Instructions to use committa/it_IT-serena-high-checkpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Piper
How to use committa/it_IT-serena-high-checkpoint with Piper:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
it_IT-serena β Piper training checkpoint (Italian, high)
Finetuning base checkpoint for Piper TTS (VITS), fine-tuned on Italian.
- Model: piper (piper_train), quality high, 22.05 kHz
- Base voice: serena β female, neutral Standard Italian (no regional accent), synthetic
- Training: 18 epochs of fine-tuning (resumed from the v1 checkpoint, 99 epochs
from scratch) on
committa/serena-synthetic-it-28h(30,842 clips, 28 h) - Dataset: committa/serena-synthetic-it-28h
- File:
epoch=18-step=105450.ckptβ piper_train convention:epoch=N-step=M, which tooling uses to pick the highest checkpoint - Best validation: val_mel 0.3567 (epoch 18)
Why this checkpoint
Piper publishes no official pretrained checkpoint for Italian (the rhasspy/piper-checkpoints
catalog has no it entry). This is the only public Italian high checkpoint that can be
used as a finetuning base. It is the base used to build it_IT-serena-high, and it is
fine-tuned on an extended dataset (serena-synthetic-it-28h) with targeted
sentence-initial phrases for improved prosody at the start of sentences.
Sample
Audio of the base voice (it_IT-serena-high):
Use as a base for finetuning other Italian voices
Any checkpoint of the same quality config (high) can be resumed with piper_train:
python -m piper_train \
--dataset-dir /path/to/your_voice_training_folder \
--resume_from_checkpoint epoch=18-step=105450.ckpt \
--quality high
Because the base already speaks Italian (correct phonemes and prosody), a new Italian voice converges in far fewer epochs than from scratch β and with less data. This works for both female and male target voices: the timbre is learned from the new dataset, so with enough epochs the new voice fully takes over.
License
CC-BY-4.0 β derived from committa/serena-synthetic-it-28h. If you use this checkpoint to build a voice, credit the dataset and this model.
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