---
language: gl
license: apache-2.0
datasets:
- CRPIH_UVigo-GL-Voices/Sabela 
tags:
- TTS
- speech-synthesis
- Galician
- female-speaker
- VITS
- coqui.ai 

---

# Celtia: Nos Project's Galician TTS Model
## Model description

This model was trained from scratch using the [Coqui TTS](https://github.com/coqui-ai/TTS) Python library on the corpus [Nos_Celtia-GL](https://zenodo.org/record/7716958).

A live inference demo can be found in our official page, [here](https://tts.nos.gal/).

This model was trained using graphemes, so no preprocessing is needed for the input text.

## Intended uses and limitations

You can use this model to generate synthetic speech in Galician.

## How to use
### Usage

Required libraries:

```bash
pip install TTS
```

Synthesize a speech using python:

```bash
import tempfile
import numpy as np
import os
import json

from typing import Optional
from TTS.config import load_config
from TTS.utils.manage import ModelManager
from TTS.utils.synthesizer import Synthesizer
model_path = # Absolute path to the model checkpoint.pth
config_path = # Absolute path to the model config.json
text = "Text to synthetize"
synthesizer = Synthesizer(
    model_path, config_path, None, None, None, None,
)
wavs = synthesizer.tts(text)
```


## Training
### Training Procedure
### Data preparation


### Hyperparameter

The model is based on VITS proposed by [Kim et al](https://arxiv.org/abs/2106.06103). The following hyperparameters were set in the coqui framework.

| Hyperparameter                     | Value                            |
|------------------------------------|----------------------------------|
| Model                              | vits                             |
| Batch Size                         | 26                               |
| Eval Batch Size                    | 16                               |
| Mixed Precision                    | true                             |
| Window Length                      | 1024                             |
| Hop Length                         | 256                              |
| FTT size                           | 1024                             |
| Num Mels                           | 80                               |
| Phonemizer                         | null                             |
| Phoneme Lenguage                   | en-us                            |
| Text Cleaners                      | multilingual_cleaners            |
| Formatter                          | nos_fonemas                      |
| Optimizer                          | adam                             |
| Adam betas                         | (0.8, 0.99)                      |
| Adam eps                           | 1e-09                            |
| Adam weight decay                  | 0.01                             |
| Learning Rate Gen                  | 0.0002                           |
| Lr. schedurer Gen                  | ExponentialLR                    |
| Lr. schedurer Gamma Gen            | 0.999875                         |
| Learning Rate Disc                 | 0.0002                           |
| Lr. schedurer Disc                 | ExponentialLR                    |
| Lr. schedurer Gamma Disc           | 0.999875                         |

The model was trained for 457900 steps.

The nos_fonemas formatter is a modification of the LJSpeech formatter with one extra column for the normalized input (extended numbers and acronyms).

## Additional information

### Authors
Carmen Magariños

### Contact information
For further information, send an email to proxecto.nos@usc.gal

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

### Funding

This research was funded by “The Nós project: Galician in the society and economy of Artificial Intelligence”, resulting from the agreement 2021-CP080 between the Xunta de Galicia and the University of Santiago de Compostela, and thanks to the Investigo program, within the National Recovery, Transformation and Resilience Plan, within the framework of the European Recovery Fund (NextGenerationEU).

### Citation information

If you use this model, please cite as follows:

Magariños, Carmen. 2023. Nos_TTS-celtia-vits-graphemes. URL: https://huggingface.co/proxectonos/Nos_TTS-celtia-vits-graphemes