language: gl
license: apache-2.0
datasets:
- CRPIH_UVigo-GL-Voices/Sabela
tags:
- TTS
- speech-synthesis
- Galician
- female-speaker
- VITS
- coqui.ai
Celtia: Nós Project's Galician TTS Model
Model description
Celtia is a Galician TTS model created under the Nós project. It was trained from scratch using the Coqui TTS Python library on the corpus Nos_Celtia-GL. This corpus comprises a total of 20,000 sentences recorded by a professional voice talent. Specifically, a subset of 13,000 sentences, corresponding to 15.5 hours of speech, was used to train the model.
The model was trained directly on grapheme inputs, so no phonetic transcription is required. The Cotovía tool can be used to normalize the input text.
You can test the model in our live inference demo (Nós-TTS) or in our spaces (Galician TTS).
Intended uses and limitations
You can use this model to generate synthetic speech in Galician.
Installation
Cotovía
For text normalization, you can use the front-end of Cotovía. This software is available for download on the SourceForge website. The required Debian packages are cotovia_0.5_amd64.deb and cotovia-lang-gl_0.5_all.deb, which can be installed using the following commands:
sudo dpkg -i cotovia_0.5_amd64.deb
sudo dpkg -i cotovia-lang-gl_0.5_all.deb
TTS library
To synthesize speech, you need to install the Coqui TTS library:
pip install TTS
How to use
Command-line usage
The following command normalizes and synthesizes the input text using the Celtia model:
echo "Son Celtia, unha voz creada con intelixencia artificial" | cotovia -p -n -S | iconv -f iso88591 -t utf8 | tts --text "$(cat -)" --model_path celtia.pth --config_path celtia_config.json --out_path celtia.wav
The output synthesized speech is saved to the specified audio file.
Python usage
Normalization and synthesis can also be performed within Python:
import argparse
import string
import subprocess
from TTS.utils.synthesizer import Synthesizer
def sanitize_filename(filename):
"""Remove or replace any characters that are not allowed in file names."""
return ''.join(c for c in filename if c.isalnum() or c in (' ', '_', '-')).rstrip()
def to_cotovia(text):
# Input and output Cotovía files
COTOVIA_IN_TXT_PATH = res + '.txt'
COTOVIA_IN_TXT_PATH_ISO = 'iso8859-1' + res + '.txt'
COTOVIA_OUT_PRE_PATH = 'iso8859-1' + res + '.pre'
COTOVIA_OUT_PRE_PATH_UTF8 = 'utf8' + res + '.pre'
with open(COTOVIA_IN_TXT_PATH, 'w') as f:
f.write(text + '\n')
# utf-8 to iso8859-1
subprocess.run(["iconv", "-f", "utf-8", "-t", "iso8859-1", COTOVIA_IN_TXT_PATH, "-o", COTOVIA_IN_TXT_PATH_ISO], stdout=subprocess.DEVNULL, stderr=subprocess.STDOUT)
subprocess.run(["cotovia", "-i", COTOVIA_IN_TXT_PATH_ISO, "-p"], stdout=subprocess.DEVNULL, stderr=subprocess.STDOUT)
subprocess.run(["iconv", "-f", "iso8859-1", "-t", "utf-8", COTOVIA_OUT_PRE_PATH, "-o", COTOVIA_OUT_PRE_PATH_UTF8], stdout=subprocess.DEVNULL, stderr=subprocess.STDOUT)
segs = []
try:
with open(COTOVIA_OUT_PRE_PATH_UTF8, 'r') as f:
segs = [line.rstrip() for line in f]
except:
print("ERROR: Couldn't read cotovia output")
subprocess.run(["rm", COTOVIA_IN_TXT_PATH, COTOVIA_IN_TXT_PATH_ISO, COTOVIA_OUT_PRE_PATH, COTOVIA_OUT_PRE_PATH_UTF8], stdout=subprocess.DEVNULL, stderr=subprocess.STDOUT)
return segs
def text_preprocess(text):
cotovia_preproc_text = to_cotovia(text)
# convert list to string
cotovia_preproc_text_res = ' '.join(cotovia_preproc_text)
# add final punctuation if missing
if cotovia_preproc_text_res[-1] not in string.punctuation:
cotovia_preproc_text_res += '.'
return cotovia_preproc_text_res
def main():
parser = argparse.ArgumentParser(description='Cotovía text normalisation')
parser.add_argument('text', type=str, help='Text to synthetize')
parser.add_argument('model_path', type=str, help='Absolute path to the model checkpoint.pth')
parser.add_argument('config_path', type=str, help='Absolute path to the model config.json')
args = parser.parse_args()
print("Text before preprocessing: ", args.text)
text = text_preprocess(args.text)
print("Text after preprocessing: ", text)
synthesizer = Synthesizer(
args.model_path, args.config_path, None, None, None, None,
)
# Step 1: Extract the first word from the text
first_word = args.text.split()[0] if args.text.split() else "audio"
first_word = sanitize_filename(first_word) # Sanitize to make it a valid filename
# Step 2: Use synthesizer's built-in function to synthesize and save the audio
wavs = synthesizer.tts(text)
filename = f"{first_word}.wav"
synthesizer.save_wav(wavs, filename)
print(f"Audio file saved as: {filename}")
if __name__ == "__main__":
main()
This Python code takes an input text, normalizes it using Cotovía’s front-end, synthesizes speech from the normalized text, and saves the synthetic output speech as a .wav file.
A more advanced version, including additional text preprocessing, can be found in the script synthesize.py, avaliable in this repository. You can use this script to synthesise speech from an input text as follows:
python synthesize.py text model_path config_path
Training
Hyperparameter
The model is based on VITS proposed by Kim et al. 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
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