Wav2vec2-base-1 Open-Source Speech Recognition Model - Free to Use and Accurately Identify Speech Content

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Wav2vec2 Base 1

Developed by jiobiala24

A fine-tuned speech recognition model based on facebook/wav2vec2-base trained on the common_voice dataset

Speech Recognition

Transformers

Open Source License:Apache-2.0 #Speech Recognition #Low Word Error Rate #Multilingual Support

Downloads 20

Release Time : 3/2/2022

Model Overview

This model is a fine-tuned version for speech recognition tasks, based on the wav2vec2 architecture and trained on the common_voice dataset, supporting automatic speech-to-text functionality.

Model Features

Efficient Fine-tuning

Fine-tuned based on the pre-trained wav2vec2-base model, leveraging the powerful feature extraction capabilities of the pre-trained model

Good Performance

Achieves a word error rate (WER) of 0.3216 on the evaluation set, outperforming many similar models

Optimized Training

Utilizes linear learning rate scheduling and 1000-step warmup, ensuring stable and efficient training

Model Capabilities

Speech-to-Text

Automatic Speech Recognition

Use Cases

Speech Transcription

Meeting Minutes

Automatically convert meeting recordings into text transcripts

Approximately 68% accuracy (inferred based on WER 0.3216)

Subtitle Generation

Automatically generate subtitles for video content

Voice Assistants

Voice Command Recognition

Recognize user voice commands and convert them into executable commands

Training Loss	Epoch	Step	Validation Loss	Wer
1.6597	2.2	1000	0.8904	0.5388
0.4751	4.41	2000	0.7009	0.3976
0.3307	6.61	3000	0.7068	0.3672
0.2574	8.81	4000	0.7320	0.3544
0.2096	11.01	5000	0.7803	0.3418
0.177	13.22	6000	0.7768	0.3423
0.1521	15.42	7000	0.8113	0.3375
0.1338	17.62	8000	0.8153	0.3325
0.1168	19.82	9000	0.8851	0.3306
0.104	22.03	10000	0.8811	0.3277
0.0916	24.23	11000	0.8722	0.3254
0.083	26.43	12000	0.9527	0.3265
0.0766	28.63	13000	0.9254	0.3216

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Wav2vec2 Base 1

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 wav2vec2-base-1

🚀 Quick Start

📚 Documentation

Training and evaluation data

Model description

Intended uses & limitations

Training procedure

Training hyperparameters

Training results

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