--- title: MiMo-V2.5-ASR canonical_url: "https://www.modelscope.ai/models/XiaomiMiMo/MiMo-V2.5-ASR" md_url: "https://www.modelscope.ai/models/XiaomiMiMo/MiMo-V2.5-ASR.md" repository: XiaomiMiMo/MiMo-V2.5-ASR last_updated: 2026-04-25 license: mit model_type: - qwen2 architectures: - MiMoV2ASRForCausalLM parameters: 8.0B tensor_type: - F32 library_name: - safetensors - pytorch frameworks: - pytorch language: - zh - en - yue downloads: 147 stars: 7 tags: - safetensors - text-generation-inference --- # MiMo-V2.5-ASR > MiMo-V2.5-ASR - An open-source model by XiaomiMiMo on ModelScope. ━━━━━━━━━━━━━━━━━━━━━━━━━━━ MiMo-V2.5-ASR: Robust Speech Recognition Across Languages, Dialects, and Complex Acoustic Scenarios ━━━━━━━━━━━━━━━━━━━━━━━━━━━ XiaomiMiMo/MiMo-V2.5-ASR is a 8.0B-parameter machine learning model on ModelScope. licensed under mit. - **Repository**: XiaomiMiMo/MiMo-V2.5-ASR - **License**: mit - **Parameters**: 8.0B - **Tags**: safetensors, text-generation-inference - **Downloads**: 147 - **Stars**: 7 - **Last updated**: 2026-04-25 Source: https://www.modelscope.ai/models/XiaomiMiMo/MiMo-V2.5-ASR ---
Xiaomi-MiMo

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MiMo-V2.5-ASR: Robust Speech Recognition Across
Languages, Dialects, and Complex Acoustic Scenarios
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| 💻 GitHub  | 🚀 Online Demo  | 📰 Blog  |

