A speech signal processing library with emphasis on deep learning.
Project description
audlib
A speech signal processing library in Python with emphasis on deep learning.
audlib provides a collection of utilities for developing speech-related applications using both signal processing and deep learning. The package offers the following high-level features:
- Speech signal processing utilities with ready-to-use applications
- Deep learning architectures for speech processing tasks in PyTorch
- PyTorch-compatible interface (similar to torchvision) for batch processing
- A command-line interface with a unix-pipe-like syntax
- I/O utilities for interfacing with CMUSPHINX
Some use cases of audlib are:
- Extracting common speech features for your backend
- Integrating CMUSPHINX with modern deep learning architectures
- Developing your own deep-learning-based tools for speech tasks
- Quickly try out speech processors and visualize the spectrogram in command line
audlib focuses on correctness, efficiency, and simplicity. Signal processing functionalities are mathematically checked whenever possible (e.g. constant overlap-add, istft(stft(X))==X
). Deep neural networks follow the PyTorch's convention.
Breaking Changes
- 0.0.3
transform.stlogm
is removed
- 0.0.2
audioread
follows the interface ofsoundfile.read
audiowrite
follows the interface ofsoundfile.write
- The argument
sr
is removed from all short-time transforms
Installation
pip install audlib
Developer Installation
In the source directory, install the library with test dependencies:
pip install ".[tests]"
Run test:
python -m pytest tests
Release flow
- Bump version in setup.py.
- Package release:
python setup.py sdist bdist_wheel
- Upload release:
twine upload --repository-url https://upload.pypi.org/legacy/ dist/*
Usage example
More extensive examples can be found in examples/
.
Release history
- 0.0.3
- First release of the command-line tool audpipe
- 0.0.2
- Streamlines optional installation
- Improves API (see breaking changes)
- Adds coverage test
- 0.0.1
- First release on PyPI
Authors
Raymond Xia - raymondxia@cmu.edu
Mahmoud Alismail - mahmoudi@andrew.cmu.edu
Shangwu Yao - shangwuyao@gmail.com
Andrew Wu - anwu.andrew@hotmail.com
Feel free to send us any issue you find and question you have.
Contributing
Please contact one of the authors.
License
Distributed under the MIT license. See LICENSE
for more information.
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