112 lines
4.1 KiB
Python
112 lines
4.1 KiB
Python
#!/usr/bin/env python
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#coding: utf-8
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""" This work is licensed under a Creative Commons Attribution 3.0 Unported License.
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Frank Zalkow, 2012-2013
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http://www.frank-zalkow.de/en/code-snippets/create-audio-spectrograms-with-python.html?i=1
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"""
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# %matplotlib inline
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import numpy as np
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from matplotlib import pyplot as plt
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from pysndfile import sndio as snd
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from numpy.lib import stride_tricks
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""" short time fourier transform of audio signal """
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def stft(sig, frameSize, overlapFac=0.5, window=np.hanning):
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win = window(frameSize)
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hopSize = int(frameSize - np.floor(overlapFac * frameSize))
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# zeros at beginning (thus center of 1st window should be for sample nr. 0)
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# sig = (sig*255).astype(np.uint8)
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# import pdb;pdb.set_trace()
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count = int(np.floor(frameSize/2.0))
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# import pdb;pdb.set_trace()
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samples = np.append(np.zeros(count), sig)
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# cols for windowing
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cols = int(np.ceil( (len(samples) - frameSize) / float(hopSize)) + 1)
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# zeros at end (thus samples can be fully covered by frames)
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samples = np.append(samples, np.zeros(frameSize))
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frames = stride_tricks.as_strided(samples, shape=(cols, frameSize), strides=(samples.strides[0]*hopSize, samples.strides[0])).copy()
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frames *= win
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return np.fft.rfft(frames)
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""" scale frequency axis logarithmically """
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def logscale_spec(spec, sr=44100, factor=20.):
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timebins, freqbins = np.shape(spec)
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scale = np.linspace(0, 1, freqbins) ** factor
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scale *= (freqbins-1)/max(scale)
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scale = np.unique(np.round(scale)).astype(np.uint32)
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# import pdb;pdb.set_trace()
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# create spectrogram with new freq bins
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newspec = np.complex128(np.zeros([timebins, len(scale)]))
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for i in range(0, len(scale)):
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if i == len(scale)-1:
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newspec[:,i] = np.sum(spec[:,scale[i]:], axis=1)
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else:
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newspec[:,i] = np.sum(spec[:,scale[i]:scale[i+1]], axis=1)
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# list center freq of bins
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allfreqs = np.abs(np.fft.fftfreq(freqbins*2, 1./sr)[:freqbins+1])
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freqs = []
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for i in range(0, len(scale)):
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if i == len(scale)-1:
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freqs += [np.mean(allfreqs[scale[i]:])]
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else:
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freqs += [np.mean(allfreqs[scale[i]:scale[i+1]])]
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return newspec, freqs
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""" generate spectrogram for aiff audio with 150ms windows and 50ms overlap"""
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def generate_aiff_spectrogram(audiopath):
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samples,samplerate,_ = snd.read(audiopath)
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# samplerate, samples = wav.read(audiopath)
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# s = stft(samples, binsize)
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s = stft(samples, samplerate*150//1000,1.0/3)
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sshow, freq = logscale_spec(s, factor=1.0, sr=samplerate)
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ims = 20.*np.log10(np.abs(sshow)/10e-6)
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return ims
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""" plot spectrogram"""
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def plotstft(audiopath, binsize=2**10, plotpath=None, colormap="jet"):
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samples,samplerate,_ = snd.read(audiopath)
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# samplerate, samples = wav.read(audiopath)
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# s = stft(samples, binsize)
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# print(samplerate*150//1000)
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s = stft(samples, samplerate*150//1000,1.0/3)
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sshow, freq = logscale_spec(s, factor=1.0, sr=samplerate)
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ims = 20.*np.log10(np.abs(sshow)/10e-6) # amplitude to decibel
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timebins, freqbins = np.shape(ims)
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# import pdb;pdb.set_trace()
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plt.figure(figsize=(15, 7.5))
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plt.imshow(np.transpose(ims), origin="lower", aspect="auto", cmap=colormap, interpolation="none")
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plt.colorbar()
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plt.xlabel("time (s)")
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plt.ylabel("frequency (hz)")
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plt.xlim([0, timebins-1])
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plt.ylim([0, freqbins])
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xlocs = np.float32(np.linspace(0, timebins-1, 5))
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plt.xticks(xlocs, ["%.02f" % l for l in ((xlocs*len(samples)/timebins)+(0.5*binsize))/samplerate])
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ylocs = np.int16(np.round(np.linspace(0, freqbins-1, 10)))
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plt.yticks(ylocs, ["%.02f" % freq[i] for i in ylocs])
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if plotpath:
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plt.savefig(plotpath, bbox_inches="tight")
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else:
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plt.show()
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plt.clf()
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if __name__ == '__main__':
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plotstft('./outputs/sunflowers-Alex-150-normal-589.aiff')
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plotstft('./outputs/sunflowers-Alex-180-normal-4763.aiff')
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plotstft('./outputs/sunflowers-Victoria-180-normal-870.aiff')
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plotstft('./outputs/sunflowers-Fred-180-phoneme-9733.aiff')
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plotstft('./outputs/sunflowers-Fred-180-normal-6515.aiff')
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