WaveRNN is a vocoder, it can convert mel-spectrogram to wav file. In this tutorial, we will introduce you how to do.
WaveRNN is built based on GRU. We can find a tensorflow version here. It can generate waveform from audio mel-spectrogram.
How to convert mel-spectrogram to WAV audio using WaveRNN?
Open run_wavernn.py and remove all @click
In this file, the main function is inference(). In this function, it will do:
read audio data using librosa.load()
use compute_spectrogram() function to compute mel-spectrogram, we also can use librosa.feature.melspectrogram() to get:
Then, we can call run_wavernn() to create waveform using mel-spectrogram.
Finally, we will use librosa.output.write_wav() to save wave file.
However, you may encounter error: AttributeError: module ‘librosa’ has no attribute ‘output’ , you can find the solution here:
We can use inference() function as follows:
if __name__ == '__main__': wav = r'samples/1221306.wav' model = r'models/frozen.pb' output = 'wavernn_1.wav' inference(wav, model, output)
Run this code, we will create a new wave file. However, the effect of the new wave file my be worse than origin. Because you should fine-tune wavernn model based on your own dataset.
We also can create new wavefrom using Griffin-Lim algorithm. Here is the tutorial: