import argparse import os import warnings from typing import TYPE_CHECKING, Optional, Tuple, Union import numpy as np import torch import tqdm 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_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, 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, 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 = [] 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 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) 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 = [] 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, ) word_end_timestamps = [ w["end"] for s in current_segments for w in s["words"] ] if len(word_end_timestamps) > 0: last_speech_timestamp = word_end_timestamps[-1] if not single_timestamp_ending and len(word_end_timestamps) > 0: seek_shift = round( (word_end_timestamps[-1] - time_offset) * FRAMES_PER_SECOND ) if seek_shift > 0: seek = previous_seek + seek_shift 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, ) def cli(): from . import available_models # 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", choices=available_models(), 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("--threads", type=optional_int, default=0, help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS") # 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"] 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") 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) if __name__ == "__main__": cli()