OpenAIが提供する音声認識オープンソースWhisperとは(5)

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transcribe関数の定義

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,
    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.

    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.
    """
    dtype = torch.float16 if decode_options.get("fp16", True) else torch.float32
    if model.device == torch.device("cpu"):
        if torch.cuda.is_available():
            warnings.warn("Performing inference on CPU when CUDA is available")
        if dtype == torch.float16:
            warnings.warn("FP16 is not supported on CPU; using FP32 instead")
            dtype = torch.float32

    if dtype == torch.float32:
        decode_options["fp16"] = False

    # Pad 30-seconds of silence to the input audio, for slicing
    mel = log_mel_spectrogram(audio, model.dims.n_mels, padding=N_SAMPLES)
    content_frames = mel.shape[-1] - N_FRAMES
    content_duration = float(content_frames * HOP_LENGTH / SAMPLE_RATE)

    if decode_options.get("language", None) is None:
        if not model.is_multilingual:
            decode_options["language"] = "en"
        else:
            if verbose:
                print(
                    "Detecting language using up to the first 30 seconds. Use `--language` to specify the language"
                )
            mel_segment = pad_or_trim(mel, N_FRAMES).to(model.device).to(dtype)
            _, probs = model.detect_language(mel_segment)
            decode_options["language"] = max(probs, key=probs.get)
            if verbose is not None:
                print(
                    f"Detected language: {LANGUAGES[decode_options['language']].title()}"
                )

    language: str = decode_options["language"]
    task: str = decode_options.get("task", "transcribe")
    tokenizer = get_tokenizer(
        model.is_multilingual,
        num_languages=model.num_languages,
        language=language,
        task=task,
    )

    if isinstance(clip_timestamps, str):
        clip_timestamps = [
            float(ts) for ts in (clip_timestamps.split(",") if clip_timestamps else [])
        ]
    seek_points: List[int] = [round(ts * FRAMES_PER_SECOND) for ts in clip_timestamps]
    if len(seek_points) == 0:
        seek_points.append(0)
    if len(seek_points) % 2 == 1:
        seek_points.append(content_frames)
    seek_clips: List[Tuple[int, int]] = list(zip(seek_points[::2], seek_points[1::2]))

    punctuation = "\"'“¿([{-\"'.。,,!!??::”)]}、"

    if word_timestamps and task == "translate":
        warnings.warn("Word-level timestamps on translations may not be reliable.")

    def decode_with_fallback(segment: torch.Tensor) -> DecodingResult:
        temperatures = (
            [temperature] if isinstance(temperature, (int, float)) else temperature
        )
        decode_result = None

        for t in temperatures:
            kwargs = {**decode_options}
            if t > 0:
                # disable beam_size and patience when t > 0
                kwargs.pop("beam_size", None)
                kwargs.pop("patience", None)
            else:
                # disable best_of when t == 0
                kwargs.pop("best_of", None)

            options = DecodingOptions(**kwargs, temperature=t)
            decode_result = model.decode(segment, options)

            needs_fallback = False
            if (
                compression_ratio_threshold is not None
                and decode_result.compression_ratio > compression_ratio_threshold
            ):
                needs_fallback = True  # too repetitive
            if (
                logprob_threshold is not None
                and decode_result.avg_logprob < logprob_threshold
            ):
                needs_fallback = True  # average log probability is too low
            if (
                no_speech_threshold is not None
                and decode_result.no_speech_prob > no_speech_threshold
            ):
                needs_fallback = False  # silence
            if not needs_fallback:
                break

        return decode_result

    clip_idx = 0
    seek = seek_clips[clip_idx][0]
    input_stride = exact_div(
        N_FRAMES, model.dims.n_audio_ctx
    )  # mel frames per output token: 2
    time_precision = (
        input_stride * HOP_LENGTH / SAMPLE_RATE
    )  # time per output token: 0.02 (seconds)
    all_tokens = []
    all_segments = []
    prompt_reset_since = 0

    if initial_prompt is not None:
        initial_prompt_tokens = tokenizer.encode(" " + initial_prompt.strip())
        all_tokens.extend(initial_prompt_tokens)
    else:
        initial_prompt_tokens = []

    def new_segment(
        *, start: float, end: float, tokens: torch.Tensor, result: DecodingResult
    ):
        tokens = tokens.tolist()
        text_tokens = [token for token in tokens if token < tokenizer.eot]
        return {
            "seek": seek,
            "start": start,
            "end": end,
            "text": tokenizer.decode(text_tokens),
            "tokens": tokens,
            "temperature": result.temperature,
            "avg_logprob": result.avg_logprob,
            "compression_ratio": result.compression_ratio,
            "no_speech_prob": result.no_speech_prob,
        }

