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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 single-speaker Piper/VITS2 voice trained on full, unchunked Wikipedia recordings whose audio/transcript agreement passed AvaSanj. It uses a case-sensitive Negara-style phonetic/Finglish frontend and produces 22,050 Hz mono audio.
## Current release
This revision replaces the earlier segmented-data v2 model with the user-selected unchunked/AvaSanj-approved 50K run.
- Full checkpoint: `checkpoint/gooya-rizehpizeh-v2-unchunked-avasanj-step50000-val_mel0.4138.ckpt`
- Checkpoint SHA-256: `8b52b3b6060b4698aba0c8bbd2f9b338aa6ba811d28a620e5f492c0fa1ae4d2b`
- Piper/browser model: `gooya-rizehpizeh-v2.onnx`
- ONNX SHA-256: `3f85f450fc21415e1eddaa0aa894c667e76d14aa9a342c4e77f1f9009b96f170`
- ONNX config SHA-256: `981770210ac1aea618cb8134597127911051deabf9900824df3388f209a884fa`
- Generator parameters: approximately 23.7M
- Sample rate: 22,050 Hz
- Speakers: 1
- Phoneme type: `text`
- Symbol inventory: 256 IDs
## Training data
The parent corpus comes from the approved Persian rows in [`Reza2kn/Wikipedia-FA-EN-DeepSeek-V4-Flash-0731`](https://huggingface.co/datasets/Reza2kn/Wikipedia-FA-EN-DeepSeek-V4-Flash-0731).
- Train: 1,290 full recordings, 21.1455 hours
- Parent-disjoint validation: 100 full recordings, 1.6445 hours
- Untouched sealed-eval agreement slice: 253 recordings, 4.1080 hours
- Recording duration: 38.68–95.96 seconds; mean 58.94 seconds
- Phoneme IDs per recording: 1,711–3,423; mean 2,174.65
- AvaSanj compact CER threshold: `<= 0.12`
- Train/validation/sealed-eval parent overlap: zero
- Audio transform: none
- Silence trimming: disabled
- Segmentation/chunking: none
The 253-row sealed-eval agreement slice was not consumed by training or checkpoint selection. The complete selection and hash receipt is in `training/unchunked-avasanj-agree-report.json`.
## Lineage and training
The original v1.5 production ONNX had no matching resumable checkpoint. Its 350 inference-time generator tensors were reconstructed into a compatible trainable Piper checkpoint; training-only branches absent from the ONNX came from the compatible Mana template. Recovery receipts remain under `training/`.
The promoted run resumed from that recovered v1.5 state and used:
- BF16 mixed precision
- Batch size 1
- Generator learning rate `5e-6`
- Discriminator learning rate `2.5e-6`
- Configured limit: 50,000 optimizer steps
- Final/best validation mel: `0.4138`
The exact continuation script is `training/train_unchunked_avasanj_agree_full50k.sh`.
## Validation
The published ONNX is self-contained and has no external tensor file. Direct ONNX Runtime CPU inference passed on both the canonical and long colloquial probes with finite, positive-length output. The same WAVs used for human listening are included under `samples/`.
- Inputs: `input`, `input_lengths`, `scales`
- Output: `output`
- File size: 63,516,051 bytes
- Canonical sample: 4.7137 seconds
- Long colloquial sample: 12.6084 seconds
Machine-readable receipts are under `browser/` and `training/`.
## Inference
Input must already be case-sensitive Negara-compatible phonetic text:
```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, split at semantic sentence or clause boundaries into substantial spans and join them with boundary-aware pauses. Tiny fixed chunks tend to sound breathy and robotic.
## Limitations
- G2P/Finglish quality remains the dominant source of pronunciation errors.
- The approved training labels are flat Negara phoneme sequences; existing internal punctuation was not available in those labels.
- Very long single-pass phoneme sequences can still collapse or omit content; use substantial semantic spans.
- Proper nouns may require corrected phonetic input.
## License
MIT. See `LICENSE`.
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