Wav2vec2-base-timit-demo-colab Open Source Speech Recognition Model - Precise Recognition, Free Deployment, Extremely Practical

Wav2vec2 Base Timit Demo Colab

Developed by murdockthedude

A speech recognition model fine-tuned based on facebook/wav2vec2-base, trained on the TIMIT dataset with a Word Error Rate (WER) of 0.3518

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Release Time : 5/10/2022

Model Overview

This is a model for English speech recognition, fine-tuned based on the wav2vec2 architecture, suitable for converting English speech to text.

Efficient Fine-tuning

Fine-tuned on the TIMIT dataset based on the wav2vec2-base model, retaining the powerful feature extraction capabilities of the original model

Low Word Error Rate

Achieves a Word Error Rate (WER) of 0.3518 on the evaluation set, demonstrating good performance

Training Optimization

Uses linear learning rate scheduling and warm-up strategies for stable training

English Speech Recognition

Speech-to-Text

Speech Transcription

Meeting Minutes

Automatically convert English meeting recordings into text transcripts

Approximately 65% accuracy (inferred based on WER 0.3518)

Voice Notes

Convert English voice notes into searchable text

Training Loss	Epoch	Step	Validation Loss	Wer
3.4716	4.0	500	1.3023	0.9254
0.5958	8.0	1000	0.4582	0.4399
0.2223	12.0	1500	0.4477	0.3886
0.1373	16.0	2000	0.4791	0.3630
0.101	20.0	2500	0.4676	0.3561
0.0724	24.0	3000	0.4539	0.3510
0.0513	28.0	3500	0.4627	0.3518

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