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"""
ModelManager: HuggingFace model download, caching, and lazy loading.
Handles automatic model downloads on first run, local caching, and singleton
pattern to ensure models are loaded only once per process.
"""
import logging
import os
from pathlib import Path
from typing import Any, Dict, Optional
logger = logging.getLogger(__name__)
class ModelManager:
"""
Manages HuggingFace model downloads and caching.
Implements singleton pattern to ensure models are loaded once and reused
across all processing operations.
"""
_instance: Optional["ModelManager"] = None
_models: Dict[str, Any] = {}
_models_loaded: bool = False
def __new__(cls):
"""Singleton pattern - one instance per process."""
if cls._instance is None:
cls._instance = super().__new__(cls)
return cls._instance
def __init__(self, cache_dir: str = "./models"):
"""
Initialize ModelManager.
Args:
cache_dir: Directory for caching downloaded models (default: ./models)
"""
self.cache_dir = Path(cache_dir)
self.cache_dir.mkdir(parents=True, exist_ok=True)
# Set HuggingFace cache environment variable
os.environ["HF_HOME"] = str(self.cache_dir)
os.environ["TRANSFORMERS_CACHE"] = str(self.cache_dir)
def get_hf_token(self) -> Optional[str]:
"""
Get HuggingFace authentication token.
Checks multiple sources in order:
1. HF_TOKEN environment variable
2. ~/.cache/huggingface/token file (from huggingface-cli login)
Returns:
HuggingFace token or None if not found
"""
# Check environment variable
token = os.environ.get("HF_TOKEN")
if token:
return token
# Check huggingface-cli token file
token_file = Path.home() / ".cache" / "huggingface" / "token"
if token_file.exists():
return token_file.read_text().strip()
return None
def load_speaker_diarization(self, progress_callback=None) -> Any:
"""
Load pyannote speaker diarization model.
Downloads on first run (~150MB), then loads from cache.
Requires HuggingFace authentication and license acceptance at:
https://huggingface.co/pyannote/speaker-diarization-3.1
Args:
progress_callback: Optional callback(progress: float, message: str)
Returns:
Loaded pyannote Pipeline object
Raises:
RuntimeError: If authentication fails or model cannot be loaded
"""
if "diarization" in self._models:
return self._models["diarization"]
if progress_callback:
progress_callback(0.0, "Loading speaker diarization model...")
logger.info("Loading speaker diarization model (first run downloads ~150MB)")
try:
import torch
from pyannote.audio import Pipeline
token = self.get_hf_token()
if not token:
raise RuntimeError("HuggingFace token not found. Please run: huggingface-cli login")
pipeline = Pipeline.from_pretrained(
"pyannote/speaker-diarization-3.1", token=token, cache_dir=str(self.cache_dir)
)
# Force CPU execution
pipeline.to(torch.device("cpu"))
self._models["diarization"] = pipeline
if progress_callback:
progress_callback(1.0, "Speaker diarization model loaded")
logger.info("✓ Speaker diarization model loaded successfully")
return pipeline
except Exception as e:
error_msg = str(e)
if "401" in error_msg or "authentication" in error_msg.lower():
raise RuntimeError(
"Authentication failed for pyannote/speaker-diarization-3.1. "
"Please:\n"
"1. Run: huggingface-cli login\n"
"2. Accept license at: https://huggingface.co/pyannote/speaker-diarization-3.1"
)
elif "disk" in error_msg.lower() or "space" in error_msg.lower():
raise RuntimeError(
f"Insufficient disk space to download model. "
f"Need ~600MB free in {self.cache_dir}"
)
else:
raise RuntimeError(f"Failed to load speaker diarization model: {error_msg}")
def load_embedding_model(self, progress_callback=None) -> Any:
"""
Load pyannote embedding model for voice matching.
Downloads on first run (~17MB), then loads from cache.
