153 lines
6.7 KiB
Python
153 lines
6.7 KiB
Python
import hashlib
|
|
import io
|
|
import os
|
|
import urllib
|
|
import warnings
|
|
from typing import List, Optional, Union
|
|
|
|
import torch
|
|
from tqdm import tqdm
|
|
|
|
from .audio import load_audio, log_mel_spectrogram, pad_or_trim
|
|
from .decoding import DecodingOptions, DecodingResult, decode, detect_language
|
|
from .model import ModelDimensions, Whisper
|
|
from .transcribe import transcribe
|
|
from .version import __version__
|
|
|
|
_MODELS = {
|
|
"tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt",
|
|
"tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt",
|
|
"base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt",
|
|
"base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt",
|
|
"small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt",
|
|
"small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt",
|
|
"medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt",
|
|
"medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
|
|
"large-v1": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large-v1.pt",
|
|
"large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
|
|
"large-v3": "https://openaipublic.azureedge.net/main/whisper/models/e5b1a55b89c1367dacf97e3e19bfd829a01529dbfdeefa8caeb59b3f1b81dadb/large-v3.pt",
|
|
"large": "https://openaipublic.azureedge.net/main/whisper/models/e5b1a55b89c1367dacf97e3e19bfd829a01529dbfdeefa8caeb59b3f1b81dadb/large-v3.pt",
|
|
}
|
|
|
|
# base85-encoded (n_layers, n_heads) boolean arrays indicating the cross-attention heads that are
|
|
# highly correlated to the word-level timing, i.e. the alignment between audio and text tokens.
|
|
_ALIGNMENT_HEADS = {
|
|
"tiny.en": b"ABzY8J1N>@0{>%R00Bk>$p{7v037`oCl~+#00",
|
|
"tiny": b"ABzY8bu8Lr0{>%RKn9Fp%m@SkK7Kt=7ytkO",
|
|
"base.en": b"ABzY8;40c<0{>%RzzG;p*o+Vo09|#PsxSZm00",
|
|
"base": b"ABzY8KQ!870{>%RzyTQH3`Q^yNP!>##QT-<FaQ7m",
|
|
"small.en": b"ABzY8>?_)10{>%RpeA61k&I|OI3I$65C{;;pbCHh0B{qLQ;+}v00",
|
|
"small": b"ABzY8DmU6=0{>%Rpa?J`kvJ6qF(V^F86#Xh7JUGMK}P<N0000",
|
|
"medium.en": b"ABzY8usPae0{>%R7<zz_OvQ{)4kMa0BMw6u5rT}kRKX;$NfYBv00*Hl@qhsU00",
|
|
"medium": b"ABzY8B0Jh+0{>%R7}kK1fFL7w6%<-Pf*t^=N)Qr&0RR9",
|
|
"large-v1": b"ABzY8r9j$a0{>%R7#4sLmoOs{s)o3~84-RPdcFk!JR<kSfC2yj",
|
|
"large-v2": b"ABzY8zd+h!0{>%R7=D0pU<_bnWW*tkYAhobTNnu$jnkEkXqp)j;w1Tzk)UH3X%SZd&fFZ2fC2yj",
|
|
"large-v3": b"ABzY8gWO1E0{>%R7(9S+Kn!D~%ngiGaR?*L!iJG9p-nab0JQ=-{D1-g00",
|
|
"large": b"ABzY8gWO1E0{>%R7(9S+Kn!D~%ngiGaR?*L!iJG9p-nab0JQ=-{D1-g00",
|
|
}
|
|
|
|
|
|
def _download(url: str, root: str, in_memory: bool) -> Union[bytes, str]:
|
|
os.makedirs(root, exist_ok=True)
|
|
|
|
expected_sha256 = url.split("/")[-2]
|
|
download_target = os.path.join(root, os.path.basename(url))
|
|
|
|
if os.path.exists(download_target) and not os.path.isfile(download_target):
|
|
raise RuntimeError(f"{download_target} exists and is not a regular file")
|
|
|
|
if os.path.isfile(download_target):
|
|
with open(download_target, "rb") as f:
|
|
model_bytes = f.read()
|
|
if hashlib.sha256(model_bytes).hexdigest() == expected_sha256:
|
|
return model_bytes if in_memory else download_target
|
|
else:
|
|
warnings.warn(
|
|
f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file"
|
|
)
|
|
|
|
with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
|
|
with tqdm(
|
|
total=int(source.info().get("Content-Length")),
|
|
ncols=80,
|
|
unit="iB",
|
|
unit_scale=True,
|
|
unit_divisor=1024,
|
|
) as loop:
|
|
while True:
|
|
buffer = source.read(8192)
|
|
if not buffer:
|
|
break
|
|
|
|
output.write(buffer)
|
|
loop.update(len(buffer))
|
|
|
|
model_bytes = open(download_target, "rb").read()
|
|
if hashlib.sha256(model_bytes).hexdigest() != expected_sha256:
|
|
raise RuntimeError(
|
|
"Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model."
|
|
)
|
|
|
|
return model_bytes if in_memory else download_target
|
|
|
|
|
|
def available_models() -> List[str]:
|
|
"""Returns the names of available models"""
|
|
return list(_MODELS.keys())
|
|
|
|
|
|
def load_model(
|
|
name: str,
|
|
device: Optional[Union[str, torch.device]] = None,
|
|
download_root: str = None,
|
|
in_memory: bool = False,
|
|
) -> Whisper:
|
|
"""
|
|
Load a Whisper ASR model
|
|
|
|
Parameters
|
|
----------
|
|
name : str
|
|
one of the official model names listed by `whisper.available_models()`, or
|
|
path to a model checkpoint containing the model dimensions and the model state_dict.
|
|
device : Union[str, torch.device]
|
|
the PyTorch device to put the model into
|
|
download_root: str
|
|
path to download the model files; by default, it uses "~/.cache/whisper"
|
|
in_memory: bool
|
|
whether to preload the model weights into host memory
|
|
|
|
Returns
|
|
-------
|
|
model : Whisper
|
|
The Whisper ASR model instance
|
|
"""
|
|
|
|
if device is None:
|
|
device = "cuda" if torch.cuda.is_available() else "cpu"
|
|
if download_root is None:
|
|
default = os.path.join(os.path.expanduser("~"), ".cache")
|
|
download_root = os.path.join(os.getenv("XDG_CACHE_HOME", default), "whisper")
|
|
|
|
if name in _MODELS:
|
|
checkpoint_file = _download(_MODELS[name], download_root, in_memory)
|
|
alignment_heads = _ALIGNMENT_HEADS[name]
|
|
elif os.path.isfile(name):
|
|
checkpoint_file = open(name, "rb").read() if in_memory else name
|
|
alignment_heads = None
|
|
else:
|
|
raise RuntimeError(f"Model {name} not found; available models = {available_models()}")
|
|
|
|
with io.BytesIO(checkpoint_file) if in_memory else open(checkpoint_file, "rb") as fp:
|
|
checkpoint = torch.load(fp, map_location=device)
|
|
del checkpoint_file
|
|
|
|
dims = ModelDimensions(**checkpoint["dims"])
|
|
model = Whisper(dims)
|
|
model.load_state_dict(checkpoint["model_state_dict"])
|
|
|
|
if alignment_heads is not None:
|
|
model.set_alignment_heads(alignment_heads)
|
|
|
|
return model.to(device)
|