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--- |
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license: apache-2.0 |
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language: |
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- en |
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tags: |
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- medical |
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--- |
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# Model Card for Fine-Tuned Whisper Model: Hypernasality Identification |
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This model card details the Whisper model, fine-tuned to identify hypernasality in patients from audio recordings. The model is developed to assist in the diagnosis and monitoring of speech disorders. |
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## Model Details |
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### Model Description |
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- **Developed by**: The Data Science team at the Vanderbilt Data Science Institute |
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- **Funded by**: This project was supported by healthcare-focused research grants and institutional funding. |
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- **Shared by**: The model is shared by Vanderbilt University with the aim to assist clinicians and speech therapists. |
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- **Model type**: Deep Learning, Neural Network (Fine-Tuned Whisper Model) |
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- **Language(s)** (NLP): The model primarily focuses on English language audio samples but has the potential to be adapted for multilingual use. |
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- **License**: The model is released under an open-source license for non-commercial use, details of which can be found in the repository. |
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- **Finetuned from model**: The base model is OpenAI's Whisper, which has been fine-tuned using a specialized dataset for hypernasality detection. |
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### Model Sources |
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- **Repository**: https://github.com/jaewoocho/Transformers_whisper_cleft/tree/main?tab=readme-ov-file |
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- **Demo** : https://huggingface.co/spaces/jcho02/Transformers_whisper_cleft |
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## Uses |
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### Direct Use |
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- This model can be directly used by healthcare professionals to analyze patient speech samples for signs of hypernasality, aiding in diagnosis and treatment planning. |
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### Downstream Use |
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- Potential use in automated speech therapy tools or in research settings for studying speech disorders. |
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### Out-of-Scope Use |
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- Not intended for non-medical use or as a sole diagnostic tool without clinical oversight. |
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## Bias, Risks, and Limitations |
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- The model might have limitations in accurately identifying hypernasality across diverse accents and languages. User discretion is advised, and results should be considered alongside clinical assessments. |
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## Recommendations |
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- Healthcare professionals should use this tool as a supplement to traditional diagnostic methods. Continuous updating with diverse datasets is recommended to improve accuracy. |
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## How to Get Started with the Model |
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- Use the following code snippet to get started with the model: |
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- https://github.com/jaewoocho/Transformers_whisper_cleft/tree/main?tab=readme-ov-file |
