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import os |
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import argparse |
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import traceback |
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from modelscope.pipelines import pipeline |
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from modelscope.utils.constant import Tasks |
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from tqdm import tqdm |
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path_denoise = "tools/denoise-model/speech_frcrn_ans_cirm_16k" |
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path_denoise = path_denoise if os.path.exists(path_denoise) else "damo/speech_frcrn_ans_cirm_16k" |
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ans = pipeline(Tasks.acoustic_noise_suppression, model=path_denoise) |
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def execute_denoise(input_folder, output_folder): |
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os.makedirs(output_folder, exist_ok=True) |
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for name in tqdm(os.listdir(input_folder)): |
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try: |
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ans("%s/%s" % (input_folder, name), output_path="%s/%s" % (output_folder, name)) |
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except: |
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traceback.print_exc() |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser() |
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parser.add_argument( |
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"-i", "--input_folder", type=str, required=True, help="Path to the folder containing WAV files." |
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) |
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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( |
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"-p", "--precision", type=str, default="float16", choices=["float16", "float32"], help="fp16 or fp32" |
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) |
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cmd = parser.parse_args() |
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execute_denoise( |
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input_folder=cmd.input_folder, |
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output_folder=cmd.output_folder, |
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) |
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