Machine Learning Use Case
Classification is one of the most common deep learning use cases for audio data with applications like virtual assistants, music identification, speech-to-text, call center automation, and more. Learn how to build an end-to-end audio classification pipeline that predicts the health of patients using respiratory audio files and tabular patient data with Predibase and Ludwig
The Predibase Solution
Turn audio recordings into powerful predictive insights
- Easily preprocess unstructured audio files along with tabular features like age, gender and BMI
- Rapidly train a series of neural networks and tune model parameters with little to no-code to improve performance
- Automate the classification of patients as healthy or unhealthy using audio data to improve healthcare decisions
Potential Use Cases
Train your first audio classification model in < 20 lines of a config file
At the core of Predibase is Ludwig, an open-source declarative ML framework, that automates complex model development with a simple configuration file. Predibase builds on these capabilities with a collaborative, easy-to-use, and fully managed ML platform in the cloud.
Resources to Get Started
Run the Notebook
Get started with audio classification using this open-source Ludwig notebook including sample code and data.
Visit the Resource Center
Visit the Predibase resource center to see our collection of ebooks and webinars on topics from multimodal ML to computer vision.
Learn About Ludwig
Read this deep dive tutorial to learn how to build an end-to-end deep learning pipeline for topic classification.