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
Some use cases of audlib are:
- Extracting common speech features for your backend
- 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
sig.util.freqz
becomessig.util.fftfreqz
nn.strfnet
is removed. See the official repository here.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
Contact
Please reach out to Raymond Xia (raymondxia@cmu.edu) for comments.
More information about our research groups can be found on at
License
Distributed under the MIT license. See LICENSE
for more information.
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