Kimi-Audio - Moonshot AI's open-source audio foundation model

What is Kimi-Audio?

Kimi-Audio is an open-source audio foundation model from Moonshot AI, focusing on audio understanding, generation, and dialogue tasks. Pre-trained on over 13 million hours of diverse audio data, it possesses powerful audio inference and language understanding capabilities. Its core architecture employs hybrid audio input (continuous acoustics + discrete semantic tags), combined with an LLM-based design, supporting parallel generation of text and audio tags, while achieving low-latency audio generation through a block-based streaming decoder.

Kimi-Audio's main functions

• Automatic Speech Recognition (ASR)It can convert speech signals into text content and supports multiple languages and dialects.
• Speech Emotion Recognition (SER)It analyzes emotional information in speech to determine the speaker's emotional state (such as happiness, sadness, anger, etc.), and can be used in customer service systems, sentiment analysis, etc.
• Sound event/scene classification (SEC/ASC)It can identify and classify environmental sounds (such as car horns, dog barks, rain sounds, etc.) or scenes (such as offices, streets, forests, etc.).
• Audio subtitle generation (AAC)It automatically generates subtitles based on audio content, helping people with hearing impairments to better understand audio information.
• Audio Q&A (AQA)Generate corresponding audio answers based on the user's questions.
• End-to-end voice dialogueSupports the generation of natural and fluent voice dialogue content.
• Multi-turn dialogue managementIt can handle complex multi-turn dialogue tasks, understand contextual information, and generate coherent speech responses.
• Text-to-speech (TTS)It converts text content into natural and fluent speech, supporting a variety of timbres and intonations.
• Audio content analysisIt involves a comprehensive analysis of the semantics, emotions, and events in audio to extract key information.
• Audio quality assessmentAnalyze audio clarity, noise levels, etc., to provide a reference for audio processing.

Kimi-Audio's technical principles

• Mixed audio inputKimi-Audio uses a mixed audio input method, dividing the input audio into two parts:
  • Discrete semantic taggingThe audio is converted into discrete semantic tags at a frequency of 12.5 Hz using vector quantization technology.
  • Continuous acoustic characteristicsThe Whisper encoder is used to extract continuous acoustic features, which are then downsampled to 12.5Hz. This hybrid input approach combines discrete semantics and continuous acoustic information, enabling the model to more comprehensively understand and process audio content.
• LLM-based core architectureKimi-Audio's core is a Transformer-based Language Model (LLM), initialized from pre-trained text LLMs (such as Qwen 2.5 7B).
• Block-based streaming decodingKimi-Audio employs a block-based streaming decoder based on stream matching, supporting low-latency audio generation. By processing audio data in blocks, the model can output audio in real time during the generation process, significantly reducing latency. It also supports a look-ahead mechanism, further optimizing the smoothness and coherence of audio generation.
• Large-scale pre-trainingKimi-Audio was pre-trained on over 13 million hours of diverse audio data, including speech, music, and various sounds. This gives the model powerful audio inference and language understanding capabilities, enabling it to handle a variety of complex audio tasks, such as speech recognition, audio question answering, and emotion recognition.
• Flow matching modelUsed to convert discrete markers into continuous audio signals.
• Vocoder (BigVGAN)Used to generate high-quality audio waveforms. Ensures the naturalness and smoothness of the generated audio.

Kimi-Audio's project address

• Github repository:https://github.com/MoonshotAI/Kimi-Audio

Kimi-Audio's performance

• Automatic Speech Recognition (ASR)On the LibriSpeech test set, Kimi-Audio achieved a word error rate (WER) of [missing value]. 1.28%(test-clean) and 2.42%(test-other), significantly lower than other models. On the AISHELL-1 dataset, its WER is only 0.60%They performed exceptionally well.
• Audio understandingIn audio understanding tasks, Kimi-Audio has achieved near- or state-of-the-art results on multiple datasets. For example, on the ClothoAQA dataset, its test set performance reached [performance level missing]. 73.18%On the VocalSound dataset, the accuracy reached [percentage missing]. 94.85%.
• Audio Q&A (AQA)In the audio question answering task, Kimi-Audio achieved [achievements] on the development set of the ClothoAQA dataset. 73.18% The accuracy rate demonstrates its powerful ability to understand and generate audio question-and-answer content.
• audio dialogueIn voice dialogue tasks, Kimi-Audio also performs exceptionally well across multiple benchmarks. For example, on the VoiceBench AlpacaEval dataset, its performance reaches [performance target value missing]. 75.73%It performs exceptionally well in terms of the fluency and coherence of voice conversations.
• Audio generationKimi-Audio performs exceptionally well in nonspeech audio generation, achieving an accuracy of [percentage missing] on the Nonspeech7k dataset. 93.93%This demonstrates its ability to generate high-quality audio content.

Application scenarios of Kimi-Audio

• Intelligent voice assistantKimi-Audio can be used to develop intelligent voice assistants, supporting speech recognition, speech synthesis, and multi-turn dialogue. It can understand users' voice commands and generate natural and fluent voice responses.
• Speech recognition and transcriptionKimi-Audio efficiently converts speech signals into text. It supports multiple languages and dialects, making it suitable for scenarios such as meeting minutes, voice notes, and real-time translation.
• Audio content generationKimi-Audio can generate high-quality audio content, including text-to-speech (TTS), audio captioning (AAC), and audio question-and-answer (AQA). It can generate natural and fluent speech based on text content, and can also generate audio answers to questions, making it suitable for audiobooks, video captioning, and intelligent customer service.
• Sentiment Analysis and Speech Emotion RecognitionKimi-Audio can analyze emotional information in speech and determine the speaker's emotional state (such as happiness, sadness, anger, etc.).
• Education and LearningKimi-Audio has various applications in education, such as English speaking practice and language learning assistance. It can help users practice pronunciation, correct grammatical errors, and provide real-time feedback through voice interaction.