68 lines
2.7 KiB
Markdown
68 lines
2.7 KiB
Markdown
# Voice-chat tests
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Two tiers.
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## Unit tests — fast, GPU-free
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```
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python -m pytest tests/unit -v
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```
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These exercise pure logic: config parsing, prompt derivation, LoRA spec
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parsing, frame-length fitting, library round-robin selection, the
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pipeline's video branch, and ffmpeg mux argument shaping. They do not
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touch CUDA, Wan2.2, MuseTalk, or a real ffmpeg binary. Safe to run on
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Windows, outside Docker, without any models installed.
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Current unit files:
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- `test_video_config.py` — `VideoConfig.from_dict` round-trip, LoRA target validation
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- `test_video_engine_logic.py` — prompt derivation, library cursor, frame fitting
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- `test_pipeline_video_branch.py` — pipeline takes the video path iff engine is ready
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- `test_musetalk_fit_frames.py` — frame-length adjustment to match audio duration
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- `test_muxer_ffmpeg.py` — ffmpeg command construction
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## Component tests — slow, GPU-required, run inside Docker
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Each script in `tests/component/` exercises one subsystem end-to-end
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against the real models. The numbered prefix reflects the implementation
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phase each script gates, and also serves as a reasonable run order when
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debugging a fresh environment:
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| Script | Phase | Tests |
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|---|---|---|
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| `test_01_video_skeleton.py` | 1 | VideoEngine loads, config gate respected |
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| `test_02_wan22_loras.py` | 2 | Wan2.2 pipeline loads, LoRA stack applies |
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| `test_03_idle_clip.py` | 3 | `set_avatar` → idle MP4, written to disk for eyeballing |
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| `test_04_library_prebake.py` | 4 | library mode pre-bakes N base clips |
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| `test_05_musetalk_lipsync.py` | 5 | MuseTalk lip-sync on library frames + ffmpeg mux |
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| `test_06_reflective.py` | 6 | reflective mode: fresh Wan2.2 per reply |
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| `test_07_endpoints.py` | 7 | HTTP endpoints return sane responses |
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| `test_08_lora_reload.py` | 8 | `/api/reload-loras` swaps LoRAs live |
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| `test_09_gguf_generate.py` | 9 | GGUF-quantised DIT end-to-end I2V generation |
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| `test_10_t5_encode.py` | 10 | T5 encoder (optionally fp8-quantised) on CUDA |
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| `test_11_image_encode.py` | 11 | Avatar image → VAE latent path |
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| `test_12_dit_single_step.py` | 12 | Single DIT step on the loaded expert(s) |
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| `test_13_vae_decode.py` | 13 | VAE decode back to RGB frames |
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Tests 09-13 are focused on the GGUF + Blackwell (SM120) path and are how
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new quant schemes / attention backends get validated before wiring them
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into the full pipeline.
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Run one:
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```
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# Inside the container:
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docker compose exec voice-chat python -m tests.component.test_03_idle_clip
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```
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Run all (slow, ~20+ minutes on a 5090):
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```
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docker compose exec voice-chat python -m tests.component.run_all
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```
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Each component script writes its artifacts (MP4s, PNG frame dumps, logs)
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to `tests/component/_out/` so you can visually inspect results. That
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directory is gitignored.
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