实战STT本地部署——OpenAI大厂出品whisper工具

import whisper
from whisper.utils import get_writer


def get_transcribe(tp: str = 'small', audio_path: str = "", language: str = 'zh'):
    model = whisper.load_model(tp,download_root="./model/")
    audio = whisper.load_audio(audio_path)
    #audio = whisper.pad_or_trim(audio)
    return model.transcribe(audio=audio, language=language, verbose=True, initial_prompt="风波不信菱枝弱，月露谁教桂叶香。直道相思了无益，未妨惆怅是清狂！", beam_size=5)

def save_file(results, format='srt'):
    writer = get_writer(format, './output/')
    writer(results, f'transcribe.{format}')


if __name__ == '__main__':
    result = get_transcribe(tp="turbo", audio_path=r'./input/sample_audio.m4a')
    print('-'*50)
    print(result.get('text',''))
    save_file(result, 'srt')
    save_file(result, 'txt')

def transcribe(
    model: "Whisper",
    audio: Union[str, np.ndarray, torch.Tensor],
    *,
    verbose: Optional[bool] = None,
    temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
    compression_ratio_threshold: Optional[float] = 2.4,
    logprob_threshold: Optional[float] = -1.0,
    no_speech_threshold: Optional[float] = 0.6,
    condition_on_previous_text: bool = True,
    initial_prompt: Optional[str] = None,
    carry_initial_prompt: bool = False,
    word_timestamps: bool = False,
    prepend_punctuations: str = "\"'“¿([{-",
    append_punctuations: str = "\"'.。,，!！?？:：”)]}、",
    clip_timestamps: Union[str, List[float]] = "0",
    hallucination_silence_threshold: Optional[float] = None,
    **decode_options,
):
    """
    Transcribe an audio file using Whisper

    Parameters
    ----------
    model: Whisper
        The Whisper model instance

    audio: Union[str, np.ndarray, torch.Tensor]
        The path to the audio file to open, or the audio waveform

    verbose: bool
        Whether to display the text being decoded to the console. If True, displays all the details,
        If False, displays minimal details. If None, does not display anything

    temperature: Union[float, Tuple[float, ...]]
        Temperature for sampling. It can be a tuple of temperatures, which will be successively used
        upon failures according to either `compression_ratio_threshold` or `logprob_threshold`.

    compression_ratio_threshold: float
        If the gzip compression ratio is above this value, treat as failed

    logprob_threshold: float
        If the average log probability over sampled tokens is below this value, treat as failed

    no_speech_threshold: float
        If the no_speech probability is higher than this value AND the average log probability
        over sampled tokens is below `logprob_threshold`, consider the segment as silent

    condition_on_previous_text: bool
        if True, the previous output of the model is provided as a prompt for the next window;
        disabling may make the text inconsistent across windows, but the model becomes less prone to
        getting stuck in a failure loop, such as repetition looping or timestamps going out of sync.

    word_timestamps: bool
        Extract word-level timestamps using the cross-attention pattern and dynamic time warping,
        and include the timestamps for each word in each segment.

    prepend_punctuations: str
        If word_timestamps is True, merge these punctuation symbols with the next word

    append_punctuations: str
        If word_timestamps is True, merge these punctuation symbols with the previous word

    initial_prompt: Optional[str]
        Optional text to provide as a prompt for the first window. This can be used to provide, or
        "prompt-engineer" a context for transcription, e.g. custom vocabularies or proper nouns
        to make it more likely to predict those word correctly.

    carry_initial_prompt: bool
        If carry_initial_prompt is True, `initial_prompt` is prepended to the prompt of each internal
        `decode()` call. If there is not enough context space at the start of the prompt, it is
        left-sliced to make space.

    decode_options: dict
        Keyword arguments to construct `DecodingOptions` instances

    clip_timestamps: Union[str, List[float]]
        Comma-separated list start,end,start,end,... timestamps (in seconds) of clips to process.
        The last end timestamp defaults to the end of the file.

    hallucination_silence_threshold: Optional[float]
        When word_timestamps is True, skip silent periods longer than this threshold (in seconds)
        when a possible hallucination is detected

    Returns
    -------
    A dictionary containing the resulting text ("text") and segment-level details ("segments"), and
    the spoken language ("language"), which is detected when `decode_options["language"]` is None.
    """