"console_scripts": ["whisper=whisper.transcribe:cli"],

エントリポイント whisper.transcribe:cli のコード確認

def cli():
    from . import available_models

    def valid_model_name(name):
        if name in available_models() or os.path.exists(name):
            return name
        raise ValueError(
            f"model should be one of {available_models()} or path to a model checkpoint"

    # fmt: off
    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    parser.add_argument("audio", nargs="+", type=str, help="audio file(s) to transcribe")
    parser.add_argument("--model", default="small", type=valid_model_name, help="name of the Whisper model to use")
    parser.add_argument("--model_dir", type=str, default=None, help="the path to save model files; uses ~/.cache/whisper by default")
    parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu", help="device to use for PyTorch inference")
    parser.add_argument("--output_dir", "-o", type=str, default=".", help="directory to save the outputs")
    parser.add_argument("--output_format", "-f", type=str, default="all", choices=["txt", "vtt", "srt", "tsv", "json", "all"], help="format of the output file; if not specified, all available formats will be produced")
    parser.add_argument("--verbose", type=str2bool, default=True, help="whether to print out the progress and debug messages")

    parser.add_argument("--task", type=str, default="transcribe", choices=["transcribe", "translate"], help="whether to perform X->X speech recognition ('transcribe') or X->English translation ('translate')")
    parser.add_argument("--language", type=str, default=None, choices=sorted(LANGUAGES.keys()) + sorted([k.title() for k in TO_LANGUAGE_CODE.keys()]), help="language spoken in the audio, specify None to perform language detection")

    parser.add_argument("--temperature", type=float, default=0, help="temperature to use for sampling")
    parser.add_argument("--best_of", type=optional_int, default=5, help="number of candidates when sampling with non-zero temperature")
    parser.add_argument("--beam_size", type=optional_int, default=5, help="number of beams in beam search, only applicable when temperature is zero")
    parser.add_argument("--patience", type=float, default=None, help="optional patience value to use in beam decoding, as in, the default (1.0) is equivalent to conventional beam search")
    parser.add_argument("--length_penalty", type=float, default=None, help="optional token length penalty coefficient (alpha) as in, uses simple length normalization by default")

    parser.add_argument("--suppress_tokens", type=str, default="-1", help="comma-separated list of token ids to suppress during sampling; '-1' will suppress most special characters except common punctuations")
    parser.add_argument("--initial_prompt", type=str, default=None, help="optional text to provide as a prompt for the first window.")
    parser.add_argument("--condition_on_previous_text", type=str2bool, default=True, help="if True, provide the previous output of the model 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")
    parser.add_argument("--fp16", type=str2bool, default=True, help="whether to perform inference in fp16; True by default")

    parser.add_argument("--temperature_increment_on_fallback", type=optional_float, default=0.2, help="temperature to increase when falling back when the decoding fails to meet either of the thresholds below")
    parser.add_argument("--compression_ratio_threshold", type=optional_float, default=2.4, help="if the gzip compression ratio is higher than this value, treat the decoding as failed")
    parser.add_argument("--logprob_threshold", type=optional_float, default=-1.0, help="if the average log probability is lower than this value, treat the decoding as failed")
    parser.add_argument("--no_speech_threshold", type=optional_float, default=0.6, help="if the probability of the <|nospeech|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence")
    parser.add_argument("--word_timestamps", type=str2bool, default=False, help="(experimental) extract word-level timestamps and refine the results based on them")
    parser.add_argument("--prepend_punctuations", type=str, default="\"\'“¿([{-", help="if word_timestamps is True, merge these punctuation symbols with the next word")
    parser.add_argument("--append_punctuations", type=str, default="\"\'.。,,!!??::”)]}、", help="if word_timestamps is True, merge these punctuation symbols with the previous word")
    parser.add_argument("--highlight_words", type=str2bool, default=False, help="(requires --word_timestamps True) underline each word as it is spoken in srt and vtt")
    parser.add_argument("--max_line_width", type=optional_int, default=None, help="(requires --word_timestamps True) the maximum number of characters in a line before breaking the line")
    parser.add_argument("--max_line_count", type=optional_int, default=None, help="(requires --word_timestamps True) the maximum number of lines in a segment")
    parser.add_argument("--max_words_per_line", type=optional_int, default=None, help="(requires --word_timestamps True, no effect with --max_line_width) the maximum number of words in a segment")
    parser.add_argument("--threads", type=optional_int, default=0, help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS")
    parser.add_argument("--clip_timestamps", type=str, default="0", help="comma-separated list start,end,start,end,... timestamps (in seconds) of clips to process, where the last end timestamp defaults to the end of the file")
    parser.add_argument("--hallucination_silence_threshold", type=optional_float, help="(requires --word_timestamps True) skip silent periods longer than this threshold (in seconds) when a possible hallucination is detected")
    # fmt: on

    args = parser.parse_args().__dict__
    model_name: str = args.pop("model")
    model_dir: str = args.pop("model_dir")
    output_dir: str = args.pop("output_dir")
    output_format: str = args.pop("output_format")
    device: str = args.pop("device")
    os.makedirs(output_dir, exist_ok=True)

    if model_name.endswith(".en") and args["language"] not in {"en", "English"}:
        if args["language"] is not None:
                f"{model_name} is an English-only model but receipted '{args['language']}'; using English instead."
        args["language"] = "en"

    temperature = args.pop("temperature")
    if (increment := args.pop("temperature_increment_on_fallback")) is not None:
        temperature = tuple(np.arange(temperature, 1.0 + 1e-6, increment))
        temperature = [temperature]

    if (threads := args.pop("threads")) > 0:

    from . import load_model

    model = load_model(model_name, device=device, download_root=model_dir)

    writer = get_writer(output_format, output_dir)
    word_options = [
    if not args["word_timestamps"]:
        for option in word_options:
            if args[option]:
                parser.error(f"--{option} requires --word_timestamps True")
    if args["max_line_count"] and not args["max_line_width"]:
        warnings.warn("--max_line_count has no effect without --max_line_width")
    if args["max_words_per_line"] and args["max_line_width"]:
        warnings.warn("--max_words_per_line has no effect with --max_line_width")
    writer_args = {arg: args.pop(arg) for arg in word_options}
    for audio_path in args.pop("audio"):
            result = transcribe(model, audio_path, temperature=temperature, **args)
            writer(result, audio_path, **writer_args)
        except Exception as e:
            print(f"Skipping {audio_path} due to {type(e).__name__}: {str(e)}")


            result = transcribe(model, audio_path, temperature=temperature, **args)
            writer(result, audio_path, **writer_args)