mirror of
https://github.com/jamiepine/voicebox.git
synced 2026-09-20 15:20:39 -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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