MoonshotAI/Kimi-Audio
Kimi Audio
Kimi-Audio is a large language model audio foundation model designed to understand audio input and generate high-fidelity speech responses in real time. It functions as a unified system encompassing a text-to-speech synthesis engine and a speech-to-text transcription tool.
The project enables real-time audio conversations through a multi-modal conversation loop and chunk-wise streaming detokenization to reduce playback latency. It provides controls over speech speed, accent, and emotional tone during conversational audio generation.
The system covers audio intelligence capabilities, including audio content analysis, emotion recognition, scene classification, and captioning. It also includes an audio model fine-tuning toolkit for instruction-based adaptation and a benchmarking suite for evaluating performance via standardized metrics and side-by-side comparisons.
Features
- Unified Audio-Text Transformers - Processes speech and text tokens in a shared embedding space using a single transformer for seamless modality switching.
- Multi-Turn Speech Conversations - Maintains context across multiple spoken exchanges, generating both text and audio replies.
- Rolling Context Windows - Maintains a rolling context window of audio and text exchanges to support coherent multi-turn spoken dialogue.
- End-to-End Speech Conversations - Engages in a spoken dialogue that understands audio input and responds with both text and synthesized speech.
- Spoken Dialogue Systems - Ships a real-time spoken dialogue system that both understands audio input and generates speech responses.
- Text-to-Speech Response Generators - Produces high-fidelity spoken audio replies from text or conversational context in real time.
- LoRA Fine-Tuning Pipelines - Ships a LoRA-style fine-tuning pipeline for adapting pre-trained audio models to custom tasks on user-provided audio-text pairs.
- Audio - Loads a pretrained audio foundation model and runs inference on audio inputs to produce speech responses.
- Real-Time Speech Synthesis - Processes an audio stream and produces natural-sounding speech output in real time for conversational use.
- Speech to Text Transcription - Converts spoken audio into written text with high accuracy across multiple languages and acoustic conditions.
- Audio Chat Interfaces - Accepts spoken questions or commands and returns relevant text-based responses in a conversational format.
- Text-to-Speech - Ships a flow-matching detokenizer and vocoder for high-fidelity text-to-speech synthesis.
- Audio Semantic Token Extractors - Encodes raw audio into discrete semantic tokens via a pretrained encoder for efficient downstream processing.
- High-Fidelity Speech Synthesis - Produces natural-sounding spoken audio from text or semantic tokens using a flow-matching detokenizer.
- Generative Audio Chunking - Generates audio in small overlapping chunks and plays incrementally to minimize end-to-end latency during conversation.
- Speech Transcription Engines - Converts spoken audio input into accurate text output using a state-of-the-art speech recognition model.
- Neural Vocoders - Implements a flow-matching neural vocoder that converts semantic token sequences into high-fidelity raw audio waveforms.
- Audio Semantic Understanding - Analyzes audio clips to identify sounds, music, speech, and environmental scenes for classification or question answering.
- Audio Understanding Fine-Tuning - Trains a pretrained model on custom audio understanding data, such as automatic speech recognition, to adapt it to specific domains.
- Controllable Speech Generation - Adjusts the speed, accent, emotion, and style of generated speech to match desired expressive qualities.
- Custom Data Fine-Tunings - Adapts the pre-trained model to new tasks or domains by training on user-provided audio and text pairs.
- Audio Performance Benchmarks - Ships a benchmarking harness with standardized metrics and side-by-side inference recipes for audio models.
- Model Fine-Tuning - Adapts the pre-trained audio foundation model to custom domains or tasks using provided lightweight training code.
- Audio - Adapts the pre-trained audio foundation model to custom tasks or domains using lightweight fine-tuning scripts.
- Controllable Speech Conversations - Provides controls over speaking speed, accent, emotion, and style during conversational audio generation.
- Audio Question Answering - Responds to natural-language queries about the content of an audio clip, such as identifying sounds or answering factual questions.
- Audio Event Classification - Identifies audio categories such as speech, music, or environmental sounds from a clip.
- Audio Emotion Classifiers - Detects the emotional tone of a speaker's voice from an audio recording.
- Audio Content Analysis - Generates natural-language descriptions of audio clip content automatically.
- Side-by-Side Inference Recipes - Provides standardized inference recipes and metric calculators for reproducible side-by-side evaluation of audio foundation models.
