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