MiMo-V2.5-ASR-oQ4

<div align="center"> <picture> <source srcset="https://huggingface.co/XiaomiMiMo/MiMo-V2.5-ASR/resolve/main/assets/XiaomiMIMO.png" media="(prefers-color-scheme: dark)"> <img src="https://huggingface.co/XiaomiMiMo/MiMo-V2.5-ASR/resolve/main/assets/XiaomiMIMO.png" width="60%" alt="Xiaomi-MiMo" /> </picture> </div> <div align="center"> <h3> <b> <span>━━━━━━━━━━━━━━━━━━━━━━━━━━━</span><br/> MiMo-V2.5-ASR: Robust Speech Recognition Across<br/> Languages, Dialects, and Complex Acoustic Scenarios<br/> <span>━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> </b> </h3> </div> <br/> <div align="center" style="line-height: 1;"> | <a href="https://github.com/XiaomiMiMo/MiMo-V2.5-ASR" target="_blank">💻 GitHub</a> &nbsp;| <a href="https://huggingface.co/spaces/XiaomiMiMo/MiMo-V2.5-ASR" target="_blank">🚀 Online Demo</a> &nbsp;| <a href="https://mimo.xiaomi.com/mimo-v2-5-asr" target="_blank">📰 Blog</a> &nbsp;| <br/> </div> <br/>

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 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.

For per-benchmark numbers and specific qualitative cases, please refer to our blog.

Model Download

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

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

pip install /path/to/flash_attn-2.7.4.post1+cu12torch2.6cxx11abiFALSE-cp312-cp312-linux_x86_64.whl

Run the Demo

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.

<p align="center"> <img src="https://huggingface.co/XiaomiMiMo/MiMo-V2.5-ASR/resolve/main/assets/MiMo_ASR_Demo.png" alt="MiMo-V2.5-ASR Gradio Demo" width="90%" /> <br/> <em>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.</em> </p>

To load the model and tokenizer automatically at startup, pass their paths on the command line:

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:

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="<chinese>")
text_en = model.asr_sft("path/to/audio.wav", audio_tag="<english>")

Citation

@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 [email protected] or open an issue if you have any questions.