pyAudioProcessing 1.1.6

Audio processing-feature extraction and building machine learning models from audio data.

pyAudioProcessing

A Python based library for processing audio data into features and building Machine Learning models.
This was written using Python 3.7.6, and should work with python 3.6+.

Getting Started

1. One way to install pyAudioProcessing and it's dependencies is from PyPI using pip

pip install pyAudioProcessing

To upgrade to the latest version of pyAudioProcessing, the following pip command can be used.

pip install -U pyAudioProcessing

2. Or, you could also clone the project and get it setup

git clone [e-mail]:jsingh811/pyAudioProcessing.git
cd pyAudioProcessing
pip install -e .

and then, get the requirements by running

pip install -r requirements/requirements.txt

Choices

Feature options

You can choose between features mfcc, gfcc, spectral, chroma or any combination of those, example gfcc,mfcc,spectral,chroma, to extract from your audio files for classification or just saving extracted feature for other uses.

Classifier options

You can choose between svm, svm_rbf, randomforest, logisticregression, knn, gradientboosting and extratrees.
Hyperparameter tuning is included in the code for each using grid search.

Training and Testing Data structuring

Let's say you have 2 classes that you have training data for (music and speech), and you want to use pyAudioProcessing to train a model using available feature options. Save each class as a directory and all the training audio .wav files under the respective class directories. Example:

.
├── training_data
├── music
│   ├── music_sample1.wav
│   ├── music_sample2.wav
│   ├── music_sample3.wav
│   ├── music_sample4.wav
├── speech
│   ├── speech_sample1.wav
│   ├── speech_sample2.wav
│   ├── speech_sample3.wav
│   ├── speech_sample4.wav

Similarly, for any test data (with known labels) you want to pass through the classifier, structure it similarly as

.
├── testing_data
├── music
│   ├── music_sample5.wav
│   ├── music_sample6.wav
├── speech
│   ├── speech_sample5.wav
│   ├── speech_sample6.wav

If you want to classify audio samples without any known labels, structure the data similarly as

.
├── data
├── unknown
│   ├── sample1.wav
│   ├── sample2.wav

Training and Classifying Audio files

Audio data can be trained, tested and classified using pyAudioProcessing. Please see feature options and classifier model options for more information.

Examples

Code example of using gfcc,spectral,chroma feature and svm classifier. Sample data can be found here. Please refer to the section on Training and Testing Data structuring to use your own data instead.

from pyAudioProcessing.run_classification import train_and_classify
# Training
train_and_classify("data_samples/training", "train", ["gfcc", "spectral", "chroma"], "svm", "svm_clf")

The above logs files analyzed, hyperparameter tuning results for recall, precision and F1 score, along with the final confusion matrix.

To classify audio samples with the classifier you created above,

# Classify data
train_and_classify("data_samples/testing", "classify", ["gfcc", "spectral", "chroma"], "svm", "svm_clf")

The above logs the filename where the classification results are saved along with the details about testing files and the classifier used.

If you cloned the project via git, the following command line example of training and classification with gfcc,spectral,chroma features and svm classifier can be used as well. Sample data can be found here. Please refer to the section on Training and Testing Data structuring to use your own data instead.

Training:

python pyAudioProcessing/run_classification.py -f "data_samples/training" -clf "svm" -clfname "svm_clf" -t "train" -feats "gfcc,spectral,chroma"

Classifying:

python pyAudioProcessing/run_classification.py -f "data_samples/testing" -clf "svm" -clfname "svm_clf" -t "classify" -feats "gfcc,spectral,chroma"

Classification results get saved in classifier_results.json.

Extracting features from audios

This feature lets the user extract aggregated data features calculated per audio file. See feature options for more information on choices of features available.

Examples

Code example for performing gfcc and mfcc feature extraction can be found below. To use your own audio data for feature extraction, pass the path to get_features in place of data_samples/testing. Please refer to the format of directory data_samples/testing or the section on Training and Testing Data structuring.

from pyAudioProcessing.extract_features import get_features
# Feature extraction
features = get_features("data_samples/testing", ["gfcc", "mfcc"])
# features is a dictionary that will hold data of the following format
"""
{
  subdir1_name: {file1_path: {"features": <list>, "feature_names": list}, ...},
  subdir2_name: {file1_path: {"features": <list>, "feature_names": list}, ...},
  ...
}
"""

To save features in a json file,

from pyAudioProcessing import utils
utils.write_to_json("audio_features.json",features)

If you cloned the project via git, the following command line example of for gfcc and mfcc feature extractions can be used as well. The features argument should be a comma separated string, example gfcc,mfcc.
To use your own audio files for feature extraction, pass in the directory path containing .wav files as the -f argument. Please refer to the format of directory data_samples/testing or the section on Training and Testing Data structuring.

python pyAudioProcessing/extract_features.py -f "data_samples/testing"  -feats "gfcc,mfcc"

Features extracted get saved in audio_features.json.

Author

Jyotika Singh
Data Scientist
https://twitter.com/jyotikasingh_/ https://www.linkedin.com/in/jyotikasingh/