live-voice-chat/tests/README.md

Voice-chat tests

Two tiers.

Unit tests — fast, GPU-free

python -m pytest tests/unit -v

These exercise pure logic: config parsing, prompt derivation, LoRA spec parsing, frame-length fitting, library round-robin selection, the pipeline's video branch, and ffmpeg mux argument shaping. They do not touch CUDA, Wan2.2, MuseTalk, or a real ffmpeg binary. Safe to run on Windows, outside Docker, without any models installed.

Current unit files:

• test_video_config.py — VideoConfig.from_dict round-trip, LoRA target validation
• test_video_engine_logic.py — prompt derivation, library cursor, frame fitting
• test_pipeline_video_branch.py — pipeline takes the video path iff engine is ready
• test_musetalk_fit_frames.py — frame-length adjustment to match audio duration
• test_muxer_ffmpeg.py — ffmpeg command construction

Component tests — slow, GPU-required, run inside Docker

Each script in tests/component/ exercises one subsystem end-to-end against the real models. The numbered prefix reflects the implementation phase each script gates, and also serves as a reasonable run order when debugging a fresh environment:

Script	Phase	Tests
test_01_video_skeleton.py	1	VideoEngine loads, config gate respected
test_02_wan22_loras.py	2	Wan2.2 pipeline loads, LoRA stack applies
test_03_idle_clip.py	3	set_avatar → idle MP4, written to disk for eyeballing
test_04_library_prebake.py	4	library mode pre-bakes N base clips
test_05_musetalk_lipsync.py	5	MuseTalk lip-sync on library frames + ffmpeg mux
test_06_reflective.py	6	reflective mode: fresh Wan2.2 per reply
test_07_endpoints.py	7	HTTP endpoints return sane responses
test_08_lora_reload.py	8	/api/reload-loras swaps LoRAs live
test_09_gguf_generate.py	9	GGUF-quantised DIT end-to-end I2V generation
test_10_t5_encode.py	10	T5 encoder (optionally fp8-quantised) on CUDA
test_11_image_encode.py	11	Avatar image → VAE latent path
test_12_dit_single_step.py	12	Single DIT step on the loaded expert(s)
test_13_vae_decode.py	13	VAE decode back to RGB frames

Tests 09-13 are focused on the GGUF + Blackwell (SM120) path and are how new quant schemes / attention backends get validated before wiring them into the full pipeline.

Run one:

# Inside the container:
docker compose exec voice-chat python -m tests.component.test_03_idle_clip

Run all (slow, ~20+ minutes on a 5090):

docker compose exec voice-chat python -m tests.component.run_all

Each component script writes its artifacts (MP4s, PNG frame dumps, logs) to tests/component/_out/ so you can visually inspect results. That directory is gitignored.