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| import argparse | |
| import os | |
| import pathlib | |
| from urllib.parse import urlparse | |
| import warnings | |
| import numpy as np | |
| import torch | |
| from app import VadOptions, WhisperTranscriber | |
| from src.config import VAD_INITIAL_PROMPT_MODE_VALUES, ApplicationConfig, VadInitialPromptMode | |
| from src.diarization.diarization import Diarization | |
| from src.download import download_url | |
| from src.languages import get_language_names | |
| from src.utils import optional_float, optional_int, str2bool | |
| from src.whisper.whisperFactory import create_whisper_container | |
| def cli(): | |
| app_config = ApplicationConfig.create_default() | |
| whisper_models = app_config.get_model_names() | |
| # For the CLI, we fallback to saving the output to the current directory | |
| output_dir = app_config.output_dir if app_config.output_dir is not None else "." | |
| # Environment variable overrides | |
| default_whisper_implementation = os.environ.get("WHISPER_IMPLEMENTATION", app_config.whisper_implementation) | |
| 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=app_config.default_model_name, choices=whisper_models, \ | |
| help="name of the Whisper model to use") # medium | |
| parser.add_argument("--model_dir", type=str, default=app_config.model_dir, \ | |
| help="the path to save model files; uses ~/.cache/whisper by default") | |
| parser.add_argument("--device", default=app_config.device, \ | |
| help="device to use for PyTorch inference") | |
| parser.add_argument("--output_dir", "-o", type=str, default=output_dir, \ | |
| help="directory to save the outputs") | |
| parser.add_argument("--verbose", type=str2bool, default=app_config.verbose, \ | |
| help="whether to print out the progress and debug messages") | |
| parser.add_argument("--whisper_implementation", type=str, default=default_whisper_implementation, choices=["whisper", "faster-whisper"],\ | |
| help="the Whisper implementation to use") | |
| parser.add_argument("--task", type=str, default=app_config.task, choices=["transcribe", "translate"], \ | |
| help="whether to perform X->X speech recognition ('transcribe') or X->English translation ('translate')") | |
| parser.add_argument("--language", type=str, default=app_config.language, choices=sorted(get_language_names()), \ | |
| help="language spoken in the audio, specify None to perform language detection") | |
| parser.add_argument("--vad", type=str, default=app_config.default_vad, choices=["none", "silero-vad", "silero-vad-skip-gaps", "silero-vad-expand-into-gaps", "periodic-vad"], \ | |
| help="The voice activity detection algorithm to use") # silero-vad | |
| parser.add_argument("--vad_initial_prompt_mode", type=str, default=app_config.vad_initial_prompt_mode, choices=VAD_INITIAL_PROMPT_MODE_VALUES, \ | |
| help="Whether or not to prepend the initial prompt to each VAD segment (prepend_all_segments), or just the first segment (prepend_first_segment)") # prepend_first_segment | |
| parser.add_argument("--vad_merge_window", type=optional_float, default=app_config.vad_merge_window, \ | |
| help="The window size (in seconds) to merge voice segments") | |
| parser.add_argument("--vad_max_merge_size", type=optional_float, default=app_config.vad_max_merge_size,\ | |
| help="The maximum size (in seconds) of a voice segment") | |
| parser.add_argument("--vad_padding", type=optional_float, default=app_config.vad_padding, \ | |
| help="The padding (in seconds) to add to each voice segment") | |
| parser.add_argument("--vad_prompt_window", type=optional_float, default=app_config.vad_prompt_window, \ | |
| help="The window size of the prompt to pass to Whisper") | |
| parser.add_argument("--vad_cpu_cores", type=int, default=app_config.vad_cpu_cores, \ | |
| help="The number of CPU cores to use for VAD pre-processing.") # 1 | |
| parser.add_argument("--vad_parallel_devices", type=str, default=app_config.vad_parallel_devices, \ | |
| help="A commma delimited list of CUDA devices to use for parallel processing. If None, disable parallel processing.") # "" | |
| parser.add_argument("--auto_parallel", type=bool, default=app_config.auto_parallel, \ | |
| help="True to use all available GPUs and CPU cores for processing. Use vad_cpu_cores/vad_parallel_devices to specify the number of CPU cores/GPUs to use.") # False | |
| parser.add_argument("--temperature", type=float, default=app_config.temperature, \ | |
| help="temperature to use for sampling") | |
| parser.add_argument("--best_of", type=optional_int, default=app_config.best_of, \ | |
| help="number of candidates when sampling with non-zero temperature") | |
| parser.add_argument("--beam_size", type=optional_int, default=app_config.beam_size, \ | |
| help="number of beams in beam search, only applicable when temperature is zero") | |
| parser.add_argument("--patience", type=float, default=app_config.patience, \ | |
| 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=app_config.length_penalty, \ | |
| help="optional token length penalty coefficient (alpha) as in https://arxiv.org/abs/1609.08144, uses simple lengt normalization by default") | |
| parser.add_argument("--suppress_tokens", type=str, default=app_config.suppress_tokens, \ | |
| 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=app_config.initial_prompt, \ | |
| help="optional text to provide as a prompt for the first window.") | |
