import argparse import gc import os import warnings import numpy as np import torch from .alignment import align, load_align_model from .asr import load_model from .audio import load_audio from .diarize import DiarizationPipeline, assign_word_speakers from .utils import (LANGUAGES, TO_LANGUAGE_CODE, get_writer, optional_float, optional_int, str2bool) def cli(): # 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", 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("--batch_size", default=8, type=int, help="device to use for PyTorch inference") parser.add_argument("--compute_type", default="float16", type=str, choices=["float16", "float32", "int8"], help="compute type for computation") 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", "vtt", "txt", "tsv", "json"], 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("--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_onset", type=float, default=0.500, help="Onset threshold for VAD (see pyannote.audio), reduce this if speech is not being detected") parser.add_argument("--vad_offset", type=float, default=0.363, help="Offset threshold for VAD (see pyannote.audio), reduce this if speech is not being detected.") # 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=False, 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("--max_line_width", type=optional_int, default=None, help="(not possible with --no_align) 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 --no_align) the maximum number of lines in a segment") 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("--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.") parser.add_argument("--tmp_dir", default=None, help="Temporary directory to write audio file if input if not .wav format (only for VAD).") # fmt: on args = parser.parse_args().__dict__ model_name: str = args.pop("model") batch_size: int = args.pop("batch_size") output_dir: str = args.pop("output_dir") output_format: str = args.pop("output_format") device: str = args.pop("device") compute_type: str = args.pop("compute_type") # model_flush: bool = args.pop("model_flush") os.makedirs(output_dir, exist_ok=True) tmp_dir: str = args.pop("tmp_dir") if tmp_dir is not None: os.makedirs(tmp_dir, exist_ok=True) align_model: str = args.pop("align_model") interpolate_method: str = args.pop("interpolate_method") no_align: bool = args.pop("no_align") hf_token: str = args.pop("hf_token") 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") # TODO: check model loading works. 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) asr_options = { "beam_size": args.pop("beam_size"), "patience": args.pop("patience"), "length_penalty": args.pop("length_penalty"), "temperatures": temperature, "compression_ratio_threshold": args.pop("compression_ratio_threshold"), "log_prob_threshold": args.pop("logprob_threshold"), "no_speech_threshold": args.pop("no_speech_threshold"), "condition_on_previous_text": False, "initial_prompt": args.pop("initial_prompt"), } writer = get_writer(output_format, output_dir) word_options = ["highlight_words", "max_line_count", "max_line_width"] if no_align: 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} # Part 1: VAD & ASR Loop results = [] tmp_results = [] # model = load_model(model_name, device=device, download_root=model_dir) model = load_model(model_name, device=device, compute_type=compute_type, language=args['language'], asr_options=asr_options, vad_options={"vad_onset": vad_onset, "vad_offset": vad_offset},) for audio_path in args.pop("audio"): audio = load_audio(audio_path) # >> VAD & ASR print(">>Performing transcription...") result = model.transcribe(audio, batch_size=batch_size) results.append((result, audio_path)) # Unload Whisper and VAD del model gc.collect() torch.cuda.empty_cache() # Part 2: Align Loop if not no_align: tmp_results = results results = [] 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) for result, audio_path in tmp_results: # >> Align if len(tmp_results) > 1: input_audio = audio_path else: # lazily load audio from part 1 input_audio = audio if align_model is not None and len(result["segments"]) > 0: if result.get("language", "en") != 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) print(">>Performing alignment...") result = align(result["segments"], align_model, align_metadata, input_audio, device, interpolate_method=interpolate_method) results.append((result, audio_path)) # Unload align model del align_model gc.collect() torch.cuda.empty_cache() # >> Diarize 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...") tmp_results = results print(">>Performing diarization...") results = [] diarize_model = DiarizationPipeline(use_auth_token=hf_token, device=device) for result, input_audio_path in tmp_results: diarize_segments = diarize_model(input_audio_path, min_speakers=min_speakers, max_speakers=max_speakers) results_segments, word_segments = assign_word_speakers(diarize_segments, result["segments"]) result = {"segments": results_segments, "word_segments": word_segments} results.append((result, input_audio_path)) # >> Write for result, audio_path in results: # Remove pandas dataframes from result so that # we can serialize the result with json for seg in result["segments"]: seg.pop("word-segments", None) seg.pop("char-segments", None) writer(result, audio_path, writer_args) if __name__ == "__main__": cli()