeroth": (0, "th"),
+            "first": (1, "st"),
+            "second": (2, "nd"),
+            "third": (3, "rd"),
+            "fifth": (5, "th"),
+            "twelfth": (12, "th"),
+            **{
+                name + ("h" if name.endswith("t") else "th"): (value, "th")
+                for name, value in self.ones.items()
+                if value > 3 and value != 5 and value != 12
+            },
+        }
+        self.ones_suffixed = {**self.ones_plural, **self.ones_ordinal}
+
+        self.tens = {
+            "twenty": 20,
+            "thirty": 30,
+            "forty": 40,
+            "fifty": 50,
+            "sixty": 60,
+            "seventy": 70,
+            "eighty": 80,
+            "ninety": 90,
+        }
+        self.tens_plural = {
+            name.replace("y", "ies"): (value, "s") for name, value in self.tens.items()
+        }
+        self.tens_ordinal = {
+            name.replace("y", "ieth"): (value, "th") for name, value in self.tens.items()
+        }
+        self.tens_suffixed = {**self.tens_plural, **self.tens_ordinal}
+
+        self.multipliers = {
+            "hundred": 100,
+            "thousand": 1_000,
+            "million": 1_000_000,
+            "billion": 1_000_000_000,
+            "trillion": 1_000_000_000_000,
+            "quadrillion": 1_000_000_000_000_000,
+            "quintillion": 1_000_000_000_000_000_000,
+            "sextillion": 1_000_000_000_000_000_000_000,
+            "septillion": 1_000_000_000_000_000_000_000_000,
+            "octillion": 1_000_000_000_000_000_000_000_000_000,
+            "nonillion": 1_000_000_000_000_000_000_000_000_000_000,
+            "decillion": 1_000_000_000_000_000_000_000_000_000_000_000,
+        }
+        self.multipliers_plural = {
+            name + "s": (value, "s") for name, value in self.multipliers.items()
+        }
+        self.multipliers_ordinal = {
+            name + "th": (value, "th") for name, value in self.multipliers.items()
+        }
+        self.multipliers_suffixed = {**self.multipliers_plural, **self.multipliers_ordinal}
+        self.decimals = {*self.ones, *self.tens, *self.zeros}
+
+        self.preceding_prefixers = {
+            "minus": "-",
+            "negative": "-",
+            "plus": "+",
+            "positive": "+",
+        }
+        self.following_prefixers = {
+            "pound": "£",
+            "pounds": "£",
+            "euro": "€",
+            "euros": "€",
+            "dollar": "$",
+            "dollars": "$",
+            "cent": "¢",
+            "cents": "¢",
+        }
+        self.prefixes = set(
+            list(self.preceding_prefixers.values()) + list(self.following_prefixers.values())
+        )
+        self.suffixers = {
+            "per": {"cent": "%"},
+            "percent": "%",
+        }
+        self.specials = {"and", "double", "triple", "point"}
+
+        self.words = set(
+            [
+                key
+                for mapping in [
+                    self.zeros,
+                    self.ones,
+                    self.ones_suffixed,
+                    self.tens,
+                    self.tens_suffixed,
+                    self.multipliers,
+                    self.multipliers_suffixed,
+                    self.preceding_prefixers,
+                    self.following_prefixers,
+                    self.suffixers,
+                    self.specials,
+                ]
+                for key in mapping
+            ]
+        )
+        self.literal_words = {"one", "ones"}
+
+    def process_words(self, words: List[str]) -> Iterator[str]:
+        prefix: Optional[str] = None
+        value: Optional[Union[str, int]] = None
+        skip = False
+
+        def to_fraction(s: str):
+            try:
+                return Fraction(s)
+            except ValueError:
+                return None
+
+        def output(result: Union[str, int]):
+            nonlocal prefix, value
+            result = str(result)
+            if prefix is not None:
+                result = prefix + result
+            value = None
+            prefix = None
+            return result
+
+        if len(words) == 0:
+            return
+
+        for prev, current, next in windowed([None] + words + [None], 3):
+            if skip:
+                skip = False
+                continue
+
+            next_is_numeric = next is not None and re.match(r"^\d+(\.\d+)?$", next)
+            has_prefix = current[0] in self.prefixes
+            current_without_prefix = current[1:] if has_prefix else current
+            if re.match(r"^\d+(\.\d+)?$", current_without_prefix):
+                # arabic numbers (potentially with signs and fractions)
+                f = to_fraction(current_without_prefix)
+                assert f is not None
+                if value is not None:
+                    if isinstance(value, str) and value.endswith("."):
+                        # concatenate decimals / ip address components
+                        value = str(value) + str(current)
+                        continue
+                    else:
+                        yield output(value)
+
+                prefix = current[0] if has_prefix else prefix
+                if f.denominator == 1:
+                    value = f.numerator  # store integers as int
+                else:
+                    value = current_without_prefix
+            elif current not in self.words:
+                # non-numeric words
+                if value is not None:
+                    yield output(value)
+                yield output(current)
+            elif current in self.zeros:
+                value = str(value or "") + "0"
+            elif current in self.ones:
+                ones = self.ones[current]
+
+                if value is None:
+                    value = ones
+                elif isinstance(value, str) or prev in self.ones:
+                    if prev in self.tens and ones < 10:  # replace the last zero with the digit
+                        assert value[-1] == "0"
+                        value = value[:-1] + str(ones)
+                    else:
+                        value = str(value) + str(ones)
+                elif ones < 10:
+                    if value % 10 == 0:
+                        value += ones
+                    else:
+                        value = str(value) + str(ones)
+                else:  # eleven to nineteen
+                    if value % 100 == 0:
+                        value += ones
+                    else:
+                        value = str(value) + str(ones)
+            elif current in self.ones_suffixed:
+                # ordinal or cardinal; yield the number right away
+                ones, suffix = self.ones_suffixed[current]
+                if value is None:
+                    yield output(str(ones) + suffix)
+                elif isinstance(value, str) or prev in self.ones:
+                    if prev in self.tens and ones < 10:
+                        assert value[-1] == "0"
+                        yield output(value[:-1] + str(ones) + suffix)
+                    else:
+                        yield output(str(value) + str(ones) + suffix)
+                elif ones < 10:
+                    if value % 10 == 0:
+                        yield output(str(value + ones) + suffix)
+                    else:
+                        yield output(str(value) + str(ones) + suffix)
+                else:  # eleven to nineteen
+                    if value % 100 == 0:
+                        yield output(str(value + ones) + suffix)
+                    else:
+                        yield output(str(value) + str(ones) + suffix)
+                value = None
+            elif current in self.tens:
+                tens = self.tens[current]
+                if value is None:
+                    value = tens
