wav2vec2-base-timit-demo-colab0 Open-source Speech Recognition Model - Precise Recognition to Reduce Word Error Rate

Wav2vec2 Base Timit Demo Colab0

Developed by sherry7144

This model is a fine-tuned speech recognition model based on facebook/wav2vec2-base, achieving a word error rate of 0.5635 on the TIMIT dataset.

Speech Recognition

Transformers

Open Source License:Apache-2.0 #Speech Recognition #Low-resource Fine-tuning #TIMIT Dataset

Downloads 26

Release Time : 4/30/2022

Model Overview

A pre-trained model for English speech recognition, fine-tuned for speech-to-text tasks.

Model Features

Based on wav2vec2 Architecture

Uses facebook's wav2vec2-base as the base model, featuring powerful speech feature extraction capabilities.

Fine-tuned on TIMIT Dataset

Fine-tuned on the standard TIMIT speech dataset, suitable for English speech recognition tasks.

Relatively Low Word Error Rate

Achieves a word error rate of 0.5635 on the evaluation set, demonstrating good performance.

Model Capabilities

English Speech Recognition

Speech-to-Text

Use Cases

Speech Transcription

English Speech Transcription

Convert English speech content into text

Word error rate 0.5635

Voice Assistants

Basic Voice Command Recognition

Can be used to build simple English voice command recognition systems

Training Loss	Epoch	Step	Validation Loss	Wer
0.3976	13.89	500	0.8616	0.5968
0.2637	27.78	1000	0.9973	0.5826
0.1794	41.67	1500	1.0395	0.5635

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Wav2vec2 Base Timit Demo Colab0

Model Overview

Model Features

Model Capabilities

Use Cases

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Model description

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Training and evaluation data

🔧 Technical Details

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