https://github.com/k2kobayashi/sprocket
Voice Conversion Tool Kit
https://github.com/k2kobayashi/sprocket
speech-enhancement speech-synthesis sprockets voice-conversion
Last synced: 5 months ago
JSON representation
Voice Conversion Tool Kit
- Host: GitHub
- URL: https://github.com/k2kobayashi/sprocket
- Owner: k2kobayashi
- License: mit
- Created: 2017-06-21T05:50:47.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2023-02-27T12:32:56.000Z (over 2 years ago)
- Last Synced: 2025-04-12T11:55:42.636Z (6 months ago)
- Topics: speech-enhancement, speech-synthesis, sprockets, voice-conversion
- Language: Python
- Homepage:
- Size: 1.75 MB
- Stars: 600
- Watchers: 34
- Forks: 115
- Open Issues: 31
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGES.md
- License: LICENSE.txt
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sprocket
======
Voice conversion software - Voice conversion (VC) is a technique to convert a speaker identity of a source speaker into that of a target speaker. This software enables the users to develop a traditional VC system based on a Gaussian mixture model (GMM) and a vocoder-free VC system based on a differential GMM (DIFFGMM) using a parallel dataset of the source and target speakers.
## Paper and slide
- K. Kobayashi, T. Toda, "sprocket: Open-Source Voice Conversion Software," Proc. Odyssey, pp. 203-210, June 2018.
[[paper]](https://nuss.nagoya-u.ac.jp/s/h8YKnq6qxjjxtU3)
- T. Toda, "Hands on Voice Conversion," Speech Processing Courses in Crete (SPCC), July 2018.
[[slide]](https://www.slideshare.net/NU_I_TODALAB/hands-on-voice-conversion)
## Conversion samples
- Voice Conversion Challenge 2018
- [HUB Task](https://nuss.nagoya-u.ac.jp/s/3F8dxTcdQdXir9s)
- [SPOKE Task](https://nuss.nagoya-u.ac.jp/s/ixwxa6DxYa68y4N)
## Purpose
### Reproduce the typical VC systems
This software was developed to make it possible for the users to easily build the VC systems by only preparing a parallel dataset of the desired source and target speakers and executing example scripts.
The following VC methods were implemented as the typical VC methods.
#### Traditional VC method based on GMM
- T. Toda, A.W. Black, K. Tokuda, "Voice conversion based on maximum likelihood estimation of spectral parameter trajectory," IEEE Transactions on Audio, Speech and Language Processing, Vol. 15, No. 8, pp. 2222-2235, Nov. 2007.
#### Vocoder-free VC method based on DIFFGMM
- K. Kobayashi, T. Toda, S. Nakamura, "F0 transformation techniques for statistical voice conversion with direct waveform modification with spectral differential," Proc. IEEE SLT, pp. 693-700, Dec. 2016.
### Supply Python3 VC library
To make it possible to easily develop VC-based applications using Python (Python3), the VC library is also supplied, including several interfaces, such as acoustic feature analysis/synthesis, acoustic feature modeling, acoustic feature conversion, and waveform modification.
For the details of the VC library, please see sprocket documents in (coming soon).
## Installation & Run
Please use Python3.
### Current stable version
Ver. 0.18.4
### Install sprocket
```
pip install numpy==1.15.4 cython # for dependency
pip install sprocket-vc
```
### Run example
See [VC example](docs/vc_example.md)
## REPORTING BUGS
For any questions or issues please visit:
```
https://github.com/k2kobayashi/sprocket/issues
```
## COPYRIGHT
Copyright (c) 2020 Kazuhiro KOBAYASHI
Released under the MIT license
[https://opensource.org/licenses/mit-license.php](https://opensource.org/licenses/mit-license.php)
## ACKNOWLEDGEMENTS
Thank you [@r9y9](https://github.com/r9y9) and [@tats-u](https://github.com/tats-u) for lots of contributions and encouragement helps before release.
## Who we are
- Kazuhiro Kobayashi [@k2kobayashi](https://github.com/k2kobayashi) [maintainer, design and development]
- [Tomoki Toda](https://sites.google.com/site/tomokitoda/) [advisor]