+                elif isinstance(value, str):
+                    value = str(value) + str(tens)
+                else:
+                    if value % 100 == 0:
+                        value += tens
+                    else:
+                        value = str(value) + str(tens)
+            elif current in self.tens_suffixed:
+                # ordinal or cardinal; yield the number right away
+                tens, suffix = self.tens_suffixed[current]
+                if value is None:
+                    yield output(str(tens) + suffix)
+                elif isinstance(value, str):
+                    yield output(str(value) + str(tens) + suffix)
+                else:
+                    if value % 100 == 0:
+                        yield output(str(value + tens) + suffix)
+                    else:
+                        yield output(str(value) + str(tens) + suffix)
+            elif current in self.multipliers:
+                multiplier = self.multipliers[current]
+                if value is None:
+                    value = multiplier
+                elif isinstance(value, str) or value == 0:
+                    f = to_fraction(value)
+                    p = f * multiplier if f is not None else None
+                    if f is not None and p.denominator == 1:
+                        value = p.numerator
+                    else:
+                        yield output(value)
+                        value = multiplier
+                else:
+                    before = value // 1000 * 1000
+                    residual = value % 1000
+                    value = before + residual * multiplier
+            elif current in self.multipliers_suffixed:
+                multiplier, suffix = self.multipliers_suffixed[current]
+                if value is None:
+                    yield output(str(multiplier) + suffix)
+                elif isinstance(value, str):
+                    f = to_fraction(value)
+                    p = f * multiplier if f is not None else None
+                    if f is not None and p.denominator == 1:
+                        yield output(str(p.numerator) + suffix)
+                    else:
+                        yield output(value)
+                        yield output(str(multiplier) + suffix)
+                else:  # int
+                    before = value // 1000 * 1000
+                    residual = value % 1000
+                    value = before + residual * multiplier
+                    yield output(str(value) + suffix)
+                value = None
+            elif current in self.preceding_prefixers:
+                # apply prefix (positive, minus, etc.) if it precedes a number
+                if value is not None:
+                    yield output(value)
+
+                if next in self.words or next_is_numeric:
+                    prefix = self.preceding_prefixers[current]
+                else:
+                    yield output(current)
+            elif current in self.following_prefixers:
+                # apply prefix (dollars, cents, etc.) only after a number
+                if value is not None:
+                    prefix = self.following_prefixers[current]
+                    yield output(value)
+                else:
+                    yield output(current)
+            elif current in self.suffixers:
+                # apply suffix symbols (percent -> '%')
+                if value is not None:
+                    suffix = self.suffixers[current]
+                    if isinstance(suffix, dict):
+                        if next in suffix:
+                            yield output(str(value) + suffix[next])
+                            skip = True
+                        else:
+                            yield output(value)
+                            yield output(current)
+                    else:
+                        yield output(str(value) + suffix)
+                else:
+                    yield output(current)
+            elif current in self.specials:
+                if next not in self.words and not next_is_numeric:
+                    # apply special handling only if the next word can be numeric
+                    if value is not None:
+                        yield output(value)
+                    yield output(current)
+                elif current == "and":
+                    # ignore "and" after hundreds, thousands, etc.
+                    if prev not in self.multipliers:
+                        if value is not None:
+                            yield output(value)
+                        yield output(current)
+                elif current == "double" or current == "triple":
+                    if next in self.ones or next in self.zeros:
+                        repeats = 2 if current == "double" else 3
+                        ones = self.ones.get(next, 0)
+                        value = str(value or "") + str(ones) * repeats
+                        skip = True
+                    else:
+                        if value is not None:
+                            yield output(value)
+                        yield output(current)
+                elif current == "point":
+                    if next in self.decimals or next_is_numeric:
+                        value = str(value or "") + "."
+                else:
+                    # should all have been covered at this point
+                    raise ValueError(f"Unexpected token: {current}")
+            else:
+                # all should have been covered at this point
+                raise ValueError(f"Unexpected token: {current}")
+
+        if value is not None:
+            yield output(value)
+
+    def preprocess(self, s: str):
+        # replace "<number> and a half" with "<number> point five"
+        results = []
+
+        segments = re.split(r"\band\s+a\s+half\b", s)
+        for i, segment in enumerate(segments):
+            if len(segment.strip()) == 0:
+                continue
+            if i == len(segments) - 1:
+                results.append(segment)
+            else:
+                results.append(segment)
+                last_word = segment.rsplit(maxsplit=2)[-1]
+                if last_word in self.decimals or last_word in self.multipliers:
+                    results.append("point five")
+                else:
+                    results.append("and a half")
+
+        s = " ".join(results)
+
+        # put a space at number/letter boundary
+        s = re.sub(r"([a-z])([0-9])", r"\1 \2", s)
+        s = re.sub(r"([0-9])([a-z])", r"\1 \2", s)
+
+        # but remove spaces which could be a suffix
+        s = re.sub(r"([0-9])\s+(st|nd|rd|th|s)\b", r"\1\2", s)
+
+        return s
+
+    def postprocess(self, s: str):
+        def combine_cents(m: Match):
+            try:
+                currency = m.group(1)
+                integer = m.group(2)
+                cents = int(m.group(3))
+                return f"{currency}{integer}.{cents:02d}"
+            except ValueError:
+                return m.string
+
+        def extract_cents(m: Match):
+            try:
+                return f"¢{int(m.group(1))}"
+            except ValueError:
+                return m.string
+
+        # apply currency postprocessing; "$2 and ¢7" -> "$2.07"
+        s = re.sub(r"([€£$])([0-9]+) (?:and )?¢([0-9]{1,2})\b", combine_cents, s)
+        s = re.sub(r"[€£$]0.([0-9]{1,2})\b", extract_cents, s)
+
+        # write "one(s)" instead of "1(s)", just for the readability
+        s = re.sub(r"\b1(s?)\b", r"one\1", s)
+
+        return s
+
+    def __call__(self, s: str):
+        s = self.preprocess(s)
+        s = " ".join(word for word in self.process_words(s.split()) if word is not None)
+        s = self.postprocess(s)
+
+        return s
+
+
+class EnglishSpellingNormalizer:
+    """
+    Applies British-American spelling mappings as listed in [1].
