whisperkit coreml

Introduction

WhisperKit is an on-device speech recognition framework designed for Apple Silicon. It leverages Core ML to provide efficient and accurate automatic speech recognition (ASR).

Architecture

WhisperKit integrates with Core ML, utilizing Apple's machine learning framework to enable on-device processing. This approach ensures privacy and efficiency by eliminating the need for continuous cloud communication.

Training

The training details for WhisperKit are not explicitly provided in the available documentation. However, performance and accuracy benchmarks can be found on Hugging Face's dedicated benchmarking page for WhisperKit.

Guide: Running Locally

1. Clone the Repository: Obtain the WhisperKit framework from its GitHub repository.

   git clone https://github.com/argmaxinc/WhisperKit
   

2. Install Dependencies: Ensure all necessary dependencies for Core ML and WhisperKit are installed on your Apple Silicon device.

3. Build the Project: Use Xcode or a similar development environment to build the WhisperKit project.

4. Run the Model: Deploy the model on your device for on-device speech recognition tasks.

For enhanced performance, consider using cloud GPUs to experiment with different model sizes and configurations.

License

The licensing information for WhisperKit is not specified in the provided documentation. For detailed licensing terms, refer to the GitHub repository or contact the developers directly.