| --- |
| pipeline_tag: automatic-speech-recognition |
| language: |
| - nan |
| - zh |
| base_model: |
| - Qwen/Qwen3-ASR-1.7B |
| - FireRedTeam/FireRedASR2-AED |
| tags: |
| - wenetspeech-min |
| - minnan |
| - speech-recognition |
| - paired-transcript-asr |
| --- |
| |
| # WenetSpeech-Min ASR Models |
|
|
| ASR models for Minnan transcription, Mandarin transcription, and paired-transcript |
| recognition, described in [WenetSpeech-Min](https://arxiv.org/abs/2609.36834). |
| The corpus contains around 10,000 hours of Minnan speech, with paired Minnan |
| and Mandarin transcripts for each utterance. |
|
|
| ## Available Models |
|
|
| | Model | Task | Framework | |
| | --- | --- | --- | |
| | [Qwen3-ASR-WSM-Min](https://huggingface.co/ASLP-lab/WS-Min-ASR/tree/main/Qwen3-ASR-WSM-Min) | Minnan transcription | ms-swift | |
| | [Qwen3-ASR-WSM-Mandarin](https://huggingface.co/ASLP-lab/WS-Min-ASR/tree/main/Qwen3-ASR-WSM-Mandarin) | Mandarin transcription | ms-swift | |
| | [FireRedASR2-AED-WSM](https://huggingface.co/ASLP-lab/WS-Min-ASR/tree/main/FireRedASR2-AED-WSM) | Paired-transcript recognition | WeNet | |
|
|
| ## Installation |
|
|
| Follow the official installation guides: |
|
|
| - [Qwen3-ASR](https://github.com/QwenLM/Qwen3-ASR#environment-setup), including the vLLM backend for inference. |
| - [ms-swift](https://swift.readthedocs.io/en/latest/GetStarted/SWIFT-installation.html) for Qwen fine-tuning. |
| - [WeNet](https://github.com/wenet-e2e/wenet#install-for-training--deployment) for FireRed fine-tuning and inference. |
|
|
| ## Usage |
|
|
| ### Qwen3-ASR |
|
|
| Select `Qwen3-ASR-WSM-Min` for Minnan output or `Qwen3-ASR-WSM-Mandarin` |
| for Mandarin output. Both take Minnan audio as input. |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| from qwen_asr import Qwen3ASRModel |
| |
| if __name__ == "__main__": |
| subfolder = "Qwen3-ASR-WSM-Min" # Or "Qwen3-ASR-WSM-Mandarin" |
| model_root = snapshot_download( |
| repo_id="ASLP-lab/WS-Min-ASR", |
| allow_patterns=[f"{subfolder}/*.safetensors", f"{subfolder}/*.json", |
| f"{subfolder}/*.txt", f"{subfolder}/*.jinja", |
| "examples/G1216S0004.wav"], |
| ) |
| model = Qwen3ASRModel.LLM( |
| model=f"{model_root}/{subfolder}", |
| gpu_memory_utilization=0.8, |
| max_model_len=8192, |
| max_new_tokens=1024, |
| ) |
| results = model.transcribe( |
| audio=f"{model_root}/examples/G1216S0004.wav", # Or your audio path |
| language=None, |
| ) |
| print(results[0].text) |
| ``` |
|
|
| ### FireRedASR2-AED-WSM |
|
|
| Download the model and run paired-transcript recognition with the GitHub recipe: |
|
|
| ```bash |
| git clone https://github.com/ASLP-lab/WenetSpeech-Min-Repo.git |
| cd WenetSpeech-Min-Repo |
| bash FireRedASR2-AED-WSM/setup_wenet.sh |
| hf download ASLP-lab/WS-Min-ASR --include "FireRedASR2-AED-WSM/*" \ |
| --local-dir pretrained_models/WS-Min-ASR |
| CUDA_VISIBLE_DEVICES=0 bash FireRedASR2-AED-WSM/infer.sh \ |
| --model-dir pretrained_models/WS-Min-ASR/FireRedASR2-AED-WSM \ |
| --task both |
| ``` |
|
|
| Use `--task minnan` or `--task mandarin` for a single transcript. |
| Add `--audio /path/to/audio.wav` to use your own audio; otherwise the included |
| example is used. Paired output contains `text_minnan` and `text_mandarin`. |
|
|
| ## Training |
|
|
| - [Minnan transcription: Qwen3-ASR-WSM-Min](https://github.com/ASLP-lab/WenetSpeech-Min-Repo/tree/main/Qwen3-ASR-Min) |
| - [Mandarin transcription: Qwen3-ASR-WSM-Mandarin](https://github.com/ASLP-lab/WenetSpeech-Min-Repo/tree/main/Qwen3-ASR-Mandarin) |
| - [Paired-transcript recognition: FireRedASR2-AED-WSM](https://github.com/ASLP-lab/WenetSpeech-Min-Repo/tree/main/FireRedASR2-AED-WSM) |
|
|