## Introduction **MiMo-V2.5-ASR** is a state-of-the-art end-to-end automatic speech recognition (ASR) model developed by the Xiaomi MiMo team. It is built to deliver accurate and robust transcription across Mandarin Chinese and English, multiple Chinese dialects, code-switched speech, song lyrics, knowledge-intensive content, noisy acoustic environments, and multi-speaker conversations. MiMo-V2.5-ASR achieves state-of-the-art results on a wide range of public benchmarks. ## Abstract Automatic speech recognition systems are expected to faithfully transcribe speech signals that originate from diverse languages, dialects, accents, and domains, and that are captured under a wide variety of acoustic conditions. While conventional end-to-end models perform well on in-domain data, they still fall short of real-world requirements in challenging scenarios such as dialect mixing, code-switching, knowledge-intensive content, noisy environments, and multi-speaker conversations. We present **MiMo-V2.5-ASR**, a large-scale end-to-end speech recognition model developed by the Xiaomi MiMo team. Through large-scale mid-training, high-quality supervised fine-tuning, and a novel reinforcement-learning algorithm, MiMo-V2.5-ASR achieves systematic improvements along the following dimensions: - 🗣️ **Chinese Dialects**: Native support for Wu, Cantonese, Hokkien, Sichuanese, and more. - 🔀 **Code-Switch**: Seamless Chinese–English code-switching transcription with no language tags required. - 🎵 **Song Recognition**: High-precision lyrics transcription for Chinese and English songs, even with mixed accompaniment and vocals. - 🔊 **Noisy Environments**: Robust recognition under heavy noise, far-field capture, and other adverse acoustic conditions. - 👥 **Multi-Speaker**: Accurate transcription of overlapping, multi-party conversations such as meetings. - 🇬🇧 **Complex English Scenarios**: Leading performance on the [Open ASR Leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard) for challenging English benchmarks such as AMI. - 📚 **Knowledge-Intensive Recognition**: Precise recognition of classical poetry, technical terminology, personal names, place names, and other knowledge-dense material. - 📝 **Native Punctuation**: Punctuation generated natively from prosody and semantics, delivering ready-to-use transcripts with no post-processing needed. ## Results MiMo-V2.5-ASR has been evaluated across a broad set of benchmarks spanning standard Mandarin and English, Chinese dialects, lyric recognition, and internal business scenarios. The chart below summarizes the average performance of MiMo-V2.5-ASR across these scenarios. ![ASR Results](https://huggingface.co/XiaomiMiMo/MiMo-V2.5-ASR/resolve/main/assets/MiMo_ASR_Results.png) For per-benchmark numbers and specific qualitative cases, please refer to our [blog](https://mimo.xiaomi.com/mimo-v2-5-asr). ## Model Download | Models | 🤗 Hugging Face | 🤖️ ModelScope | |-------|-------|-------| | MiMo-Audio-Tokenizer | [XiaomiMiMo/MiMo-Audio-Tokenizer](https://huggingface.co/XiaomiMiMo/MiMo-Audio-Tokenizer) | [XiaomiMiMo/MiMo-Audio-Tokenizer](https://modelscope.cn/models/XiaomiMiMo/MiMo-Audio-Tokenizer)| | MiMo-V2.5-ASR | [XiaomiMiMo/MiMo-V2.5-ASR](https://huggingface.co/XiaomiMiMo/MiMo-V2.5-ASR) | [XiaomiMiMo/MiMo-V2.5-ASR](https://modelscope.cn/models/XiaomiMiMo/MiMo-V2.5-ASR) | ```bash pip install huggingface-hub hf download XiaomiMiMo/MiMo-Audio-Tokenizer --local-dir ./models/MiMo-Audio-Tokenizer hf download XiaomiMiMo/MiMo-V2.5-ASR --local-dir ./models/MiMo-V2.5-ASR ``` ## Getting Started Spin up the MiMo-V2.5-ASR 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-V2.5-ASR.git cd MiMo-V2.5-ASR 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 ```bash python run_mimo_asr.py ``` This launches a local Gradio interface for MiMo-V2.5-ASR. You can: * Upload an audio file **or** record directly from your microphone. * Optionally specify a **language tag** (Chinese / English / Auto) to bias the model for a specific language, or leave it to **Auto** for automatic language detection (recommended for code-switched speech). * The demo calls the `asr_sft()` interface under the hood. The interface provides a **Model Configuration** tab for setting local model and tokenizer paths, and a **Speech Recognition** tab where you drop in audio, pick a language tag, and hit *Transcribe* — the decoded text and processing status stream into the panels on the right.

MiMo-V2.5-ASR Gradio Demo
Figure: Gradio demo for MiMo-V2.5-ASR — upload an audio clip or record from your microphone, choose a language tag, and get the transcription on the right.

To load the model and tokenizer automatically at startup, pass their paths on the command line: ```bash python run_mimo_asr.py \ --model-path ./models/MiMo-V2.5-ASR \ --tokenizer-path ./models/MiMo-Audio-Tokenizer ``` Otherwise, enter the local paths for `MiMo-Audio-Tokenizer` and `MiMo-V2.5-ASR` in the **Model Configuration** tab, then start transcribing! ## Python API Basic usage with the `asr_sft` interface: ```python from src.mimo_audio.mimo_audio import MimoAudio model = MimoAudio( model_path="./models/MiMo-V2.5-ASR", tokenizer_path="./models/MiMo-Audio-Tokenizer", ) # Automatic language detection (recommended for code-switching) text = model.asr_sft("path/to/audio.wav") print(text) # With explicit language tag text_zh = model.asr_sft("path/to/audio.wav", audio_tag="") text_en = model.asr_sft("path/to/audio.wav", audio_tag="") ``` ## Citation ```bibtex @misc{coreteam2026mimov25asr, title={MiMo-V2.5-ASR: Robust Speech Recognition Across Languages, Dialects, and Complex Acoustic Scenarios}, author={LLM-Core-Team Xiaomi}, year={2026}, url={https://github.com/XiaomiMiMo/MiMo-V2.5-ASR}, } ``` ## Contact Please contact us at [mimo@xiaomi.com](mailto:mimo@xiaomi.com) or open an issue if you have any questions.