README.md

---
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
<details>
<summary>Click to expand</summary>

- [Model description](#model-description)
- [Intended uses and limitations](#intended-uses-and-limitations)
- [How to use](#how-to-use)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation](#citation)
- [Additional information](#additional-information)

</details>

## Model description

**Matcha-TTS** is an encoder-decoder architecture designed for fast acoustic modelling in TTS. The encoder predicts phoneme durations and their averaged acoustic features. 
The decoder backbone is essentially a U-Net inspired by [Grad-TTS](https://arxiv.org/pdf/2105.06337.pdf) based on Transformers architecture. By replacing 2D CNNs by 1D CNNs, 
a large reduction in memory consumption and fast synthesis is achieved.

**Matcha-TTS** is a non-autorregressive model trained with optimal-transport conditional flow matching (OT-CFM). 
This yields an ODE-based decoder capable of generating 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 for other languages it may will not produce intelligible samples after mapping 
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 quality of samples for each speaker.

## How to use

### Installation

```bash
pip install git+https://github.com/langtech-bsc/vocos.git@matcha
```
You need to install the Catalan phonemizer version of espeak-ng:

```bash
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 <langtech@bsc.es>.

### Copyright
Copyright(c) 2023 by Language Technologies Unit, Barcelona Supercomputing Center.

### License
[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)

### Funding
This work has been promoted and financed by the Generalitat de Catalunya through the [Aina project](https://projecteaina.cat/).
### Disclaimer