如何使用 Python 将 scipy.signal.spectrogram 反转为音频? [英] How can I reverse a scipy.signal.spectrogram to audio with Python?

问题描述

我有:

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)

然而，这会产生一个(接近)空的声音文件.

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

推荐答案

正如@DrSpill 提到的，scipy.io.wav.read 和 scipy.io.wav.write 命令是错误的，也是从 librosa 不正确.应该这样做:

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))

备注:当我听到它时，生成的文件有一个加速的速度，我认为这是由于您的处理，但经过一些调整它应该可以工作.

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.

一个不错的选择，就是只使用 librosa，如下所示:

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)
 

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