---
license: apache-2.0
language:
- en
tags:
- medical
---

# Model Card for Fine-Tuned Whisper Model: Hypernasality Identification

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.

- Model Interactive Demo: https://huggingface.co/spaces/jcho02/Transformers_whisper_cleft
## Model Details

### Model Description
- **Developed by**: The Data Science team at the Vanderbilt Data Science Institute
- **Funded by**: This project was supported by healthcare-focused research grants and institutional funding.
- **Shared by**: The model is shared by Vanderbilt University with the aim to assist clinicians and speech therapists.
- **Model type**: Deep Learning, Neural Network (Fine-Tuned Whisper Model)
- **Language(s)** (NLP): The model primarily focuses on English language audio samples but has the potential to be adapted for multilingual use.
- **License**: The model is released under an open-source license for non-commercial use, details of which can be found in the repository.
- **Finetuned from model**: The base model is OpenAI's Whisper, which has been fine-tuned using a specialized dataset for hypernasality detection.


### Model Sources
- **Repository**: https://github.com/jaewoocho/Transformers_whisper_cleft/tree/main?tab=readme-ov-file
- **Demo** : https://huggingface.co/spaces/jcho02/Transformers_whisper_cleft

## Uses

### Direct Use
- This model can be directly used by healthcare professionals to analyze patient speech samples for signs of hypernasality, aiding in diagnosis and treatment planning.

### Downstream Use 
- Potential use in automated speech therapy tools or in research settings for studying speech disorders.

### Out-of-Scope Use
- Not intended for non-medical use or as a sole diagnostic tool without clinical oversight.

## Bias, Risks, and Limitations
- 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.

## Recommendations
- Healthcare professionals should use this tool as a supplement to traditional diagnostic methods. Continuous updating with diverse datasets is recommended to improve accuracy.

## How to Get Started with the Model
- Use the following code snippet to get started with the model:
- https://github.com/jaewoocho/Transformers_whisper_cleft/tree/main?tab=readme-ov-file