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30 lines
1.3 KiB
30 lines
1.3 KiB
3 years ago
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#!/usr/bin/env python
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from python_speech_features import mfcc
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from python_speech_features import delta
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from python_speech_features import logfbank
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import scipy.io.wavfile as wav
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(rate,sig) = wav.read("english.wav")
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# note that generally nfilt=40 is used for speech recognition
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fbank_feat = logfbank(sig,nfilt=23,lowfreq=20,dither=0,wintype='povey')
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# the computed fbank coefficents of english.wav with dimension [110,23]
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# [ 12.2865 12.6906 13.1765 15.714 16.064 15.7553 16.5746 16.9205 16.6472 16.1302 16.4576 16.7326 16.8864 17.7215 18.88 19.1377 19.1495 18.6683 18.3886 20.3506 20.2772 18.8248 18.1899
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# 11.9198 13.146 14.7215 15.8642 17.4288 16.394 16.8238 16.1095 16.4297 16.6331 16.3163 16.5093 17.4981 18.3429 19.6555 19.6263 19.8435 19.0534 19.001 20.0287 19.7707 19.5852 19.1112
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# ...
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# ...
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# the same with that using kaldi commands: compute-fbank-feats --dither=0.0
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mfcc_feat = mfcc(sig,dither=0,useEnergy=True,wintype='povey')
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# the computed mfcc coefficents of english.wav with dimension [110,13]
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# [ 17.1337 -23.3651 -7.41751 -7.73686 -21.3682 -8.93884 -3.70843 4.68346 -16.0676 12.782 -7.24054 8.25089 10.7292
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# 17.1692 -23.3028 -5.61872 -4.0075 -23.287 -20.6101 -5.51584 -6.15273 -14.4333 8.13052 -0.0345329 2.06274 -0.564298
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# ...
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# ...
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# the same with that using kaldi commands: compute-mfcc-feats --dither=0.0
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