cli.py

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()