+
+    [1] https://www.tysto.com/uk-us-spelling-list.html
+    """
+
+    def __init__(self):
+        mapping_path = os.path.join(os.path.dirname(__file__), "english.json")
+        self.mapping = json.load(open(mapping_path))
+
+    def __call__(self, s: str):
+        return " ".join(self.mapping.get(word, word) for word in s.split())
+
+
+class EnglishTextNormalizer:
+    def __init__(self):
+        self.ignore_patterns = r"\b(hmm|mm|mhm|mmm|uh|um)\b"
+        self.replacers = {
+            # common contractions
+            r"\bwon't\b": "will not",
+            r"\bcan't\b": "can not",
+            r"\blet's\b": "let us",
+            r"\bain't\b": "aint",
+            r"\by'all\b": "you all",
+            r"\bwanna\b": "want to",
+            r"\bgotta\b": "got to",
+            r"\bgonna\b": "going to",
+            r"\bi'ma\b": "i am going to",
+            r"\bimma\b": "i am going to",
+            r"\bwoulda\b": "would have",
+            r"\bcoulda\b": "could have",
+            r"\bshoulda\b": "should have",
+            r"\bma'am\b": "madam",
+            # contractions in titles/prefixes
+            r"\bmr\b": "mister ",
+            r"\bmrs\b": "missus ",
+            r"\bst\b": "saint ",
+            r"\bdr\b": "doctor ",
+            r"\bprof\b": "professor ",
+            r"\bcapt\b": "captain ",
+            r"\bgov\b": "governor ",
+            r"\bald\b": "alderman ",
+            r"\bgen\b": "general ",
+            r"\bsen\b": "senator ",
+            r"\brep\b": "representative ",
+            r"\bpres\b": "president ",
+            r"\brev\b": "reverend ",
+            r"\bhon\b": "honorable ",
+            r"\basst\b": "assistant ",
+            r"\bassoc\b": "associate ",
+            r"\blt\b": "lieutenant ",
+            r"\bcol\b": "colonel ",
+            r"\bjr\b": "junior ",
+            r"\bsr\b": "senior ",
+            r"\besq\b": "esquire ",
+            # prefect tenses, ideally it should be any past participles, but it's harder..
+            r"'d been\b": " had been",
+            r"'s been\b": " has been",
+            r"'d gone\b": " had gone",
+            r"'s gone\b": " has gone",
+            r"'d done\b": " had done",  # "'s done" is ambiguous
+            r"'s got\b": " has got",
+            # general contractions
+            r"n't\b": " not",
+            r"'re\b": " are",
+            r"'s\b": " is",
+            r"'d\b": " would",
+            r"'ll\b": " will",
+            r"'t\b": " not",
+            r"'ve\b": " have",
+            r"'m\b": " am",
+        }
+        self.standardize_numbers = EnglishNumberNormalizer()
+        self.standardize_spellings = EnglishSpellingNormalizer()
+
+    def __call__(self, s: str):
+        s = s.lower()
+
+        s = re.sub(r"[<\[][^>\]]*[>\]]", "", s)  # remove words between brackets
+        s = re.sub(r"\(([^)]+?)\)", "", s)  # remove words between parenthesis
+        s = re.sub(self.ignore_patterns, "", s)
+        s = re.sub(r"\s+'", "'", s)  # standardize when there's a space before an apostrophe
+
+        for pattern, replacement in self.replacers.items():
+            s = re.sub(pattern, replacement, s)
+
+        s = re.sub(r"(\d),(\d)", r"\1\2", s)  # remove commas between digits
+        s = re.sub(r"\.([^0-9]|$)", r" \1", s)  # remove periods not followed by numbers
+        s = remove_symbols_and_diacritics(s, keep=".%$¢€£")  # keep some symbols for numerics
+
+        s = self.standardize_numbers(s)
+        s = self.standardize_spellings(s)
+
+        # now remove prefix/suffix symbols that are not preceded/followed by numbers
+        s = re.sub(r"[.$¢€£]([^0-9])", r" \1", s)
+        s = re.sub(r"([^0-9])%", r"\1 ", s)
+
+        s = re.sub(r"\s+", " ", s)  # replace any successive whitespace characters with a space
+
+        return s
diff --git a/musetalk/whisper/whisper/tokenizer.py b/musetalk/whisper/whisper/tokenizer.py
new file mode 100644
index 0000000..88142c4
--- /dev/null
+++ b/musetalk/whisper/whisper/tokenizer.py
@@ -0,0 +1,331 @@
+import os
+from dataclasses import dataclass
+from functools import lru_cache
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+from transformers import GPT2TokenizerFast
+
+LANGUAGES = {
+    "en": "english",
+    "zh": "chinese",
+    "de": "german",
+    "es": "spanish",
+    "ru": "russian",
+    "ko": "korean",
+    "fr": "french",
+    "ja": "japanese",
+    "pt": "portuguese",
+    "tr": "turkish",
+    "pl": "polish",
+    "ca": "catalan",
+    "nl": "dutch",
+    "ar": "arabic",
+    "sv": "swedish",
+    "it": "italian",
+    "id": "indonesian",
+    "hi": "hindi",
+    "fi": "finnish",
+    "vi": "vietnamese",
+    "iw": "hebrew",
+    "uk": "ukrainian",
+    "el": "greek",
+    "ms": "malay",
+    "cs": "czech",
+    "ro": "romanian",
+    "da": "danish",
+    "hu": "hungarian",
+    "ta": "tamil",
+    "no": "norwegian",
+    "th": "thai",
+    "ur": "urdu",
+    "hr": "croatian",
+    "bg": "bulgarian",
+    "lt": "lithuanian",
+    "la": "latin",
+    "mi": "maori",
+    "ml": "malayalam",
+    "cy": "welsh",
+    "sk": "slovak",
+    "te": "telugu",
+    "fa": "persian",
+    "lv": "latvian",
+    "bn": "bengali",
+    "sr": "serbian",
+    "az": "azerbaijani",
+    "sl": "slovenian",
+    "kn": "kannada",
+    "et": "estonian",
+    "mk": "macedonian",
