Kimi-Audio : Best Audio LLM
How to use the Kimi-Audio AI model for free?
It’s China’s world, and we are living in it. After DeepSeek, ByteDance, now MoonShotAI aka Kimi, is also picking up pace in Generative AI, and they have released their audio model, Kimi-Audio, which is said to be the best audio model released so far.
Kimi-Audio is the Best Audio AI model now !!
Data Science in Your Pocket – No Rocket Science
Key Features Kimi-Audio
Unified Architecture for Audio Tasks
- Combines audio understanding, generation, and conversation into a single model.
- Uses a hybrid input representation: discrete semantic tokens (12.5Hz) for efficiency and continuous acoustic vectors (from Whisper) for enhanced perception.
- Features a dual-generation mechanism with separate heads for text and audio outputs.
A single LLM for any Audio task
Advanced Tokenisation and Detokenisation:
- Tokeniser: Extracts discrete semantic tokens and continuous acoustic features, bridging the gap between text and audio sequences.
- Detokenizer: Employs a chunk-wise streaming framework with a look-ahead mechanism to ensure smooth, high-quality audio generation.
Massive and Diverse Training Data:
- Pre-trained on over 13 million hours of audio data, covering speech, music, and environmental sounds.
- Curated high-quality supervised fine-tuning (SFT) data for tasks like speech recognition, audio understanding, and conversation.
Innovative Training Strategies:
- Initialised from Qwen2.5 7B LLM, enabling strong language capabilities.
- Pre-training tasks include unimodal (audio/text), audio-text mapping (ASR/TTS), and interleaved tasks to align modalities.
- Fine-tuned with diverse instructions to enhance robustness and task generalisation.
Efficient Deployment:
- Supports real-time speech-to-speech conversation with low latency.
- Modular architecture (Tokenizer, LLM, Detokenizer services) ensures scalability in production.
Open-Source Ecosystem:
- Releases code, model checkpoints, and evaluation toolkits to foster community development.
How does Kimi-Audio work?
Audio comes in and is processed two ways: one path uses an Audio Tokeniser to chop it into manageable tokens, and the other uses Whisper + Adaptor to grab the meaning behind the sounds.
These audio tokens and embeddings are mixed together and passed into a Shared LLM Layer, which is like the model’s main thinking engine — it processes both text and audio the same way.
After processing, the model decides whether the output should be text (like subtitles) or audio (like spoken words), sending it to the appropriate head.
If it’s audio output, a little Audio Delay is added to help predict smoother, more natural sounds.
Finally, the model’s Audio Detokenizer stitches the audio tokens back into real sound waves you can hear.
Benchmarks and State-of-the-Art Performance
Kimi-Audio outperforms previous models across multiple audio tasks
Automatic Speech Recognition (ASR): Achieves the lowest Word Error Rate (WER) on datasets like LibriSpeech (1.28 on test-clean) and AISHELL-1 (0.60), surpassing Qwen2-Audio and Qwen2.5-Omni.
Audio Understanding: Excels in tasks like sound event classification (94.85 on VocalSound), emotion recognition (59.13 on MELD), and acoustic scene classification (80.99 on CochlScene).
Audio-to-Text Chat: Leads in conversational benchmarks (OpenAudioBench, VoiceBench) with superior scores in instruction following, reasoning, and question answering (e.g., 75.73 on AlpacaEval).
Speech Conversation: Rated highly for emotion control (4.27/5), speed control (4.30/5), and overall quality (3.90/5), outperforming GLM-4-Voice and GPT-4o-mini.
How to use Kimi-Audio?
The weights are open-sourced and are available at HuggingFace
moonshotai/Kimi-Audio-7B-Instruct · Hugging Face
I hope you try out the new Audio LLM
Kimi-Audio : Best Audio LLM was originally published in Data Science in Your Pocket on Medium, where people are continuing the conversation by highlighting and responding to this story.
