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language:
- fa
license: mit
pipeline_tag: text-to-speech
base_model: rhasspy/piper-voices
library_name: piper
model_type: text-to-speech
tags:
- audio
- text-to-speech
- piper
- vits
- persian
- farsi
- fonnely
pretty_name: Gooya RizehPizeh v1.5
---
# Gooya RizehPizeh v1.5
Persian (Farsi) text-to-speech voice **"gooya-fa"** for [Piper](https://github.com/rhasspy/piper),
trained with the AvaSanj/Negara improved G2P front end. Single-speaker, 22050 Hz, `phoneme_type: text`
(no espeak-ng required at inference time).
## Provenance
- **Original model:** [Piper](https://github.com/rhasspy/piper) VITS, warm-started from the **Mana Persian Piper**
checkpoint (`epoch=6012-step=4203520.ckpt`, sdp enabled). This model is therefore a **fine-tune of Piper**,
continuing from a mature Persian training run rather than training from scratch.
- **Front end:** phonemic input produced by **Negara v7.1** G2P (grapheme-to-phoneme), with phoneme ids
mapped through the Mana 256-symbol inventory (157 real phonemes).
- **Training data:** AvaSanj **clean-core v2** — 102,584 utterances whose phoneme labels were rebuilt by the
OOF (out-of-fold) listener policy:
- `oof_listener_winner` … 48,656 (OOF AvaSanj ASR margin ≥ 0.1)
- `stored_audio_prompt` … 42,244 (unchanged approved prompts)
- `three_listener_consensus` … 11,342 (unanimous multi-listener rows)
- `human_override` … 41
- `human_reviewed_v71_overlay` … 301
- 28,253 rows changed vs. the stored prompt (the G2P improvement delivered by this project).
- **Split:** 5% validation, `num_test_examples: 0`.
## Model
- Generator parameters: **23,663,792** (~23.7 M)
- Architecture (Piper/VITS): `hidden_channels 192`, `filter_channels 768`, `inter_channels 192`,
6 flow layers, 2 attention heads, `resblock 2`, upsampling rates `[8, 8, 4]` (upsample initial channel 256),
`mel_channels 80`, `use_sdp true`, `num_symbols 256`, `num_speakers 1`.
- Vocab: 157 phoneme tokens (`text` phoneme type, Mana id map with `^`/`_`/`$` control tokens).
## Inference
```bash
echo "salAm olAqe aziz hAlet Cetore" | \
piper -m gooya-fa.onnx -c gooya-fa.onnx.json -f output.wav
```
Inference-scales baked into `gooya-fa.onnx.json`: `noise_scale 0.667`, `length_scale 1.0`, `noise_w 0.8`;
sample rate `22050` Hz; `espeak.voice: fa`; `phoneme_type: text`.
## Files
- `gooya-fa.onnx` — ONNX model (inference runtime)
- `gooya-fa.onnx.json` — Piper voice/config metadata
- `checkpoint/epoch=*-val_mel=*.ckpt` — PyTorch training checkpoint (resumable) |