+    "br": "breton",
+    "eu": "basque",
+    "is": "icelandic",
+    "hy": "armenian",
+    "ne": "nepali",
+    "mn": "mongolian",
+    "bs": "bosnian",
+    "kk": "kazakh",
+    "sq": "albanian",
+    "sw": "swahili",
+    "gl": "galician",
+    "mr": "marathi",
+    "pa": "punjabi",
+    "si": "sinhala",
+    "km": "khmer",
+    "sn": "shona",
+    "yo": "yoruba",
+    "so": "somali",
+    "af": "afrikaans",
+    "oc": "occitan",
+    "ka": "georgian",
+    "be": "belarusian",
+    "tg": "tajik",
+    "sd": "sindhi",
+    "gu": "gujarati",
+    "am": "amharic",
+    "yi": "yiddish",
+    "lo": "lao",
+    "uz": "uzbek",
+    "fo": "faroese",
+    "ht": "haitian creole",
+    "ps": "pashto",
+    "tk": "turkmen",
+    "nn": "nynorsk",
+    "mt": "maltese",
+    "sa": "sanskrit",
+    "lb": "luxembourgish",
+    "my": "myanmar",
+    "bo": "tibetan",
+    "tl": "tagalog",
+    "mg": "malagasy",
+    "as": "assamese",
+    "tt": "tatar",
+    "haw": "hawaiian",
+    "ln": "lingala",
+    "ha": "hausa",
+    "ba": "bashkir",
+    "jw": "javanese",
+    "su": "sundanese",
+}
+
+# language code lookup by name, with a few language aliases
+TO_LANGUAGE_CODE = {
+    **{language: code for code, language in LANGUAGES.items()},
+    "burmese": "my",
+    "valencian": "ca",
+    "flemish": "nl",
+    "haitian": "ht",
+    "letzeburgesch": "lb",
+    "pushto": "ps",
+    "panjabi": "pa",
+    "moldavian": "ro",
+    "moldovan": "ro",
+    "sinhalese": "si",
+    "castilian": "es",
+}
+
+
+@dataclass(frozen=True)
+class Tokenizer:
+    """A thin wrapper around `GPT2TokenizerFast` providing quick access to special tokens"""
+
+    tokenizer: "GPT2TokenizerFast"
+    language: Optional[str]
+    sot_sequence: Tuple[int]
+
+    def encode(self, text, **kwargs):
+        return self.tokenizer.encode(text, **kwargs)
+
+    def decode(self, token_ids: Union[int, List[int], np.ndarray, torch.Tensor], **kwargs):
+        return self.tokenizer.decode(token_ids, **kwargs)
+
+    def decode_with_timestamps(self, tokens) -> str:
+        """
+        Timestamp tokens are above the special tokens' id range and are ignored by `decode()`.
+        This method decodes given tokens with timestamps tokens annotated, e.g. "<|1.08|>".
+        """
+        outputs = [[]]
+        for token in tokens:
+            if token >= self.timestamp_begin:
+                timestamp = f"<|{(token - self.timestamp_begin) * 0.02:.2f}|>"
+                outputs.append(timestamp)
+                outputs.append([])
+            else:
+                outputs[-1].append(token)
+        outputs = [s if isinstance(s, str) else self.tokenizer.decode(s) for s in outputs]
+        return "".join(outputs)
+
+    @property
+    @lru_cache()
+    def eot(self) -> int:
+        return self.tokenizer.eos_token_id
+
+    @property
+    @lru_cache()
+    def sot(self) -> int:
+        return self._get_single_token_id("<|startoftranscript|>")
+
+    @property
+    @lru_cache()
+    def sot_lm(self) -> int:
+        return self._get_single_token_id("<|startoflm|>")
+
+    @property
+    @lru_cache()
+    def sot_prev(self) -> int:
+        return self._get_single_token_id("<|startofprev|>")
+
+    @property
+    @lru_cache()
+    def no_speech(self) -> int:
+        return self._get_single_token_id("<|nospeech|>")
+
+    @property
+    @lru_cache()
+    def no_timestamps(self) -> int:
+        return self._get_single_token_id("<|notimestamps|>")
+
+    @property
+    @lru_cache()
+    def timestamp_begin(self) -> int:
+        return self.tokenizer.all_special_ids[-1] + 1
+
+    @property
+    @lru_cache()
+    def language_token(self) -> int:
+        """Returns the token id corresponding to the value of the `language` field"""
+        if self.language is None:
+            raise ValueError(f"This tokenizer does not have language token configured")
+
+        additional_tokens = dict(
+            zip(
+                self.tokenizer.additional_special_tokens,
+                self.tokenizer.additional_special_tokens_ids,
+            )
+        )
+        candidate = f"<|{self.language}|>"
+        if candidate in additional_tokens:
+            return additional_tokens[candidate]
+
+        raise KeyError(f"Language {self.language} not found in tokenizer.")
+
+    @property
+    @lru_cache()
+    def all_language_tokens(self) -> Tuple[int]:
+        result = []
+        for token, token_id in zip(
+            self.tokenizer.additional_special_tokens,
+            self.tokenizer.additional_special_tokens_ids,
+        ):
+            if token.strip("<|>") in LANGUAGES:
+                result.append(token_id)
+        return tuple(result)
+
+    @property
+    @lru_cache()
+    def all_language_codes(self) -> Tuple[str]:
+        return tuple(self.decode([l]).strip("<|>") for l in self.all_language_tokens)
+
+    @property
+    @lru_cache()
+    def sot_sequence_including_notimestamps(self) -> Tuple[int]:
+        return tuple(list(self.sot_sequence) + [self.no_timestamps])
+
+    @property
+    @lru_cache()
+    def non_speech_tokens(self) -> Tuple[int]:
+        """
+        Returns the list of tokens to suppress in order to avoid any speaker tags or non-speech
+        annotations, to prevent sampling texts that are not actually spoken in the audio, e.g.
