import argparse import os import warnings from typing import TYPE_CHECKING, Optional, Tuple, Union import numpy as np import torch import tempfile import ffmpeg from whisper.tokenizer import LANGUAGES, TO_LANGUAGE_CODE from whisper.audio import SAMPLE_RATE from whisper.utils import ( optional_float, optional_int, str2bool, ) from .alignment import load_align_model, align from .asr import transcribe, transcribe_with_vad from .diarize import DiarizationPipeline from .utils import get_writer from .vad import load_vad_model def cli(): from whisper 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=["all", "srt", "srt-word", "vtt", "txt", "tsv", "ass", "ass-char", "pickle", "vad"], 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") # alignment params parser.add_argument("--align_model", default=None, help="Name of phoneme-level ASR model to do alignment") parser.add_argument("--align_extend", default=2, type=float, help="Seconds before and after to extend the whisper segments for alignment (if not using VAD).") parser.add_argument("--align_from_prev", default=True, type=bool, help="Whether to clip the alignment start time of current segment to the end time of the last aligned word of the previous segment (if not using VAD)") parser.add_argument("--interpolate_method", default="nearest", choices=["nearest", "linear", "ignore"], help="For word .srt, method to assign timestamps to non-aligned words, or merge them into neighbouring.") parser.add_argument("--no_align", action='store_true', help="Do not perform phoneme alignment") # vad params parser.add_argument("--vad_filter", action="store_true", help="Whether to pre-segment audio with VAD, highly recommended! Produces more accurate alignment + timestamp see WhisperX paper https://arxiv.org/abs/2303.00747") parser.add_argument("--vad_onset", type=float, default=0.767, help="Onset threshold for VAD (see pyannote.audio)") parser.add_argument("--vad_offset", type=float, default=0.363, help="Offset threshold for VAD (see pyannote.audio).") # diarization params parser.add_argument("--diarize", action="store_true", help="Apply diarization to assign speaker labels to each segment/word") parser.add_argument("--min_speakers", default=None, type=int) parser.add_argument("--max_speakers", default=None, type=int) 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("--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("--hf_token", type=str, default=None, help="Hugging Face Access Token to access PyAnnote gated models") parser.add_argument("--model_flush", action="store_true", help="Flush memory from each model after use, reduces GPU requirement but slower processing >1 audio file.") # 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") model_flush: bool = args.pop("model_flush") os.makedirs(output_dir, exist_ok=True) align_model: str = args.pop("align_model") align_extend: float = args.pop("align_extend") align_from_prev: bool = args.pop("align_from_prev") interpolate_method: str = args.pop("interpolate_method") no_align: bool = args.pop("no_align") hf_token: str = args.pop("hf_token") vad_filter: bool = args.pop("vad_filter") vad_onset: float = args.pop("vad_onset") vad_offset: float = args.pop("vad_offset") diarize: bool = args.pop("diarize") min_speakers: int = args.pop("min_speakers") max_speakers: int = args.pop("max_speakers") if vad_filter: if hf_token is None: print("Warning, no --hf_token used, needs to be saved in environment variable, otherwise will throw error loading diarization model...") from pyannote.audio import Pipeline from pyannote.audio import Model, Pipeline vad_model = load_vad_model(torch.device(device), vad_onset, vad_offset, use_auth_token=hf_token) else: vad_model = None if diarize: if hf_token is None: print("Warning, no --hf_token used, needs to be saved in environment variable, otherwise will throw error loading diarization model...") diarize_model = DiarizationPipeline(use_auth_token=hf_token) else: diarize_model = None if no_align: align_model, align_metadata = None, None else: align_language = args["language"] if args["language"] is not None else "en" # default to loading english if not specified align_model, align_metadata = load_align_model(align_language, device, model_name=align_model) 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 whisper import load_model model = load_model(model_name, device=device, download_root=model_dir) writer = get_writer(output_format, output_dir) for audio_path in args.pop("audio"): if vad_model is not None: if not audio_path.endswith(".wav"): print("VAD requires .wav format, converting to wav as a tempfile...") tfile = tempfile.NamedTemporaryFile(delete=True, suffix=".wav") ffmpeg.input(audio_path, threads=0).output(tfile.name, ac=1, ar=SAMPLE_RATE).run(cmd=["ffmpeg"]) vad_audio_path = tfile.name else: vad_audio_path = audio_path print("Performing VAD...") result = transcribe_with_vad(model, vad_audio_path, vad_model, temperature=temperature, **args) if tfile is not None: tfile.close() else: print("Performing transcription...") result = transcribe(model, audio_path, temperature=temperature, **args) if align_model is not None: if result["language"] != align_metadata["language"]: # load new language print(f"New language found ({result['language']})! Previous was ({align_metadata['language']}), loading new alignment model for new language...") align_model, align_metadata = load_align_model(result["language"], device) result = align(result["segments"], align_model, align_metadata, audio_path, device, extend_duration=align_extend, start_from_previous=align_from_prev, interpolate_method=interpolate_method) # if diarize_model is not None: # diarize_segments = diarize_model(audio_path, min_speakers=min_speakers, max_speakers=max_speakers) # results_segments, word_segments = assign_word_speakers(diarize_segments, ) writer(result, audio_path) if __name__ == "__main__": cli()