wav2vec2-base-timit-demo-colab Open-source Speech Recognition Model - Free Deployment to Achieve English Speech-to-Text Conversion

Wav2vec2 Base Timit Demo Colab

Developed by Rafat

A speech recognition model fine-tuned on the TIMIT dataset based on the facebook/wav2vec2-base model, primarily used for English speech-to-text tasks.

Speech Recognition

Transformers

Open Source License:Apache-2.0 #Speech Recognition #Low Word Error Rate #TIMIT Dataset

Downloads 18

Release Time : 3/2/2022

Model Overview

This model is a fine-tuned version of wav2vec2-base, specializing in English speech recognition tasks and demonstrating excellent performance on the TIMIT dataset.

Model Features

Efficient Speech Recognition

Fine-tuned on the TIMIT dataset, achieving a Word Error Rate (WER) of 0.2386

Based on wav2vec2 Architecture

Utilizes the wav2vec2-base architecture developed by Facebook Research

Lightweight Model

Base model size suitable for deployment in resource-constrained environments

Model Capabilities

English Speech Recognition

Speech-to-Text

Automatic Speech Transcription

Use Cases

Speech Transcription

Meeting Minutes

Automatically convert English meeting recordings into text transcripts

Accuracy approximately 76% (inferred based on WER 0.2386)

Voice Notes

Convert English voice notes into searchable text content

Training Loss	Epoch	Step	Validation Loss	Wer
3.5486	4.0	500	2.1672	0.9876
0.6819	8.0	1000	0.4502	0.3301
0.2353	12.0	1500	0.4352	0.2841
0.1427	16.0	2000	0.4237	0.2584
0.0945	20.0	2500	0.4409	0.2545
0.0671	24.0	3000	0.4257	0.2413
0.0492	28.0	3500	0.4229	0.2386

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Wav2vec2 Base Timit Demo Colab

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 wav2vec2-base-timit-demo-colab

🚀 Quick Start

📚 Documentation

Training procedure

Training hyperparameters

Training results

Framework versions

📄 License