+
+        - ♪♪♪
+        - ( SPEAKING FOREIGN LANGUAGE )
+        - [DAVID] Hey there,
+
+        keeping basic punctuations like commas, periods, question marks, exclamation points, etc.
+        """
+        symbols = list("\"#()*+/:;<=>@[\\]^_`{|}~「」『』")
+        symbols += "<< >> <<< >>> -- --- -( -[ (' (\" (( )) ((( ))) [[ ]] {{ }} ♪♪ ♪♪♪".split()
+
+        # symbols that may be a single token or multiple tokens depending on the tokenizer.
+        # In case they're multiple tokens, suppress the first token, which is safe because:
+        # These are between U+2640 and U+267F miscellaneous symbols that are okay to suppress
+        # in generations, and in the 3-byte UTF-8 representation they share the first two bytes.
+        miscellaneous = set("♩♪♫♬♭♮♯")
+        assert all(0x2640 <= ord(c) <= 0x267F for c in miscellaneous)
+
+        # allow hyphens "-" and single quotes "'" between words, but not at the beginning of a word
+        result = {self.tokenizer.encode(" -")[0], self.tokenizer.encode(" '")[0]}
+        for symbol in symbols + list(miscellaneous):
+            for tokens in [self.tokenizer.encode(symbol), self.tokenizer.encode(" " + symbol)]:
+                if len(tokens) == 1 or symbol in miscellaneous:
+                    result.add(tokens[0])
+
+        return tuple(sorted(result))
+
+    def _get_single_token_id(self, text) -> int:
+        tokens = self.tokenizer.encode(text)
+        assert len(tokens) == 1, f"{text} is not encoded as a single token"
+        return tokens[0]
+
+
+@lru_cache(maxsize=None)
+def build_tokenizer(name: str = "gpt2"):
+    os.environ["TOKENIZERS_PARALLELISM"] = "false"
+    path = os.path.join(os.path.dirname(__file__), "assets", name)
+    tokenizer = GPT2TokenizerFast.from_pretrained(path)
+
+    specials = [
+        "<|startoftranscript|>",
+        *[f"<|{lang}|>" for lang in LANGUAGES.keys()],
+        "<|translate|>",
+        "<|transcribe|>",
+        "<|startoflm|>",
+        "<|startofprev|>",
+        "<|nospeech|>",
+        "<|notimestamps|>",
+    ]
+
+    tokenizer.add_special_tokens(dict(additional_special_tokens=specials))
+    return tokenizer
+
+
+@lru_cache(maxsize=None)
+def get_tokenizer(
+    multilingual: bool,
+    *,
+    task: Optional[str] = None,  # Literal["transcribe", "translate", None]
+    language: Optional[str] = None,
+) -> Tokenizer:
+    if language is not None:
+        language = language.lower()
+        if language not in LANGUAGES:
+            if language in TO_LANGUAGE_CODE:
+                language = TO_LANGUAGE_CODE[language]
+            else:
+                raise ValueError(f"Unsupported language: {language}")
+
+    if multilingual:
+        tokenizer_name = "multilingual"
+        task = task or "transcribe"
+        language = language or "en"
+    else:
+        tokenizer_name = "gpt2"
+        task = None
+        language = None
+
+    tokenizer = build_tokenizer(name=tokenizer_name)
+    all_special_ids: List[int] = tokenizer.all_special_ids
+    sot: int = all_special_ids[1]
+    translate: int = all_special_ids[-6]
+    transcribe: int = all_special_ids[-5]
+
+    langs = tuple(LANGUAGES.keys())
+    sot_sequence = [sot]
+    if language is not None:
+        sot_sequence.append(sot + 1 + langs.index(language))
+    if task is not None:
+        sot_sequence.append(transcribe if task == "transcribe" else translate)
+
+    return Tokenizer(tokenizer=tokenizer, language=language, sot_sequence=tuple(sot_sequence))
diff --git a/musetalk/whisper/whisper/transcribe.py b/musetalk/whisper/whisper/transcribe.py
new file mode 100644
index 0000000..745e775
--- /dev/null
+++ b/musetalk/whisper/whisper/transcribe.py
@@ -0,0 +1,207 @@
+import argparse
+import os
+import warnings
+from typing import List, Optional, Tuple, Union, TYPE_CHECKING
+
+import numpy as np
+import torch
+import tqdm
+
+from .audio import SAMPLE_RATE, N_FRAMES, HOP_LENGTH, pad_or_trim, log_mel_spectrogram
+from .decoding import DecodingOptions, DecodingResult
+from .tokenizer import LANGUAGES, TO_LANGUAGE_CODE, get_tokenizer
+from .utils import exact_div, format_timestamp, optional_int, optional_float, str2bool, write_txt, write_vtt, write_srt
+
+if TYPE_CHECKING:
+    from .model import Whisper
+
+
+def transcribe(
+        model: "Whisper",
+        audio: Union[str, np.ndarray, torch.Tensor],
+        *,
+        verbose: Optional[bool] = None,
+        temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
+        compression_ratio_threshold: Optional[float] = 2.4,
+        logprob_threshold: Optional[float] = -1.0,
+        no_speech_threshold: Optional[float] = 0.6,
+        condition_on_previous_text: bool = True,
+        force_extraction: bool = False,
+        **decode_options,
+):
+    """
+    Transcribe an audio file using Whisper
+
+    Parameters
+    ----------
+    model: Whisper
+        The Whisper model instance
+
+    audio: Union[str, np.ndarray, torch.Tensor]
+        The path to the audio file to open, or the audio waveform
+
+    verbose: bool
+        Whether to display the text being decoded to the console. If True, displays all the details,
+        If False, displays minimal details. If None, does not display anything
+
+    temperature: Union[float, Tuple[float, ...]]
