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99d05b917c
MLX streams are thread-local and mlx-audio caches one on the model at load time. Model load and inference were each dispatched through asyncio.to_thread(), which uses a multi-worker pool, so load and generate/transcribe could land on different OS threads -- the inference thread then has no Stream(gpu, N) and MLX aborts with "There is no Stream(gpu, 1) in current thread." Route every MLX call (load, generate, transcribe; TTS and STT) through a single dedicated worker thread so a model and its stream always share a thread. max_workers=1 also serialises the single local GPU. Adds a regression test covering the thread-affinity invariant. Fixes #699. Also addresses #675. Co-Authored-By: Claude Opus 4.8 (1M context) <[email protected]>
Backend Tests
Manual test scripts for debugging and validating backend functionality.
Test Files
test_generation_progress.py
Tests TTS generation with SSE progress monitoring to identify UX issues where users see download progress even when the model is already cached.
Usage:
cd backend
python tests/test_generation_progress.py
Prerequisites:
- Server must be running (
python main.py) - At least one voice profile must exist
test_real_download.py
Tests real model download with SSE progress monitoring.
Usage:
cd backend
# Delete cache first to force fresh download
rm -rf ~/.cache/huggingface/hub/models--openai--whisper-base
python tests/test_real_download.py
Prerequisites:
- Server must be running (
python main.py)
test_progress.py
Unit tests for ProgressManager and HFProgressTracker functionality.
Usage:
cd backend
python tests/test_progress.py
test_check_progress_state.py
Debugging script to inspect the internal state of ProgressManager and TaskManager.
Usage:
cd backend
python tests/test_check_progress_state.py
Notes
These are manual test scripts, not automated unit tests. They're designed for:
- Debugging progress tracking issues
- Validating SSE event streams
- Monitoring real-time download behavior
- Inspecting internal state during development