Update model card

README.md

@@ -1,3 +1,154 @@
 ---
 license: apache-2.0
+language:
+- en
+- zh
+tags:
+- automatic-speech-recognition
+- speech-recognition
+- audio
+- robust-asr
+- qwen3-asr
+pipeline_tag: automatic-speech-recognition
 ---
+
+# Mega-ASR
+
+Mega-ASR is a robust automatic speech recognition system designed for real-world audio with severe acoustic degradation. It targets noisy, reverberant, clipped, band-limited, overlapping, and otherwise difficult recording conditions where standard ASR systems often produce empty outputs, omissions, repetitions, or hallucinated text.
+
+The release contains the Qwen3-ASR-1.7B foundation model files, Mega-ASR adaptation weights, and an audio quality router. The router decides whether to use the robust Mega-ASR path or the base recognition path for each input, which helps preserve clean-speech recognition quality while improving robustness on degraded speech.
+
+## Model Details
+
+- **Model name:** Mega-ASR
+- **Task:** Automatic speech recognition
+- **Backbone:** Qwen3-ASR-1.7B
+- **Primary use case:** In-the-wild ASR under challenging acoustic conditions
+- **Default decoding:** Greedy decoding
+- **Default max new tokens:** 256 in the Mega-ASR inference wrapper
+- **Router:** Audio quality classifier with a default threshold of 0.5
+- **License:** Apache-2.0
+
+## Repository Contents
+
+```text
+Mega-ASR/
+├── Qwen3-ASR-1.7B/              # Backbone model, tokenizer, processor, and generation config
+├── mega-asr-merged/             # Mega-ASR adaptation weights used by the inference wrapper
+├── audio_quality_router/        # Audio quality router checkpoint
+└── README.md                    # Model card
+```
+
+## Intended Use
+
+Mega-ASR is intended for speech-to-text transcription of real-world audio, especially audio affected by compound acoustic distortions. Example scenarios include far-field recording, environmental noise, reverberation, low-quality microphones, compression artifacts, partial signal corruption, and mixed acoustic conditions.
+
+## Quick Start
+
+Install the Mega-ASR codebase and dependencies:
+
+```bash
+git clone https://github.com/xzf-thu/Mega-ASR.git
+cd Mega-ASR
+
+conda create -n mega-asr python=3.10 -y
+conda activate mega-asr
+pip install -r requirements.txt
+```
+
+Place this checkpoint directory at:
+
+```text
+ckpt/Mega-ASR
+```
+
+Run inference:
+
+```bash
+python infer.py --audio /path/to/audio.wav --ckpt_dir ckpt/Mega-ASR
+```
+
+Disable routing if you want to always use the robust recognition path:
+
+```bash
+python infer.py --audio /path/to/audio.wav --ckpt_dir ckpt/Mega-ASR --routing false
+```
+
+Python usage:
+
+```python
+from MegaASR.model.megaASR import MegaASR
+
+model = MegaASR(
+    model_path="ckpt/Mega-ASR/Qwen3-ASR-1.7B",
+    router_checkpoint="ckpt/Mega-ASR/audio_quality_router/best_acc_model.pt",
+    routing_enabled=True,
+)
+
+result = model.infer("/path/to/audio.wav", return_route=True)
+print(result)
+```
+
+## Decoding Defaults
+
+The Mega-ASR wrapper uses Qwen3-ASR generation defaults unless explicitly overridden. In the provided wrapper, `max_new_tokens` is set to 256.
+
+The default generation configuration is deterministic:
+
+```text
+do_sample: false
+num_beams: 1
+repetition_penalty: 1.0
+top_p: 1.0
+top_k: 50
+```
+
+Because `do_sample` is false, decoding is greedy by default and sampling controls such as temperature, top-p, and top-k do not affect normal inference.
+
+## Training Summary
+
+Mega-ASR is trained for robust speech recognition in realistic acoustic environments. The training pipeline uses acoustic-to-semantic supervised fine-tuning, where the model is exposed to progressively harder speech examples and learns to recover both local acoustic details and sentence-level semantics under degradation.
+
+The system is designed to improve recognition robustness on difficult audio while using a routing mechanism to reduce unnecessary changes on clean audio.
+
+## Evaluation
+
+Mega-ASR is evaluated on standard ASR benchmarks, noisy robustness benchmarks, and in-the-wild compound acoustic scenarios. The recommended evaluation metrics are:
+
+- **WER** for English and whitespace-tokenized languages
+- **CER** for Chinese and character-based evaluation
+
+The Mega-ASR repository includes an evaluation script:
+
+```bash
+python src/MegaASR/eval/evaluate_wer.py \
+  --ckpt_dir ckpt/Mega-ASR \
+  --input_jsonl examples/test.jsonl \
+  --output_jsonl outputs/pred_with_wer.jsonl
+```
+
+Input JSONL format:
+
+```json
+{"audio": "examples/audio/noise.wav", "answer": "I usually take the quieter road home because the main street gets crowded after work."}
+```
+
+## Citation
+
+If you use Mega-ASR, please cite the project:
+
+```bibtex
+@misc{xie2026megaasrinthewild2speechrecognition,
+      title={Mega-ASR: Towards In-the-wild^2 Speech Recognition via Scaling up Real-world Acoustic Simulation},
+      author={Zhifei Xie and Kaiyu Pang and Haobin Zhang and Deheng Ye and Xiaobin Hu and Shuicheng Yan and Chunyan Miao},
+      year={2026},
+      eprint={2605.19833},
+      archivePrefix={arXiv},
+      primaryClass={cs.SD},
+      url={https://arxiv.org/abs/2605.19833},
+}
+```
+
+## Acknowledgements
+
+Mega-ASR builds on Qwen3-ASR. We thank the Qwen3-ASR team and the creators of public speech and audio datasets used in the project.