---
language:
  - sa
  - hi
  - kn
  - ta
  - te
  - ml
license: mit
library_name: onnx
pipeline_tag: text-to-speech
tags:
  - text-to-speech
  - sanskrit
  - indic
  - vits
  - hifi-gan
  - onnxruntime-web
  - wasm
---

# `sntts` — Pan-Indic & Classical Sanskrit Neural TTS (VITS + 16 kHz HiFi-GAN)

`sntts` is a compact, single-checkpoint **Pan-Indic & Classical Sanskrit** neural text-to-speech model (`35.4M` parameters; **`54.3 MB` single-file browser ONNX**) supporting **6 Indic languages** (`Sanskrit [sa]`, `Hindi [hi]`, `Kannada [kn]`, `Tamil [ta]`, `Telugu [te]`, `Malayalam [ml]`) across **9 Brahmic scripts + ISO-15919/IAST** in Python, Node.js, and 100% client-side in the browser via `onnxruntime-web` WASM SIMD.

## Training & Evaluation Highlights
- **GCP GPU Training**: Trained on `NVIDIA L4` (`24` epochs, `3024` steps) across `1,008` studio 16 kHz utterances curated from 7 Hugging Face speech datasets (`prathoshap/sushrota-sanskrit-asr-data`, `yashnbx/gita_supersite_sanskrit_tts`, SYSPIN Hindi/Kannada/Tamil/Telugu, and IITM Malayalam).
- **Held-Out Evaluation (`N = 126` utterances, `B = 1,000` bootstrap)**:
  - Mean Mel-Cepstral Distortion (`MCD`): **5.143 dB**
  - Independent `Su-śrotā` Sanskrit ASR Round-Trip `CER`: **15.006%**
- **Files**:
  - `model.safetensors`, `config.json`, `sntts_config.json`, `vocab.json` — PyTorch FP32 checkpoint
  - `onnx/model_quantized.onnx` — Single-file FP16 browser ONNX (`54.3 MB`, zero `.data` sidecars, `r = 0.9998` parity with FP32)
  - `training_history.json`, `gcp_job_manifest.json` — Full training telemetry