Aina Project's Catalan multi-speaker text-to-speech model
Model description
This model was trained from scratch using the Coqui TTS toolkit on a combination of 3 datasets:
Festcat, OpenSLR69 and Common Voice v12.
For the training, we used 487 hours of recordings from 255 speakers.
We have trimmed and denoised the data which all except Common Voice can be found in a seperate dataset
in festcat_trimmed_denoised and openslr69_trimmed_denoised.
A live inference demo can be found in our spaces, here.
The model needs our fork of espeak-ng to work correctly. For installation and deployment please consult the docker file of our inference demo.
Intended uses and limitations
You can use this model to generate synthetic speech in Catalan with different voices.
1from TTS.utils.synthesizer import Synthesizer
23model_path =# Absolute path to the model checkpoint.pth4config_path =# Absolute path to the model config.json5speakers_file_path =# Absolute path to speakers.pth file67text ="Text to synthetize"8speaker_idx ="Speaker ID"910synthesizer = Synthesizer(11 model_path, config_path, speakers_file_path,None,None,None,12)13wavs = synthesizer.tts(text, speaker_idx)
Training
Training Procedure
Data preparation
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
16
Eval Batch Size
8
Mixed Precision
false
Window Length
1024
Hop Length
256
FTT size
1024
Num Mels
80
Phonemizer
espeak
Phoneme Lenguage
ca
Text Cleaners
multilingual_cleaners
Formatter
vctk_old
Optimizer
adam
Adam betas
(0.8, 0.99)
Adam eps
1e-09
Adam weight decay
0.01
Learning Rate Gen
0.0001
Lr. schedurer Gen
ExponentialLR
Lr. schedurer Gamma Gen
0.999875
Learning Rate Disc
0.0001
Lr. schedurer Disc
ExponentialLR
Lr. schedurer Gamma Disc
0.999875
The model was trained for 730962 steps.
Additional information
Author
Language Technologies Unit (LangTech) at the Barcelona Supercomputing Center
Contact information
For further information, send an email to aina@bsc.es
Copyright
Copyright (c) 2023 Language Technologies Unit (LangTech) at Barcelona Supercomputing Center
The training of the model was possible thanks to the compute time given by Galician Supercomputing Center CESGA (Centro de Supercomputación de Galicia).
Disclaimer
Click to expand
The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.
When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.
In no event shall the owner and creator of the models (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.
