SpeechPy Official Project Documentation

Table of Contents

• Documentation
• Which Python versions are supported
• Citation
• How to Install?
  • Local Installation
  • Pypi
• What Features are supported?
  • MFCC Features
  • Filterbank Energy Features
  • log - Filterbank Energy Features
  • Stack Frames
• Post Processing
  • Global cepstral mean and variance normalization (CMVN)
  • Local cepstral mean and variance normalization (CMVN) over a sliding window
• Tests
• Example
• Dependencies
• Acknowledgements
• Contributing
  • Pull Request Process
  • Declaring issues
  • Final Note
• Disclaimer

Documentation

Which Python versions are supported

Citation

@article{torfi2018speechpy,  title={SpeechPy-A Libraryfor Speech Processing and Recognition},  author={Torfi, Amirsina},  journal={arXiv preprint arXiv:1803.01094},  year={2018} }

How to Install?

Local Installation

Pypi

pip install speechpy

What Features are supported?

MFCC Features

defmfcc(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

defmfe(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

deflmfe(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

defstack_frames(sig,sampling_frequency,frame_length=0.020,frame_stride=0.020,Filter=lambdax: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

Global cepstral mean and variance normalization (CMVN)

defcmvn(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

defcmvnw(vec,win_size=301,variance_normalization=False):"""    This function is aimed to perform local cepstral mean and variance normalization on a sliding window.    (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))          win_size: The size of sliding window for local normalization and should be odd.                    default=301 which is around 3s if 100 Hz rate is considered(== 10ms frame stide)          variance_normalization: If the variance normilization should be performed or not.    :return: The mean(or mean+variance) normalized feature vector.    """

Tests

python -m pytest

Example

importscipy.io.wavfileaswavimportnumpyasnpimportspeechpyimportosfile_name=os.path.join(os.path.dirname(os.path.abspath(__file__)),'Alesis-Sanctuary-QCard-AcoustcBas-C2.wav')fs,signal=wav.read(file_name)signal=signal[:,0]# Example of pre-emphasizing.signal_preemphasized=speechpy.processing.preemphasis(signal,cof=0.98)# Example of staching framesframes=speechpy.processing.stack_frames(signal,sampling_frequency=fs,frame_length=0.020,frame_stride=0.01,filter=lambdax:np.ones((x,)),zero_padding=True)# Example of extracting power spectrumpower_spectrum=speechpy.processing.power_spectrum(frames,fft_points=512)print('power spectrum shape=',power_spectrum.shape)############# Extract MFCC features #############mfcc=speechpy.feature.mfcc(signal,sampling_frequency=fs,frame_length=0.020,frame_stride=0.01,num_filters=40,fft_length=512,low_frequency=0,high_frequency=None)mfcc_cmvn=speechpy.processing.cmvnw(mfcc,win_size=301,variance_normalization=True)print('mfcc(mean + variance normalized) feature shape=',mfcc_cmvn.shape)mfcc_feature_cube=speechpy.feature.extract_derivative_feature(mfcc)print('mfcc feature cube shape=',mfcc_feature_cube.shape)############# Extract logenergy features #############logenergy=speechpy.feature.lmfe(signal,sampling_frequency=fs,frame_length=0.020,frame_stride=0.01,num_filters=40,fft_length=512,low_frequency=0,high_frequency=None)logenergy_feature_cube=speechpy.feature.extract_derivative_feature(logenergy)print('logenergy features=',logenergy.shape)

Dependencies

Acknowledgements

Contributing

Pull Request Process

Declaring issues

Final Note

Disclaimer