wav2vec2-base-timit-demo-colab7 Open-Source Speech Recognition Model - Accurately Identify Speech Content with Low Error Rate

Wav2vec2 Base Timit Demo Colab7

Developed by sameearif88

This model is a fine-tuned speech recognition model based on facebook/wav2vec2-base, trained on the TIMIT dataset with a Word Error Rate (WER) of 0.5426.

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 speech-to-text tasks.

Model Features

Based on wav2vec2 Architecture

Utilizes the efficient wav2vec2 architecture proposed by Facebook, suitable for speech representation learning.

Low Word Error Rate

Achieves a Word Error Rate (WER) of 0.5426 on the evaluation set, demonstrating good performance.

Transfer Learning

Fine-tuned based on the pre-trained wav2vec2-base model, fully leveraging pre-trained knowledge.

Model Capabilities

English Speech Recognition

Speech-to-Text

Audio Feature Extraction

Use Cases

Speech Transcription

Automatic Meeting Transcription

Automatically converts English meeting recordings into text transcripts

Word Error Rate 0.5426

Voice Command Recognition

Recognizes English voice commands and converts them into executable commands

Training Loss	Epoch	Step	Validation Loss	Wer
5.1854	13.89	500	3.1687	1.0
1.7033	27.78	1000	0.7289	0.5659
0.4208	41.67	1500	0.6917	0.5426

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Wav2vec2 Base Timit Demo Colab7

Model Overview

Model Features

Model Capabilities

Use Cases

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🔧 Technical Details

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