Attempt at tracking states of the arts and recent results (bibliography) on speech recognition.
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Updated
Jun 27, 2022
Attempt at tracking states of the arts and recent results (bibliography) on speech recognition.
Evaluate your speech-to-text system with similarity measures such as word error rate (WER)
Python code for various NLP metrics
한국어 STT 출력의 CER, WER, CRR, 키워드·개체명·코퍼스 평가를 제공하는 Python 패키지
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.
A lightweight library for normalizing speech transcripts before computing WER
Calculates the word error rate of two strings, and the result is written into beautify HTML.
Machine Translation (MT) Evaluation Scripts
🐍📦 Easy-to-use Python package for lightning-fast Word Error Rate (WER) analysis
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.
This repo shows how to finetune the wav2vec2.0 model along with its prerequisites.
Word Error Rate computation using components from huggingface-evaluate and openai-whisper projects
Custom plugin for windows error reporting
Calculates the word error rate between the reference and hypothesis in ASR, then print the aligned result.
Exploring the functionality of the werpy Python package through testing within the Gradio tool for interactive user interface development.
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