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TTSLab – A voice AI agent and TTS lab running in the browser via WebGPU

TTSLab – A voice AI agent and TTS lab running in the browser via WebGPU

by MbBrainz·Feb 23, 2026·5 points·3 comments

AI Analysis

●●●BangerWizardryZero to OneShip It

Full voice agent (STT→LLM→TTS) runs locally on GPU, no backend needed.

Strengths
  • WebGPU acceleration eliminates network latency for real-time voice interactions.
  • Local inference means text and audio never leave your device — genuine privacy win over cloud APIs.
  • Instant model comparison and hardware benchmarking built in, useful for dev/research workflows.
Weaknesses
  • WebGPU browser support is still fragmented; WASM fallback may be slow on older hardware.
  • Voice agent capability is early-stage and may struggle with complex multi-turn conversations.
Category
Target Audience

Developers evaluating TTS/STT, researchers benchmarking speech models, product teams needing privacy-first voice features.

Similar To

Transformers.js (ONNX in browser) · Ollama (local model runner, but desktop-focused)

Post Description

I built TTSLab — a free, open-source tool for running text-to-speech and speech-to-text models directly in the browser using WebGPU and WASM.

No API keys, no backend, no data leaves your machine.

When you open the site, you'll hear it immediately — the landing page auto-generates speech from three different sentences right in your browser, no setup required.

You can then try any model yourself: type text, hit generate, hear it instantly. Models download once and get cached locally.

The most experimental feature: a fully in-browser Voice Agent. It chains speech-to-text → LLM → text-to-speech, all running locally on your GPU via WebGPU. You can have a spoken conversation with an AI without a single network request.

Currently supported models: - TTS: Kokoro 82M, SpeechT5, Piper (VITS) - STT: Whisper Tiny, Whisper Base

Other features: - Side-by-side model comparison - Speed benchmarking on your hardware - Streaming generation for supported models

Source: https://github.com/MbBrainz/ttslab (MIT)

Feedback I'd especially like: 1. How does performance feel on your hardware? 2. What models should I add next? 3. Did the Voice Agent work for you? That's the most experimental part.

Built on top of ONNX Runtime Web (https://onnxruntime.ai) and Transformers.js — huge thanks to those communities for making in-browser ML inference possible.

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