WenetSpeech-Min
Collection
4 items • Updated
ASR models for Minnan transcription, Mandarin transcription, and paired-transcript recognition, described in WenetSpeech-Min. The corpus contains around 10,000 hours of Minnan speech, with paired Minnan and Mandarin transcripts for each utterance.
| Model | Task | Framework |
|---|---|---|
| Qwen3-ASR-WSM-Min | Minnan transcription | ms-swift |
| Qwen3-ASR-WSM-Mandarin | Mandarin transcription | ms-swift |
| FireRedASR2-AED-WSM | Paired-transcript recognition | WeNet |
Follow the official installation guides:
Select Qwen3-ASR-WSM-Min for Minnan output or Qwen3-ASR-WSM-Mandarin
for Mandarin output. Both take Minnan audio as input.
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)
Download the model and run paired-transcript recognition with the GitHub recipe:
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.
Base model
FireRedTeam/FireRedASR2-AED