How to convert a .wav file to a spectrogram in python3
Use scipy.signal.spectrogram.
import matplotlib.pyplot as plt
from scipy import signal
from scipy.io import wavfile
sample_rate, samples = wavfile.read('path-to-mono-audio-file.wav')
frequencies, times, spectrogram = signal.spectrogram(samples, sample_rate)
plt.pcolormesh(times, frequencies, spectrogram)
plt.imshow(spectrogram)
plt.ylabel('Frequency [Hz]')
plt.xlabel('Time [sec]')
plt.show()
Be sure that your wav file is mono (single channel) and not stereo (dual channel) before trying to do this. I highly recommend reading the scipy documentation at https://docs.scipy.org/doc/scipy- 0.19.0/reference/generated/scipy.signal.spectrogram.html.
Putting plt.pcolormesh before plt.imshow seems to fix some issues, as pointed out by @Davidjb, and if unpacking error occurs, follow the steps by @cgnorthcutt below.
You can use librosa for your mp3 spectogram needs. Here is some code I found, thanks to Parul Pandey from medium. The code I used is this,
# Method described here https://stackoverflow.com/questions/15311853/plot-spectogram-from-mp3
import librosa
import librosa.display
from pydub import AudioSegment
import matplotlib.pyplot as plt
from scipy.io import wavfile
from tempfile import mktemp
def plot_mp3_matplot(filename):
"""
plot_mp3_matplot -- using matplotlib to simply plot time vs amplitude waveplot
Arguments:
filename -- filepath to the file that you want to see the waveplot for
Returns -- None
"""
# sr is for 'sampling rate'
# Feel free to adjust it
x, sr = librosa.load(filename, sr=44100)
plt.figure(figsize=(14, 5))
librosa.display.waveplot(x, sr=sr)
def convert_audio_to_spectogram(filename):
"""
convert_audio_to_spectogram -- using librosa to simply plot a spectogram
Arguments:
filename -- filepath to the file that you want to see the waveplot for
Returns -- None
"""
# sr == sampling rate
x, sr = librosa.load(filename, sr=44100)
# stft is short time fourier transform
X = librosa.stft(x)
# convert the slices to amplitude
Xdb = librosa.amplitude_to_db(abs(X))
# ... and plot, magic!
plt.figure(figsize=(14, 5))
librosa.display.specshow(Xdb, sr = sr, x_axis = 'time', y_axis = 'hz')
plt.colorbar()
# same as above, just changed the y_axis from hz to log in the display func
def convert_audio_to_spectogram_log(filename):
x, sr = librosa.load(filename, sr=44100)
X = librosa.stft(x)
Xdb = librosa.amplitude_to_db(abs(X))
plt.figure(figsize=(14, 5))
librosa.display.specshow(Xdb, sr = sr, x_axis = 'time', y_axis = 'log')
plt.colorbar()
Cheers!
I have fixed the errors you are facing for http://www.frank-zalkow.de/en/code-snippets/create-audio-spectrograms-with-python.html
This implementation is better because you can change the binsize (e.g. binsize=2**8)
import numpy as np
from matplotlib import pyplot as plt
import scipy.io.wavfile as wav
from numpy.lib import stride_tricks
""" short time fourier transform of audio signal """
def stft(sig, frameSize, overlapFac=0.5, window=np.hanning):
win = window(frameSize)
hopSize = int(frameSize - np.floor(overlapFac * frameSize))
# zeros at beginning (thus center of 1st window should be for sample nr. 0)
samples = np.append(np.zeros(int(np.floor(frameSize/2.0))), sig)
# cols for windowing
cols = np.ceil( (len(samples) - frameSize) / float(hopSize)) + 1
# zeros at end (thus samples can be fully covered by frames)
samples = np.append(samples, np.zeros(frameSize))
frames = stride_tricks.as_strided(samples, shape=(int(cols), frameSize), strides=(samples.strides[0]*hopSize, samples.strides[0])).copy()
frames *= win
return np.fft.rfft(frames)
""" scale frequency axis logarithmically """
def logscale_spec(spec, sr=44100, factor=20.):
timebins, freqbins = np.shape(spec)
scale = np.linspace(0, 1, freqbins) ** factor
scale *= (freqbins-1)/max(scale)
scale = np.unique(np.round(scale))
# create spectrogram with new freq bins
newspec = np.complex128(np.zeros([timebins, len(scale)]))
for i in range(0, len(scale)):
if i == len(scale)-1:
newspec[:,i] = np.sum(spec[:,int(scale[i]):], axis=1)
else:
newspec[:,i] = np.sum(spec[:,int(scale[i]):int(scale[i+1])], axis=1)
# list center freq of bins
allfreqs = np.abs(np.fft.fftfreq(freqbins*2, 1./sr)[:freqbins+1])
freqs = []
for i in range(0, len(scale)):
if i == len(scale)-1:
freqs += [np.mean(allfreqs[int(scale[i]):])]
else:
freqs += [np.mean(allfreqs[int(scale[i]):int(scale[i+1])])]
return newspec, freqs
""" plot spectrogram"""
def plotstft(audiopath, binsize=2**10, plotpath=None, colormap="jet"):
samplerate, samples = wav.read(audiopath)
s = stft(samples, binsize)
sshow, freq = logscale_spec(s, factor=1.0, sr=samplerate)
ims = 20.*np.log10(np.abs(sshow)/10e-6) # amplitude to decibel
timebins, freqbins = np.shape(ims)
print("timebins: ", timebins)
print("freqbins: ", freqbins)
plt.figure(figsize=(15, 7.5))
plt.imshow(np.transpose(ims), origin="lower", aspect="auto", cmap=colormap, interpolation="none")
plt.colorbar()
plt.xlabel("time (s)")
plt.ylabel("frequency (hz)")
plt.xlim([0, timebins-1])
plt.ylim([0, freqbins])
xlocs = np.float32(np.linspace(0, timebins-1, 5))
plt.xticks(xlocs, ["%.02f" % l for l in ((xlocs*len(samples)/timebins)+(0.5*binsize))/samplerate])
ylocs = np.int16(np.round(np.linspace(0, freqbins-1, 10)))
plt.yticks(ylocs, ["%.02f" % freq[i] for i in ylocs])
if plotpath:
plt.savefig(plotpath, bbox_inches="tight")
else:
plt.show()
plt.clf()
return ims
ims = plotstft(filepath)
import os
import wave
import pylab
def graph_spectrogram(wav_file):
sound_info, frame_rate = get_wav_info(wav_file)
pylab.figure(num=None, figsize=(19, 12))
pylab.subplot(111)
pylab.title('spectrogram of %r' % wav_file)
pylab.specgram(sound_info, Fs=frame_rate)
pylab.savefig('spectrogram.png')
def get_wav_info(wav_file):
wav = wave.open(wav_file, 'r')
frames = wav.readframes(-1)
sound_info = pylab.fromstring(frames, 'int16')
frame_rate = wav.getframerate()
wav.close()
return sound_info, frame_rate
for A Capella Science - Bohemian Gravity! this gives:
Use graph_spectrogram(path_to_your_wav_file).
I don't remember the blog from where I took this snippet. I will add the link whenever I see it again.