denoised_data = Denoiser.reduce_by_example(data, noise_data, rate)
denoised_audio = Denoiser.numpy_to_seg_like_seg(denoised_data, audio)
denoised_audio.export(out_file, format='mp3')
# sf.write(out_file, denoised_data, rate)
def add_leading_noise(audio_file, out_file):
data, rate = sf.read(audio_file)
noised, noise = add_noise(data, rate)
new_data = np.concatenate((noise, noised)) * 0.05
new_sample = AudioSegment(new_data.astype('float32').tobytes(),
frame_rate=rate,
sample_width=4,
channels=1)
new_sample.export(out_file, format='mp3')
if __name__ == '__main__':
# audio_file = input('Enter audio file path: ')
# # noise_file = input('Enter noise file path: ')
# out_file = input(
# 'Enter result file path (All libs in path should already exist): '
# )
audio_file = '/home/chernogor/Workspace/audio/noised_sample2.wav'
out_file = '/home/chernogor/Workspace/audio/noised_sample2.wav'
# reduce_by_example_to_mp3(audio_file, noise_file, out_file)
Denoiser.process_file_to_file(audio_file, out_file, noise_length=3)
