Tools implemented in bob.bio.spear

Summary

Speech Preprocessors

Voice Feature Extractors

Preprocessors

class bob.bio.spear.preprocessor.Base(**kwargs)

Bases: bob.bio.base.preprocessor.Preprocessor.Preprocessor

Performs color space adaptations and data type corrections for the given image

read_data(data_file)

read_original_data(original_file_name)

Reads the original wav data from file (usually .wav file) If you have different format, please overwrite this function.

write_data(data, data_file, compression=0)

Writes the given preprocessed data to a file with the given name.

class bob.bio.spear.preprocessor.Energy_2Gauss(max_iterations=10, convergence_threshold=0.0005, variance_threshold=0.0005, win_length_ms=20.0, win_shift_ms=10.0, smoothing_window=10, **kwargs)

Bases: bob.bio.spear.preprocessor.Base.Base

Extracts the Energy

read_data(data_file)

read_original_data(original_file_name)

Reads the original wav data from file (usually .wav file) If you have different format, please overwrite this function.

write_data(data, data_file, compression=0)

Writes the given preprocessed data to a file with the given name.

class bob.bio.spear.preprocessor.Energy_Thr(win_length_ms=20.0, win_shift_ms=10.0, smoothing_window=10, ratio_threshold=0.15, **kwargs)

Bases: bob.bio.spear.preprocessor.Base.Base

VAD based on the thresholded energy

read_data(data_file)

read_original_data(original_file_name)

Reads the original wav data from file (usually .wav file) If you have different format, please overwrite this function.

write_data(data, data_file, compression=0)

Writes the given preprocessed data to a file with the given name.

class bob.bio.spear.preprocessor.External(win_length_ms=20.0, win_shift_ms=10.0, **kwargs)

Bases: bob.bio.spear.preprocessor.Base.Base

Uses external VAD and converts it to fit the format used by Spear

read_data(data_file)

read_original_data(original_file_name)

Reads the original wav data from file (usually .wav file) If you have different format, please overwrite this function.

use_existing_vad(inArr, vad_file)

write_data(data, data_file, compression=0)

Writes the given preprocessed data to a file with the given name.

class bob.bio.spear.preprocessor.Mod_4Hz(max_iterations=10, convergence_threshold=0.0005, variance_threshold=0.0005, win_length_ms=20.0, win_shift_ms=10.0, smoothing_window=10, n_filters=40, f_min=0.0, f_max=4000, pre_emphasis_coef=1.0, ratio_threshold=0.1, **kwargs)

Bases: bob.bio.spear.preprocessor.Base.Base

VAD based on the modulation of the energy around 4 Hz and the energy

averaging(list_1s_shift)

bandpass_firwin(ntaps, lowcut, highcut, fs, window='hamming')

mod_4hz(rate_wavsample)

Computes and returns the 4Hz modulation energy features for the given input wave file

modulation_4hz(filtering_res, rate_wavsample)

pass_band_filtering(energy_bands, fs)

read_data(data_file)

read_original_data(original_file_name)

Reads the original wav data from file (usually .wav file) If you have different format, please overwrite this function.

write_data(data, data_file, compression=0)

Writes the given preprocessed data to a file with the given name.

Extractors

Feature extraction tools

class bob.bio.spear.extractor.Cepstral(win_length_ms=20, win_shift_ms=10, n_filters=24, dct_norm=False, f_min=0.0, f_max=4000, delta_win=2, mel_scale=True, with_energy=True, with_delta=True, with_delta_delta=True, n_ceps=19, pre_emphasis_coef=0.95, features_mask=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59]), normalize_flag=True, **kwargs)

Bases: bob.bio.base.extractor.Extractor.Extractor

Extracts the Cepstral features

load(extractor_file)

Loads the parameters required for feature extraction from the extractor file. This function usually is only useful in combination with the train() function. In this base class implementation, it does nothing.

Parameters:

extractor_file : str

The file to read the extractor from.

normalize_features(params)

read_feature(feature_file)

Reads the extracted feature from file. In this base class implementation, it uses bob.bio.base.load() to do that. If you have different format, please overwrite this function.

Parameters:

feature_file : str or bob.io.base.HDF5File

The file open for reading or the name of the file to read from.

