def transcribe_minimal(
        model: "Whisper",
        audio: Union[str, np.ndarray, torch.Tensor, bytes],
        *,
        verbose: Optional[bool] = False,
        word_timestamps: bool = True,
        regroup: Union[bool, str] = True,
        suppress_silence: bool = True,
        suppress_word_ts: bool = True,
        use_word_position: bool = True,
        q_levels: int = 20,
        k_size: int = 5,
        denoiser: Optional[str] = None,
        denoiser_options: Optional[dict] = None,
        demucs: bool = False,
        demucs_options: dict = None,
        vad: bool = False,
        vad_threshold: float = 0.35,
        vad_onnx: bool = False,
        min_word_dur: float = 0.1,
        nonspeech_error: float = 0.1,
        only_voice_freq: bool = False,
        only_ffmpeg: bool = False,
        **options) \
        -> WhisperResult:
    """
    Transcribe audio using Whisper.

    This is uses the original whisper transcribe function, :func:`whisper.transcribe.transcribe`, while still allowing
    additional preprocessing and postprocessing. The preprocessing performed on the audio includes: voice isolation /
    noise removal and low/high-pass filter. The postprocessing performed on the transcription result includes:
    adjusting timestamps with VAD and custom regrouping segments based punctuation and speech gaps.

    Parameters
    ----------
    model : whisper.model.Whisper
        An instance of Whisper ASR model.
    audio : str or numpy.ndarray or torch.Tensor or bytes
        Path/URL to the audio file, the audio waveform, or bytes of audio file.
        If audio is ``numpy.ndarray`` or ``torch.Tensor``, the audio must be already at sampled to 16kHz.
    verbose : bool or None, default False
        Whether to display the text being decoded to the console.
        Displays all the details if ``True``. Displays progressbar if ``False``. Display nothing if ``None``.
    word_timestamps : bool, default True
        Extract word-level timestamps using the cross-attention pattern and dynamic time warping,
        and include the timestamps for each word in each segment.
        Disabling this will prevent segments from splitting/merging properly.
    regroup : bool or str, default True, meaning the default regroup algorithm
        String for customizing the regrouping algorithm. False disables regrouping.
        Ignored if ``word_timestamps = False``.
    suppress_silence : bool, default True
        Whether to enable timestamps adjustments based on the detected silence.
    suppress_word_ts : bool, default True
        Whether to adjust word timestamps based on the detected silence. Only enabled if ``suppress_silence = True``.
    use_word_position : bool, default True
        Whether to use position of the word in its segment to determine whether to keep end or start timestamps if
        adjustments are required. If it is the first word, keep end. Else if it is the last word, keep the start.
    q_levels : int, default 20
        Quantization levels for generating timestamp suppression mask; ignored if ``vad = true``.
        Acts as a threshold to marking sound as silent.
        Fewer levels will increase the threshold of volume at which to mark a sound as silent.
    k_size : int, default 5
        Kernel size for avg-pooling waveform to generate timestamp suppression mask; ignored if ``vad = true``.
        Recommend 5 or 3; higher sizes will reduce detection of silence.
    denoiser : str, optional
        String of the denoiser to use for preprocessing ``audio``.
        See ``stable_whisper.audio.SUPPORTED_DENOISERS`` for supported denoisers.
    denoiser_options : dict, optional
        Options to use for ``denoiser``.
    vad : bool, default False
        Whether to use Silero VAD to generate timestamp suppression mask.
        Silero VAD requires PyTorch 1.12.0+. Official repo, https://github.com/snakers4/silero-vad.
    vad_threshold : float, default 0.35
        Threshold for detecting speech with Silero VAD. Low threshold reduces false positives for silence detection.
    vad_onnx : bool, default False
        Whether to use ONNX for Silero VAD.
    min_word_dur : float, default 0.1
        Shortest duration each word is allowed to reach for silence suppression.
    nonspeech_error : float, default 0.1
        Relative error of non-speech sections that appear in between a word for silence suppression.
    only_voice_freq : bool, default False
        Whether to only use sound between 200 - 5000 Hz, where majority of human speech are.
    only_ffmpeg : bool, default False
        Whether to use only FFmpeg (instead of not yt-dlp) for URls
    options
        Additional options used for :func:`whisper.transcribe.transcribe` and
        :func:`stable_whisper.non_whisper.transcribe_any`.
    Returns
    -------
    stable_whisper.result.WhisperResult
        All timestamps, words, probabilities, and other data from the transcription of ``audio``.

