title: Kimi-Audio Technical Report
summary: 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.
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paper_title: Kimi-Audio Technical Report
paper_summary: 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.
paper_ai_summary: Kimi-Audio, an open-source audio foundation model, achieves state-of-the-art performance across audio-related tasks through a novel LLM-based architecture and comprehensive training and evaluation processes.
paper_ai_keywords: LLM-based architecture continuous features discrete tokens flow matching chunk-wise streaming detokenizer pre-training dataset post-training data continual pre-training fine-tuning speech recognition audio understanding audio question answering speech conversation