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https://github.com/karl0ss/bazarr-ai-sub-generator.git
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60 lines
3.1 KiB
Python
60 lines
3.1 KiB
Python
import argparse
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from faster_whisper import available_models
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from utils.constants import LANGUAGE_CODES
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from main import process
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from utils.convert import str2bool, str2timeinterval
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def main():
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"""
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Main entry point for the script.
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Parses command line arguments, processes the inputs using the specified options,
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and performs transcription or translation based on the specified task.
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"""
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument("--audio_channel", default="0",
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type=int, help="audio channel index to use")
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parser.add_argument("--sample_interval", type=str2timeinterval, default=None,
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help="generate subtitles for a specific \
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fragment of the video (e.g. 01:02:05-01:03:45)")
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parser.add_argument("--model", default="small",
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choices=available_models(), help="name of the Whisper model to use")
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parser.add_argument("--device", type=str, default="auto",
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choices=["cpu", "cuda", "auto"],
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help="Device to use for computation (\"cpu\", \"cuda\", \"auto\")")
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parser.add_argument("--compute_type", type=str, default="default", choices=[
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"int8", "int8_float32", "int8_float16", "int8_bfloat16",
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"int16", "float16", "bfloat16", "float32"],
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help="Type to use for computation. \
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See https://opennmt.net/CTranslate2/quantization.html.")
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# parser.add_argument("--output_dir", "-o", type=str,
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# default=".", help="directory to save the outputs")
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parser.add_argument("--output_srt", type=str2bool, default=False,
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help="whether to output the .srt file along with the video files")
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parser.add_argument("--srt_only", type=str2bool, default=False,
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help="only generate the .srt file and not create overlayed video")
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parser.add_argument("--beam_size", type=int, default=5,
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help="model parameter, tweak to increase accuracy")
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parser.add_argument("--no_speech_threshold", type=float, default=0.6,
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help="model parameter, tweak to increase accuracy")
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parser.add_argument("--condition_on_previous_text", type=str2bool, default=True,
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help="model parameter, tweak to increase accuracy")
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parser.add_argument("--task", type=str, default="transcribe",
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choices=["transcribe", "translate"],
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help="whether to perform X->X speech recognition ('transcribe') \
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or X->English translation ('translate')")
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parser.add_argument("--language", type=str, default="auto",
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choices=LANGUAGE_CODES,
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help="What is the origin language of the video? \
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If unset, it is detected automatically.")
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args = parser.parse_args().__dict__
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process(args)
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if __name__ == '__main__':
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main()
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