---
license: mit
library_name: transformers
language:
- zh
- en
- yue
pipeline_tag: automatic-speech-recognition
tags:
- safetensors
- text-generation-inference
---
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MiMo-V2.5-ASR: Robust Speech Recognition Across
Languages, Dialects, and Complex Acoustic Scenarios
━━━━━━━━━━━━━━━━━━━━━━━━━━━
## 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.
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.
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.
