werpy and werx
The Python packages are ecosystem siblings, with one being an ultra-fast tool for calculating and analyzing Word Error Rate (WER) and the other a complementary, easy-to-use package for lightning-fast WER analysis.
About werpy
analyticsinmotion/werpy
🐍📦 Ultra-fast Python package for calculating and analyzing the Word Error Rate (WER). Built for the scalable evaluation of speech and transcription accuracy.
This tool helps you quickly and accurately measure how well a spoken phrase has been converted into written text, or how similar two pieces of text are. By comparing a "reference" (the correct text) to a "hypothesis" (the transcribed or predicted text), it calculates the Word Error Rate (WER) and shows you exactly where mistakes like insertions, deletions, or substitutions occurred. It's designed for speech-to-text engineers, transcription quality analysts, and researchers evaluating text generation systems.
About werx
analyticsinmotion/werx
🐍📦 Easy-to-use Python package for lightning-fast Word Error Rate (WER) analysis
This tool helps you quickly assess the accuracy of text transcription systems, like those used in voice-to-text applications or call center analytics. You input the correct version of what was said (reference text) and the system's transcribed output, and it calculates how many words were wrong. This is for data scientists, speech engineers, or researchers who need to evaluate and compare transcription quality efficiently.
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