#128128th of 168 in ML frameworks and librariesFor voice, speech and music
pyaudioanalysis
Python library for audio feature extraction, classification and segmentation
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- Stars
- 6.3k
- License
- Apache-2.0
- Last commit
- Oct 2026
- Language
- Python
6,209 on 21 Apr 20266,271 on 10 Nov 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
pyAudioAnalysis extracts audio features such as MFCCs, spectrograms and chromagrams, and trains classifiers and regression models on WAV segments. It also covers silence removal, supervised and unsupervised segmentation including speaker diarization, and audio thumbnails. Functions are callable from Python or through the audioAnalysis.py command line.
Who it is for: Python developers and researchers doing classical audio analysis and diarization
Strengths
- Covers features, classification, regression, diarization and silence detection in one library
- Every function is also exposed through a command-line script
- Train a classifier from folders of WAV files in one call
- Backed by a peer-reviewed PLoS ONE paper and a wiki with tutorials
Weaknesses
- Install is a git clone plus pip install -e, with no PyPI package mentioned
- No tagged releases
- Examples use classical models like SVM; no deep learning, which the group's other repos cover
- Docs split between a short README and a separate wiki
What it needs
- Models: SVM classifiers, audio regression models, WAV files
Quick answers
Is pyaudioanalysis open source?
Yes. Its code is on GitHub under the Apache-2.0 license.
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