modules/whisper_extractor/__init__.py

# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

# This module is modified from [Whisper](https://github.com/openai/whisper.git).

# ## Citations

# ```bibtex
# @inproceedings{openai-whisper,
#   author       = {Alec Radford and
#                   Jong Wook Kim and
#                   Tao Xu and
#                   Greg Brockman and
#                   Christine McLeavey and
#                   Ilya Sutskever},
#   title        = {Robust Speech Recognition via Large-Scale Weak Supervision},
#   booktitle    = {{ICML}},
#   series       = {Proceedings of Machine Learning Research},
#   volume       = {202},
#   pages        = {28492--28518},
#   publisher    = {{PMLR}},
#   year         = {2023}
# }
# ```
#

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 Whisper, ModelDimensions
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": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
}


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,
    checkpoint_file=None,
) -> 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:
        download_root = os.getenv(
            "XDG_CACHE_HOME", os.path.join(os.path.expanduser("~"), ".cache", "whisper")
        )

    if not os.path.exists(checkpoint_file):
        if name in _MODELS:
            checkpoint_file = _download(_MODELS[name], download_root, in_memory)
        elif os.path.isfile(name):
            checkpoint_file = open(name, "rb").read() if in_memory else name
        else:
            raise RuntimeError(
                f"Model {name} not found; available models = {available_models()}"
            )
    else:
        checkpoint_file = (
            open(checkpoint_file, "rb").read() if in_memory else checkpoint_file
        )

    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"])

    return model.to(device)