wav2vec2-base-timit-demo-colab52 Open-Source Speech Recognition Model

Wav2vec2 Base Timit Demo Colab52

Developed by hassnain

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

Speech Recognition

Transformers

Open Source License:Apache-2.0 #Speech Recognition #Low-resource Optimization #TIMIT Dataset

Downloads 16

Release Time : 5/1/2022

Model Overview

This is an automatic speech recognition (ASR) model based on the wav2vec2 architecture, fine-tuned on the TIMIT dataset, capable of converting English speech into text.

Model Features

Based on wav2vec2 Architecture

Utilizes the wav2vec2 base architecture developed by Facebook, with excellent speech feature extraction capabilities

Fine-tuned on TIMIT Dataset

Fine-tuned on the standard TIMIT speech dataset, improving English speech recognition accuracy

Relatively Lightweight

Based on the wav2vec2-base version, easier to deploy and use compared to larger models

Model Capabilities

English Speech Recognition

Speech-to-Text

Use Cases

Speech Transcription

English Speech Transcription

Convert English speech content into text format

Word Error Rate (WER) of 0.7501

Training Loss	Epoch	Step	Validation Loss	Wer
5.3424	7.04	500	3.3225	1.0
2.518	14.08	1000	1.5884	0.8300
1.0217	21.13	1500	1.6643	0.7719
0.6074	28.17	2000	1.7941	0.7501

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Wav2vec2 Base Timit Demo Colab52

Model Overview

Model Features

Model Capabilities

Use Cases

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