DeSTA-ntu

DeSTA-ntu/DeSTA2.5-Audio-Llama-3.1-8B

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Model Documentation

DeSTA2.5-Audio



πŸ“‘ Paper | πŸ‘©β€πŸ’» Github | πŸ€— Model | πŸ€— Dataset

DeSTA2.5-Audio: Toward General-Purpose Large Audio Language Model with Self-Generated Cross-Modal Alignment > Self-generated data is what you need for developing general-purpose LALMs!

  • πŸ§ͺ A new training framework (read the paper)
  • Highly scalable and efficient without task-specific instruction-tuning data
  • Preserves language ability and avoids catastrophic forgetting
  • Comprehensive studies on data quality in LALM development
  • πŸ“¦ Open resources for the community
  • Model checkpoints and Training scripts
  • DeSTA-AQA5M dataset (5M audio-text pairs from 7,000 hours of audio)


  • πŸš€Quickstart



    Installation

    shell
    git clone https://github.com/kehanlu/DeSTA2.5-Audio.git
    cd DeSTA2.5-Audio
    pip install -e .
    


    Inference

    python
    from desta import DeSTA25AudioModel

    Load the model from Hugging Face

    model = DeSTA25AudioModel.from_pretrained("DeSTA-ntu/DeSTA2.5-Audio-Llama-3.1-8B") model.to("cuda")

    Run inference with audio input

    messages = [ { "role": "system", "content": "Focus on the audio clips and instructions." }, { "role": "user", "content": "<|AUDIO|>\nDescribe this audio.", "audios": [{ "audio": "/path/to/audio.wav",

    Path to your audio file

    "text": None }] } ]

    outputs = model.generate( messages=messages, do_sample=False, top_p=1.0, temperature=1.0, max_new_tokens=512 )

    print(outputs.text)




    πŸ“š Citation

    bibtex
    @article{lu2025desta25Audio,
      title={DeSTA2.5-Audio: Toward General-Purpose Large Audio Language Model with Self-Generated Cross-Modal Alignment},
      author={Lu, Ke-Han and Chen, Zhehuai and Fu, Szu-Wei and Yang, Chao-Han Huck and Huang, Sung-Feng and Yang, Chih-Kai and Yu, Chee-En and Chen, Chun-Wei and Chen, Wei-Chih and Huang, Chien-yu and others},
      journal={arXiv preprint arXiv:2507.02768},
      year={2025}
    }

    @inproceedings{lu2025developing, title={Developing instruction-following speech language model without speech instruction-tuning data}, author={Lu, Ke-Han and Chen, Zhehuai and Fu, Szu-Wei and Yang, Chao-Han Huck and Balam, Jagadeesh and Ginsburg, Boris and Wang, Yu-Chiang Frank and Lee, Hung-yi}, booktitle={ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages={1--5}, year={2025}, organization={IEEE} }

    @inproceedings{lu24c_interspeech, title = {DeSTA: Enhancing Speech Language Models through Descriptive Speech-Text Alignment}, author = {Ke-Han Lu and Zhehuai Chen and Szu-Wei Fu and He Huang and Boris Ginsburg and Yu-Chiang Frank Wang and Hung-yi Lee}, year = {2024}, booktitle = {Interspeech 2024}, pages = {4159--4163}, doi = {10.21437/Interspeech.2024-457}, issn = {2958-1796}, }




    πŸ‘₯ Contributors

    Ke-Han Lu, Zhehuai Chen, Szu-Wei Fu, Chao-Han Huck Yang, Sung-Feng Huang, Chih-Kai Yang, Chee-En Yu, Chun-Wei Chen, Wei-Chih Chen, Chien-yu Huang, Yi-Cheng Lin, Yu-Xiang Lin, Chi-An Fu, Chun-Yi Kuan, Wenze Ren, Xuanjun Chen, Wei-Ping Huang, En-Pei Hu, Tzu-Quan Lin, Yuan-Kuei Wu, Kuan-Po Huang, Hsiao-Ying Huang, Huang-Cheng Chou, Kai-Wei Chang, Cheng-Han Chiang, Boris Ginsburg, Yu-Chiang Frank Wang, Hung-yi Lee

    Files & Weights

    FilenameSizeAction
    model.safetensors 0.49 GB