ASLP-lab/WSChuan-ASR

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WSChuan-ASR
├── paraformer_large_chuan/
│ ├── config.yaml
│ ├── model.pt
│ └── infer.py
│
├── Qwen2.5-omni3B/
| ├──added_tokens.json
| ├──args.json
| ├──char_template.jinja
| ├──config.json
| ├──generation_config.json
| ├──merges.txt
| ├──model-00001-of-00003.safetensors
| ├──model-00002-of-00003.safetensors
| ├──model-00003-of-00003.safetensors
| ├──model.safetensors.index.json
| ├──preprocessor_config.json
| ├──special_tokens_map.json
| ├──spk_dict.pt
| ├──tokenizer_config.json
| ├──tokenizer.json
| ├──video_preprocessor_config.json
| └──vocab.json
│
├── .gitattributes
└── README.md

ASR Leaderboard

Model	Model Size	WSC-Eval-ASR - Easy	WSC-Eval-ASR - Hard	WSC-Eval-ASR - Total	Magicdata - Conversation	Magicdata - Daily-Use	Avg.
with LLM
Kimi-Audio	7B	16.65	28.66	17.66	24.67	5.77	18.68
FireRedASR-LLM	8.3B	12.80	25.27	14.40	17.68	6.69	15.37
Qwen2.5-omni	3B	16.94	26.01	18.20	20.40	6.32	17.69
Qwen2.5-omni-WSC-Finetune⭐	3B	14.36	24.14	15.61	18.45	6.15	15.74
Qwen2.5-omni+internal data⭐	3B	13.17	23.36	14.81	18.50	5.88	15.14
Qwen2.5-omni-WSC-Finetune + internal data⭐	3B	12.93	23.19	14.25	17.95	5.89	14.84
without LLM
SenseVoice-small	234M	17.43	28.38	18.39	23.50	8.77	19.29
Whisper	244M	52.06	63.99	53.59	55.88	52.03	55.51
FireRedASR-AED	1.1B	13.29	23.64	14.62	17.84	6.69	15.14
Paraformer	220M	14.34	24.61	15.66	19.81	8.16	16.52
Paraformer-WSC-Finetune⭐	220M	12.15	22.60	13.51	16.60	8.02	14.58
Paraformer + internal data⭐	220M	11.93	21.82	13.14	15.61	6.77	13.85
Paraformer-WSC-Finetune + internal data⭐	220M	11.59	21.59	12.87	14.59	6.28	13.38

ASR Inference

Paraformer_large_Chuan

export CUDA_VISIBLE_DEVICES=7
root_dir=./test_data
test_sets=("WSC-Eval-ASR" "WSC-Eval-ASR-Hard" "WSC-Eval-ASR-Easy")
model_dir=./model_dir

out_rootdir=./results
mkdir -p $out_rootdir
python infer_paraformer.py \
  --model $model_dir \
  --wav_scp_file $root_dir/$test_data/wav.scp \
  --output_dir $out_rootdir/debug \
  --device "cuda" \
  --output_file $out_dir/hyp.txt

---

Qwen2.5-Omni-3B_Chuan

python infer_qwen2.5omni.py \
    --wavs_path /path/to/your/wav.scp \
    --out_path /path/to/your/results.txt \
    --gpu 0 \
    --model /path/to/your/model