I'm doing:
import librosa
D = librosa.stft(samples, n_fft=nperseg,
hop_length=overlap, win_length=nperseg,
window=scipy.signal.windows.hamming)
spect, _ = librosa.magphase(D)
audio_signal = librosa.griffinlim(spect, n_iter=1024,
win_length=nperseg, hop_length=overlap,
window=signal.windows.hamming)
print(audio_signal, audio_signal.shape)
sf.write('test.wav', audio_signal, sample_rate)
And it is introducing noticeable distortion in the reconstructed audio signal. What can I do to improve that?
You should use neural network based vocoder like WaveNet for reconstruction
You need to use a window function that is centered so that the windowed signal is zero-phase, i.e. it is perfectly symmetrical around the middle of the window. In this case, you can use the hann window, which is a raised cosine window with non-zero endpoints.
D = librosa.stft(samples, n_fft=nperseg,
hop_length=overlap, win_length=nperseg,
window=scipy.signal.windows.hann)
spect, _ = librosa.magphase(D)
audio_signal = librosa.griffinlim(spect, n_iter=1024,
win_length=nperseg, hop_length=overlap,
window=signal.windows.hann)
print(audio_signal, audio_signal.shape)
sf.write('test.wav', audio_signal, sample_rate)