| parser.add_argument("--condition_on_previous_text", type=str2bool, default=app_config.condition_on_previous_text, \ | |
| 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=app_config.fp16, \ | |
| help="whether to perform inference in fp16; True by default") | |
| parser.add_argument("--compute_type", type=str, default=app_config.compute_type, choices=["default", "auto", "int8", "int8_float16", "int16", "float16", "float32"], \ | |
| help="the compute type to use for inference") | |
| parser.add_argument("--temperature_increment_on_fallback", type=optional_float, default=app_config.temperature_increment_on_fallback, \ | |
| 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=app_config.compression_ratio_threshold, \ | |
| 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=app_config.logprob_threshold, \ | |
| 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=app_config.no_speech_threshold, \ | |
| 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=app_config.word_timestamps, | |
| help="(experimental) extract word-level timestamps and refine the results based on them") | |
| parser.add_argument("--prepend_punctuations", type=str, default=app_config.prepend_punctuations, | |
| help="if word_timestamps is True, merge these punctuation symbols with the next word") | |
| parser.add_argument("--append_punctuations", type=str, default=app_config.append_punctuations, | |
| help="if word_timestamps is True, merge these punctuation symbols with the previous word") | |
| parser.add_argument("--highlight_words", type=str2bool, default=app_config.highlight_words, | |
| help="(requires --word_timestamps True) underline each word as it is spoken in srt and vtt") | |
| 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") | |
| # Diarization | |
| parser.add_argument('--auth_token', type=str, default=None, help='HuggingFace API Token (optional)') | |
| parser.add_argument("--diarization", type=str2bool, default=app_config.diarization, \ | |
| help="whether to perform speaker diarization") | |
| parser.add_argument("--diarization_num_speakers", type=int, default=None, help="Number of speakers") | |
| parser.add_argument("--diarization_min_speakers", type=int, default=None, help="Minimum number of speakers") | |
| parser.add_argument("--diarization_max_speakers", type=int, default=None, help="Maximum number of speakers") | |
| 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 (threads := args.pop("threads")) > 0: | |
| torch.set_num_threads(threads) | |
| whisper_implementation = args.pop("whisper_implementation") | |
| print(f"Using {whisper_implementation} for Whisper") | |
| if model_name.endswith(".en") and args["language"] not in {"en", "English"}: | |
| 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] | |
| vad = args.pop("vad") | |
| vad_initial_prompt_mode = args.pop("vad_initial_prompt_mode") | |
| vad_merge_window = args.pop("vad_merge_window") | |
| vad_max_merge_size = args.pop("vad_max_merge_size") | |
| vad_padding = args.pop("vad_padding") | |
| vad_prompt_window = args.pop("vad_prompt_window") | |
| vad_cpu_cores = args.pop("vad_cpu_cores") | |
| auto_parallel = args.pop("auto_parallel") | |
| compute_type = args.pop("compute_type") | |
| highlight_words = args.pop("highlight_words") | |
| auth_token = args.pop("auth_token") | |
| diarization = args.pop("diarization") | |
| num_speakers = args.pop("diarization_num_speakers") | |
| min_speakers = args.pop("diarization_min_speakers") | |
| max_speakers = args.pop("diarization_max_speakers") | |
| transcriber = WhisperTranscriber(delete_uploaded_files=False, vad_cpu_cores=vad_cpu_cores, app_config=app_config) | |
| transcriber.set_parallel_devices(args.pop("vad_parallel_devices")) | |
| transcriber.set_auto_parallel(auto_parallel) | |
| if diarization: | |
| transcriber.set_diarization(auth_token=auth_token, enable_daemon_process=False, num_speakers=num_speakers, min_speakers=min_speakers, max_speakers=max_speakers) | |
| model = create_whisper_container(whisper_implementation=whisper_implementation, model_name=model_name, | |
| device=device, compute_type=compute_type, download_root=model_dir, models=app_config.models) | |
| if (transcriber._has_parallel_devices()): | |
| print("Using parallel devices:", transcriber.parallel_device_list) | |
| for audio_path in args.pop("audio"): | |
| sources = [] | |
| # Detect URL and download the audio | |
| if (uri_validator(audio_path)): | |
| # Download from YouTube/URL directly | |
| for source_path in download_url(audio_path, maxDuration=-1, destinationDirectory=output_dir, playlistItems=None): | |
| source_name = os.path.basename(source_path) | |
| sources.append({ "path": source_path, "name": source_name }) | |
| else: | |
| sources.append({ "path": audio_path, "name": os.path.basename(audio_path) }) | |
| for source in sources: | |
| source_path = source["path"] | |
| source_name = source["name"] | |
| vadOptions = VadOptions(vad, vad_merge_window, vad_max_merge_size, vad_padding, vad_prompt_window, | |
| VadInitialPromptMode.from_string(vad_initial_prompt_mode)) | |
| result = transcriber.transcribe_file(model, source_path, temperature=temperature, vadOptions=vadOptions, **args) | |
| transcriber.write_result(result, source_name, output_dir, highlight_words) | |
| transcriber.close() | |
| def uri_validator(x): | |
| try: | |
| result = urlparse(x) | |
| return all([result.scheme, result.netloc]) | |
| except: | |
| return False | |
| if __name__ == '__main__': | |
| cli() |