wav2vec_mle Open-source Speech Recognition Model - Based on Pre-training and Fine-tuning, with Word Error Rate as Low as 1.0

Wav2vec Mle

Developed by eugenetanjc

A speech recognition model fine-tuned based on facebook/wav2vec2-base-960h, achieving a word error rate of 1.0 on the evaluation set

Speech Recognition

Transformers

Open Source License:Apache-2.0 #Speech Recognition #Fine-tuned Model #Low Word Error Rate

Downloads 68

Release Time : 6/24/2022

Model Overview

This model is a speech recognition model based on the wav2vec2 architecture, fine-tuned for speech-to-text tasks

Model Features

Based on wav2vec2 Architecture

Uses Facebook's wav2vec2-base-960h as the base model

Fine-tuning Optimization

Fine-tuned on specific datasets to optimize speech recognition performance

Model Capabilities

Speech-to-Text

Automatic Speech Recognition

Use Cases

Speech Transcription

Meeting Minutes

Automatically convert meeting recordings into text transcripts

Voice Notes

Convert voice memos into searchable text

🚀 wav2vec_mle

This model is a fine - tuned version of facebook/wav2vec2-base-960h, which can achieve specific results on the evaluation set, providing a basis for speech - related tasks.

🚀 Quick Start

This model is a fine - tuned version of facebook/wav2vec2-base-960h on the None dataset. It achieves the following results on the evaluation set:

Loss: 4.3076
Wer: 1.0

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0003
train_batch_size: 6
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 12
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e - 08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 20
num_epochs: 60

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
7.3604	3.33	30	4.4612	1.0
4.502	6.67	60	4.5906	1.0
4.2842	10.0	90	4.4217	1.0
4.3833	13.33	120	4.3967	1.0
4.2631	16.67	150	4.3469	1.0
4.3357	20.0	180	4.3372	1.0
4.3941	23.33	210	4.3187	1.0
4.393	26.67	240	4.2981	1.0
4.3619	30.0	270	4.3049	1.0
4.3849	33.33	300	4.3138	1.0
4.3186	36.67	330	4.3123	1.0
4.3196	40.0	360	4.3097	1.0
4.3212	43.33	390	4.3279	1.0
4.3108	46.67	420	4.3249	1.0
4.3112	50.0	450	4.3093	1.0
4.2994	53.33	480	4.3198	1.0
4.2958	56.67	510	4.3071	1.0
4.2905	60.0	540	4.3076	1.0