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Wav2vec2 Base Timit Demo Colab3000
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Wav2vec2 Base Timit Demo Colab3000

Developed by hassnain
A speech recognition model fine-tuned on the TIMIT dataset based on the facebook/wav2vec2-base model, suitable for English speech-to-text tasks.
Speech Recognition
Transformers
Open Source License:Apache-2.0#Speech Recognition#Low Word Error Rate#TIMIT Dataset
Downloads 23
Release Time : 5/2/2022

Model Overview

This model is a fine-tuned version of wav2vec2-base, specifically designed for English speech recognition tasks, trained and evaluated on the TIMIT dataset.

Model Features

Based on wav2vec2 Architecture
Utilizes the foundational wav2vec2 architecture proposed by Facebook, featuring robust speech feature extraction capabilities.
Fine-tuned on TIMIT Dataset
Fine-tuned on the standard TIMIT speech dataset, optimizing performance for English speech recognition.
Efficient Inference
Capable of processing approximately 9.8 samples per second during evaluation, demonstrating high processing efficiency.

Model Capabilities

English Speech Recognition
Audio-to-Text Conversion
Continuous Speech Recognition

Use Cases

Speech Transcription
English Speech Transcription
Convert English speech content into text format
Word Error Rate (WER) of 0.3845
Educational Applications
Pronunciation Assessment
Can be used for evaluating pronunciation accuracy in language learning
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