https://github.com/aavache/caesynth
This repository holds the implementation for achieving timbre and pitch disentanglement for musical data.
https://github.com/aavache/caesynth
Last synced: 9 months ago
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This repository holds the implementation for achieving timbre and pitch disentanglement for musical data.
- Host: GitHub
- URL: https://github.com/aavache/caesynth
- Owner: Aavache
- License: mit
- Created: 2020-07-03T07:07:26.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-02-10T08:42:27.000Z (almost 4 years ago)
- Last Synced: 2025-03-28T09:51:31.358Z (10 months ago)
- Language: Python
- Size: 38.8 MB
- Stars: 5
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# CAESynth: Real-Time Timbre Intepolation and Pitch Control with Conditional Autoencoders
This is the original python implementation of the [CAESynth](https://ieeexplore.ieee.org/document/9596414) paper, presented at the IEEE International Workshop on Machine Learning for Signal Processing MLSP 2021. Please cite our work!.
```
@INPROCEEDINGS{9596414,
author={Puche, Aaron Valero and Lee, Sukhan},
booktitle={2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)},
title={Caesynth: Real-Time Timbre Interpolation and Pitch Control with Conditional Autoencoders},
year={2021}, volume={}, number={}, pages={1-6},
doi={10.1109/MLSP52302.2021.9596414}}
```
## Dependencies
The necessary python libraries to run our experience can be directly downloaded executing the following command:
```
pip install -r requirements.txt
```
## Datasets
Both [NSynth](https://magenta.tensorflow.org/datasets/nsynth#files) and [FreeSoundDataset50k](https://zenodo.org/record/4060432#.YXjuK3UzZhE) can be downloaded at the provided links. The datasets should be stored in the `./data/` directory.
## Training
In this implementation, we opt for summarizing the training configuration with external json files located in the `./option/` directory. Customize your own configuration file following similar structure to the already existing examples. Once the configuration file is ready, start the training with the following command.
```
python train.py --opt_file "./options/config_file_name.json"
```