    # show the progress bar when verbose is False (if True, transcribed text will be printed)
    with tqdm.tqdm(
        total=content_frames, unit="frames", disable=verbose is not False
    ) as pbar:
        last_speech_timestamp = 0.0
        # NOTE: This loop is obscurely flattened to make the diff readable.
        # A later commit should turn this into a simpler nested loop.
        # for seek_clip_start, seek_clip_end in seek_clips:
        #     while seek < seek_clip_end
        while clip_idx < len(seek_clips):
            seek_clip_start, seek_clip_end = seek_clips[clip_idx]
            if seek < seek_clip_start:
                seek = seek_clip_start
            if seek >= seek_clip_end:
                clip_idx += 1
                if clip_idx < len(seek_clips):
                    seek = seek_clips[clip_idx][0]
                continue
            time_offset = float(seek * HOP_LENGTH / SAMPLE_RATE)
            window_end_time = float((seek + N_FRAMES) * HOP_LENGTH / SAMPLE_RATE)
            segment_size = min(N_FRAMES, content_frames - seek, seek_clip_end - seek)
            mel_segment = mel[:, seek : seek + segment_size]
            segment_duration = segment_size * HOP_LENGTH / SAMPLE_RATE
            mel_segment = pad_or_trim(mel_segment, N_FRAMES).to(model.device).to(dtype)

            decode_options["prompt"] = all_tokens[prompt_reset_since:]
            result: DecodingResult = decode_with_fallback(mel_segment)
            tokens = torch.tensor(result.tokens)

            if no_speech_threshold is not None:
                # no voice activity check
                should_skip = result.no_speech_prob > no_speech_threshold
                if (
                    logprob_threshold is not None
                    and result.avg_logprob > logprob_threshold
                ):
                    # don't skip if the logprob is high enough, despite the no_speech_prob
                    should_skip = False

                if should_skip:
                    seek += segment_size  # fast-forward to the next segment boundary
                    continue

            previous_seek = seek
            current_segments = []

            # anomalous words are very long/short/improbable
            def word_anomaly_score(word: dict) -> float:
                probability = word.get("probability", 0.0)
                duration = word["end"] - word["start"]
                score = 0.0
                if probability < 0.15:
                    score += 1.0
                if duration < 0.133:
                    score += (0.133 - duration) * 15
                if duration > 2.0:
                    score += duration - 2.0
                return score

            def is_segment_anomaly(segment: Optional[dict]) -> bool:
                if segment is None or not segment["words"]:
                    return False
                words = [w for w in segment["words"] if w["word"] not in punctuation]
                words = words[:8]
                score = sum(word_anomaly_score(w) for w in words)
                return score >= 3 or score + 0.01 >= len(words)

            def next_words_segment(segments: List[dict]) -> Optional[dict]:
                return next((s for s in segments if s["words"]), None)

            timestamp_tokens: torch.Tensor = tokens.ge(tokenizer.timestamp_begin)
            single_timestamp_ending = timestamp_tokens[-2:].tolist() == [False, True]

            consecutive = torch.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[0]
            consecutive.add_(1)
            if len(consecutive) > 0:
                # if the output contains two consecutive timestamp tokens
                slices = consecutive.tolist()
                if single_timestamp_ending:
                    slices.append(len(tokens))

                last_slice = 0
                for current_slice in slices:
                    sliced_tokens = tokens[last_slice:current_slice]
                    start_timestamp_pos = (
                        sliced_tokens[0].item() - tokenizer.timestamp_begin
                    )
                    end_timestamp_pos = (
                        sliced_tokens[-1].item() - tokenizer.timestamp_begin
                    )
                    current_segments.append(
                        new_segment(
                            start=time_offset + start_timestamp_pos * time_precision,
                            end=time_offset + end_timestamp_pos * time_precision,
                            tokens=sliced_tokens,
                            result=result,
                        )
                    )
                    last_slice = current_slice

