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Wav2vec2 Timit Demo

Developed by asini
A speech recognition model fine-tuned on the TIMIT dataset based on the facebook/wav2vec2-base model
Speech Recognition
Transformers
Open Source License:Apache-2.0#Speech Recognition#Low Word Error Rate#TIMIT Dataset
Downloads 21
Release Time : 3/2/2022

Model Overview

This is a pre-trained model for English speech recognition, achieving good word error rate performance through fine-tuning on the TIMIT dataset.

Model Features

Efficient Fine-tuning
Fine-tuned based on the powerful wav2vec2-base model, fully leveraging the advantages of the pre-trained model
Low Word Error Rate
Achieved a word error rate (WER) of 34.62% on the TIMIT dataset
Lightweight
Based on the wav2vec2-base architecture, more computationally efficient compared to larger models

Model Capabilities

English Speech Recognition
Audio to Text
Speech Content Analysis

Use Cases

Speech Transcription
Meeting Minutes
Automatically convert English meeting recordings into text transcripts
Accuracy approximately 65.38% (based on 1-WER calculation)
Voice Notes
Convert English voice notes into searchable text
Speech Analysis
Speech Content Analysis
Analyze keywords and topics in speech content
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