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--- |
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license: mit |
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language: |
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- en |
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base_model: |
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- Qwen/Qwen2-Audio-7B-Instruct |
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--- |
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- data process |
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- Place the WAV and JSON files in `dev_data`. |
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To distinguish the recognition performance of each part, the file names of the training audio for Part One need to be prefixed with fold1-d-, those for Part Two need to be prefixed with fold1-a-, fold1-b-, fold1-c-, and those for Part Three need to be prefixed with fold1-e-. If the training audio file names for Part One and Part Three do not have the prefixes fold1-d- and fold1-e-, you will need to add them yourself. For example, if the file name of the training audio for Part One is 5402400A, then add the prefix to make it fold1-d-5402400A. If the file name of the audio for Part Three is audio_0001405, then add the prefix to make it fold1-e-audio_0001405. The process for the development set is the same, except that fold1 should be changed to fold2. |
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Download the pre-trained Sentence-BERT model and tokenizer from the following URL |
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and Place the downloaded pre-trained model and tokenizer inside the `../../qwen2_audio_baseline/Bert_pretrain` |
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- Example commands |
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``` |
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git clone https://huggingface.co/PeacefulData/2025_DCASE_AudioQA_Baselines |
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cd 2025_DCASE_AudioQA_Baselines |
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mkdir Bert_pretrain |
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cd Bert_pretrain |
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git clone https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2/tree/main |
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``` |
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- Environment |
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```bash |
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cd ../qwen2_audio_baseline |
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pip install -r requirements.txt |
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``` |
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You can also use a mirror source to speed up the process. |
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`pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple` |
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- Run Audio QA Inference Baseline |
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```bash |
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sh qwen_audio_test.sh |
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``` |
