The wav2vec2-base-finetuned-ks open-source speech recognition model - The accuracy of the evaluation set reaches 87.27%, and it works well.

Wav2vec2 Base Finetuned Ks

Developed by FerhatDk

A speech recognition model fine-tuned based on facebook/wav2vec2-base, achieving an accuracy of 87.27% on the evaluation set.

Downloads 38

Release Time : 9/22/2022

Model Overview

This model is a fine-tuned version based on the wav2vec2-base architecture, primarily used for speech recognition tasks.

High Accuracy

Achieves an accuracy of 87.27% on the evaluation set.

Based on wav2vec2 Architecture

Uses facebook's wav2vec2-base as the base model.

Optimized Training

Fine-tuned for 25 epochs using cosine learning rate scheduler and Adam optimizer.

Speech Recognition

Audio Feature Extraction

Speech Processing

Voice Command Recognition

Recognizes simple voice commands

Accuracy: 87.27%

This model is a fine - tuned version of facebook/wav2vec2-base, aiming to achieve high - performance speech processing.

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

More information needed

More information needed

More information needed

The following hyperparameters were used during training:

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	8	0.6840	0.6
0.6867	2.0	16	0.6780	0.6364
0.6742	3.0	24	0.6601	0.6182
0.6446	4.0	32	0.6294	0.6364
0.6299	5.0	40	0.6002	0.6727
0.6299	6.0	48	0.5755	0.7091
0.6021	7.0	56	0.5530	0.7273
0.5678	8.0	64	0.5036	0.8182
0.5512	9.0	72	0.4753	0.8545
0.4784	10.0	80	0.4184	0.9273
0.4784	11.0	88	0.4102	0.8909
0.4515	12.0	96	0.4444	0.8182
0.4878	13.0	104	0.3780	0.9091
0.4418	14.0	112	0.4570	0.8
0.4746	15.0	120	0.3870	0.8545
0.4746	16.0	128	0.3932	0.8364
0.4226	17.0	136	0.2779	0.9636
0.4301	18.0	144	0.3125	0.9455
0.3482	19.0	152	0.3212	0.9091
0.3611	20.0	160	0.3925	0.8364
0.3611	21.0	168	0.3389	0.8909
0.3507	22.0	176	0.3099	0.8727
0.3241	23.0	184	0.3120	0.8727
0.2533	24.0	192	0.2313	0.9455
0.2466	25.0	200	0.3550	0.8727