Approximate Accuracy Comparison Between Locally Hosted Whisper Models Of Varying Sizes (tiny, Small, Medium, Large), Based On Their Typical Word Error Rates.

This project conducts an approximate accuracy comparison between locally hosted Whisper models categorized by size—tiny, small, medium, and large—based on their typical Word Error Rates. The analysis aims to determine how model size impacts transcription accuracy when processing audio input. By examining these variations, users can better understand the trade-offs between model performance and computational resource requirements.

The project utilizes the Whisper transcription models developed by OpenAI, leveraging their performance metrics to evaluate accuracy across different configurations. It employs Python for data analysis and visualization, allowing for clear representation of results. This analysis serves as a valuable resource for developers and researchers in the fields of speech recognition and natural language processing, particularly those considering the implementation of Whisper models in applications requiring transcription services. It also aids in making informed decisions regarding model selection based on specific use cases.