    Examples
    --------
    >>> import stable_whisper
    >>> model = stable_whisper.load_model('base')
    >>> result = model.transcribe_minimal('audio.mp3', vad=True)
    >>> result.to_srt_vtt('audio.srt')
    Saved: audio.srt
    """
    audioloader_not_supported(audio)
    from ..non_whisper import transcribe_any
    inference_kwargs = dict(
        model=model,
        audio=audio,
        word_timestamps=word_timestamps,
        verbose=verbose
    )
    extra_options = isolate_useful_options(options, transcribe_any, True)
    denoiser, denoiser_options = convert_demucs_kwargs(
        denoiser, denoiser_options, demucs=demucs, demucs_options=demucs_options
    )
    if not isinstance(audio, (str, bytes)):
        if 'input_sr' not in extra_options:
            extra_options['input_sr'] = SAMPLE_RATE

    if denoiser or only_voice_freq:
        if 'audio_type' not in extra_options:
            extra_options['audio_type'] = 'torch'
        if 'model_sr' not in extra_options:
            extra_options['model_sr'] = SAMPLE_RATE
    inference_kwargs.update(options)
    return transcribe_any(
        inference_func=whisper.transcribe,
        audio=audio,
        inference_kwargs=inference_kwargs,
        verbose=verbose,
        regroup=regroup,
        suppress_silence=suppress_silence,
        suppress_word_ts=suppress_word_ts,
        q_levels=q_levels,
        k_size=k_size,
        denoiser=denoiser,
        denoiser_options=denoiser_options,
        vad=vad,
        vad_threshold=vad_threshold,
        vad_onnx=vad_onnx,
        min_word_dur=min_word_dur,
        nonspeech_error=nonspeech_error,
        use_word_position=use_word_position,
        only_voice_freq=only_voice_freq,
        only_ffmpeg=only_ffmpeg,
        force_order=True,
        **extra_options
    )

    Transcribe audio using Whisper.

    This is uses the original whisper transcribe function, :func:`whisper.transcribe.transcribe`, while still allowing
    additional preprocessing and postprocessing. The preprocessing performed on the audio includes: voice isolation /
    noise removal and low/high-pass filter. The postprocessing performed on the transcription result includes:
    adjusting timestamps with VAD and custom regrouping segments based punctuation and speech gaps.

    Parameters
    ----------
    model : whisper.model.Whisper
        An instance of Whisper ASR model.
    audio : str or numpy.ndarray or torch.Tensor or bytes
        Path/URL to the audio file, the audio waveform, or bytes of audio file.
        If audio is ``numpy.ndarray`` or ``torch.Tensor``, the audio must be already at sampled to 16kHz.
    verbose : bool or None, default False
        Whether to display the text being decoded to the console.
        Displays all the details if ``True``. Displays progressbar if ``False``. Display nothing if ``None``.
    word_timestamps : bool, default True
        Extract word-level timestamps using the cross-attention pattern and dynamic time warping,
        and include the timestamps for each word in each segment.
        Disabling this will prevent segments from splitting/merging properly.
    regroup : bool or str, default True, meaning the default regroup algorithm
        String for customizing the regrouping algorithm. False disables regrouping.
        Ignored if ``word_timestamps = False``.
    suppress_silence : bool, default True
        Whether to enable timestamps adjustments based on the detected silence.
    suppress_word_ts : bool, default True
        Whether to adjust word timestamps based on the detected silence. Only enabled if ``suppress_silence = True``.
    use_word_position : bool, default True
        Whether to use position of the word in its segment to determine whether to keep end or start timestamps if
        adjustments are required. If it is the first word, keep end. Else if it is the last word, keep the start.
    q_levels : int, default 20
        Quantization levels for generating timestamp suppression mask; ignored if ``vad = true``.
        Acts as a threshold to marking sound as silent.
        Fewer levels will increase the threshold of volume at which to mark a sound as silent.
    k_size : int, default 5
        Kernel size for avg-pooling waveform to generate timestamp suppression mask; ignored if ``vad = true``.
        Recommend 5 or 3; higher sizes will reduce detection of silence.
    denoiser : str, optional
        String of the denoiser to use for preprocessing ``audio``.
        See ``stable_whisper.audio.SUPPORTED_DENOISERS`` for supported denoisers.
    denoiser_options : dict, optional
        Options to use for ``denoiser``.
    vad : bool, default False
        Whether to use Silero VAD to generate timestamp suppression mask.
        Silero VAD requires PyTorch 1.12.0+. Official repo, https://github.com/snakers4/silero-vad.
    vad_threshold : float, default 0.35
        Threshold for detecting speech with Silero VAD. Low threshold reduces false positives for silence detection.
    vad_onnx : bool, default False
        Whether to use ONNX for Silero VAD.
    min_word_dur : float, default 0.1
        Shortest duration each word is allowed to reach for silence suppression.
    nonspeech_error : float, default 0.1
        Relative error of non-speech sections that appear in between a word for silence suppression.
    only_voice_freq : bool, default False
        Whether to only use sound between 200 - 5000 Hz, where majority of human speech are.
    only_ffmpeg : bool, default False
        Whether to use only FFmpeg (instead of not yt-dlp) for URls
    options
        Additional options used for :func:`whisper.transcribe.transcribe` and
        :func:`stable_whisper.non_whisper.transcribe_any`.
    Returns
    -------
    stable_whisper.result.WhisperResult
        All timestamps, words, probabilities, and other data from the transcription of ``audio``.

    Examples
    --------
    >>> import stable_whisper
    >>> model = stable_whisper.load_model('base')
    >>> result = model.transcribe_minimal('audio.mp3', vad=True)
    >>> result.to_srt_vtt('audio.srt')
    Saved: audio.srt
    
stable_whisper/whisper_word_level/original_whisper.py