+        Temperature for sampling. It can be a tuple of temperatures, which will be successfully used
+        upon failures according to either `compression_ratio_threshold` or `logprob_threshold`.
+
+    compression_ratio_threshold: float
+        If the gzip compression ratio is above this value, treat as failed
+
+    logprob_threshold: float
+        If the average log probability over sampled tokens is below this value, treat as failed
+
+    no_speech_threshold: float
+        If the no_speech probability is higher than this value AND the average log probability
+        over sampled tokens is below `logprob_threshold`, consider the segment as silent
+
+    condition_on_previous_text: bool
+        if True, the previous output of the model is provided as a prompt for the next window;
+        disabling may make the text inconsistent across windows, but the model becomes less prone to
+        getting stuck in a failure loop, such as repetition looping or timestamps going out of sync.
+
+    decode_options: dict
+        Keyword arguments to construct `DecodingOptions` instances
+
+    Returns
+    -------
+    A dictionary containing the resulting text ("text") and segment-level details ("segments"), and
+    the spoken language ("language"), which is detected when `decode_options["language"]` is None.
+    """
+    dtype = torch.float16 if decode_options.get("fp16", True) else torch.float32
+    if model.device == torch.device("cpu"):
+        if torch.cuda.is_available():
+            warnings.warn("Performing inference on CPU when CUDA is available")
+        if dtype == torch.float16:
+            warnings.warn("FP16 is not supported on CPU; using FP32 instead")
+            dtype = torch.float32
+
+    if dtype == torch.float32:
+        decode_options["fp16"] = False
+
+    mel = log_mel_spectrogram(audio)
+   
+    all_segments = []
+    def add_segment(
+            *, start: float, end: float, encoder_embeddings
+    ):
+      
+        all_segments.append(
+            {
+                "start": start,
+                "end": end,
+                "encoder_embeddings":encoder_embeddings,
+            }
+        )
+    # show the progress bar when verbose is False (otherwise the transcribed text will be printed)
+    num_frames = mel.shape[-1]
+    seek = 0
+    previous_seek_value = seek
+    sample_skip = 3000 # 
+    with tqdm.tqdm(total=num_frames, unit='frames', disable=verbose is not False) as pbar:
+        while seek < num_frames:
+            # seek是开始的帧数
+            end_seek = min(seek + sample_skip, num_frames)
+            segment = pad_or_trim(mel[:,seek:seek+sample_skip], N_FRAMES).to(model.device).to(dtype)
+            
+            single = segment.ndim == 2
+            if single:
+                segment = segment.unsqueeze(0)
+            if dtype == torch.float16:
+                segment = segment.half()
+            audio_features, embeddings  = model.encoder(segment, include_embeddings = True)
+            
+            encoder_embeddings = embeddings
+            #print(f"encoder_embeddings shape {encoder_embeddings.shape}")
+            add_segment(
+                start=seek,
+                end=end_seek,
+                #text_tokens=tokens,
+                #result=result,
+                encoder_embeddings=encoder_embeddings,
+            )
+            seek+=sample_skip
+    
+    return dict(segments=all_segments)
+
+
+def cli():
+    from . import available_models
+
+    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    parser.add_argument("audio", nargs="+", type=str, help="audio file(s) to transcribe")
+    parser.add_argument("--model", default="small", choices=available_models(), help="name of the Whisper model to use")
+    parser.add_argument("--model_dir", type=str, default=None, help="the path to save model files; uses ~/.cache/whisper by default")
+    parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu", help="device to use for PyTorch inference")
+    parser.add_argument("--output_dir", "-o", type=str, default=".", help="directory to save the outputs")
+    parser.add_argument("--verbose", type=str2bool, default=True, help="whether to print out the progress and debug messages")
+
+    parser.add_argument("--task", type=str, default="transcribe", choices=["transcribe", "translate"], help="whether to perform X->X speech recognition ('transcribe') or X->English translation ('translate')")
+    parser.add_argument("--language", type=str, default=None, choices=sorted(LANGUAGES.keys()) + sorted([k.title() for k in TO_LANGUAGE_CODE.keys()]), help="language spoken in the audio, specify None to perform language detection")
+
+    parser.add_argument("--temperature", type=float, default=0, help="temperature to use for sampling")
+    parser.add_argument("--best_of", type=optional_int, default=5, help="number of candidates when sampling with non-zero temperature")
+    parser.add_argument("--beam_size", type=optional_int, default=5, help="number of beams in beam search, only applicable when temperature is zero")
+    parser.add_argument("--patience", type=float, default=None, help="optional patience value to use in beam decoding, as in https://arxiv.org/abs/2204.05424, the default (1.0) is equivalent to conventional beam search")
+    parser.add_argument("--length_penalty", type=float, default=None, help="optional token length penalty coefficient (alpha) as in https://arxiv.org/abs/1609.08144, uses simple length normalization by default")
+
+    parser.add_argument("--suppress_tokens", type=str, default="-1", help="comma-separated list of token ids to suppress during sampling; '-1' will suppress most special characters except common punctuations")
+    parser.add_argument("--initial_prompt", type=str, default=None, help="optional text to provide as a prompt for the first window.")
+    parser.add_argument("--condition_on_previous_text", type=str2bool, default=True, help="if True, provide the previous output of the model as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop")
+    parser.add_argument("--fp16", type=str2bool, default=True, help="whether to perform inference in fp16; True by default")
+
+    parser.add_argument("--temperature_increment_on_fallback", type=optional_float, default=0.2, help="temperature to increase when falling back when the decoding fails to meet either of the thresholds below")
+    parser.add_argument("--compression_ratio_threshold", type=optional_float, default=2.4, help="if the gzip compression ratio is higher than this value, treat the decoding as failed")
+    parser.add_argument("--logprob_threshold", type=optional_float, default=-1.0, help="if the average log probability is lower than this value, treat the decoding as failed")
+    parser.add_argument("--no_speech_threshold", type=optional_float, default=0.6, help="if the probability of the <|nospeech|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence")
+    parser.add_argument("--threads", type=optional_int, default=0, help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS")
+
+    args = parser.parse_args().__dict__
+    model_name: str = args.pop("model")
+    model_dir: str = args.pop("model_dir")
+    output_dir: str = args.pop("output_dir")
+    device: str = args.pop("device")
+    os.makedirs(output_dir, exist_ok=True)
+
+    if model_name.endswith(".en") and args["language"] not in {"en", "English"}:
+        if args["language"] is not None:
+            warnings.warn(f"{model_name} is an English-only model but receipted '{args['language']}'; using English instead.")
