--- license: mit pipeline_tag: any-to-any tags: - Audio-to-Text - Text-to-Audio - Audio-to-Audio - Text-to-Text - Audio-Text-to-Text ---
Xiaomi-MiMo

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MiMo Audio: Audio Language Models are Few-Shot Learners
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| 🤖 GitHub  | 📄 Paper  | 📰 Blog  | 🔥 Online Demo  | 📊 MiMo-Audio-Eval  |

## Introduction Existing audio language models typically rely on task-specific fine-tuning to accomplish particular audio tasks. In contrast, humans are able to generalize to new audio tasks with only a few examples or simple instructions. GPT-3 has shown that scaling next-token prediction pretraining enables strong generalization capabilities in text, and we believe this paradigm is equally applicable to the audio domain. By scaling MiMo-Audio's pretraining data to over one hundred million of hours, we observe the emergence of few-shot learning capabilities across a diverse set of audio tasks. We develop a systematic evaluation of these capabilities and find that MiMo-Audio-7B-Base achieves SOTA performance on both speech intelligence and audio understanding benchmarks among open-source models. Beyond standard metrics, MiMo-Audio-7B-Base generalizes to tasks absent from its training data, such as voice conversion, style transfer, and speech editing. MiMo-Audio-7B-Base also demonstrates powerful speech continuation capabilities, capable of generating highly realistic talk shows, recitations, livestreaming and debates. At the post-training stage, we curate a diverse instruction-tuning corpus and introduce thinking mechanisms into both audio understanding and generation. MiMo-Audio-7B-Instruct achieves open-source SOTA on audio understanding benchmarks, spoken dialogue benchmarks and instruct-TTS evaluations, approaching or surpassing closed-source models.

## Architecture ### MiMo-Audio-Tokenizer MiMo-Audio-Tokenizer is a 1.2B-parameter Transformer operating at 25 Hz. It employs an eight-layer RVQ stack to generate 200 tokens per second. By jointly optimizing semantic and reconstruction objectives, we train MiMo-Audio-Tokenizer from scratch on a 10-million-hour corpus, achieving superior reconstruction quality and facilitating downstream language modeling.

MiMo-Audio couples a patch encoder, an LLM, and a patch decoder to improve modeling efficiency for high-rate sequences and bridge the length mismatch between speech and text. The patch encoder aggregates four consecutive time steps of RVQ tokens into a single patch, downsampling the sequence to a 6.25 Hz representation for the LLM. The patch decoder autoregressively generates the full 25 Hz RVQ token sequence via a delayed-generation scheme. ### MiMo-Audio

## Explore MiMo-Audio Now! 🚀🚀🚀 - 🎧 **Try the Hugging Face demo:** [MiMo-Audio Demo](https://huggingface.co/spaces/XiaomiMiMo/mimo_audio_chat) - 📰 **Read the Official Blog:** [MiMo-Audio Blog](https://xiaomimimo.github.io/MiMo-Audio-Demo) - 📄 **Dive into the Technical Report:** [MiMo-Audio Technical Report](https://github.com/XiaomiMiMo/MiMo-Audio/blob/main/MiMo-Audio-Technical-Report.pdf) ## Model Download | Models | 🤗 Hugging Face | |-------|-------| | MiMo-Audio-Tokenizer | [XiaomiMiMo/MiMo-Audio-Tokenizer](https://huggingface.co/XiaomiMiMo/MiMo-Audio-Tokenizer) | | MiMo-Audio-7B-Base | [XiaomiMiMo/MiMo-Audio-7B-Base](https://huggingface.co/XiaomiMiMo/MiMo-Audio-7B-Base) | | MiMo-Audio-7B-Instruct | [XiaomiMiMo/MiMo-Audio-7B-Instruct](https://huggingface.co/XiaomiMiMo/MiMo-Audio-7B-Instruct) | ```bash pip install huggingface-hub hf download XiaomiMiMo/MiMo-Audio-Tokenizer --local-dir ./models/MiMo-Audio-Tokenizer hf download XiaomiMiMo/MiMo-Audio-7B-Base --local-dir ./models/MiMo-Audio-7B-Base hf download XiaomiMiMo/MiMo-Audio-7B-Instruct --local-dir ./models/MiMo-Audio-7B-Instruct ``` ## Getting Started Spin up the MiMo-Audio demo in minutes with the built-in Gradio app. ### Prerequisites (Linux) * Python 3.12 * CUDA >= 12.0 ### Installation ```bash git clone https://github.com/XiaomiMiMo/MiMo-Audio.git cd MiMo-Audio pip install -r requirements.txt pip install flash-attn==2.7.4.post1 ``` > \[!Note] > If the compilation of flash-attn takes too long, you can download the precompiled wheel and install it manually: > > * [Download Precompiled Wheel](https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.4.post1/flash_attn-2.7.4.post1+cu12torch2.6cxx11abiFALSE-cp312-cp312-linux_x86_64.whl) > > ```sh > pip install /path/to/flash_attn-2.7.4.post1+cu12torch2.6cxx11abiFALSE-cp312-cp312-linux_x86_64.whl > ``` ### Run the demo ``` sh python run_mimo_audio.py ``` This launches a local Gradio interface where you can try MiMo-Audio interactively.

Enter the local paths for `MiMo-Audio-Tokenizer` and `MiMo-Audio-7B-Instruct`, then enjoy the full functionality of MiMo-Audio! ## Inference Scripts ### Base Model We provide an example script to explore the **in-context learning** capabilities of `MiMo-Audio-7B-Base`. See: [`inference_example_pretrain.py`](https://github.com/XiaomiMiMo/MiMo-Audio/blob/main/inference_example_pretrain.py) ### Instruct Model To try the instruction-tuned model `MiMo-Audio-7B-Instruct`, use the corresponding inference script. See: [`inference_example_sft.py`](https://github.com/XiaomiMiMo/MiMo-Audio/blob/main/inference_example_sft.py) ## Evaluation Toolkit Full evaluation suite are available at 🌐[MiMo-Audio-Eval](https://github.com/XiaomiMiMo/MiMo-Audio-Eval). This toolkit is designed to evaluate MiMo-Audio and other recent audio LLMs as mentioned in the paper. It provides a flexible and extensible framework, supporting a wide range of datasets, tasks, and models. ## Citation ```bibtex @misc{coreteam2025mimoaudio, title={MiMo-Audio: Audio Language Models are Few-Shot Learners}, author={LLM-Core-Team Xiaomi}, year={2025}, url={GitHub - XiaomiMiMo/MiMo-Audio}, } ``` ## Contact Please contact us at [mimo@xiaomi.com](mailto:mimo@xiaomi.com) or open an issue if you have any questions.