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39 lines
1.4 KiB
Python
39 lines
1.4 KiB
Python
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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# print(input_folder)
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# print(list(os.listdir(input_folder).sort()))
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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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) # 还没接入
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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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