+        args["language"] = "en"
+
+    temperature = args.pop("temperature")
+    temperature_increment_on_fallback = args.pop("temperature_increment_on_fallback")
+    if temperature_increment_on_fallback is not None:
+        temperature = tuple(np.arange(temperature, 1.0 + 1e-6, temperature_increment_on_fallback))
+    else:
+        temperature = [temperature]
+
+    threads = args.pop("threads")
+    if threads > 0:
+        torch.set_num_threads(threads)
+
+    from . import load_model
+    model = load_model(model_name, device=device, download_root=model_dir)
+
+    for audio_path in args.pop("audio"):
+        result = transcribe(model, audio_path, temperature=temperature, **args)
+
+        audio_basename = os.path.basename(audio_path)
+
+        # save TXT
+        with open(os.path.join(output_dir, audio_basename + ".txt"), "w", encoding="utf-8") as txt:
+            write_txt(result["segments"], file=txt)
+
+        # save VTT
+        with open(os.path.join(output_dir, audio_basename + ".vtt"), "w", encoding="utf-8") as vtt:
+            write_vtt(result["segments"], file=vtt)
+
+        # save SRT
+        with open(os.path.join(output_dir, audio_basename + ".srt"), "w", encoding="utf-8") as srt:
+            write_srt(result["segments"], file=srt)
+
+
+if __name__ == '__main__':
+    cli()
diff --git a/musetalk/whisper/whisper/utils.py b/musetalk/whisper/whisper/utils.py
new file mode 100644
index 0000000..87c91f2
--- /dev/null
+++ b/musetalk/whisper/whisper/utils.py
@@ -0,0 +1,87 @@
+import zlib
+from typing import Iterator, TextIO
+
+
+def exact_div(x, y):
+    assert x % y == 0
+    return x // y
+
+
+def str2bool(string):
+    str2val = {"True": True, "False": False}
+    if string in str2val:
+        return str2val[string]
+    else:
+        raise ValueError(f"Expected one of {set(str2val.keys())}, got {string}")
+
+
+def optional_int(string):
+    return None if string == "None" else int(string)
+
+
+def optional_float(string):
+    return None if string == "None" else float(string)
+
+
+def compression_ratio(text) -> float:
+    return len(text) / len(zlib.compress(text.encode("utf-8")))
+
+
+def format_timestamp(seconds: float, always_include_hours: bool = False, decimal_marker: str = '.'):
+    assert seconds >= 0, "non-negative timestamp expected"
+    milliseconds = round(seconds * 1000.0)
+
+    hours = milliseconds // 3_600_000
+    milliseconds -= hours * 3_600_000
+
+    minutes = milliseconds // 60_000
+    milliseconds -= minutes * 60_000
+
+    seconds = milliseconds // 1_000
+    milliseconds -= seconds * 1_000
+
+    hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else ""
+    return f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}"
+
+
+def write_txt(transcript: Iterator[dict], file: TextIO):
+    for segment in transcript:
+        print(segment['text'].strip(), file=file, flush=True)
+
+
+def write_vtt(transcript: Iterator[dict], file: TextIO):
+    print("WEBVTT\n", file=file)
+    for segment in transcript:
+        print(
+            f"{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}\n"
+            f"{segment['text'].strip().replace('-->', '->')}\n",
+            file=file,
+            flush=True,
+        )
+
+
+def write_srt(transcript: Iterator[dict], file: TextIO):
+    """
+    Write a transcript to a file in SRT format.
+
+    Example usage:
+        from pathlib import Path
+        from whisper.utils import write_srt
+
+        result = transcribe(model, audio_path, temperature=temperature, **args)
+
+        # save SRT
+        audio_basename = Path(audio_path).stem
+        with open(Path(output_dir) / (audio_basename + ".srt"), "w", encoding="utf-8") as srt:
+            write_srt(result["segments"], file=srt)
+    """
+    for i, segment in enumerate(transcript, start=1):
+        # write srt lines
+        print(
+            f"{i}\n"
+            f"{format_timestamp(segment['start'], always_include_hours=True, decimal_marker=',')} --> "
+            f"{format_timestamp(segment['end'], always_include_hours=True, decimal_marker=',')}\n"
+            f"{segment['text'].strip().replace('-->', '->')}\n",
+            file=file,
+            flush=True,
+        )
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..cf74616
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,11 @@
+torch==2.0.1  --index-url https://download.pytorch.org/whl/cu118 
+torchvision==0.15.2  --index-url https://download.pytorch.org/whl/cu118 
+torchaudio==2.0.2  --index-url https://download.pytorch.org/whl/cu118
+diffusers==0.27.2
+accelerate==0.28.0
+tensorflow==2.12.0
+tensorboard==2.12.0
+opencv-python==4.9.0.80
+soundfile==0.12.1
+transformers==4.39.2
+
diff --git a/scripts/inference.py b/scripts/inference.py
new file mode 100644
index 0000000..5c659ea
--- /dev/null
+++ b/scripts/inference.py
@@ -0,0 +1,142 @@
+import argparse
+import os
+from omegaconf import OmegaConf
+import numpy as np
+import cv2
+import torch
+import glob
+import pickle
+from tqdm import tqdm
+import copy
+
+from musetalk.utils.utils import get_file_type,get_video_fps,datagen
+from musetalk.utils.preprocessing import get_landmark_and_bbox,read_imgs,coord_placeholder
+from musetalk.utils.blending import get_image
+from musetalk.utils.utils import load_all_model
+
+# load model weights
+audio_processor,vae,unet,pe  = load_all_model()
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+timesteps = torch.tensor([0], device=device)
+
+@torch.no_grad()
+def main(args):
+    inference_config = OmegaConf.load(args.inference_config)
+    print(inference_config)
+    for task_id in inference_config:
+        video_path = inference_config[task_id]["video_path"]
+        audio_path = inference_config[task_id]["audio_path"]
+
+        input_basename = os.path.basename(video_path).split('.')[0]
+        audio_basename  = os.path.basename(audio_path).split('.')[0]
+        output_basename = f"{input_basename}_{audio_basename}"
+        crop_coord_save_path = os.path.join(args.result_dir, input_basename+".pkl") # only related to video input
