---
license: apache-2.0
library_name: mlx
pipeline_tag: automatic-speech-recognition
tags:
- mlx
- asr
- automatic-speech-recognition
- speech-recognition
- mimo
base_model:
- XiaomiMiMo/MiMo-V2.5-ASR
language:
- zh
- en
---
Current variant: `bf16`
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MiMo-V2.5-ASR: Robust Speech Recognition Across
Languages, Dialects, and Complex Acoustic Scenarios
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## MLX Note
This repository is a community MLX conversion of the official `XiaomiMiMo/MiMo-V2.5-ASR` release for Apple silicon. The original model description below is preserved from the official release, and the MLX-specific material in this page is added as an incremental note for local MLX deployment.
## MLX Usage
Current MLX usage is documented in the GitHub forks below:
- [ailuntx/MiMo-V2.5-ASR-MLX](https://github.com/ailuntx/MiMo-V2.5-ASR-MLX)
- [ailuntx/MiMo-Audio-Tokenizer-MLX](https://github.com/ailuntx/MiMo-Audio-Tokenizer-MLX)
Install the current MLX path:
```bash
pip install git+https://github.com/ailuntx/mlx-audio@feat/mimo-v25-asr
```
Download the MLX checkpoints:
```bash
hf download mlx-community/MiMo-Audio-Tokenizer --local-dir ./models/MiMo-Audio-Tokenizer
hf download mlx-community/MiMo-V2.5-ASR-MLX --local-dir ./models/MiMo-V2.5-ASR-MLX
```
Run transcription from the helper script in `ailuntx/MiMo-V2.5-ASR-MLX`:
```bash
git clone https://github.com/ailuntx/MiMo-V2.5-ASR-MLX.git
cd MiMo-V2.5-ASR-MLX
python run_mimo_asr_mlx.py \
--model ./models/MiMo-V2.5-ASR-MLX \
--audio path/to/audio.wav
```
Python:
```python
from mlx_audio.stt import load
model = load("./models/MiMo-V2.5-ASR-MLX")
result = model.generate("path/to/audio.wav", language="en")
print(result.text)
```
Notes:
- `mlx-community/MiMo-V2.5-ASR-MLX` resolves `mlx-community/MiMo-Audio-Tokenizer` through `mlx_manifest.json`.
- The current install path depends on the MiMo support branch in `ailuntx/mlx-audio`.
- The usage section here will be simplified once MiMo lands in upstream `mlx-audio` and `mlx-audio-swift`.
## 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. Therefore, we present **MiMo-V2.5-ASR**, an 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 the official [blog](https://mimo.xiaomi.com/mimo-v2-5-asr).
## Model Download
| Models | 🤗 Hugging Face |
|-------|-------|
| MiMo-Audio-Tokenizer | [XiaomiMiMo/MiMo-Audio-Tokenizer](https://huggingface.co/XiaomiMiMo/MiMo-Audio-Tokenizer) |
| MiMo-V2.5-ASR | [XiaomiMiMo/MiMo-V2.5-ASR](https://huggingface.co/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
```
## MLX Releases
The following repositories are MLX conversions derived from the official release:
| Variant | Precision | Size | Local smoke time | Smoke result |
| --- | --- | ---: | ---: | --- |
| `MiMo-V2.5-ASR-MLX` | 4bit | 4.2 GB | 0.88 s | `Intention.` |
| `MiMo-V2.5-ASR-MLX-4bit` | 4bit | 4.2 GB | 0.88 s | `Intention.` |
| `MiMo-V2.5-ASR-MLX-8bit` | 8bit | 8.0 GB | 10.80 s | `Intention.` |
| `MiMo-V2.5-ASR-MLX-bf16` | bf16 | 15 GB | - | dense reference export |
| `MiMo-V2.5-ASR-MLX-fp32` | fp32 | 30 GB | - | dense reference export |
MLX conversion notes:
- Base model: `XiaomiMiMo/MiMo-V2.5-ASR`
- Tokenizer resolution: automatic via `mlx-community/MiMo-Audio-Tokenizer`
- Conversion date: `2026-05-12`
- Local validation runtimes: `mlx-audio` and `mlx-audio-swift`
- Recommended default: `MiMo-V2.5-ASR-MLX`
Example downloads:
```bash
hf download mlx-community/MiMo-V2.5-ASR-MLX --local-dir ./models/MiMo-V2.5-ASR-MLX
hf download mlx-community/MiMo-V2.5-ASR-MLX-8bit --local-dir ./models/MiMo-V2.5-ASR-MLX-8bit
```
## Validation
Local smoke validation was run with `mlx-audio` and `mlx-audio-swift`.
- `intention.wav` -> `Intention.`
- `conversational_a.wav` -> expected coffee / Kaldi paragraph
## Getting Started
The following section is preserved from the official project and describes the original Python/CUDA workflow.
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-MLX
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.
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
The following API example is preserved from the official project.
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 [mimo@xiaomi.com](mailto:mimo@xiaomi.com) or open an issue in the official project if you have questions about the original model.
