language:
- ca
licence:
- apache-2.0
tags:
- matcha-tts
- acoustic modelling
- speech
- multispeaker
pipeline_tag: text-to-speech
datasets:
- projecte-aina/festcat_trimmed_denoised
- projecte-aina/openslr-slr69-ca-trimmed-denoised
Matcha-TTS Catalan Multispeaker
Table of Contents
Click to expand
Model description
Matcha-TTS is an encoder-decoder architecture designed for fast acoustic modelling in TTS. The encoder predicts phoneme durations and its average acoustic features. And the decoder is essentially a U-Net inspired by Grad-TTS, that is based on Transformers architecture but combined with 1D instead of 2D CNNs, making a high reduction on memory consumption and speedy synthesis.
Matcha-TTS is non-autorregressive model and is trained using optimal-transport conditional flow matching (OT-CFM). This yields an ODE-based decoder capable of high output quality in fewer synthesis steps than models trained using score matching.
Intended uses and limitations
This model is intended to serve as an acoustic feature generator for multispeaker text-to-speech systems for the Catalan language. It has been finetuned using a Catalan phonemizer, therefore if the model is used in other languages it may will not produce intelligible samples after converting its output into a speech waveform.
The quality of the samples can vary depending on the speaker. This may be due to the sensitivity of the model in learning specific frequencies and also due to the samples used for each speaker.
How to use
Installation
pip install git+https://github.com/langtech-bsc/vocos.git@matcha
You need to install the Catalan phonemizer version of espeak-ng:
git clone https://github.com/projecte-aina/espeak-ng.git
export PYTHON=/path/to/env/<env_name>/bin/python
cd /path/to/espeak-ng
./autogen.sh
./configure --prefix=/path/to/espeak-ng
make
make install
pip cache purge
pip install mecab-python3
pip install unidic-lite
Generate
Training
Adaptation
Training data
The model was trained on 2 Catalan speech datasets
| Dataset | Language | Hours |
|---|---|---|
| Festcat | ca | 22 |
| OpenSLR69 | ca | 5 |
Languages
Data comes from two different datasets: festcat and openslr69
Framework
Evaluation
Results
Citation
If this code contributes to your research, please cite the work:
@misc{mehta2024matchatts,
title={Matcha-TTS: A fast TTS architecture with conditional flow matching},
author={Shivam Mehta and Ruibo Tu and Jonas Beskow and Éva Székely and Gustav Eje Henter},
year={2024},
eprint={2309.03199},
archivePrefix={arXiv},
primaryClass={eess.AS}
}
Additional information
Author
The Language Technologies Unit from Barcelona Supercomputing Center.
Contact
For further information, please send an email to [email protected].
Copyright
Copyright(c) 2023 by Language Technologies Unit, Barcelona Supercomputing Center.
License
Funding
This work has been promoted and financed by the Generalitat de Catalunya through the Aina project.