https://github.com/fakufaku/torchiva

Blind source separation with independent vector analysis family of algorithm in torch
https://github.com/fakufaku/torchiva

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Blind source separation with independent vector analysis family of algorithm in torch

• Host: GitHub
• URL: https://github.com/fakufaku/torchiva
• Owner: fakufaku
• License: mit
• Created: 2022-07-21T23:38:54.000Z (over 2 years ago)
• Default Branch: master
• Last Pushed: 2023-01-30T05:09:58.000Z (almost 2 years ago)
• Last Synced: 2024-10-31T15:38:00.543Z (8 days ago)
• Language: Python
• Homepage: https://torchiva.readthedocs.io/en/latest/
• Size: 13.6 MB
• Stars: 88
• Watchers: 5
• Forks: 5
• Open Issues: 1
• Metadata Files:
  • Readme: README.rst
  • Changelog: CHANGELOG.rst
  • License: LICENSE

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README

        
TORCHIVA

========

A package for blind source separation and beamforming in `pytorch `_ .

* supports many BSS and beamforming methods

* supports memory efficient gradient computation for training neural source models

* supports batched computations

* can run on GPU via pytorch

Quick Start

-----------

The package can be installed via pip::

    pip install torchiva

Separation using Pre-trained Model

----------------------------------

We provide a pre-trained model in `trained_models/tiss`.

You can easily try separation with the pre-trained model::

    # Separation

    python -m torchiva.separate INPUT OUTPUT

where ``INPUT`` is either a multichannel wav file or a folder containing

multichannel wav files.  If a folder, then all the files inside are separted.

The output is saved to ``OUTPUT``.

The model stored in ``trained_models/tiss`` is automatically downloaded to

``$HOME/.torchiva_models``. The path or url to the model can also be

manually provided via the ``--model`` option.

The model was trained on the `WSJ1-mix dataset

`_ with the same configuration

as ``./examples/configs/tiss.json``.

Training

--------

We provide some simple training scripts.

We support training of **T-ISS**, **MWF**, **MVDR**, **GEV**::

    cd examples

    # install some modules necessary for training

    pip install -r requirements.txt

    # training

    python train.py PATH_TO_CONFIG PATH_TO_DATASET

Note that our example scripts assumes using WSJ1-mix dataset.

If you want to use other datasets, please change the script in the part that loads audios.

Test your trained model with checkpoint from epoch 128::

    # python ./test.py --dataset ../wsj1_6ch --n_fft 2048 --hop 512 --n_iter 40 --iss-hparams checkpoints/tiss_delay1tap5_2ch/lightning_logs/version_0/hparams.yaml --epoch 128 --test

Export the trained model for later use::

    python ./export_model.py ../trained_models/tiss checkpoints/tiss_delay1tap5_2ch/lightning_logs/version_0 128 146 148 138 122 116 112 108 104 97

Run the example script using the exported model::

    python ./example_dnn.py ../wsj1_6ch ../trained_models/tiss -m 2 -r 100

Authors

-------

* `Robin Scheibler `_

* Kohei Saijo

License

-------

2022 (c) Robin Scheibler, Kohei Saijo, LINE Corporation.

All of this code is released under `MIT License `_