Returns:

feature : object (usually numpy.ndarray)

The feature read from file.

train(training_data, extractor_file)

This function can be overwritten to train the feature extractor. If you do this, please also register the function by calling this base class constructor and enabling the training by requires_training = True.

Parameters:

training_data : [object] or [[object]]

A list of preprocessed data that can be used for training the extractor. Data will be provided in a single list, if split_training_features_by_client = False was specified in the constructor, otherwise the data will be split into lists, each of which contains the data of a single (training-)client.

extractor_file : str

The file to write. This file should be readable with the load() function.

write_feature(feature, feature_file)

Writes the given extracted feature to a file with the given name. In this base class implementation, we simply use bob.bio.base.save() for that. If you have a different format, please overwrite this function.

Parameters:

feature : object

The extracted feature, i.e., what is returned from __call__().

feature_file : str or bob.io.base.HDF5File

The file open for writing, or the name of the file to write.

class bob.bio.spear.extractor.HTKFeatures(features_mask=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59]), normalize_flag=True, **kwargs)

Bases: bob.bio.base.extractor.Extractor.Extractor

Extracts the Cepstral features

HTKReader(input_file)

load(extractor_file)

Loads the parameters required for feature extraction from the extractor file. This function usually is only useful in combination with the train() function. In this base class implementation, it does nothing.

Parameters:

extractor_file : str

The file to read the extractor from.

normalize_features(params)

read_feature(feature_file)

Reads the extracted feature from file. In this base class implementation, it uses bob.bio.base.load() to do that. If you have different format, please overwrite this function.

Parameters:

feature_file : str or bob.io.base.HDF5File

The file open for reading or the name of the file to read from.

Returns:

feature : object (usually numpy.ndarray)

The feature read from file.

train(training_data, extractor_file)

This function can be overwritten to train the feature extractor. If you do this, please also register the function by calling this base class constructor and enabling the training by requires_training = True.

Parameters:

training_data : [object] or [[object]]

A list of preprocessed data that can be used for training the extractor. Data will be provided in a single list, if split_training_features_by_client = False was specified in the constructor, otherwise the data will be split into lists, each of which contains the data of a single (training-)client.

extractor_file : str

The file to write. This file should be readable with the load() function.

write_feature(feature, feature_file)

Writes the given extracted feature to a file with the given name. In this base class implementation, we simply use bob.bio.base.save() for that. If you have a different format, please overwrite this function.

Parameters:

feature : object

The extracted feature, i.e., what is returned from __call__().

feature_file : str or bob.io.base.HDF5File

The file open for writing, or the name of the file to write.

class bob.bio.spear.extractor.SPROFeatures(features_mask=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59]), normalize_flag=True, **kwargs)

Bases: bob.bio.base.extractor.Extractor.Extractor

Extracts the Cepstral features

SPRORead(input_file)

load(extractor_file)

Loads the parameters required for feature extraction from the extractor file. This function usually is only useful in combination with the train() function. In this base class implementation, it does nothing.

Parameters:

extractor_file : str

The file to read the extractor from.

normalize_features(params)

read_feature(feature_file)

Reads the extracted feature from file. In this base class implementation, it uses bob.bio.base.load() to do that. If you have different format, please overwrite this function.

Parameters:

feature_file : str or bob.io.base.HDF5File

The file open for reading or the name of the file to read from.

Returns:

feature : object (usually numpy.ndarray)

The feature read from file.

train(training_data, extractor_file)

This function can be overwritten to train the feature extractor. If you do this, please also register the function by calling this base class constructor and enabling the training by requires_training = True.

Parameters:

training_data : [object] or [[object]]

A list of preprocessed data that can be used for training the extractor. Data will be provided in a single list, if split_training_features_by_client = False was specified in the constructor, otherwise the data will be split into lists, each of which contains the data of a single (training-)client.

extractor_file : str

The file to write. This file should be readable with the load() function.

write_feature(feature, feature_file)

Writes the given extracted feature to a file with the given name. In this base class implementation, we simply use bob.bio.base.save() for that. If you have a different format, please overwrite this function.

Parameters:

feature : object

The extracted feature, i.e., what is returned from __call__().

feature_file : str or bob.io.base.HDF5File

The file open for writing, or the name of the file to write.