mirror of
https://github.com/jamiepine/voicebox.git
synced 2026-09-15 04:40:40 -07:00
Refactor MLX and PyTorch Backend Model Loading
- Updated hidden imports in build_binary.py to replace 'mlx_audio.asr' with 'mlx_audio.stt'. - Enhanced model loading logic in MLX and PyTorch backends to ensure proper progress tracking during model downloads. - Improved error handling and context management for progress tracking in both backends. - Bumped version to 0.1.10 in Cargo.lock to reflect recent changes.
This commit is contained in:
Jamie Pine
@@ -341,21 +341,34 @@ class MLXSTTBackend:
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def _load_model_sync(self, model_size: str):
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"""Synchronous model loading."""
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try:
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from mlx_audio.asr import load
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# MLX Whisper model naming
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model_name = f"mlx-community/whisper-{model_size}"
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# Set up progress tracking
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# IMPORTANT: Set up progress tracking BEFORE importing mlx_audio
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# This ensures tqdm is patched before any HuggingFace Hub imports
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progress_manager = get_progress_manager()
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progress_model_name = f"whisper-{model_size}"
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# Set up progress callback and tracker
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progress_callback = create_hf_progress_callback(progress_model_name, progress_manager)
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tracker = HFProgressTracker(progress_callback)
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# Patch tqdm BEFORE importing mlx_audio
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# This is critical because mlx_audio imports huggingface_hub which imports tqdm
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print("[DEBUG] Starting tqdm patch BEFORE mlx_audio import")
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tracker_context = tracker.patch_download()
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tracker_context.__enter__()
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print("[DEBUG] tqdm patched, now importing mlx_audio")
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# NOW import mlx_audio - it will use our patched tqdm
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from mlx_audio.stt import load
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# MLX Whisper uses the standard OpenAI models
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model_name = f"openai/whisper-{model_size}"
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# Start tracking download task
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task_manager = get_task_manager()
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task_manager.start_download(progress_model_name)
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print(f"Loading MLX Whisper model {model_size}...")
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# Initialize progress state
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progress_manager.update_progress(
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model_name=progress_model_name,
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@@ -364,14 +377,13 @@ class MLXSTTBackend:
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filename="",
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status="downloading",
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)
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# Set up progress callback
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progress_callback = create_hf_progress_callback(progress_model_name, progress_manager)
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tracker = HFProgressTracker(progress_callback)
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# Use progress tracker during download
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with tracker.patch_download():
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# Load the model (tqdm is already patched from above)
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try:
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self.model = load(model_name)
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finally:
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# Exit the patch context
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tracker_context.__exit__(None, None, None)
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self.model_size = model_size
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@@ -85,21 +85,31 @@ class PyTorchTTSBackend:
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def _load_model_sync(self, model_size: str):
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"""Synchronous model loading."""
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try:
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from qwen_tts import Qwen3TTSModel
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# Get model path (local or HuggingFace Hub ID)
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model_path = self._get_model_path(model_size)
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# Set up progress tracking
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# IMPORTANT: Set up progress tracking BEFORE importing qwen_tts
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# This ensures tqdm is patched before any HuggingFace Hub imports
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progress_manager = get_progress_manager()
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model_name = f"qwen-tts-{model_size}"
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# Set up progress callback and tracker
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progress_callback = create_hf_progress_callback(model_name, progress_manager)
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tracker = HFProgressTracker(progress_callback)
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# Patch tqdm BEFORE importing qwen_tts
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tracker_context = tracker.patch_download()
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tracker_context.__enter__()
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# NOW import qwen_tts - it will use our patched tqdm
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from qwen_tts import Qwen3TTSModel
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# Get model path (local or HuggingFace Hub ID)
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model_path = self._get_model_path(model_size)
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print(f"Loading TTS model {model_size} on {self.device}...")
