---
language:
- fa
license: mit
pipeline_tag: text-to-speech
base_model: Reza2kn/Gooya-RizehPizeh-v1.5
library_name: piper
model_type: text-to-speech
tags:
- audio
- text-to-speech
- piper
- vits
- persian
- farsi
- negara
pretty_name: Gooya RizehPizeh v2
---

# Gooya RizehPizeh v2

Persian (Farsi) single-speaker Piper/VITS2 voice fine-tuned on approved Gemini TTS Wikipedia audio. The model uses a case-sensitive Negara-style phonetic/Finglish frontend and produces 22,050 Hz mono audio.

## Selected checkpoint

This release selects the checkpoint at **global step 50,000** (`val_mel = 0.3951`). Training continued to 100,000 global steps, but later validation was worse, so the terminal state is intentionally not promoted.

- Generator parameters: approximately 23.7M
- Full trainable checkpoint: `checkpoint/gooya-rizehpizeh-v2-step50000-val_mel0.3951.ckpt`
- Checkpoint SHA-256: `0c2d22718894e20fb6361c7bf2d75ee54d61db3a167983ce5d843ff6ed438938`
- Sample rate: 22,050 Hz
- Speakers: 1
- Phoneme type: `text`
- Symbol inventory: 256 IDs

## Model lineage

The original v1.5 production ONNX did not have a matching resumable checkpoint. A trainable hybrid checkpoint was reconstructed by copying all 350 inference-time generator tensors from the production ONNX into a compatible Mana Piper training checkpoint. Training-only branches absent from ONNX came from that Mana template.

Before fine-tuning, a fresh ONNX round-trip matched the production model on 19/19 deterministic test rows:

- Worst maximum absolute error: `1.0348856449127197e-05`
- Minimum SNR: `110.0768 dB`
- Minimum correlation: `0.999999999995`

The complete recovery and parity receipts are under `training/`.

## Fine-tuning data

The parent corpus came from approved rows in [`Reza2kn/Wikipedia-FA-EN-DeepSeek-V4-Flash-0731`](https://huggingface.co/datasets/Reza2kn/Wikipedia-FA-EN-DeepSeek-V4-Flash-0731).

- Requested parent clips: 4,186 (`3,517` train, `669` sealed eval)
- Successfully aligned parents: 4,116
- Rejected during alignment: 70
- Materialized segments: 30,965
- Training segments consumed: 26,053
- Sealed eval segments, not consumed by training: 4,912
- Total materialized duration: 68.9853 hours
- Segment duration: mean `8.0203s`, min `0.6307s`, max `21.6656s`

Audio timing came from AvaSanj v1 CTC forced alignment. Training phoneme labels came from Negara v7.1. Parent-level train/eval boundaries were preserved.

## Training

- Precision: BF16 mixed precision
- Generator learning rate: `1e-5`
- Discriminator learning rate: `5e-6`
- Batch size: 1
- Selected validation metric: `val_mel`
- Selected checkpoint: global step 50,000, `val_mel = 0.3951`

The 100K run was resumed from a successful 2K signal checkpoint. The sealed 669-parent eval split was not used for checkpoint selection.

## Inference and long-form use

Input must already be case-sensitive Negara-compatible phonetic text. Example:

```bash
echo "zurxAneye vahdat yek makAne varzeSi dar Sahre borujerd ast." | \
  piper --model gooya-rizehpizeh-v2.onnx \
        --config gooya-rizehpizeh-v2.onnx.json \
        --output_file output.wav
```

For long-form narration, do not send an article as one model input. Split at semantic sentence/clause boundaries into substantial spans, synthesize each span independently, trim quiet waveform edges, and join with boundary-aware pauses. The final output duration can be arbitrarily long even though each model invocation remains bounded.

## Limitations

- Lower validation mel loss did not guarantee universally better pronunciation. Human listening found some words, including proper nouns, better at the earlier 2K checkpoint or the original v1.5 model.
- G2P/Finglish quality remains the dominant source of pronunciation errors.
- Very long single-pass phoneme sequences can collapse or omit content; use chunked synthesis.
- Overly small chunks sound breathy and robotic. Prefer substantial semantic spans instead of fixed tiny fragments.
- This release has not yet been promoted over v1.5 on Gouya.app.

## License

MIT. See `LICENSE`.