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cleanup
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@ -2,8 +2,6 @@ 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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@ -12,15 +10,20 @@ def main():
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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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# Create an ArgumentParser object with a specific formatter for default values
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter
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)
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# Add argument for selecting the Whisper model
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parser.add_argument(
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"--model",
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default="small",
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choices=available_models(),
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help="name of the Whisper model to use",
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)
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# Add argument for specifying the device to use (CPU, CUDA, or auto-detect)
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parser.add_argument(
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"--device",
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type=str,
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@ -28,35 +31,24 @@ def main():
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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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)
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# parser.add_argument(
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# "--compute_type",
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# type=str,
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# default="default",
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# choices=[
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# "int8",
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# "int8_float32",
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# "int8_float16",
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# "int8_bfloat16",
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# "int16",
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# "float16",
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# "bfloat16",
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# "float32",
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# ],
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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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# )
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# Add argument for processing a single file
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parser.add_argument(
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"--file",
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type=str,
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default=None,
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help="Process a single file"
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)
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# Add argument for processing all videos in a folder
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parser.add_argument(
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"--folder",
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type=str,
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default=None,
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help="Process all videos in folder"
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)
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# Add argument for specifying the task: transcribe or translate
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parser.add_argument(
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"--show",
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type=str,
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@ -64,6 +56,8 @@ def main():
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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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)
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# Add argument for setting the origin language of the video, with auto-detection as default
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parser.add_argument(
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"--language",
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type=str,
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@ -72,16 +66,20 @@ def main():
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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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)
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# Add argument for selecting the backend: whisper or faster_whisper
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parser.add_argument(
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"--backend",
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type=str,
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default="whisper",
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choices=["whisper", "faster_whisper"],
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)
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# Parse the command line arguments into a dictionary
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args = parser.parse_args().__dict__
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# Call the process function with the parsed arguments
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process(args)
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if __name__ == "__main__":
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main()
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