Evaluate your speech-to-text system with similarity measures such as word error rate (WER)
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Updated
Feb 15, 2025 - Python
Evaluate your speech-to-text system with similarity measures such as word error rate (WER)
STT 한글 문장 인식기 출력 스크립트의 외자 오류율(CER), 단어 오류율(WER)을 계산하는 Python 함수 패키지
Calculates the word error rate of two strings, and the result is written into beautify HTML.
🐍📦 Ultra-fast Python package for calculating and analyzing the Word Error Rate (WER). Built for the scalable evaluation of speech and transcription accuracy.
Python tools to compare output transcript to reference
🐍📦 Easy-to-use Python package for lightning-fast Word Error Rate (WER) analysis
A simple Python package to calculate word error rate (WER).
Implementation of a couple of heuristics that estimate OCR quality without reliance on ground truth data, focusing on historical documents written in English.
Toolkit for using Whisper to transcribe YouTube videos. Includes Whisper transcription of YouTube videos, conversion of YouTube video into HuggingFace dataset (using audio and subtitles) and evaluation of Whisper transcription against YouTube subtitles
Exploring the functionality of the werpy Python package through testing within the Gradio tool for interactive user interface development.
ASR-LE is an advanced ASR evaluation + observability toolkit that goes beyond WER: it shows where errors happen in time, estimates streaming p95 first-word latency, generates “worst moments” automatically, and produces reusable artifacts (report.json, timeline bins, moments, etc.)
Phonetic Fidelity Voice Conversion evaluates how well voice conversion models preserve phonetic details by comparing source and converted speech using Phoneme Error Rate (PER), complementing traditional text-based metrics like WER and CER
Calculate the word error rate (WER) from provided correct and actual text file(s), for measuring the accuracy of automated speech recognition systems.
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