Kimi-Audio Technical Report
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Cited by 44 in LLM reasoning, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
We present Kimi-Audio, an open-source audio foundation model that excels in audio understanding, generation, and conversation. We detail the practices in building Kimi-Audio, including model architecture, data curation, training recipe, inference deployment, and evaluation. Specifically, we leverage a 12.5Hz audio tokenizer, design a novel LLM-based architecture with continuous features as input and discrete tokens as output, and develop a chunk-wise streaming detokenizer based on flow matching. We curate a pre-training dataset that consists of more than 13 million hours of audio data covering a wide range of modalities including speech, sound, and music, and build a pipeline to construct high-quality and diverse post-training data. Initialized from a pre-trained LLM, Kimi-Audio is continual pre-trained on both audio and text data with several carefully designed tasks, and then fine-tuned to support a diverse of audio-related tasks. Extensive evaluation shows that Kimi-Audio achieves state-of-the-art performance on a range of audio benchmarks including speech recognition, audio understanding, audio question answering, and speech conversation. We release the codes, model checkpoints, as well as the evaluation toolkits in https://github.com/MoonshotAI/Kimi-Audio.
Key facts. Cited by 222 papers in the map (data through 14 Oct 2026). Builds on 69 of its 86 references that are in the map. First posted on arXiv 25 Apr 2025.
Builds on
69 of 86 references.
References · 69
- 2024The Llama 3 Herd of Modelscited by 14,715
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- 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
- 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,384
- 2025Kimi k1.5: Scaling Reinforcement Learning with LLMscited by 1,210
- 2024Tulu 3: Pushing Frontiers in Open Language Model Post-Trainingcited by 1,053
- 2024VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMscited by 771
- 2023Neural Codec Language Models are Zero-Shot Text to Speech Synthesizerscited by 752
- 2025Qwen2.5-Omni Technical Reportcited by 736
- 2019LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speechcited by 649
- 2020VGGSound: A Large-scale Audio-Visual Datasetcited by 648
38 more
- 2018MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversationscited by 643
- 2022AudioLM: a Language Modeling Approach to Audio Generationcited by 616
- 2023Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Modelscited by 608
- 2024Moshi: a speech-text foundation model for real-time dialoguecited by 568
- 2024Qwen2-Audio Technical Reportcited by 560
- 2023SALMONN: Towards Generic Hearing Abilities for Large Language Modelscited by 506
- 2020MLS: A Large-Scale Multilingual Dataset for Speech Researchcited by 476
- 2022FLEURS: Few-shot Learning Evaluation of Universal Representations of Speechcited by 469
- 2021VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretationcited by 463
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- 2021GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audiocited by 399
- 2023Voicebox: Text-Guided Multilingual Universal Speech Generation at Scalecited by 379
- 2017AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baselinecited by 373
- 2019Clotho: An Audio Captioning Datasetcited by 358
- 2024Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothingcited by 346
- 2020FSD50K: An Open Dataset of Human-Labeled Sound Eventscited by 342
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- 2023Google USM: Scaling Automatic Speech Recognition Beyond 100 Languagescited by 231
- 2024GLM-4-Voice: Towards Intelligent and Human-Like End-to-End Spoken Chatbotcited by 224
- 2024Mini-Omni: Language Models Can Hear, Talk While Thinking in Streamingcited by 210
- 2024Emilia: An Extensive, Multilingual, and Diverse Speech Dataset for Large-Scale Speech Generationcited by 204
- 2024VoiceBench: Benchmarking LLM-Based Voice Assistantscited by 200
- 2021WenetSpeech: A 10000+ Hours Multi-domain Mandarin Corpus for Speech Recognitioncited by 197
- 2022Learning to Answer Questions in Dynamic Audio-Visual Scenarioscited by 191
- 2024Audio Flamingo: A Novel Audio Language Model with Few-Shot Learning and Dialogue Abilitiescited by 186
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- 2022Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognitioncited by 144
- 2024Freeze-Omni: A Smart and Low Latency Speech-to-speech Dialogue Model with Frozen LLMcited by 141
- 2018AISHELL-2: Transforming Mandarin ASR Research Into Industrial Scalecited by 128
- 2023UniAudio: An Audio Foundation Model Toward Universal Audio Generationcited by 127
- 2021Emotional Voice Conversion: Theory, Databases and ESDcited by 115
- 2020AISHELL-3: A Multi-speaker Mandarin TTS Corpus and the Baselinescited by 111
- 2022Clotho-AQA: A Crowdsourced Dataset for Audio Question Answeringcited by 85
- 2023Libriheavy: a 50,000 hours ASR corpus with punctuation casing and contextcited by 85
- 2025Step-Audio: Unified Understanding and Generation in Intelligent Speech Interactioncited by 83
50 most cited of 69 All 69 (JSON)
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Citing papers per year: 2025: 84; 2026: 138.
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50 newest of 222 All 222 (JSON)
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12 newest of 44 All 44 (JSON)
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