wav2vec2-base-timit-demo-colab51 Open-source Speech Recognition Model - Free Deployment for Efficient Speech Recognition

Wav2vec2 Base Timit Demo Colab51

Developed by hassnain

This model is a fine-tuned speech recognition model based on facebook/wav2vec2-base, achieving a word error rate of 0.748 on the TIMIT dataset.

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

Efficient Fine-tuning

Fine-tuned based on the powerful wav2vec2-base model, achieving good results even with limited data.

Low Word Error Rate

Achieved a word error rate (WER) of 0.748 on the evaluation set, demonstrating good performance.

End-to-End Training

Adopts an end-to-end training approach, directly mapping audio input to text output.

Model Capabilities

English Speech Recognition

Audio to Text Conversion

Automatic Speech Transcription

Use Cases

Speech Transcription

Automated Meeting Minutes

Automatically convert meeting recordings into text transcripts

Approximately 75.2% accuracy

Voice Command Recognition

Recognize simple voice commands

Training Loss	Epoch	Step	Validation Loss	Wer
5.481	7.04	500	3.2834	1.0
2.2521	14.08	1000	1.6333	0.8093
0.9467	21.13	1500	1.7458	0.7560
0.5888	28.17	2000	1.8395	0.7480

Featured Recommended AI Models

Empowering the Future, Your AI Solution Knowledge Base

English 简体中文繁體中文にほんご

Wav2vec2 Base Timit Demo Colab51

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 wav2vec2-base-timit-demo-colab51

🚀 Quick Start

📚 Documentation

Model description

Intended uses & limitations

Training and evaluation data

🔧 Technical Details

Training procedure

Training hyperparameters

Training results

Framework versions

📄 License