Kimi-Audio: open source audio base model unlocks multitasking audio processing

brief

Kimi-Audio is an open source audio base model developed by the MoonshotAI team that has received 3.5k stars and 208 forks on GitHub.The model specializes inAudio comprehension, generation and dialogThree core capabilities with an innovative hybrid architecture designed to handle diverse audio tasks from speech recognition to sentiment analysis.

core functionality

1. All-around audio processing
   • Speech Recognition (ASR): Industry Leading Accuracy Rate
   • Audio Quiz (AQA): Understanding audio content and answering questions
   • Audio Description (AAC): generates a text description for the audio
   • Sentiment Recognition (SER): analyzing emotions in speech
   • Sound Event Classification (SEC): recognizing specific sound events
2. Innovative Architecture Design
   • Adopts the three-stage architecture of "Audio Parser + LLM Core + Audio Descrambler".
   • Supports efficient audio feature extraction at 12.5Hz
   • Low-latency audio generation based on flow-matching
3. multimodal dialog capabilities
   • Supports audio-only, text-only or mixed-mode dialog interactions
   • Generate both voice and text responses
   • Provides voice style control such as emotion and speech rate

Technical Highlights

• Hyperscale pre-training: Based on 13 million hours of diverse audio data (voice, music, ambient sound)
• mixed representation learning (MRL): Simultaneous use of discrete semantic tokens and continuous acoustic features
• Efficient Reasoning: Low-latency response with chunked streaming processing
• full open source (computing): Provides pre-training and instructions for fine-tuning model weights

performance

Kimi-Audio has set new records in several prestigious reviews:

1. speech recognition::
   • LibriSpeech test set: WER (word error rate) only 1.281 TP3T (clean) and 2.421 TP3T (other)
   • Chinese AISHELL-1 test set: WER as low as 0.61 TP3T
2. Audio comprehension::
   • MMAU music comprehension task: accuracy 61.68%
   • Sound Scene Classification (CochlScene): accuracy nearly 80%
3. dialog skills::
   • Multiple firsts in OpenAudioBench reviews
   • Voice Style Control Score 4.3 out of 5

Fits the crowd

1. developers::
   • App developers who need to integrate advanced audio features
   • Voice Interaction System Builder
   • Developer of multimedia content analysis tools
2. research worker::
   • Academic researcher in the field of audio AI
   • Multimodal Learning Explorer
   • Low Resource Language Processing (LRLP) researcher
3. business user::
   • Intelligent customer service system builder
   • Content Audit Platform
   • Accessible Service Providers

Experience

Experience the power of Kimi-Audio through a simple Python API:

from kimia_infer.api.kimia import KimiAudio# Initialization Model model = KimiAudio(model_path="moonshotai/Kimi-Audio-7B-Instruct") # Speech Recognition Example messages = [ {"role": "user", "message_type": "text", "content": "Please transcribe the following audio:"}, {"role": "user", "message_type": "audio", " content": "test.wav"}]_, text_output = model.generate(messages, output_type="text")print(text_output)

Strengths and limitations

✅ dominance::

• One-stop solution for multiple audio processing needs
• Chinese scenes are particularly well represented
• Open Source Community Support for Continuous Updates
• Reasoning efficiency optimization in place

⚠️ limitations::

• Currently mainly for Chinese and English
• Requires some GPU computing resources
• Real-time still has room for improvement

Acquisition method

1. Model Download::
   • Basic Edition:Kimi-Audio-7B
   • Instruction Edition:Kimi-Audio-7B-Instructions
2. code repository::git clone https://github.com/MoonshotAI/Kimi-Audio.git
3. Assessment Toolkit:: Kimi Audio Evaluation Kit

summarize

Kimi-Audio represents the top level of current open source audio macromodeling and is especially suitable for developers who need to deal with Chinese audio scenarios. Its innovative architectural design and comprehensive capability coverage make it ideal for building smart audio applications. With continued contributions from the open source community, the potential of this model will be further unleashed.

byword: Kimi-Audio, open source audio modeling, speech recognition, audio understanding, speech generation, multimodal dialog, Chinese speech processing, MoonshotAI