transcribe.py

import argparse
import os
import traceback
import warnings
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np
import torch
import tqdm
import pdb

from .audio import (
    FRAMES_PER_SECOND,
    HOP_LENGTH,
    N_FRAMES,
    N_SAMPLES,
    SAMPLE_RATE,
    log_mel_spectrogram,
    pad_or_trim,
)
from .decoding import DecodingOptions, DecodingResult
from .timing import add_word_timestamps
from .tokenizer import LANGUAGES, TO_LANGUAGE_CODE, get_tokenizer
from .utils import (
    exact_div,
    format_timestamp,
    get_end,
    get_writer,
    make_safe,
    optional_float,
    optional_int,
    str2bool,
)

if TYPE_CHECKING:
    from .model import Whisper


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 = "\"'.。,，!！?？:：”)]}、",
    **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

    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

    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 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

    seek = 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 = []
    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,
        }

    # ******** calculate the first mel segment outside while loop ***********
    time_offset = float(seek * HOP_LENGTH / SAMPLE_RATE)
    mel_segment = mel[:, seek : seek + N_FRAMES]
    segment_size = min(N_FRAMES, content_frames - seek)
    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)
    decode_options["beam_size"]
    seeks = [0] * decode_options["beam_size"] # one seek variable for each hypothesis

    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:
            seeks = [seek + segment_size for seek in seeks]  # fast-forward to the next segment boundary
    #pdb.set_trace()
    current_segments_list = [] # value per each hypothesis, where value is a list of segments, where a segment is a dict
    current_tokens_list = [] # value per each hypothesis, where value is a list

    # for loop over all hypotheses outside while loop for the first mel segment
    for j in range(len(result.tokens)):
        current_segments = []
        hypothesis = torch.tensor(result.tokens[j])

        timestamp_tokens: torch.Tensor = hypothesis.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(hypothesis))

            last_slice = 0
            for current_slice in slices:
                sliced_tokens = hypothesis[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.
                seeks[j] += segment_size
            else:
                # otherwise, ignore the unfinished segment and seek to the last timestamp
                last_timestamp_pos = (
                    hypothesis[last_slice - 1].item() - tokenizer.timestamp_begin
                )
                seeks[j] += last_timestamp_pos * input_stride
        else:
            duration = segment_duration
            timestamps = hypothesis[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=hypothesis,
                    result=result,
                )
            )
            seeks[j] += segment_size
        #pdb.set_trace()
        print(j)
        try:
            current_segments_list[j].extend([current_segments])
        except IndexError:
            current_segments_list.append([current_segments])

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

        # populate current_tokens_list for this hypothesis 
        try:
            current_tokens_list[j].extend([token for segment in current_segments for token in segment["tokens"]])
        except IndexError:
            current_tokens_list.append([token for segment in current_segments for token in segment["tokens"]])

    # loop through seek values corresponding to hypotheses
    # s_index will have the same range as number of hypotheses
    for s_index in range(len(seeks)):
        seek = seeks[s_index]
        while seek < content_frames:
            time_offset = float(seek * HOP_LENGTH / SAMPLE_RATE)
            mel_segment = mel[:, seek : seek + N_FRAMES]
            segment_size = min(N_FRAMES, content_frames - seek)
            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)
            hypothesis = result.tokens[s_index] # get corresponding hypothesis
            hypothesis = torch.tensor(hypothesis)

            current_segments = []

            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

            timestamp_tokens: torch.Tensor = hypothesis.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(hypothesis))

                last_slice = 0
                for current_slice in slices:
                    sliced_tokens = hypothesis[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 = (
                        hypothesis[last_slice - 1].item() - tokenizer.timestamp_begin
                    )
                    seek += last_timestamp_pos * input_stride
            else:
                duration = segment_duration
                timestamps = hypothesis[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=hypothesis,
                        result=result,
                    )
                )
                seek += segment_size

            # if a segment is instantaneous or does not contain text, clear it
            for segments in current_segments:
                if segment["start"] == segment["end"] or segment["text"].strip() == "":
                    segment["text"] = ""
                    segment["tokens"] = []
                    segment["words"] = []
        
            current_segments_list[s_index].extend([current_segments])
            current_tokens_list[s_index].extend([token for segment in current_segments for token in segment["tokens"]])

    # loop through each hypothesis
    out_dicts = []
    for all_toks, segs in zip(current_tokens_list, current_segments_list):
        segs_list = [segment for sublist in segs for segment in sublist]
        out_dicts.append(dict(text=tokenizer.decode(all_toks[len(initial_prompt_tokens) :]), segments=segs_list, language=language))

    return out_dicts

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 https://arxiv.org/abs/2204.05424, 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 https://arxiv.org/abs/1609.08144, 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:
            warnings.warn(
                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))
    else:
        temperature = [temperature]

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

    from . import load_model

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

    writer = get_writer(output_format, output_dir)
    word_options = [
        "highlight_words",
        "max_line_count",
        "max_line_width",
        "max_words_per_line",
    ]
    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"):
        try:
            result = transcribe(model, audio_path, temperature=temperature, **args)
            writer(result, audio_path, **writer_args)
        except Exception as e:
            traceback.print_exc()
            print(f"Skipping {audio_path} due to {type(e).__name__}: {str(e)}")


if __name__ == "__main__":
    cli()