Instructions to use Reza2kn/Gooya-RizehPizeh-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Piper
How to use Reza2kn/Gooya-RizehPizeh-v2 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
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.
- 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: 38.68โ95.96 seconds; mean 58.94 seconds
- Phoneme IDs per recording: 1,711โ3,423; 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:
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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# 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