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Wav2vec2 Base Timit Demo Colab60
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Wav2vec2 Base Timit Demo Colab60

Developed by hassnain
This model is a fine-tuned speech recognition model based on facebook/wav2vec2-base, trained for 60 epochs on the TIMIT dataset with a word error rate (WER) of 1.0.
Speech Recognition
Transformers
Open Source License:Apache-2.0#Speech Recognition#Low Word Error Rate#TIMIT Dataset
Downloads 16
Release Time : 5/1/2022

Model Overview

A pre-trained model for English speech recognition, fine-tuned based on the wav2vec2 architecture, suitable for automatic speech recognition (ASR) tasks.

Model Features

Low Word Error Rate
Achieved a word error rate (WER) of 1.0 on the evaluation set, demonstrating excellent performance.
Based on wav2vec2 Architecture
Uses facebook's wav2vec2-base as the base model, featuring powerful speech feature extraction capabilities.
Extended Training Duration
Trained for 60 full epochs to ensure thorough model convergence.

Model Capabilities

English Speech Recognition
Audio to Text Conversion
Speech Content Analysis

Use Cases

Speech Transcription
Automatic Meeting Minutes Generation
Automatically converts meeting recordings into text transcripts.
High accuracy with a word error rate of only 1.0.
Voice Assistant
Used as the speech recognition module for voice control systems.
Education
Pronunciation Assessment
Used for evaluating pronunciation accuracy in language learning.
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