                if single_timestamp_ending:
                    # single timestamp at the end means no speech after the last timestamp.
                    seek += segment_size
                else:
                    # otherwise, ignore the unfinished segment and seek to the last timestamp
                    last_timestamp_pos = (
                        tokens[last_slice - 1].item() - tokenizer.timestamp_begin
                    )
                    seek += last_timestamp_pos * input_stride
            else:
                duration = segment_duration
                timestamps = tokens[timestamp_tokens.nonzero().flatten()]
                if (
                    len(timestamps) > 0
                    and timestamps[-1].item() != tokenizer.timestamp_begin
                ):
                    # no consecutive timestamps but it has a timestamp; use the last one.
                    last_timestamp_pos = (
                        timestamps[-1].item() - tokenizer.timestamp_begin
                    )
                    duration = last_timestamp_pos * time_precision

                current_segments.append(
                    new_segment(
                        start=time_offset,
                        end=time_offset + duration,
                        tokens=tokens,
                        result=result,
                    )
                )
                seek += segment_size

            if word_timestamps:
                add_word_timestamps(
                    segments=current_segments,
                    model=model,
                    tokenizer=tokenizer,
                    mel=mel_segment,
                    num_frames=segment_size,
                    prepend_punctuations=prepend_punctuations,
                    append_punctuations=append_punctuations,
                    last_speech_timestamp=last_speech_timestamp,
                )

                if not single_timestamp_ending:
                    last_word_end = get_end(current_segments)
                    if last_word_end is not None and last_word_end > time_offset:
                        seek = round(last_word_end * FRAMES_PER_SECOND)

                # skip silence before possible hallucinations
                if hallucination_silence_threshold is not None:
                    threshold = hallucination_silence_threshold
                    if not single_timestamp_ending:
                        last_word_end = get_end(current_segments)
                        if last_word_end is not None and last_word_end > time_offset:
                            remaining_duration = window_end_time - last_word_end
                            if remaining_duration > threshold:
                                seek = round(last_word_end * FRAMES_PER_SECOND)
                            else:
                                seek = previous_seek + segment_size

                    # if first segment might be a hallucination, skip leading silence
                    first_segment = next_words_segment(current_segments)
                    if first_segment is not None and is_segment_anomaly(first_segment):
                        gap = first_segment["start"] - time_offset
                        if gap > threshold:
                            seek = previous_seek + round(gap * FRAMES_PER_SECOND)
                            continue

                    # skip silence before any possible hallucination that is surrounded
                    # by silence or more hallucinations
                    hal_last_end = last_speech_timestamp
                    for si in range(len(current_segments)):
                        segment = current_segments[si]
                        if not segment["words"]:
                            continue
                        if is_segment_anomaly(segment):
                            next_segment = next_words_segment(
                                current_segments[si + 1 :]
                            )
                            if next_segment is not None:
                                hal_next_start = next_segment["words"][0]["start"]
                            else:
                                hal_next_start = time_offset + segment_duration
                            silence_before = (
                                segment["start"] - hal_last_end > threshold
                                or segment["start"] < threshold
                                or segment["start"] - time_offset < 2.0
                            )
                            silence_after = (
                                hal_next_start - segment["end"] > threshold
                                or is_segment_anomaly(next_segment)
                                or window_end_time - segment["end"] < 2.0
                            )
                            if silence_before and silence_after:
                                seek = round(
                                    max(time_offset + 1, segment["start"])
                                    * FRAMES_PER_SECOND
                                )
                                if content_duration - segment["end"] < threshold:
                                    seek = content_frames
                                current_segments[si:] = []
                                break
                        hal_last_end = segment["end"]

                last_word_end = get_end(current_segments)
                if last_word_end is not None:
                    last_speech_timestamp = last_word_end

            if verbose:
                for segment in current_segments:
                    start, end, text = segment["start"], segment["end"], segment["text"]
                    line = f"[{format_timestamp(start)} --> {format_timestamp(end)}] {text}"
                    print(make_safe(line))

