BayLing-Models/BayLing-Speech

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3146 stars · 225 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

LLaMA-Omni is an end-to-end speech interaction model built on Llama-3.1-8B-Instruct that generates simultaneous text and speech responses from speech instructions with latency as low as 226ms. It combines a pretrained speech encoder, speech adaptor, LLM, and streaming speech decoder, trained on the InstructS2S-200K dataset.

Use cases

• build a voice assistant that talks to an LLM in real time
• generate speech and text responses from spoken instructions
• run a low-latency speech-to-speech conversation model locally
• fine-tune an open-source LLM with speech capabilities
• research speech-language model architectures
• create a hands-free voice chatbot without transcription

When to choose

• you need open-source real-time voice interaction with an LLM
• you want simultaneous text and speech output from speech input
• you need low-latency speech responses without ASR transcription
• you're researching speech-language models on Llama backbones

When to avoid

• you only need text-based chat without audio
• you need a production-ready hosted voice API rather than a research model
• you lack GPU resources for 8B-parameter inference
• you need non-English speech interaction

Facets

library · maturity active

speech-recognition tts llm-inference machine-learning speech-processing large-language-models artificial-intelligence python cli speech-language-model speech-to-speech multimodal llama voice-assistant streaming-speech-decoder low-latency natural-language-processing gpu linux

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