wav2vec2-base-timit-demo-colab-1 Open-Source Speech Recognition Model - Accurately Identify Speech Content, Free to Use

Wav2vec2 Base Timit Demo Colab 1

Developed by Prasadi

This model is a fine-tuned speech recognition model based on facebook/wav2vec2-base, trained on the TIMIT dataset with an evaluation set word error rate (WER) of 0.3874.

Speech Recognition

Transformers

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

Downloads 15

Release Time : 3/2/2022

Model Overview

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

Model Features

Low Word Error Rate

Achieves a word error rate (WER) of 0.3874 on the evaluation set, demonstrating good performance.

Based on wav2vec2 Architecture

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

Fine-tuned Training

Fine-tuned on the TIMIT dataset, making it suitable for specific speech recognition scenarios.

Model Capabilities

English Speech Recognition

Audio to Text Conversion

Use Cases

Speech Transcription

Automatic Meeting Transcription

Automatically converts English meeting recordings into text transcripts

Word error rate approximately 38.74%

Voice Command Recognition

Recognizes English voice commands and converts them into executable commands

Training Loss	Epoch	Step	Validation Loss	Wer
3.4285	2.01	500	1.4732	0.9905
0.7457	4.02	1000	0.5278	0.4960
0.3463	6.02	1500	0.4245	0.4155
0.2034	8.03	2000	0.3857	0.3874

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Wav2vec2 Base Timit Demo Colab 1

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 wav2vec2-base-timit-demo-colab-1

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📚 Documentation

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

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