Comparative analysis of the Moonshine and Faster-Whisper Tiny transcription models, focusing on latency and Word Error Rate (WER)
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Updated
Oct 27, 2024 - Jupyter Notebook
Comparative analysis of the Moonshine and Faster-Whisper Tiny transcription models, focusing on latency and Word Error Rate (WER)
Correcting the noisy OCR output on Bangla language using seq2seq Model
中文 ASR 评测工具箱 · micro-CER 对比 FunASR/Whisper/llama.cpp · 一条命令出报告 · 自带迷你测试集 · Mandarin ASR benchmark toolkit
his repository contains an Automatic Speech Recognition (ASR) system for Amharic built by fine-tuning Facebook’s Wav2Vec2.0 model using Hugging Face Transformers. The goal is to provide an open-source Amharic speech-to-text model, making it easier for developers and researchers to work with Amharic audio data.
Python tool to evaluate Automatic Speech Recognition (ASR) outputs by calculating Word Error Rate (WER) for multiple models. Ideal for NLP research, ASR benchmarking, and improving transcription accuracy.
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