Args:
progress_callback: Optional callback(progress: float, message: str)
Returns:
Loaded pyannote Model object
"""
if "embedding" in self._models:
return self._models["embedding"]
if progress_callback:
progress_callback(0.0, "Loading voice embedding model...")
logger.info("Loading voice embedding model (first run downloads ~17MB)")
try:
from pyannote.audio import Model
token = self.get_hf_token()
if not token:
raise RuntimeError("HuggingFace token not found. Please run: huggingface-cli login")
model = Model.from_pretrained(
"pyannote/embedding", token=token, cache_dir=str(self.cache_dir)
)
self._models["embedding"] = model
if progress_callback:
progress_callback(1.0, "Voice embedding model loaded")
logger.info("✓ Voice embedding model loaded successfully")
return model
except Exception as e:
raise RuntimeError(f"Failed to load embedding model: {str(e)}")
def load_ast_classifier(self, progress_callback=None) -> Any:
"""
Load Audio Spectrogram Transformer for speech classification.
Downloads on first run (~340MB), then loads from cache.
Args:
progress_callback: Optional callback(progress: float, message: str)
Returns:
Tuple of (feature_extractor, classifier_model)
"""
if "ast" in self._models:
return self._models["ast"]
if progress_callback:
progress_callback(0.0, "Loading audio classifier model...")
logger.info("Loading audio classifier model (first run downloads ~340MB)")
try:
from transformers import ASTFeatureExtractor, ASTForAudioClassification
feature_extractor = ASTFeatureExtractor.from_pretrained(
"MIT/ast-finetuned-audioset-10-10-0.4593", cache_dir=str(self.cache_dir)
)
classifier = ASTForAudioClassification.from_pretrained(
"MIT/ast-finetuned-audioset-10-10-0.4593", cache_dir=str(self.cache_dir)
)
classifier.eval() # Set to inference mode
self._models["ast"] = (feature_extractor, classifier)
if progress_callback:
progress_callback(1.0, "Audio classifier model loaded")
logger.info("✓ Audio classifier model loaded successfully")
return self._models["ast"]
except Exception as e:
raise RuntimeError(f"Failed to load AST classifier: {str(e)}")
def load_vad_model(self, progress_callback=None) -> Any:
"""
Load Silero VAD model for voice activity detection.
Downloads on first run (~1.5MB), then loads from cache.
Args:
progress_callback: Optional callback(progress: float, message: str)
Returns:
Loaded Silero VAD model
"""
if "vad" in self._models:
return self._models["vad"]
if progress_callback:
progress_callback(0.0, "Loading voice activity detection model...")
logger.info("Loading VAD model (first run downloads ~1.5MB)")
try:
import torch
# Silero VAD uses torch.hub
model, utils = torch.hub.load(
repo_or_dir="snakers4/silero-vad",
model="silero_vad",
force_reload=False,
onnx=False,
)
model.eval()
self._models["vad"] = (model, utils)
if progress_callback:
progress_callback(1.0, "VAD model loaded")
logger.info("✓ VAD model loaded successfully")
return self._models["vad"]
except Exception as e:
raise RuntimeError(f"Failed to load VAD model: {str(e)}")
def models_are_cached(self) -> bool:
"""
Check if all required models are already downloaded.
Returns:
True if all models are cached locally, False otherwise
"""
required_models = [
"pyannote--speaker-diarization-3.1",
"pyannote--embedding",
"MIT--ast-finetuned-audioset-10-10-0.4593",
]
hub_cache = self.cache_dir / "hub"
if not hub_cache.exists():
return False
for model_name in required_models:
model_path = hub_cache / f"models--{model_name}"
if not model_path.exists():
return False
return True
def get_cache_size(self) -> int:
"""
Get total size of cached models in bytes.
Returns:
Total cache size in bytes
"""
total_size = 0
for path in self.cache_dir.rglob("*"):
if path.is_file():
total_size += path.stat().st_size
return total_size
def clear_cache(self):
"""
Clear all cached models.
WARNING: This will force re-download on next use (~600MB).
"""
import shutil
if self.cache_dir.exists():
shutil.rmtree(self.cache_dir)
self.cache_dir.mkdir(parents=True, exist_ok=True)
logger.info(f"Cleared model cache at {self.cache_dir}")
# Reset loaded models
self._models.clear()
self._models_loaded = False
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