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# Start tracking download task
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task_manager = get_task_manager()
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task_manager.start_download(model_name)
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# Initialize progress state to show download has started
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progress_manager.update_progress(
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model_name=model_name,
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@@ -108,19 +118,17 @@ class PyTorchTTSBackend:
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filename="",
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status="downloading",
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)
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# Set up progress callback
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progress_callback = create_hf_progress_callback(model_name, progress_manager)
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tracker = HFProgressTracker(progress_callback)
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# Use progress tracker during download
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with tracker.patch_download():
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# Load the model - downloads will happen automatically with progress tracking
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# Load the model (tqdm is already patched from above)
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try:
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self.model = Qwen3TTSModel.from_pretrained(
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model_path,
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device_map=self.device,
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torch_dtype=torch.float32 if self.device == "cpu" else torch.bfloat16,
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)
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finally:
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# Exit the patch context
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tracker_context.__exit__(None, None, None)
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# Mark as complete
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progress_manager.mark_complete(model_name)
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@@ -314,40 +322,61 @@ class PyTorchSTTBackend:
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async def load_model_async(self, model_size: Optional[str] = None):
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"""
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Lazy load the Whisper model.
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Args:
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model_size: Model size (tiny, base, small, medium, large)
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"""
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print(f"[DEBUG] load_model_async called with size: {model_size}")
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if model_size is None:
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model_size = self.model_size
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print(f"[DEBUG] Model already loaded? {self.model is not None}, current size: {self.model_size}, requested: {model_size}")
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if self.model is not None and self.model_size == model_size:
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print(f"[DEBUG] Early return - model already loaded")
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return
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print(f"[DEBUG] Calling asyncio.to_thread for _load_model_sync")
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# Run blocking load in thread pool
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await asyncio.to_thread(self._load_model_sync, model_size)
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print(f"[DEBUG] asyncio.to_thread completed")
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# Alias for compatibility
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load_model = load_model_async
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def _load_model_sync(self, model_size: str):
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"""Synchronous model loading."""
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print(f"[DEBUG] _load_model_sync called for Whisper {model_size}")
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try:
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from transformers import WhisperProcessor, WhisperForConditionalGeneration
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model_name = f"openai/whisper-{model_size}"
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# Set up progress tracking
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# IMPORTANT: Set up progress tracking BEFORE importing transformers
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# This ensures tqdm is patched before any HuggingFace Hub imports
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progress_manager = get_progress_manager()
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progress_model_name = f"whisper-{model_size}"
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# Set up progress callback and tracker
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progress_callback = create_hf_progress_callback(progress_model_name, progress_manager)
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tracker = HFProgressTracker(progress_callback)
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# Patch tqdm BEFORE importing transformers
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print("[DEBUG] Starting tqdm patch BEFORE transformers import")
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tracker_context = tracker.patch_download()
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tracker_context.__enter__()
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print("[DEBUG] tqdm patched, now importing transformers")
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# NOW import transformers - it will use our patched tqdm
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from transformers import WhisperProcessor, WhisperForConditionalGeneration
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model_name = f"openai/whisper-{model_size}"
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print(f"[DEBUG] Model name: {model_name}")
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# Start tracking download task
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task_manager = get_task_manager()
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task_manager.start_download(progress_model_name)
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print(f"[DEBUG] Task manager started download")
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print(f"Loading Whisper model {model_size} on {self.device}...")
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# Initialize progress state to show download has started
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print(f"[DEBUG] Calling update_progress...")
