Machine Learning Use Case

Audio Classification

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

Multimodal Data

Audio Recordings
Audio Recordings
Recording Device Data
Recording Device Data
Recording Metadata
Recording Metadata
Demographics
Demographics
Machinery Information
Machinery Information
Audio Classification

Potential Use Cases

Early detection of chronic disease
Early detection of chronic disease
Customer service automation
Customer service automation
Preventative maintenance on machinery
Preventative maintenance on machinery

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.

Sample Ludwig configuration and Automated ML pipeline

Resources to Get Started

Ready to accelerate your model building?