library_name: piper
license: cc-by-4.0
language:
- it
tags:
- text-to-speech
- tts
- piper
- checkpoint
- finetuning
datasets:
- committa/serena-synthetic-it-28h
it_IT-serena — Piper training checkpoint (Italian, medium)
Finetuning base checkpoint for Piper TTS (VITS), fine-tuned on Italian.
- Model: piper (piper_train), quality medium, 22.05 kHz
- Base voice: serena — female, neutral Standard Italian (no regional accent), synthetic
- Training: 14 epochs of fine-tuning (resumed from the v1 checkpoint, ~94 epochs
from scratch) on
committa/serena-synthetic-it-28h(30,842 clips, 28 h) - Dataset: committa/serena-synthetic-it-28h
- File:
epoch=14-step=83250.ckpt— piper_train convention:epoch=N-step=M, which tooling uses to pick the highest checkpoint - Best validation: val_mel 0.3763 (epoch 14)
Why this checkpoint
This is the fixed version of the published Italian medium checkpoint: the
previous release had a defect (sentence-initial words after .?! spoken with
collapsed duration). This checkpoint is the fine-tuned base used to build the
fixed it_IT-serena-medium voice, and can be used as a base for further
finetuning of other Italian voices.
Sample
Audio of the base voice (it_IT-serena-medium):
Use as a base for finetuning other Italian voices
Any checkpoint of the same quality config (medium) can be resumed with piper_train:
python -m piper_train \
--dataset-dir /path/to/your_voice_training_folder \
--resume_from_checkpoint epoch=14-step=83250.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-28h. If you use this checkpoint to build a voice, credit the dataset and this model.