            # if a segment is instantaneous or does not contain text, clear it
            for i, segment in enumerate(current_segments):
                if segment["start"] == segment["end"] or segment["text"].strip() == "":
                    segment["text"] = ""
                    segment["tokens"] = []
                    segment["words"] = []

            all_segments.extend(
                [
                    {"id": i, **segment}
                    for i, segment in enumerate(
                        current_segments, start=len(all_segments)
                    )
                ]
            )
            all_tokens.extend(
                [token for segment in current_segments for token in segment["tokens"]]
            )

            if not condition_on_previous_text or result.temperature > 0.5:
                # do not feed the prompt tokens if a high temperature was used
                prompt_reset_since = len(all_tokens)

            # update progress bar
            pbar.update(min(content_frames, seek) - previous_seek)

    return dict(
        text=tokenizer.decode(all_tokens[len(initial_prompt_tokens) :]),
        segments=all_segments,
        language=language,
    )

transcribe関数の呼び出し

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

log_mel_spectrogram関数の定義

def log_mel_spectrogram(
    audio: Union[str, np.ndarray, torch.Tensor],
    n_mels: int = 80,
    padding: int = 0,
    device: Optional[Union[str, torch.device]] = None,
):
    """
    Compute the log-Mel spectrogram of

    Parameters
    ----------
    audio: Union[str, np.ndarray, torch.Tensor], shape = (*)
        The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz

    n_mels: int
        The number of Mel-frequency filters, only 80 is supported

    padding: int
        Number of zero samples to pad to the right

    device: Optional[Union[str, torch.device]]
        If given, the audio tensor is moved to this device before STFT

    Returns
    -------
    torch.Tensor, shape = (80, n_frames)
        A Tensor that contains the Mel spectrogram
    """
    if not torch.is_tensor(audio):
        if isinstance(audio, str):
            audio = load_audio(audio)
        audio = torch.from_numpy(audio)

    if device is not None:
        audio = audio.to(device)
    if padding > 0:
        audio = F.pad(audio, (0, padding))
    window = torch.hann_window(N_FFT).to(audio.device)
    stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True)
    magnitudes = stft[..., :-1].abs() ** 2

    filters = mel_filters(audio.device, n_mels)
    mel_spec = filters @ magnitudes

    log_spec = torch.clamp(mel_spec, min=1e-10).log10()
    log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
    log_spec = (log_spec + 4.0) / 4.0
    return log_spec

この関数は、与えられたオーディオのログメルスペクトログラムを計算します。以下は関数の各パラメータの説明です:

  • audio: 入力オーディオデータです。文字列 (ファイルパス)、NumPy配列、またはPyTorchテンソルが受け入れられます。
  • n_mels: メルフィルタバンクの数を指定します。デフォルトは80です。
  • padding: 右に追加されるゼロサンプルの数を指定します。デフォルトは0です。
  • device: 処理を実行するデバイスを指定します。指定された場合、オーディオテンソルはこのデバイスに移動されます。

この関数は以下の手順でログメルスペクトログラムを計算します:

  1. 入力オーディオデータがテンソルでない場合、テンソルに変換します。
  2. 必要に応じて、オーディオテンソルを指定されたデバイスに移動します。
  3. パディングを適用します(必要な場合)。
  4. ハニング窓を作成します。
  5. 短時間フーリエ変換(STFT)を実行し、複素数形式で結果を取得します。
  6. スペクトログラムの振幅を計算します。
  7. メルフィルタバンクを作成し、スペクトログラムに適用します。
  8. メルスペクトログラムに対して対数を取り、クリッピングとスケーリングを行います。
  9. 処理されたログメルスペクトログラムを返します。

log_mel_spectrogram関数の呼び出し

    mel = log_mel_spectrogram(audio, model.dims.n_mels, padding=N_SAMPLES)

smallモデルの場合、n_mels=80

また、paddingに設定するN_SAMPLES値は以下のように決めている。

SAMPLE_RATE = 16000
CHUNK_LENGTH = 30
N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE  # 480000 samples in a 30-second chunk

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