https://github.com/shreyaskarnik/svara-tts-webgpu

Browser-native multilingual Indic TTS via WebGPU + transformers.js v4 (Svara + SNAC)
https://github.com/shreyaskarnik/svara-tts-webgpu

Last synced: 10 days ago
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Browser-native multilingual Indic TTS via WebGPU + transformers.js v4 (Svara + SNAC)

• Host: GitHub
• URL: https://github.com/shreyaskarnik/svara-tts-webgpu
• Owner: shreyaskarnik
• Created: 2026-04-26T23:02:32.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2026-05-06T05:51:57.000Z (about 1 month ago)
• Last Synced: 2026-05-06T07:30:45.816Z (about 1 month ago)
• Language: JavaScript
• Size: 685 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
---

title: Svāra TTS WebGPU

emoji: 🗣️

colorFrom: yellow

colorTo: red

sdk: static

pinned: false

license: apache-2.0

short_description: Multilingual Indic TTS in your browser, via WebGPU

models:

  - shreyask/svara-tts-v1-ONNX

  - onnx-community/snac_24khz-ONNX

tags:

  - text-to-speech

  - indic

  - webgpu

  - on-device

---

# Svāra TTS · WebGPU

Browser-native multilingual TTS for **19 Indian languages** powered by [Svara](https://huggingface.co/kenpath/svara-tts-v1), [SNAC](https://huggingface.co/hubertsiuzdak/snac_24khz), and [Transformers.js v4](https://huggingface.co/docs/transformers.js). Runs 100% locally in the browser after the one-time model download.

This build adds an explicit model load step, browser-side caching, multilingual voice switching, prompt presets, and a WebGPU worker tuned around the ONNX-exported Svāra model.

## Architecture

```

text → tokenizer → Llama-3.2-3B (q4f16, transformers.js v4 + WebGPU) →

  audio token IDs in [128266, 156938) →

  group every 7 → SNAC frame (3 hierarchical levels) →

  SNAC decoder ONNX (q4f16/fp16 from onnx-community/snac_24khz-ONNX) →

  24 kHz mono PCM → WAV blob → 

```

## Models

| Repo | Size | Notes |

|------|------|-------|

| [`shreyask/svara-tts-v1-ONNX`](https://huggingface.co/shreyask/svara-tts-v1-ONNX) | ~1.95 GB | Llama-3.2-3B q4f16, GQA, KV-cache |

| [`onnx-community/snac_24khz-ONNX`](https://huggingface.co/onnx-community/snac_24khz-ONNX) | ~26 MB (fp16) | SNAC decoder |

## Run locally

```sh

npm install

npm run dev   # http://localhost:5173

```

First run downloads the selected model into the browser cache (LM + codec + tokenizer). Subsequent runs reuse the cached weights.

## Voices

Use a string of the form `" ()"`. **38 voices across 19 languages**: Hindi, Bengali, Marathi, Telugu, Kannada, Tamil, Malayalam, Gujarati, Punjabi, Assamese, Bhojpuri, Magahi, Maithili, Chhattisgarhi, Bodo, Dogri, Nepali, Sanskrit, English (Indian) — male + female each.

## Notes

- `q4f16` is the fastest cold-start option and works well for short prompts.

- `q8` is heavier but can sound cleaner on more difficult prompts.

- Emotion tags such as `` and `` can be appended at the end of a line.

- Everything stays local to the browser after the model has loaded.

## Credits

- [Kenpath](https://huggingface.co/kenpath) — Svara TTS v1 base model.

- [Canopy Labs](https://huggingface.co/canopylabs) — Orpheus 3B Hindi base.

- [Hugging Face](https://github.com/huggingface/transformers.js-examples/tree/main/text-to-speech-webgpu) — original `text-to-speech-webgpu` scaffold this project forked from.

- License: Apache 2.0.