modal-labs/quillman
A voice chat app
What it solves
QuiLLMan is a voice chat application that enables near-instantaneous, human-like speech interaction. It solves the latency issues typically associated with voice AI by using bidirectional streaming and efficient audio compression.
How it works
The app uses a speech-to-speech language model (Moshi) and a streaming encoder/decoder (Mimi) to continuously listen, plan, and respond. It employs a bidirectional websocket streaming architecture and the Opus audio codec to compress audio data across the network, ensuring low-latency responses.
Who it’s for
Developers looking for a starting point or playground for building language model-based voice applications.
Highlights
- Speech-to-Speech: Uses the Moshi model for continuous listening and responding.
- Low Latency: Achieves near-instantaneous response times via bidirectional websockets and Opus compression.
- Full Stack: Includes a React frontend and a FastAPI backend hosted on Modal.
- Serverless Deployment: Designed for easy deployment to Modal, allowing the application to scale to zero when not in use.
Related
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dimastatz/whisper-flowWhisper Flow is a FastAPI‑based service that turns OpenAI’s Whisper model into a real‑time streaming speech‑to‑text engine. Clients stream 16 kHz mono PCM audio over a WebSocket; the server buffers audio into tumbling windows, runs Whisper, and pushes back partial and final JSON transcripts with sub‑second latency (≈275 ms) and ~7 % WER on an M1 Mac. It can be run locally, via Docker, or installed as a Python package for integration.
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datascale-ai/opentalkingAn open-source orchestration framework for real-time digital-human conversations, integrating LLMs, TTS, STT, and video rendering via WebRTC.
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KoljaB/RealtimeTTSA Python library that provides a unified, low-latency interface for streaming text-to-speech, designed to integrate seamlessly with LLM token streams and various local or cloud-based audio engines.