Instructions to use committa/it_IT-serena-medium-checkpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Piper
How to use committa/it_IT-serena-medium-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
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library_name: piper
license: cc-by-4.0
language:
- it
tags:
- text-to-speech
- tts
- piper
- checkpoint
- finetuning
datasets:
- committa/serena-synthetic-it-27h
---
# it_IT-serena — Piper training checkpoint (Italian, medium)
Finetuning base checkpoint for Piper TTS (VITS), trained **from scratch** on Italian.
- Model: piper (piper_train), quality **medium**, 22.05 kHz
- Base voice: **serena** — female, neutral Standard Italian (no regional accent), synthetic
- Training: **94 epochs** from scratch, batch 10, 29,470 clips (27 h)
- Dataset: [committa/serena-synthetic-it-27h](https://huggingface.co/datasets/committa/serena-synthetic-it-27h)
- File: `epoch=94-step=559930.ckpt` — **keep this filename** (piper_train convention:
`epoch=N-step=M`), which tooling uses to pick the highest checkpoint
## Why this checkpoint
Piper publishes **no official pretrained checkpoint for Italian** (the `rhasspy/piper-checkpoints`
catalog has no `it` entry; the only official Italian voices are `it_IT-paola-medium`, which was
finetuned from a U.S. English base, and `it_IT-riccardo-x_low`, trained from scratch at x_low
quality — neither exposes a trainable checkpoint). This is the only public Italian **medium**
checkpoint that can be used as a finetuning base.
## Sample
Audio of the base voice (it_IT-serena-medium):
<audio controls src="https://huggingface.co/committa/it_IT-serena-medium-checkpoint/resolve/main/samples/speaker_0.mp3"></audio>
## Use as a base for finetuning other Italian voices
Any checkpoint of the same quality config (medium) can be resumed with piper_train:
```sh
python -m piper_train \
--dataset-dir /path/to/your_voice_training_folder \
--resume_from_checkpoint epoch=94-step=559930.ckpt \
--quality medium
```
Because the base already speaks Italian (correct phonemes and prosody), a new Italian voice
converges in **far fewer epochs** than from scratch — 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 — derived from [committa/serena-synthetic-it-27h](https://huggingface.co/datasets/committa/serena-synthetic-it-27h).
If you use this checkpoint to build a voice, credit the dataset and this model.
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