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W2v Timit Ft 4001
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W2v Timit Ft 4001

Developed by devin132
A speech recognition model based on Wav2Vec 2.0 architecture, fine-tuned on the TIMIT dataset, suitable for English speech-to-text tasks
Speech Recognition
Transformers
#English speech recognition#High-precision speech-to-text#Low-resource speech processing
Downloads 22
Release Time : 3/2/2022

Model Overview

This model is a variant of Facebook's Wav2Vec 2.0, specifically fine-tuned for the TIMIT speech dataset, designed for high-precision English speech recognition

Model Features

End-to-end speech recognition
Directly generates text from raw audio waveforms, eliminating the need for traditional acoustic feature extraction steps in speech recognition pipelines
Self-supervised pretraining
Utilizes a two-stage training approach with large-scale unsupervised pretraining followed by supervised fine-tuning
Context-aware
Transformer architecture captures long-range speech context dependencies

Model Capabilities

English speech recognition
Direct audio waveform processing
Speaker-independent recognition

Use Cases

Speech transcription
Meeting minutes automation
Automatically converts English meeting recordings into text transcripts
Achieves approximately 5% word error rate on TIMIT test set
Assistive technology
Voice control interface
Provides speech recognition capabilities for device control by individuals with disabilities
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