README.md

metadata

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