mirror of
https://github.com/Monadical-SAS/reflector.git
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58 lines
1.8 KiB
Python
Executable File
58 lines
1.8 KiB
Python
Executable File
import argparse
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import os
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import moviepy.editor
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from loguru import logger
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import whisper
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WHISPER_MODEL_SIZE = "base"
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def init_argparse() -> argparse.ArgumentParser:
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parser = argparse.ArgumentParser(
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usage="%(prog)s <LOCATION> <OUTPUT>",
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description="Creates a transcript of a video or audio file using the OpenAI Whisper model"
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)
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parser.add_argument("location", help="Location of the media file")
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parser.add_argument("output", help="Output file path")
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return parser
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def main():
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import sys
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sys.setrecursionlimit(10000)
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parser = init_argparse()
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args = parser.parse_args()
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media_file = args.location
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logger.info(f"Processing file: {media_file}")
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# Check if the media file is a valid audio or video file
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if os.path.isfile(media_file) and not media_file.endswith(('.mp3', '.wav', '.ogg', '.flac', '.mp4', '.avi', '.flv')):
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logger.error(f"Invalid file format: {media_file}")
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return
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# If the media file we just retrieved is an audio file then skip extraction step
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audio_filename = media_file
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logger.info(f"Found audio-only file, skipping audio extraction")
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audio = moviepy.editor.AudioFileClip(audio_filename)
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logger.info("Selected extracted audio")
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# Transcribe the audio file using the OpenAI Whisper model
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logger.info("Loading Whisper speech-to-text model")
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whisper_model = whisper.load_model(WHISPER_MODEL_SIZE)
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logger.info(f"Transcribing file: {media_file}")
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whisper_result = whisper_model.transcribe(media_file)
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logger.info("Finished transcribing file")
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# Save the transcript to the specified file.
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logger.info(f"Saving transcript to: {args.output}")
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transcript_file = open(args.output, "w")
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transcript_file.write(whisper_result["text"])
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transcript_file.close()
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if __name__ == "__main__":
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main()
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