How can I reverse a scipy.signal.spectrogram to audio with Python?

Question

I have:

import librosa
from scipy import signal 
import scipy.io.wavfile as sf    

samples, sample_rate = sf.read(args.file)
nperseg = int(sample_rate * 0.001 * 20)
frequencies, times, spectrogram = signal.spectrogram(samples, 
                                                     sample_rate, 
                                                     nperseg=nperseg, 
                                                     window=signal.hann(nperseg))

audio_signal = librosa.griffinlim(spectrogram)
print(audio_signal, audio_signal.shape)

sf.write('test.wav', audio_signal, sample_rate)

However, this produces a (near) empty sound file.

To use Griffin Lim, you need a magnitude spectrogram. I'd try to specify the mode in your signal.spectrogram(... mode='magnitude') call. Haven't tested. — Hendrik
– Hendrik, Commented Feb 24, 2020 at 14:09
I can't comment about the librosa library. Assuming, that is not the problem, did you try the scipy.io library for read and write of the audio file? scipy.io.wavfile.read and scipy.io.wavfile.write. Note that the order changes from signal, signal_rate to signal_rate, signal. (docs.scipy.org/doc/scipy/reference/…) — DrSpill
– DrSpill, Commented Feb 27, 2020 at 11:17

SuperKogito · Accepted Answer · 2020-10-13 09:24:21Z

8

+50

As @DrSpill mentioned, scipy.io.wav.read and scipy.io.wav.write orders were wrong and also the import from librosa was not correct. This should do it:

import librosa
import numpy as np
import scipy.signal
import scipy.io.wavfile

# read file
file    = "temp/processed_file.wav"
fs, sig = scipy.io.wavfile.read(file)
nperseg = int(fs * 0.001 * 20)

# process
frequencies, times, spectrogram = scipy.signal.spectrogram(sig, 
                                                           fs, 
                                                           nperseg=nperseg, 
                                                           window=scipy.signal.hann(nperseg))
audio_signal = librosa.core.spectrum.griffinlim(spectrogram)
print(audio_signal, audio_signal.shape)

# write output
scipy.io.wavfile.write('test.wav', fs, np.array(audio_signal, dtype=np.int16))

Remark: The resulting file had an accelerated tempo when I heard it, I think this is due to your processing but with some tweaking it should work.

A good alternative, would be to only use librosa, like this:

import librosa
import numpy as np

# read file
file    = "temp/processed_file.wav"
sig, fs = librosa.core.load(file, sr=8000)

# process
abs_spectrogram = np.abs(librosa.core.spectrum.stft(sig))
audio_signal = librosa.core.spectrum.griffinlim(abs_spectrogram)

print(audio_signal, audio_signal.shape)

# write output
librosa.output.write_wav('test2.wav', audio_signal, fs)

edited Oct 13, 2020 at 9:24

answered Feb 27, 2020 at 11:32

SuperKogito

2,9663 gold badges20 silver badges40 bronze badges

Sign up to request clarification or add additional context in comments.

2 Comments

Shamoon Over a year ago

Does the reconstructed file have the same number of samples?

SuperKogito Over a year ago

Sorry for the late answer, I just further tested this and in the second solution, one should specify the input sampling rate (I edited the code accordingly). Using ffmpeg I verified that the input and output signals have the same sampling rates. However, you should be careful about the bitrates and the encoding when using the second solution. For more please refer to the librosa-documentation.

Chris · Accepted Answer · 2021-01-15 17:22:45Z

1

librosa.output was removed. It is no longer providing its deprecated output module. Instead try soundfile.write:

import numpy as np
import soundfile as sf
sf.write('stereo_file.wav', np.random.randn(10, 2), 44100, 'PCM_24')

#Per your code you could try:
sf.write('test.wav', audio_signal, sample_rate, 'PCM_24')

answered Jan 15, 2021 at 17:22

Chris

19k15 gold badges64 silver badges80 bronze badges

Collectives™ on Stack Overflow

How can I reverse a scipy.signal.spectrogram to audio with Python?

2 Answers 2

2 Comments

Comments

Your Answer

Linked

Hot Network Questions

Collectives™ on Stack Overflow

2 Answers 2

2 Comments

Comments

Your Answer

Sign up or log in

Post as a guest

Linked

Related