Summary
Developers have open-sourced a new library designed to normalize text in Speech-to-Text (STT) evaluations, preventing Word Error Rate (WER) from unfairly penalizing formatting differences like "$50" vs "fifty dollars." This tool ensures WER accurately reflects actual recognition quality by standardizing both reference and hypothesis texts before scoring, addressing a common issue in STT benchmarking.
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