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https://github.com/jameslyons/python_speech_features

This library provides common speech features for ASR including MFCCs and filterbank energies.
https://github.com/jameslyons/python_speech_features

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This library provides common speech features for ASR including MFCCs and filterbank energies.

Host: GitHub
URL: https://github.com/jameslyons/python_speech_features
Owner: jameslyons
License: mit
Created: 2013-10-31T02:42:08.000Z (about 11 years ago)
Default Branch: master
Last Pushed: 2021-10-20T10:08:48.000Z (about 3 years ago)
Last Synced: 2024-10-01T20:18:09.311Z (about 1 month ago)
Language: Python
Homepage:
Size: 216 KB
Stars: 2,366
Watchers: 88
Forks: 618
Open Issues: 25
Metadata Files:
- Readme: README.rst
- License: LICENSE

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README

        ======================

python_speech_features

======================

This library provides common speech features for ASR including MFCCs and filterbank energies.

If you are not sure what MFCCs are, and would like to know more have a look at this 

`MFCC tutorial `_

`Project Documentation `_

To cite, please use: James Lyons et al. (2020, January 14). jameslyons/python_speech_features: release v0.6.1 (Version 0.6.1). Zenodo. http://doi.org/10.5281/zenodo.3607820

Installation

============

This `project is on pypi `_

To install from pypi:: 

	pip install python_speech_features

	

From this repository::

	git clone https://github.com/jameslyons/python_speech_features

	python setup.py develop

Usage

=====

Supported features:

- Mel Frequency Cepstral Coefficients

- Filterbank Energies

- Log Filterbank Energies

- Spectral Subband Centroids

`Example use `_

From here you can write the features to a file etc.

MFCC Features

=============

The default parameters should work fairly well for most cases, 

if you want to change the MFCC parameters, the following parameters are supported::

	python

	def mfcc(signal,samplerate=16000,winlen=0.025,winstep=0.01,numcep=13,

			 nfilt=26,nfft=512,lowfreq=0,highfreq=None,preemph=0.97,

             ceplifter=22,appendEnergy=True)

=============	===========

Parameter 		Description

=============	===========

signal			the audio signal from which to compute features. Should be an N*1 array

samplerate 		the samplerate of the signal we are working with.

winlen 			the length of the analysis window in seconds. Default is 0.025s (25 milliseconds)

winstep 		the step between successive windows in seconds. Default is 0.01s (10 milliseconds)

numcep			the number of cepstrum to return, default 13

nfilt			the number of filters in the filterbank, default 26.

nfft			the FFT size. Default is 512

lowfreq			lowest band edge of mel filters. In Hz, default is 0

highfreq		highest band edge of mel filters. In Hz, default is samplerate/2

preemph			apply preemphasis filter with preemph as coefficient. 0 is no filter. Default is 0.97

ceplifter		apply a lifter to final cepstral coefficients. 0 is no lifter. Default is 22

appendEnergy	if this is true, the zeroth cepstral coefficient is replaced with the log of the total frame energy.

returns			A numpy array of size (NUMFRAMES by numcep) containing features. Each row holds 1 feature vector.

=============	===========

Filterbank Features

===================

These filters are raw filterbank energies. 

For most applications you will want the logarithm of these features.

The default parameters should work fairly well for most cases. 

If you want to change the fbank parameters, the following parameters are supported::

	python

	def fbank(signal,samplerate=16000,winlen=0.025,winstep=0.01,

              nfilt=26,nfft=512,lowfreq=0,highfreq=None,preemph=0.97)

=============	===========

Parameter 		Description

=============	===========

signal			the audio signal from which to compute features. Should be an N*1 array

samplerate		the samplerate of the signal we are working with

winlen			the length of the analysis window in seconds. Default is 0.025s (25 milliseconds)

winstep			the step between successive windows in seconds. Default is 0.01s (10 milliseconds)

nfilt			the number of filters in the filterbank, default 26.

nfft			the FFT size. Default is 512.

lowfreq			lowest band edge of mel filters. In Hz, default is 0

highfreq		highest band edge of mel filters. In Hz, default is samplerate/2

preemph			apply preemphasis filter with preemph as coefficient. 0 is no filter. Default is 0.97

returns			A numpy array of size (NUMFRAMES by nfilt) containing features. Each row holds 1 feature vector. The second return value is the energy in each frame (total energy, unwindowed)

=============	===========

Reference

=========

sample english.wav obtained from::

	wget http://voyager.jpl.nasa.gov/spacecraft/audio/english.au

	sox english.au -e signed-integer english.wav