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progress_manager.update_progress(
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model_name=progress_model_name,
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current=0,
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@@ -355,15 +384,15 @@ class PyTorchSTTBackend:
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filename="",
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status="downloading",
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)
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# Set up progress callback
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progress_callback = create_hf_progress_callback(progress_model_name, progress_manager)
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tracker = HFProgressTracker(progress_callback)
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# Use progress tracker during download
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with tracker.patch_download():
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print(f"[DEBUG] update_progress called, listeners: {len(progress_manager._listeners.get(progress_model_name, []))}")
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# Load models (tqdm is already patched from above)
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try:
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self.processor = WhisperProcessor.from_pretrained(model_name)
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self.model = WhisperForConditionalGeneration.from_pretrained(model_name)
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finally:
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# Exit the patch context
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tracker_context.__exit__(None, None, None)
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self.model.to(self.device)
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self.model_size = model_size
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@@ -80,7 +80,7 @@ def build_server():
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'--hidden-import', 'mlx.nn',
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'--hidden-import', 'mlx_audio',
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'--hidden-import', 'mlx_audio.tts',
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'--hidden-import', 'mlx_audio.asr',
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'--hidden-import', 'mlx_audio.stt',
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'--collect-submodules', 'mlx',
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'--collect-submodules', 'mlx_audio',
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# Collect MLX data files including Metal shader libraries (.metallib)
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+5
-1
@@ -1393,7 +1393,11 @@ async def trigger_model_download(request: models.ModelDownloadRequest):
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async def download_in_background():
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"""Download model in background without blocking the HTTP request."""
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try:
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await asyncio.to_thread(config["load_func"])
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# Call the load function (which may be async)
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result = config["load_func"]()
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# If it's a coroutine, await it
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if asyncio.iscoroutine(result):
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await result
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task_manager.complete_download(request.model_name)
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except Exception as e:
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task_manager.error_download(request.model_name, str(e))
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@@ -29,8 +29,9 @@ class HFProgressTracker:
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class TrackedTqdm(original_tqdm):
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"""A tqdm subclass that reports progress to our tracker."""
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def __init__(self, *args, **kwargs):
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print(f"[DEBUG TrackedTqdm] __init__ called with desc: {kwargs.get('desc', '')}")
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# Extract filename from desc before passing to parent
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desc = kwargs.get("desc", "")
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if not desc and args:
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@@ -79,8 +80,9 @@ class HFProgressTracker:
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}
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def update(self, n=1):
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print(f"[DEBUG TrackedTqdm] update called with n={n}")
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result = super().update(n)
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# Report progress
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with tracker._lock:
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if id(self) in tracker._active_tqdms:
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@@ -118,11 +120,13 @@ class HFProgressTracker:
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@contextmanager
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def patch_download(self):
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"""Context manager to patch tqdm for progress tracking."""
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print("[DEBUG HFProgressTracker] patch_download called")
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try:
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import tqdm as tqdm_module
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# Store original tqdm class
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self._original_tqdm_class = tqdm_module.tqdm
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print(f"[DEBUG HFProgressTracker] Original tqdm class: {self._original_tqdm_class}")
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# Reset totals
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with self._lock:
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@@ -135,18 +139,22 @@ class HFProgressTracker:
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# Create our tracked tqdm class
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tracked_tqdm = self._create_tracked_tqdm_class()
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print(f"[DEBUG HFProgressTracker] Created TrackedTqdm class: {tracked_tqdm}")
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# Patch tqdm.tqdm
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tqdm_module.tqdm = tracked_tqdm
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print(f"[DEBUG HFProgressTracker] Patched tqdm.tqdm")
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# Also patch tqdm.auto.tqdm if it exists (used by huggingface_hub)
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self._original_tqdm_auto = None
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if hasattr(tqdm_module, "auto") and hasattr(tqdm_module.auto, "tqdm"):
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self._original_tqdm_auto = tqdm_module.auto.tqdm
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tqdm_module.auto.tqdm = tracked_tqdm
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print(f"[DEBUG HFProgressTracker] Patched tqdm.auto.tqdm")
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# Patch in sys.modules to catch already-imported references
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self._patched_modules = {}
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patched_count = 0
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for module_name in list(sys.modules.keys()):
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if "huggingface" in module_name or module_name.startswith("tqdm"):
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try:
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@@ -159,8 +167,11 @@ class HFProgressTracker:
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):
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self._patched_modules[module_name] = attr
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setattr(module, "tqdm", tracked_tqdm)
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patched_count += 1
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print(f"[DEBUG HFProgressTracker] Patched {module_name}.tqdm")
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except (AttributeError, TypeError):
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pass
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print(f"[DEBUG HFProgressTracker] Patched {patched_count} modules in sys.modules")
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yield
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@@ -65,7 +65,7 @@ class ProgressManager:
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):
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"""
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Update progress for a model download.
