Speechpy
:speech_balloon: SpeechPy - A Library for Speech Processing and Recognition: http://speechpy.readthedocs.io/en/latest/
Install / Use
/learn @astorfi/SpeechpyREADME
.. image:: _images/speechpy_logo.gif :target: https://github.com/astorfi/speech_feature_extraction/blob/master/images/speechpy_logo.gif
===============================================
SpeechPy Official Project Documentation_
.. image:: https://pepy.tech/badge/speechpy :target: https://pepy.tech/project/speechpy .. image:: https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat :target: https://github.com/astorfi/speechpy/pulls .. image:: https://coveralls.io/repos/github/astorfi/speechpy/badge.svg?branch=master :target: https://coveralls.io/github/astorfi/speechpy?branch=master .. image:: https://codecov.io/gh/astorfi/speechpy/branch/master/graph/badge.svg :target: https://codecov.io/gh/astorfi/speechpy .. image:: https://badge.fury.io/py/speechpy.svg :target: https://badge.fury.io/py/speechpy .. image:: http://joss.theoj.org/papers/10.21105/joss.00749/status.svg :target: https://doi.org/10.21105/joss.00749 .. image:: https://img.shields.io/twitter/follow/amirsinatorfi.svg?label=Follow&style=social :target: https://twitter.com/amirsinatorfi
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.. _SpeechPy Official Project Documentation: http://speechpy.readthedocs.io
========================== Table of Contents
.. contents:: :local: :depth: 3
Documentation
This library provides most frequent used speech features including MFCCs and filterbank energies alongside with the log-energy of filterbanks.
If you are interested to see what are MFCCs and how they are generated please refer to this
wiki <https://github.com/astorfi/speech_feature_extraction/wiki/>_ page.
.. image:: _images/speech.gif
Please refer to the following links for further informations:
SpeechPy Official Project Documentation_
Paper_
.. _SpeechPy Official Project Documentation: http://speechpy.readthedocs.io .. _Paper: https://doi.org/10.21105/joss.00749
Which Python versions are supported
Currently, the package has been tested and verified using Python 2.7, 3.4 and 3.5.
Citation
If you used this package, please kindly cite it as follows:
.. code:: bash
@article{torfi2018speechpy,
title={SpeechPy-A Library for Speech Processing and Recognition},
author={Torfi, Amirsina},
journal={arXiv preprint arXiv:1803.01094},
year={2018}
}
How to Install?
There are two possible ways for installation of this package: local installation and PyPi.
Local Installation
For local installation at first the repository must be cloned::
git clone https://github.com/astorfi/speech_feature_extraction.git
After cloning the reposity, root to the repository directory then execute::
python setup.py develop
Pypi
The package is available on PyPi. For direct installation simply execute the following:
.. code-block:: shell
pip install speechpy
What Features are supported?
- Mel Frequency Cepstral Coefficients(MFCCs)
- Filterbank Energies
- Log Filterbank Energies
Please refer to SpeechPy Official Project Documentation_ for details about the supported features.
MFCC Features
|pic1| |pic2|
.. |pic1| image:: _images/Speech_GIF.gif :width: 45%
.. |pic2| image:: _images/pipeline.jpg :width: 45%
The supported attributes for generating MFCC features can be seen by investigating the related function:
.. code-block:: python
def mfcc(signal, sampling_frequency, frame_length=0.020, frame_stride=0.01,num_cepstral =13,
num_filters=40, fft_length=512, low_frequency=0, high_frequency=None, dc_elimination=True):
"""Compute MFCC features from an audio signal.
:param signal: the audio signal from which to compute features. Should be an N x 1 array
:param sampling_frequency: the sampling frequency of the signal we are working with.
:param frame_length: the length of each frame in seconds. Default is 0.020s
:param frame_stride: the step between successive frames in seconds. Default is 0.02s (means no overlap)
:param num_filters: the number of filters in the filterbank, default 40.
:param fft_length: number of FFT points. Default is 512.
:param low_frequency: lowest band edge of mel filters. In Hz, default is 0.