+        result_img_save_path = os.path.join(args.result_dir, output_basename) # related to video & audio inputs
+        os.makedirs(result_img_save_path,exist_ok =True)
+        
+        if args.output_vid_name=="":
+            output_vid_name = os.path.join(args.result_dir, output_basename+".mp4")
+        else:
+            output_vid_name = os.path.join(args.result_dir, args.output_vid_name)
+        ############################################## extract frames from source video ##############################################
+        if get_file_type(video_path)=="video":
+            save_dir_full = os.path.join(args.result_dir, input_basename)
+            os.makedirs(save_dir_full,exist_ok = True)
+            cmd = f"ffmpeg -i {video_path} -start_number 0 {save_dir_full}/%08d.png"
+            os.system(cmd)
+            input_img_list = sorted(glob.glob(os.path.join(save_dir_full, '*.[jpJP][pnPN]*[gG]')))
+            fps = get_video_fps(video_path)
+        else: # input img folder
+            input_img_list = glob.glob(os.path.join(video_path, '*.[jpJP][pnPN]*[gG]'))
+            input_img_list = sorted(input_img_list, key=lambda x: int(os.path.splitext(os.path.basename(x))[0]))
+            fps = args.fps
+        #print(input_img_list)
+        ############################################## extract audio feature ##############################################
+        whisper_feature = audio_processor.audio2feat(audio_path)
+        whisper_chunks = audio_processor.feature2chunks(feature_array=whisper_feature,fps=fps)
+        ############################################## preprocess input image  ##############################################
+        if os.path.exists(crop_coord_save_path) and args.use_saved_coord:
+            print("using extracted coordinates")
+            with open(crop_coord_save_path,'rb') as f:
+                coord_list = pickle.load(f)
+            frame_list = read_imgs(input_img_list)
+        else:
+            print("extracting landmarks...time consuming")
+            coord_list, frame_list = get_landmark_and_bbox(input_img_list,args.bbox_shift)
+            with open(crop_coord_save_path, 'wb') as f:
+                pickle.dump(coord_list, f)
+                
+        i = 0
+        input_latent_list = []
+        for bbox, frame in zip(coord_list, frame_list):
+            if bbox == coord_placeholder:
+                continue
+            x1, y1, x2, y2 = bbox
+            crop_frame = frame[y1:y2, x1:x2]
+            crop_frame = cv2.resize(crop_frame,(256,256),interpolation = cv2.INTER_LANCZOS4)
+            latents = vae.get_latents_for_unet(crop_frame)
+            input_latent_list.append(latents)
+    
+        # to smooth the first and the last frame
+        frame_list_cycle = frame_list + frame_list[::-1]
+        coord_list_cycle = coord_list + coord_list[::-1]
+        input_latent_list_cycle = input_latent_list + input_latent_list[::-1]
+        ############################################## inference batch by batch ##############################################
+        print("start inference")
+        video_num = len(whisper_chunks)
+        batch_size = args.batch_size
+        gen = datagen(whisper_chunks,input_latent_list_cycle,batch_size)
+        res_frame_list = []
+        for i, (whisper_batch,latent_batch) in enumerate(tqdm(gen,total=int(np.ceil(float(video_num)/batch_size)))):
+            
+            tensor_list = [torch.FloatTensor(arr) for arr in whisper_batch]
+            audio_feature_batch = torch.stack(tensor_list).to(unet.device) # torch, B, 5*N,384
+            audio_feature_batch = pe(audio_feature_batch)
+            
+            pred_latents = unet.model(latent_batch, timesteps, encoder_hidden_states=audio_feature_batch).sample
+            recon = vae.decode_latents(pred_latents)
+            for res_frame in recon:
+                res_frame_list.append(res_frame)
+                
+        ############################################## pad to full image ##############################################
+        print("pad talking image to original video")
+        for i, res_frame in enumerate(tqdm(res_frame_list)):
+            bbox = coord_list_cycle[i%(len(coord_list_cycle))]
+            ori_frame = copy.deepcopy(frame_list_cycle[i%(len(frame_list_cycle))])
+            x1, y1, x2, y2 = bbox
+            try:
+                res_frame = cv2.resize(res_frame.astype(np.uint8),(x2-x1,y2-y1))
+            except:
+#                 print(bbox)
+                continue
+            
+            combine_frame = get_image(ori_frame,res_frame,bbox)
+            cv2.imwrite(f"{result_img_save_path}/{str(i).zfill(8)}.png",combine_frame)
+            
+        cmd_img2video = f"ffmpeg -y -v fatal -r {fps} -f image2 -i {result_img_save_path}/%08d.png -vcodec libx264 -vf format=rgb24,scale=out_color_matrix=bt709,format=yuv420p -crf 18 temp.mp4"
+        print(cmd_img2video)
+        os.system(cmd_img2video)
+        
+        cmd_combine_audio = f"ffmpeg -i {audio_path} -i temp.mp4 {output_vid_name} -y"
+        print(cmd_combine_audio)
+        os.system(cmd_combine_audio)
+        
+        os.system("rm temp.mp4")
+        os.system(f"rm -r {result_img_save_path}")
+        print(f"result is save to {output_vid_name}")
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--inference_config",type=str, default="configs/inference/test_img.yaml")
+    parser.add_argument("--bbox_shift",type=int, default=0)
+    parser.add_argument("--result_dir", default='./results', help="path to output")
+
+    parser.add_argument("--fps",type=int, default=25)
+    parser.add_argument("--batch_size",type=int, default=8)
+    parser.add_argument("--output_vid_name",type=str,default='')
+    parser.add_argument("--use_saved_coord",action="store_true", help='use saved coordinate to save time')
+
+
+    args = parser.parse_args()
+    main(args)
+    
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