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.

STT 한글 문장 인식기 출력 스크립트의 외자 오류율(CER), 단어 오류율(WER)을 계산하는 Python 함수 패키지

Evaluate results from ASR/Speech-to-Text quickly

Calculates the word error rate of two strings, and the result is written into beautify HTML.

Takes audio and reference transcriptions in bulk and generates WER

🐍📦 Rapidly calculate and analyze the Word Error Rate (WER) with this powerful yet lightweight Python package.

Python tools to compare output transcript to reference

A simple Python package to calculate word error rate (WER).

Calculates the word error rate between the reference and hypothesis in ASR, then print the aligned result.

Implementation of a couple of heuristics that estimate OCR quality without reliance on ground truth data, focusing on historical documents written in English.

CMU sphinx evaluation scripts

d-ser-t quantifies speech recognition accuracy of the MSFT speech service and/or user created MSFT custom speech service models.

Evaluating the werpy Python package for UI development by testing it within PyScript for seamless integration and user interface exploration

Exploring the functionality of the werpy Python package through testing within the Gradio tool for interactive user interface development.

Small utility which helps to get most important metrics for STT

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.

A word error rate util for golang

Word Error Rate computation using components from huggingface-evaluate and openai-whisper projects