:param high_frequency: highest band edge of mel filters. In Hz, default is samplerate/2
:param num_cepstral: Number of cepstral coefficients.
:param dc_elimination: hIf the first dc component should be eliminated or not.
:returns: A numpy array of size (num_frames x num_cepstral) containing mfcc features.
"""
Filterbank Energy Features
.. code-block:: python
def mfe(signal, sampling_frequency, frame_length=0.020, frame_stride=0.01,
num_filters=40, fft_length=512, low_frequency=0, high_frequency=None):
"""Compute Mel-filterbank energy features from an audio signal.
:param signal: the audio signal from which to compute features. Should be an N x 1 array
:param sampling_frequency: the sampling frequency of the signal we are working with.
:param frame_length: the length of each frame in seconds. Default is 0.020s
:param frame_stride: the step between successive frames in seconds. Default is 0.02s (means no overlap)
:param num_filters: the number of filters in the filterbank, default 40.
:param fft_length: number of FFT points. Default is 512.
:param low_frequency: lowest band edge of mel filters. In Hz, default is 0.
:param high_frequency: highest band edge of mel filters. In Hz, default is samplerate/2
:returns:
features: the energy of fiterbank: num_frames x num_filters
frame_energies: the energy of each frame: num_frames x 1
"""
log - Filterbank Energy Features
The attributes for log_filterbank energies are the same for filterbank energies too.
.. code-block:: python
def lmfe(signal, sampling_frequency, frame_length=0.020, frame_stride=0.01,
num_filters=40, fft_length=512, low_frequency=0, high_frequency=None):
"""Compute log Mel-filterbank energy features from an audio signal.
:param signal: the audio signal from which to compute features. Should be an N x 1 array
:param sampling_frequency: the sampling frequency of the signal we are working with.
:param frame_length: the length of each frame in seconds. Default is 0.020s
:param frame_stride: the step between successive frames in seconds. Default is 0.02s (means no overlap)
:param num_filters: the number of filters in the filterbank, default 40.
:param fft_length: number of FFT points. Default is 512.
:param low_frequency: lowest band edge of mel filters. In Hz, default is 0.
:param high_frequency: highest band edge of mel filters. In Hz, default is samplerate/2
:returns:
features: the energy of fiterbank: num_frames x num_filters
frame_log_energies: the log energy of each frame: num_frames x 1
"""
Stack Frames
In Stack_Frames function, the stack of frames will be generated from the signal.
.. code-block:: python
def stack_frames(sig, sampling_frequency, frame_length=0.020, frame_stride=0.020, Filter=lambda x: numpy.ones((x,)),
zero_padding=True):
"""Frame a signal into overlapping frames.
:param sig: The audio signal to frame of size (N,).
:param sampling_frequency: The sampling frequency of the signal.
:param frame_length: The length of the frame in second.
:param frame_stride: The stride between frames.
:param Filter: The time-domain filter for applying to each frame. By default it is one so nothing will be changed.
:param zero_padding: If the samples is not a multiple of frame_length(number of frames sample), zero padding will
be done for generating last frame.
:returns: Array of frames. size: number_of_frames x frame_len.
"""
Post Processing
There are some post-processing operation that are supported in speechpy.
Global cepstral mean and variance normalization (CMVN)
This function performs global cepstral mean and variance normalization (CMVN) to remove the channel effects. The code assumes that there is one observation per row.
.. code-block:: python
def cmvn(vec, variance_normalization=False):
"""
This function is aimed to perform global cepstral mean and variance normalization
(CMVN) on input feature vector "vec". The code assumes that there is one observation per row.
:param:
vec: input feature matrix (size:(num_observation,num_features))
variance_normalization: If the variance normilization should be performed or not.
:return:
The mean(or mean+variance) normalized feature vector.
"""
Local cepstral mean and variance normalization (CMVN) over a sliding window
This function performs local cepstral mean and variance normalization (CMVN) over sliding windows. The code assumes that there is one observation per row.
.. code-block:: python
def cmvnw(vec, win_siz
