pattaraearth
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Update README.md
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README.md
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| Pathumma-llm-audio-1.0.0 | 12.03 | 12.20 | 11.36 | 42.30, 36.88 | 90.30, 92.07 | -->
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## Limitations and Future Work
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At present, our model remains in the experimental research phase and is not yet fully suitable for practical applications as an assistant. The model currently has an input duration limit, processing audio inputs
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## Acknowledgements
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We are grateful to ThaiSC, also known as NSTDA Supercomputer Centre, for providing the LANTA that was utilised for model training and finetuning. Additionally, we would like to express our gratitude to the SALMONN team for making their code publicly available, and to Typhoon Audio at SCB 10X for making available the huggingface project, source code, and technical paper, which served as a valuable guide for us. Many other open-source projects have contributed valuable information, code, data, and model weights; we are grateful to them all.
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| Pathumma-llm-audio-1.0.0 | 12.03 | 12.20 | 11.36 | 42.30, 36.88 | 90.30, 92.07 | -->
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## Limitations and Future Work
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At present, our model remains in the experimental research phase and is not yet fully suitable for practical applications as an assistant. The model currently has an input duration limit, **processing audio inputs up to 30 seconds**, which restricts its usability for longer audio tasks. Future work will focus on upgrading the language model to a newer version [Pathumma-llm-text-1.0.0](https://huggingface.co/nectec/Pathumma-llm-text-1.0.0), and curating more refined and robust datasets to improve performance. Additionally, we aim to address and prioritize the safety and reliability of the model's outputs.
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## Acknowledgements
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We are grateful to ThaiSC, also known as NSTDA Supercomputer Centre, for providing the LANTA that was utilised for model training and finetuning. Additionally, we would like to express our gratitude to the SALMONN team for making their code publicly available, and to Typhoon Audio at SCB 10X for making available the huggingface project, source code, and technical paper, which served as a valuable guide for us. Many other open-source projects have contributed valuable information, code, data, and model weights; we are grateful to them all.
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