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Delete demo_with_detailed_comments.py

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- from clearvoice import ClearVoice # Import the ClearVoice class for speech processing tasks
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-
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- if __name__ == '__main__':
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- ## ----------------- Demo One: Using a Single Model ----------------------
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- if True: # This block demonstrates how to use a single model for speech enhancement
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- # Initialize ClearVoice for the task of speech enhancement using the MossFormerGAN_SE_16K model
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- myClearVoice = ClearVoice(task='speech_enhancement', model_names=['MossFormerGAN_SE_16K'])
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-
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- # 1st calling method:
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- # Process an input waveform and return the enhanced output waveform
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- # - input_path: Path to the input noisy audio file (input.wav)
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- # - The returned value is the enhanced output waveform
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- output_wav = myClearVoice(input_path='input.wav')
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- # Write the processed waveform to an output file
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- # - output_wav: The enhanced waveform data
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- # - output_path: Path to save the enhanced audio file (output.wav)
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- myClearVoice.write(output_wav, output_path='output.wav')
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-
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- # 2nd calling method:
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- # Process and write audio files directly
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- # - input_path: Directory of input noisy audio files
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- # - online_write=True: Enables writing the enhanced audio directly to files during processing
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- # - output_path: Directory where the enhanced audio files will be saved
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- myClearVoice(input_path='path_to_input_wavs', online_write=True, output_path='path_to_output_wavs')
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-
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- # 3rd calling method:
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- # Use an .scp file to specify input audio paths
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- # - input_path: Path to an .scp file listing multiple audio file paths
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- # - online_write=True: Directly writes the enhanced output during processing
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- # - output_path: Directory to save the enhanced output files
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- myClearVoice(input_path='data/cv_webrtc_test_set_20200521_16k.scp', online_write=True, output_path='path_to_output_waves')
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-
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-
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- ## ---------------- Demo Two: Using Multiple Models -----------------------
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- if False: # This block demonstrates using multiple models for speech enhancement
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- # Initialize ClearVoice for the task of speech enhancement using two models: FRCRN_SE_16K and MossFormerGAN_SE_16K
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- myClearVoice = ClearVoice(task='speech_enhancement', model_names=['FRCRN_SE_16K', 'MossFormerGAN_SE_16K'])
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-
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- # 1st calling method:
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- # Process an input waveform using the multiple models and return the enhanced output waveform
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- # - input_path: Path to the input noisy audio file (input.wav)
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- # - The returned value is the enhanced output waveform after being processed by the models
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- output_wav = myClearVoice(input_path='input.wav')
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- # Write the processed waveform to an output file
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- # - output_wav: The enhanced waveform data
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- # - output_path: Path to save the enhanced audio file (output.wav)
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- myClearVoice.write(output_wav, output_path='output.wav')
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-
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- # 2nd calling method:
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- # Process and write audio files directly using multiple models
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- # - input_path: Directory of input noisy audio files
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- # - online_write=True: Enables writing the enhanced audio directly to files during processing
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- # - output_path: Directory where the enhanced audio files will be saved
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- myClearVoice(input_path='path_to_input_wavs', online_write=True, output_path='path_to_output_wavs')
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-
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- # 3rd calling method:
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- # Use an .scp file to specify input audio paths for multiple models
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- # - input_path: Path to an .scp file listing multiple audio file paths
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- # - online_write=True: Directly writes the enhanced output during processing
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- # - output_path: Directory to save the enhanced output files
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- myClearVoice(input_path='data/cv_webrtc_test_set_20200521_16k.scp', online_write=True, output_path='path_to_output_waves')