The shift in audio AI is underway — from narrow, task-specific systems to unified, foundation-level architectures. Just like NLP had its LLM moment, audio is entering its own revolution.
Kimi-Audio is a major leap: an open-source, real-time, instruction-following foundation model for audio understanding, generation, and conversation — all within one architecture.
🧠 What makes it different?
Kimi-Audio uses a hybrid input representation to bridge text and audio:
• Discrete semantic tokens (12.5Hz) from an ASR-based tokenizer
• Continuous acoustic vectors from Whisper, also downsampled to 12.5Hz
These are fed into a shared transformer initialized from a large language model, with:
• Dual heads for generating both text and discrete audio tokens
• A streaming detokenizer for real-time synthesis using flow matching + BigVGAN
• A novel look-ahead mechanism that smooths audio at chunk boundaries
The result: low-latency, high-quality, multimodal generation from a single, unified system.
🚀 Why it matters
Kimi-Audio changes how we build audio systems:
• Unified architecture replaces separate ASR, TTS, and sound models
• Real-time, instruction-following capabilities open new application domains
• Designed to support diverse data: speech, sound, music, and text
• Open-source release promotes transparency, reproducibility, and extensibility
This model is part of a broader vision for general-purpose audio intelligence — where models can hear, understand, and speak in natural, context-aware ways.
🛠️ Built for the community
Kimi-Audio was pre-trained on 13M+ hours of audio and fine-tuned on 300K hours of labeled tasks. It comes with:
• Full codebase
• Released model checkpoints
• Evaluation toolkit
🔗 Explore: https://github.com/MoonshotAI/Kimi-Audio