A Python based library for processing audio data into features (GFCC, MFCC, spectral, chroma) and building Machine Learning models.
This was written using Python 3.7.6, and has been tested to work with Python >= 3.6, <4.
- 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
- Or, you could also clone the project and get it setup
git clone [email protected]:jsingh811/pyAudioProcessing.git
cd pyAudioProcessing
pip install -e .
You can also get the requirements by running
pip install -r requirements/requirements.txt
If you are on Python 3.9 and experience any issues with the code samples regarding numpy, please run
pip install -U numpy
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.
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.
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.wavSimilarly, 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.wavIf you want to classify audio samples without any known labels, structure the data similarly as
.
├── data
├── unknown
│ ├── sample1.wav
│ ├── sample2.wavThere are three models that have been pre-trained and provided in this project under the /models directory. They are as follows.
music genre: Contains SVM classifier to classify audio into 10 music genres - blues, classical, country, disco, hiphop, jazz, metal, pop, reggae, rock. This classifier was trained using mfcc, gfcc, spectral and chroma features. In order to classify your audio files using this classifier, please follow the audio files structuring guidelines. The following commands in Python can be used to classify your data.
musicVSspeech: Contains SVM classifier that classifying audio into two possible classes - music and speech. This classifier was trained using mfcc, spectral and chroma features.
musicVSspeechVSbirds: Contains SVM classifier that classifying audio into three possible classes - music, speech and birds. This classifier was trained using mfcc, spectral and chroma features.
In order to classify your audio files using any of these classifier, please follow the audio files structuring guidelines. The following commands in Python can be used to classify your data.
from pyAudioProcessing.run_classification import train_and_classify
# musicVSspeech classification
train_and_classify("../test_data", "classify", ["spectral", "chroma", "mfcc"], "svm", "models/musicVSspeech/svm_clf")
# musicVSspeechVSbirds classification
train_and_classify("../test_data", "classify", ["spectral", "chroma", "mfcc"], "svm", "models/musicVSspeechVSbirds/svm_clf")
# music genre classification
train_and_classify("../test_data", "classify", ["gfcc", "spectral", "chroma", "mfcc"], "svm", "models/music genre/svm_clf")
Audio data can be trained, tested and classified using pyAudioProcessing. Please see feature options and classifier model options for more information.
Sample spoken location name dataset for spoken instances of "london" and "boston" can be found here.
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.
This feature lets the user extract aggregated data features calculated per audio file. See feature options for more information on choices of features available.
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.
You can convert you audio in .mp4, .mp3, .m4a and .aac to .wav. This will allow you to use audio feature generation and classification functionalities.
In order to convert your audios, the following code sample can be used.
from pyAudioProcessing.convert_audio import convert_files_to_wav
# dir_path is the path to the directory/folder on your machine containing audio files
dir_path = "data/mp4_files"
# simple change audio_format to "mp3", "m4a" or "acc" depending on the format
# of audio that you are trying to convert to wav
convert_files_to_wav(dir_path, audio_format="mp4")
# the converted wav files will be saved in the same dir_path location.
To remove low-activity regions from your audio clip, the following sample usage can be referred to.
from pyAudioProcessing import clean
clean.remove_silence(
<path to wav file>,
output_file=<path where you want to store cleaned wav file>
)
To see time-domain view of the audios, and the spectrogram of the audios, please refer to the following sample usage.
from pyAudioProcessing import plot
# spectrogram plot
plot.spectrogram(
<path to wav file>,
show=True, # set to False if you do not want the plot to show
save_to_disk=True, # set to False if you do not want the plot to save
output_file=<path where you want to store spectrogram as a png>
)
# time-series plot
plot.time(
<path to wav file>,
show=True, # set to False if you do not want the plot to show
save_to_disk=True, # set to False if you do not want the plot to save
output_file=<path where you want to store the plot as a png>
)
Using pyAudioProcessing in your research? Please cite as follows.
Jyotika Singh. (2021, July 22). jsingh811/pyAudioProcessing: Audio processing, feature extraction and classification (Version v1.2.0). Zenodo. http://doi.org/10.5281/zenodo.5121041
Bibtex
@software{jyotika_singh_2021_5121041,
author = {Jyotika Singh},
title = {{jsingh811/pyAudioProcessing: Audio processing,
feature extraction and classification}},
month = jul,
year = 2021,
publisher = {Zenodo},
version = {v1.2.0},
doi = {10.5281/zenodo.5121041},
url = {https://doi.org/10.5281/zenodo.5121041}
}
Jyotika Singh
Data Scientist
https://twitter.com/jyotikasingh_/
https://www.linkedin.com/in/jyotikasingh/
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