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