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https://github.com/RVC-Boss/GPT-SoVITS.git
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42 lines
1.9 KiB
Python
42 lines
1.9 KiB
Python
import os
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import argparse
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import os
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os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
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from glob import glob
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from faster_whisper import WhisperModel
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def main(input_folder, output_folder, output_filename, language):
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model = WhisperModel("large-v3", device="cuda", compute_type="float16")
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output_file = os.path.join(output_folder, output_filename)
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if not os.path.exists(output_folder):
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os.makedirs(output_folder)
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with open(output_file, 'w', encoding='utf-8') as f:
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for file in glob(os.path.join(input_folder, '**/*.wav'), recursive=True):
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segments, _ = model.transcribe(file, beam_size=10, vad_filter=True,
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vad_parameters=dict(min_silence_duration_ms=700), language=language)
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segments = list(segments)
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filename = os.path.basename(file).replace('.wav', '')
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directory = os.path.dirname(file)
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result_line = f"{file}|{language.upper()}|{segments[0].text}\n"
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f.write(result_line)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument("-i", "--input_folder", type=str, required=True,
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help="Path to the folder containing WAV files.")
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parser.add_argument("-o", "--output_folder", type=str, required=True, help="Output folder to store transcriptions.")
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parser.add_argument("-f", "--output_filename", type=str, default="transcriptions.txt", help="Name of the output text file.")
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parser.add_argument("-l", "--language", type=str, default='zh', choices=['zh', 'en', ...],
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help="Language of the audio files.")
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cmd = parser.parse_args()
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input_folder = cmd.input_folder
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output_folder = cmd.output_folder
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output_filename = cmd.output_filename
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language = cmd.language
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main(input_folder, output_folder, output_filename, language) |