import argparse import os import warnings from typing import List, Optional, Tuple, Union, TYPE_CHECKING import numpy as np import torch import tqdm from .audio import SAMPLE_RATE, N_FRAMES, HOP_LENGTH, pad_or_trim, log_mel_spectrogram from .decoding import DecodingOptions, DecodingResult from .tokenizer import LANGUAGES, TO_LANGUAGE_CODE, get_tokenizer from .utils import exact_div, format_timestamp, optional_int, optional_float, str2bool, write_txt, write_vtt, write_srt 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, **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 successfully 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. 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 mel = log_mel_spectrogram(audio) 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") segment = pad_or_trim(mel, N_FRAMES).to(model.device).to(dtype) _, probs = model.detect_language(segment) decode_options["language"] = max(probs, key=probs.get) if verbose is not None: print(f"Detected language: {LANGUAGES[decode_options['language']].title()}") language = decode_options["language"] task = decode_options.get("task", "transcribe") tokenizer = get_tokenizer(model.is_multilingual, language=language, task=task) 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 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 initial_prompt = decode_options.pop("initial_prompt", None) or [] if initial_prompt: initial_prompt = tokenizer.encode(" " + initial_prompt.strip()) all_tokens.extend(initial_prompt) def add_segment( *, start: float, end: float, text_tokens: torch.Tensor, result: DecodingResult ): text = tokenizer.decode([token for token in text_tokens if token < tokenizer.eot]) if len(text.strip()) == 0: # skip empty text output return all_segments.append( { "id": len(all_segments), "seek": seek, "start": start, "end": end, "text": text, "tokens": text_tokens.tolist(), "temperature": result.temperature, "avg_logprob": result.avg_logprob, "compression_ratio": result.compression_ratio, "no_speech_prob": result.no_speech_prob, } ) if verbose: print(f"[{format_timestamp(start)} --> {format_timestamp(end)}] {text}") # show the progress bar when verbose is False (otherwise the transcribed text will be printed) num_frames = mel.shape[-1] previous_seek_value = seek with tqdm.tqdm(total=num_frames, unit='frames', disable=verbose is not False) as pbar: while seek < num_frames: timestamp_offset = float(seek * HOP_LENGTH / SAMPLE_RATE) segment = pad_or_trim(mel[:, seek:], N_FRAMES).to(model.device).to(dtype) segment_duration = segment.shape[-1] * HOP_LENGTH / SAMPLE_RATE decode_options["prompt"] = all_tokens[prompt_reset_since:] result: DecodingResult = decode_with_fallback(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.shape[-1] # fast-forward to the next segment boundary continue timestamp_tokens: torch.Tensor = tokens.ge(tokenizer.timestamp_begin) consecutive = torch.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[0].add_(1) if len(consecutive) > 0: # if the output contains two consecutive timestamp tokens last_slice = 0 for current_slice in consecutive: sliced_tokens = tokens[last_slice:current_slice] start_timestamp_position = ( sliced_tokens[0].item() - tokenizer.timestamp_begin ) end_timestamp_position = ( sliced_tokens[-1].item() - tokenizer.timestamp_begin ) add_segment( start=timestamp_offset + start_timestamp_position * time_precision, end=timestamp_offset + end_timestamp_position * time_precision, text_tokens=sliced_tokens[1:-1], result=result, ) last_slice = current_slice last_timestamp_position = ( tokens[last_slice - 1].item() - tokenizer.timestamp_begin ) seek += last_timestamp_position * input_stride all_tokens.extend(tokens[: last_slice + 1].tolist()) 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. # single timestamp at the end means no speech after the last timestamp. last_timestamp_position = timestamps[-1].item() - tokenizer.timestamp_begin duration = last_timestamp_position * time_precision add_segment( start=timestamp_offset, end=timestamp_offset + duration, text_tokens=tokens, result=result, ) seek += segment.shape[-1] all_tokens.extend(tokens.tolist()) 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(num_frames, seek) - previous_seek_value) previous_seek_value = seek return dict(text=tokenizer.decode(all_tokens[len(initial_prompt):]), segments=all_segments, language=language) def cli(): from . import available_models 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("--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("--threads", type=optional_int, default=0, help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS") 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") 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") temperature_increment_on_fallback = args.pop("temperature_increment_on_fallback") if temperature_increment_on_fallback is not None: temperature = tuple(np.arange(temperature, 1.0 + 1e-6, temperature_increment_on_fallback)) else: temperature = [temperature] threads = args.pop("threads") if threads > 0: torch.set_num_threads(threads) from . import load_model model = load_model(model_name, device=device, download_root=model_dir) for audio_path in args.pop("audio"): result = transcribe(model, audio_path, temperature=temperature, **args) audio_basename = os.path.basename(audio_path) # save TXT with open(os.path.join(output_dir, audio_basename + ".txt"), "w", encoding="utf-8") as txt: write_txt(result["segments"], file=txt) # save VTT with open(os.path.join(output_dir, audio_basename + ".vtt"), "w", encoding="utf-8") as vtt: write_vtt(result["segments"], file=vtt) # save SRT with open(os.path.join(output_dir, audio_basename + ".srt"), "w", encoding="utf-8") as srt: write_srt(result["segments"], file=srt) if __name__ == '__main__': cli()