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Thread-safe: can be called from background threads.
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Args:
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@@ -89,16 +89,26 @@ class ProgressManager:
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"status": status,
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"timestamp": datetime.now().isoformat(),
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}
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print(f"[DEBUG] update_progress called: {model_name}, {progress_pct:.1f}%")
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# Thread-safe update of progress dict
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with self._lock:
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self._progress[model_name] = progress_data
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# Notify all listeners (thread-safe)
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listener_count = len(self._listeners.get(model_name, []))
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print(f"[DEBUG] Listener count for {model_name}: {listener_count}")
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print(f"[DEBUG] All listeners: {list(self._listeners.keys())}")
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print(f"[DEBUG] Main loop set: {self._main_loop is not None}")
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if self._main_loop:
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print(f"[DEBUG] Main loop running: {self._main_loop.is_running()}")
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if listener_count > 0:
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logger.debug(f"Notifying {listener_count} listeners for {model_name}: {progress_pct:.1f}% ({filename})")
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print(f"[DEBUG] About to notify listeners...")
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self._notify_listeners_threadsafe(model_name, progress_data)
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print(f"[DEBUG] Notified listeners")
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else:
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logger.debug(f"No listeners for {model_name}, progress update stored: {progress_pct:.1f}%")
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@@ -4,7 +4,7 @@ from PyInstaller.utils.hooks import collect_submodules
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from PyInstaller.utils.hooks import copy_metadata
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datas = []
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hiddenimports = ['backend', 'backend.main', 'backend.config', 'backend.database', 'backend.models', 'backend.profiles', 'backend.history', 'backend.tts', 'backend.transcribe', 'backend.platform_detect', 'backend.backends', 'backend.backends.pytorch_backend', 'backend.utils.audio', 'backend.utils.cache', 'backend.utils.progress', 'backend.utils.hf_progress', 'backend.utils.validation', 'torch', 'transformers', 'fastapi', 'uvicorn', 'sqlalchemy', 'librosa', 'soundfile', 'qwen_tts', 'qwen_tts.inference', 'qwen_tts.inference.qwen3_tts_model', 'qwen_tts.inference.qwen3_tts_tokenizer', 'qwen_tts.core', 'qwen_tts.cli', 'pkg_resources.extern', 'backend.backends.mlx_backend', 'mlx', 'mlx.core', 'mlx.nn', 'mlx_audio', 'mlx_audio.tts', 'mlx_audio.asr']
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hiddenimports = ['backend', 'backend.main', 'backend.config', 'backend.database', 'backend.models', 'backend.profiles', 'backend.history', 'backend.tts', 'backend.transcribe', 'backend.platform_detect', 'backend.backends', 'backend.backends.pytorch_backend', 'backend.utils.audio', 'backend.utils.cache', 'backend.utils.progress', 'backend.utils.hf_progress', 'backend.utils.validation', 'torch', 'transformers', 'fastapi', 'uvicorn', 'sqlalchemy', 'librosa', 'soundfile', 'qwen_tts', 'qwen_tts.inference', 'qwen_tts.inference.qwen3_tts_model', 'qwen_tts.inference.qwen3_tts_tokenizer', 'qwen_tts.core', 'qwen_tts.cli', 'pkg_resources.extern', 'backend.backends.mlx_backend', 'mlx', 'mlx.core', 'mlx.nn', 'mlx_audio', 'mlx_audio.tts', 'mlx_audio.stt']
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datas += collect_data_files('qwen_tts')
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datas += collect_data_files('mlx')
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datas += collect_data_files('mlx_audio')
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Generated
+1
-1
@@ -5041,7 +5041,7 @@ checksum = "0b928f33d975fc6ad9f86c8f283853ad26bdd5b10b7f1542aa2fa15e2289105a"
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[[package]]
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name = "voicebox"
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version = "0.1.9"
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version = "0.1.10"
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dependencies = [
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"base64 0.22.1",
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"core-foundation-sys",
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