File size: 7,150 Bytes
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import logging
from pathlib import Path
log = logging.getLogger("LiveTrans.ModelManager")
APP_DIR = Path(__file__).parent
MODELS_DIR = APP_DIR / "models"
ASR_MODEL_IDS = {
"sensevoice": "iic/SenseVoiceSmall",
"funasr-nano": "FunAudioLLM/Fun-ASR-Nano-2512",
"funasr-mlt-nano": "FunAudioLLM/Fun-ASR-MLT-Nano-2512",
}
ASR_DISPLAY_NAMES = {
"sensevoice": "SenseVoice Small",
"funasr-nano": "Fun-ASR-Nano",
"funasr-mlt-nano": "Fun-ASR-MLT-Nano",
"whisper": "Whisper",
}
_MODEL_SIZE_BYTES = {
"silero-vad": 2_000_000,
"sensevoice": 940_000_000,
"funasr-nano": 1_050_000_000,
"funasr-mlt-nano": 1_050_000_000,
"whisper-tiny": 78_000_000,
"whisper-base": 148_000_000,
"whisper-small": 488_000_000,
"whisper-medium": 1_530_000_000,
"whisper-large-v3": 3_100_000_000,
}
_WHISPER_SIZES = ["tiny", "base", "small", "medium", "large-v3"]
_CACHE_MODELS = [
("SenseVoice Small", "iic/SenseVoiceSmall"),
("Fun-ASR-Nano", "FunAudioLLM/Fun-ASR-Nano-2512"),
("Fun-ASR-MLT-Nano", "FunAudioLLM/Fun-ASR-MLT-Nano-2512"),
]
def apply_cache_env():
"""Point all model caches to ./models/."""
resolved = str(MODELS_DIR.resolve())
os.environ["MODELSCOPE_CACHE"] = os.path.join(resolved, "modelscope")
os.environ["HF_HOME"] = os.path.join(resolved, "huggingface")
os.environ["TORCH_HOME"] = os.path.join(resolved, "torch")
log.info(f"Cache env set: {resolved}")
def is_silero_cached() -> bool:
torch_hub = MODELS_DIR / "torch" / "hub"
return any(torch_hub.glob("snakers4_silero-vad*")) if torch_hub.exists() else False
def _ms_model_path(org, name):
"""Return the first existing ModelScope cache path, or the default."""
for sub in (
MODELS_DIR / "modelscope" / org / name,
MODELS_DIR / "modelscope" / "hub" / "models" / org / name,
):
if sub.exists():
return sub
return MODELS_DIR / "modelscope" / org / name
def is_asr_cached(engine_type, model_size="medium", hub="ms") -> bool:
if engine_type in ("sensevoice", "funasr-nano", "funasr-mlt-nano"):
model_id = ASR_MODEL_IDS[engine_type]
org, name = model_id.split("/")
# Accept cache from either hub to avoid redundant downloads
if _ms_model_path(org, name).exists():
return True
if (MODELS_DIR / "huggingface" / "hub" / f"models--{org}--{name}").exists():
return True
return False
elif engine_type == "whisper":
return (
MODELS_DIR
/ "huggingface"
/ "hub"
/ f"models--Systran--faster-whisper-{model_size}"
).exists()
return True
def get_missing_models(engine, model_size, hub) -> list:
missing = []
if not is_silero_cached():
missing.append(
{
"name": "Silero VAD",
"type": "silero-vad",
"estimated_bytes": _MODEL_SIZE_BYTES["silero-vad"],
}
)
if not is_asr_cached(engine, model_size, hub):
key = engine if engine != "whisper" else f"whisper-{model_size}"
display = ASR_DISPLAY_NAMES.get(engine, engine)
if engine == "whisper":
display = f"Whisper {model_size}"
missing.append(
{
"name": display,
"type": key,
"estimated_bytes": _MODEL_SIZE_BYTES.get(key, 0),
}
)
return missing
def get_local_model_path(engine_type, hub="ms"):
"""Return local snapshot path if model is cached, else None.
Checks the preferred hub first, then falls back to the other hub.
"""
if engine_type not in ASR_MODEL_IDS:
return None
model_id = ASR_MODEL_IDS[engine_type]
org, name = model_id.split("/")
def _try_ms():
local = _ms_model_path(org, name)
return str(local) if local.exists() else None
def _try_hf():
snap_dir = (
MODELS_DIR / "huggingface" / "hub" / f"models--{org}--{name}" / "snapshots"
)
if snap_dir.exists():
snaps = sorted(snap_dir.iterdir())
if snaps:
return str(snaps[-1])
return None
if hub == "ms":
return _try_ms() or _try_hf()
else:
return _try_hf() or _try_ms()
def download_silero():
import torch
log.info("Downloading Silero VAD...")
model, _ = torch.hub.load(
repo_or_dir="snakers4/silero-vad",
model="silero_vad",
trust_repo=True,
)
del model
log.info("Silero VAD downloaded")
def download_asr(engine, model_size="medium", hub="ms"):
resolved = str(MODELS_DIR.resolve())
ms_cache = os.path.join(resolved, "modelscope")
hf_cache = os.path.join(resolved, "huggingface", "hub")
if engine in ("sensevoice", "funasr-nano", "funasr-mlt-nano"):
model_id = ASR_MODEL_IDS[engine]
if hub == "ms":
from modelscope import snapshot_download
log.info(f"Downloading {model_id} from ModelScope...")
snapshot_download(model_id=model_id, cache_dir=ms_cache)
else:
from huggingface_hub import snapshot_download
log.info(f"Downloading {model_id} from HuggingFace...")
snapshot_download(repo_id=model_id, cache_dir=hf_cache)
elif engine == "whisper":
from huggingface_hub import snapshot_download
model_id = f"Systran/faster-whisper-{model_size}"
log.info(f"Downloading {model_id} from HuggingFace...")
snapshot_download(repo_id=model_id, cache_dir=hf_cache)
log.info(f"ASR model downloaded: {engine}")
def dir_size(path) -> int:
total = 0
try:
for f in Path(path).rglob("*"):
if f.is_file():
total += f.stat().st_size
except (OSError, PermissionError):
pass
return total
def format_size(size_bytes: int) -> str:
if size_bytes < 1024:
return f"{size_bytes} B"
elif size_bytes < 1024**2:
return f"{size_bytes / 1024:.1f} KB"
elif size_bytes < 1024**3:
return f"{size_bytes / (1024**2):.1f} MB"
else:
return f"{size_bytes / (1024**3):.2f} GB"
def get_cache_entries():
"""Scan ./models/ for cached models."""
entries = []
hf_base = MODELS_DIR / "huggingface" / "hub"
torch_base = MODELS_DIR / "torch" / "hub"
for name, model_id in _CACHE_MODELS:
org, model = model_id.split("/")
ms_path = _ms_model_path(org, model)
hf_path = hf_base / f"models--{org}--{model}"
if ms_path.exists():
entries.append((f"{name} (ModelScope)", ms_path))
if hf_path.exists():
entries.append((f"{name} (HuggingFace)", hf_path))
for size in _WHISPER_SIZES:
hf_path = hf_base / f"models--Systran--faster-whisper-{size}"
if hf_path.exists():
entries.append((f"Whisper {size}", hf_path))
if torch_base.exists():
for d in sorted(torch_base.glob("snakers4_silero-vad*")):
if d.is_dir():
entries.append(("Silero VAD", d))
break
return entries
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