XTTS v2 — PT-BR Fine-tuning (Paraíba Accent)

GPT fine-tuning of XTTS v2 (coqui/XTTS-v2) on a Brazilian Portuguese voice corpus (April 2026). Checkpoints and configs at the root; graficos/ with curves and metrics; inferencias/ with evaluation audio.

Checkpoints

File	Step
`checkpoint_8868.pth`	8,868 (1 epoch)
`checkpoint_17736.pth`	17,736
`checkpoint_26604.pth`	26,604
`checkpoint_35472.pth`	35,472
`checkpoint_44340.pth`	44,340
`best_model_53205.pth`	53,205 (best eval loss)
`best_model.pth`	copy of the best model

config.json holds the full training configuration; experiment_config.json and relatorio_experimento.md document the experiment.

Training environment

GPU: NVIDIA GeForce RTX 4060 Ti 16 GB
PyTorch 2.5.1 + CUDA 12.1, Python 3.10, Windows 10
Details in machine_report_thesis.md

Files

*.pth, config.json, experiment_config.json — checkpoints and configs
relatorio_experimento.md, training_statistics.json — training reports
graficos/ — loss and training-time curves (PNG + CSV/JSON)
inferencias/ — xtts_model_*_frase*.wav (5 phrases per checkpoint, incl. xtts_model_original_* for comparison against the base model), infer_finetuned_* × ref_* pairs, and manifests
metadata_train.csv, metadata_eval.csv — dataset manifests
xtts_finetune_paraiba_experiment.py — experiment script

Usage

Load with Coqui TTS (Xtts.init_from_config + load_checkpoint). The original XTTS v2 support files (dvae, vocab, etc.) are not included — download them from the official Coqui repository.

License: derived from XTTS v2, subject to the Coqui Public Model License (non-commercial use).

Citation

These checkpoints are fine-tunes of coqui/XTTS-v2. If you use them, please cite:

DINIZ, Thomaz. Incorporando regionalismos e sotaques em modelos de síntese de fala. 2026. Dissertação (Mestrado em Ciência da Computação) — Universidade Federal de Campina Grande, Campina Grande, 2026.