Add model card README

README.md

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-license: apache-2.0
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+---
+license: mit
+library_name: transformers
+---
+
+<div align="center">
+  <picture>
+    <source srcset="https://github.com/XiaomiMiMo/MiMo-VL/raw/main/figures/Xiaomi_MiMo_darkmode.png?raw=true" media="(prefers-color-scheme: dark)">
+    <img src="https://github.com/XiaomiMiMo/MiMo-VL/raw/main/figures/Xiaomi_MiMo.png?raw=true" 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://huggingface.co/collections/XiaomiMiMo/mimo-v2.5-asr" target="_blank">🤗 HuggingFace</a>
+  &nbsp;|
+  <a href="https://github.com/XiaomiMiMo/MiMo-V2.5-ASR" target="_blank">💻 GitHub</a>
+  &nbsp;|
+  <a href="https://xiaomimimo.github.io/MiMo-V2.5-ASR-Demo" 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, Min-nan, Cantonese, Sichuanese, and other major Chinese dialects.
+- 🔀 **Code-Switch**: Fluent transcription of Chinese–English code-switched speech without any language tag prompting.
+- 🎵 **Song Lyrics**: Accurate lyric transcription for both Chinese and English songs, even when vocals are mixed with accompaniment.
+- 🔊 **Noisy Conditions**: Robust recognition in high-noise and far-field environments.
+- 👥 **Multi-Speaker**: Accurate transcription of overlapping and cross-talk conversations, such as meeting scenarios.
+- 🇬🇧 **Complex English Scenarios**: Leading performance among non-English-only models on English multi-speaker meeting benchmarks such as AMI.
+- 📚 **Knowledge-Intensive Recognition**: Precise recognition of classical poetry, technical terminology, and named entities (people, places, organizations).
+
+## Results
+
+MiMo-V2.5-ASR has been evaluated across a broad set of benchmarks spanning standard Mandarin and English, Chinese dialects, singing, code-switching, noisy conditions, and multi-speaker scenarios. Highlights of our results are shown below.
+
+### Standard Mandarin Chinese
+
+![Results - Standard Chinese](https://github.com/XiaomiMiMo/MiMo-V2.5-ASR/raw/main/assets/results_chinese.png)
+
+### Standard English
+
+![Results - Standard English](https://github.com/XiaomiMiMo/MiMo-V2.5-ASR/raw/main/assets/results_english.png)
+
+### Chinese Dialects
+
+![Results - Chinese Dialects](https://github.com/XiaomiMiMo/MiMo-V2.5-ASR/raw/main/assets/results_dialect.png)
+
+### Singing & Code-Switch
+
+![Results - Singing and Code-Switch](https://github.com/XiaomiMiMo/MiMo-V2.5-ASR/raw/main/assets/results_singing_codeswitch.png)
+
+## 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
+```
+
+## 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.
+
+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="<chinese>")
+text_en = model.asr_sft("path/to/audio.wav", audio_tag="<english>")
+```
+
+## 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.