wav2vec2-base-timit-demo-colab10 Open-source Speech Recognition Model - Accurate English Speech-to-Text Conversion

Wav2vec2 Base Timit Demo Colab10

Developed by sameearif88

This model is a fine-tuned speech recognition model based on facebook/wav2vec2-base on the TIMIT dataset, focusing on English speech-to-text tasks.

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

This is a model for English Automatic Speech Recognition (ASR), fine-tuned based on the wav2vec2 architecture, capable of converting English speech into text.

Model Features

Based on wav2vec2 Architecture

Utilizes Facebook's wav2vec2-base model architecture with excellent speech feature extraction capabilities

Fine-tuning Optimization

Fine-tuned on the TIMIT dataset, optimized for English speech recognition tasks

Relatively Lightweight

Based on the base version rather than the large version, suitable for deployment in resource-constrained environments

Model Capabilities

English Speech Recognition

Speech-to-Text

Continuous Speech Recognition

Use Cases

Speech Transcription

English Speech to Text

Convert English speech content into text transcripts

Word Error Rate (WER) of 0.3425

Educational Technology

English Pronunciation Assessment

Can be used in pronunciation evaluation systems for English learners

Training Loss	Epoch	Step	Validation Loss	Wer
4.9891	3.52	500	3.1554	1.0
1.71	7.04	1000	0.7122	0.5811
0.6164	10.56	1500	0.5149	0.4880
0.4188	14.08	2000	0.4726	0.4344
0.3038	17.61	2500	0.4765	0.4092
0.2312	21.13	3000	0.4387	0.3765
0.1867	24.65	3500	0.4411	0.3583
0.1582	28.17	4000	0.4460	0.3425

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Wav2vec2 Base Timit Demo Colab10

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 wav2vec2-base-timit-demo-colab10

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Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

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