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Wav2vec2 Base Timit Demo Colab 1

Developed by zasheza
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 0.4398.
Downloads 18
Release Time : 5/1/2022

Model Overview

A speech recognition model based on the wav2vec2 architecture, suitable for English speech-to-text tasks.

Model Features

Based on wav2vec2 Architecture
Utilizes the open-source wav2vec2-base model architecture from Facebook, which has excellent speech feature extraction capabilities.
Fine-tuned Optimization
Fine-tuned on the TIMIT dataset for optimized performance on specific speech recognition tasks.
Relatively Low Word Error Rate
Achieves a word error rate (WER) of 0.4398 on the evaluation set, outperforming the base model.

Model Capabilities

English Speech Recognition
Speech-to-Text

Use Cases

Speech Transcription
Meeting Minutes
Automatically transcribe English meeting recordings into text
Accuracy approximately 56.02% (1-WER)
Voice Notes
Convert English voice notes into searchable text
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