Evaluate your speech-to-text system with similarity measures such as word error rate (WER)
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Updated
Sep 3, 2026 - Python
Evaluate your speech-to-text system with similarity measures such as word error rate (WER)
An efficient OpenFST-based tool for calculating WER and aligning two transcript sequences.
한국어 STT 출력의 CER, WER, CRR, 키워드·개체명·코퍼스 평가를 제공하는 Python 패키지
Comprehensive voice cloning evaluation: seven automatic speech metrics and ready-to-use benchmark datasets. Score any model’s audio with Python or CLI.
Evaluate results from ASR/Speech-to-Text quickly
🐍📦 Ultra-fast Python package for calculating and analyzing the Word Error Rate (WER). Built for the scalable evaluation of speech and transcription accuracy.
Calculates the word error rate of two strings, and the result is written into beautify HTML.
agent-oriented wer open metrics
🐍📦 Easy-to-use Python package for lightning-fast Word Error Rate (WER) analysis
Python tools to compare output transcript to reference
WhisperCPP (FP32) INT8, INT4, INT5, quantization effect on model latency and WER experiment
A simple Python package to calculate word error rate (WER).
Developed a Marathi speech-to-text application using the Hugging Face whisper ASR models. Trained the model with a custom audio dataset and fine-tuned it for optimized performance. Deployed the model on the Hugging Face Model Hub, achieving a WER of 0.74 for the base model.
Implementation of a couple of heuristics that estimate OCR quality without reliance on ground truth data, focusing on historical documents written in English.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
Word Error Rate computation using components from huggingface-evaluate and openai-whisper projects
A word error rate util for golang
d-ser-t quantifies speech recognition accuracy of the MSFT speech service and/or user created MSFT custom speech service models.
Calculates the word error rate between the reference and hypothesis in ASR, then print the aligned result.
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