https://github.com/rybandrei2014/open-unmix-pytorch-clj
Clojure bindings for PyTorch implementation of Open-Unmix
https://github.com/rybandrei2014/open-unmix-pytorch-clj
audio-separation clojure clojure-library clojure-python-interop deep-learning libpython-clj musdb museval music-separation norbert open-unmix pytorch sound-separation
Last synced: 3 months ago
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Clojure bindings for PyTorch implementation of Open-Unmix
- Host: GitHub
- URL: https://github.com/rybandrei2014/open-unmix-pytorch-clj
- Owner: rybandrei2014
- License: epl-2.0
- Created: 2020-01-15T20:11:53.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2024-03-21T16:56:07.000Z (about 1 year ago)
- Last Synced: 2025-01-17T17:17:20.302Z (5 months ago)
- Topics: audio-separation, clojure, clojure-library, clojure-python-interop, deep-learning, libpython-clj, musdb, museval, music-separation, norbert, open-unmix, pytorch, sound-separation
- Language: Clojure
- Size: 41 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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README
# open-unmix-pytorch-clj
Clojure bindings for PyTorch implementation of __Open-Unmix__,
a deep neural network reference implementation for music source separation,
applicable for researchers, audio engineers and artists.
[](https://clojars.org/dragoon/open-unmix-pytorch-clj)
* utilizes great JNA libpython bindings library.
* implementation based on related python implementation.
## Get Started
* install python 3.6, pip
* install python dependencies
```bash
pip3 install -r requirements.txt
```
* add dependency to the project.clj
```clj
[dragoon000320/open-unmix-pytorch-clj "0.1.4-ALPHA"]
```
## Usage
A little demo how to use it
```clj
;; require namespaces
(require '[open-unmix-pytorch-clj.core :refer :all])
(require '[open-unmix-pytorch-clj.io :refer :all])
(require '[open-unmix-pytorch-clj.convert :refer :all])
(-> "your-audio-file.wav"
;; reads audio file
soundfile-read
;; converts audio data to 2 channel one
->2-channels
;; separates audio data into required audio sources
(separate ["vocals" "drums" "other" "bass"]
:device "cpu"
;; or if you have cuda enabled uncomment line below
;; :device "cuda"
)
;; writes estimates for each audio source to the output directory
(write-estimates "out-dir"))
```
## Disclaimer
The library is in __alpha__ at the moment, current state of library is described below:
* for now only __separation__ of audio source implemented
* so there is no implementation of __model training__
* performance must be further improved
* a lot more to do...
## Contributions
It is an open-source project so contributions are welcomed (pull-requests, issue reports).
## Special Thanks To
* Libpython-clj
* Numpy
* Open-Unmix
* Norbert
* Museval
* Musdb18
* PyTorch
## License
Copyright © 2020
Distributed under the Eclipse Public License 2.0