Automatic Subtitle Generation for Bengali Multimedia Using Deep Learning.
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Updated
Oct 29, 2023 - Jupyter Notebook
Automatic Subtitle Generation for Bengali Multimedia Using Deep Learning.
Calculate the word error rate (WER) from provided correct and actual text file(s), for measuring the accuracy of automated speech recognition systems.
Exploring the functionality of the werpy Python package through testing within the Gradio tool for interactive user interface development.
Word Error Rate computation using components from huggingface-evaluate and openai-whisper projects
A word error rate util for golang
Evaluating the werpy Python package for UI development by testing it within PyScript for seamless integration and user interface exploration
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.
d-ser-t quantifies speech recognition accuracy of the MSFT speech service and/or user created MSFT custom speech service models.
Implementation of a couple of heuristics that estimate OCR quality without reliance on ground truth data, focusing on historical documents written in English.
Calculates the word error rate between the reference and hypothesis in ASR, then print the aligned result.
A simple Python package to calculate word error rate (WER).
Python tools to compare output transcript to reference
🐍📦 Rapidly calculate and analyze the Word Error Rate (WER) with this powerful yet lightweight Python package.
Takes audio and reference transcriptions in bulk and generates WER
Calculates the word error rate of two strings, and the result is written into beautify HTML.
Evaluate results from ASR/Speech-to-Text quickly
STT 한글 문장 인식기 출력 스크립트의 외자 오류율(CER), 단어 오류율(WER)을 계산하는 Python 함수 패키지
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