wav2vec2-base-STTTest

Giseok/wav2vec2-base-STTTest

wav2vec2-base-STTTest is a automatic speech recognition model from Giseok. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Automatic Speech Recognition
Library: transformers
License: apache-2.0
Model type: wav2vec2
Access: Public
Created: May 26, 2022
Updated: May 27, 2022
SHA: 3a8337c4

Model card

From the Hugging Face model README

wav2vec2-base-STTTest

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

Loss: 0.5198
Wer: 0.3393

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: 0.0001
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1000
num_epochs: 30
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.231	1.0	500	0.4337	0.4100
0.1845	2.01	1000	0.4296	0.3931
0.1551	3.01	1500	0.4397	0.3770
0.1479	4.02	2000	0.4524	0.3827
0.1186	5.02	2500	0.5182	0.3795
0.1079	6.02	3000	0.4799	0.3737
0.0974	7.03	3500	0.4966	0.3860
0.0878	8.03	4000	0.4993	0.3699
0.0788	9.04	4500	0.5183	0.3678
0.0732	10.04	5000	0.5064	0.3635
0.0664	11.04	5500	0.5330	0.3663
0.0596	12.05	6000	0.5147	0.3516
0.0538	13.05	6500	0.5254	0.3581
0.0535	14.06	7000	0.4902	0.3534
0.0492	15.06	7500	0.5115	0.3488
0.0455	16.06	8000	0.5250	0.3472
0.0434	17.07	8500	0.5338	0.3515
0.0351	18.07	9000	0.5365	0.3444
0.0341	19.08	9500	0.4886	0.3439
0.0332	20.08	10000	0.5234	0.3475
0.0289	21.08	10500	0.5375	0.3464
0.028	22.09	11000	0.5395	0.3478
0.0225	23.09	11500	0.5236	0.3428
0.0244	24.1	12000	0.5122	0.3402
0.0246	25.1	12500	0.5212	0.3390
0.0214	26.1	13000	0.5198	0.3393
0.0179	27.11	13500	0.5198	0.3393
0.0194	28.11	14000	0.5198	0.3393
0.0193	29.12	14500	0.5198	0.3393

Framework versions

Transformers 4.17.0
Pytorch 1.10.1+cu111
Datasets 1.18.3
Tokenizers 0.12.1

Task: Automatic Speech Recognition
Library: transformers
Type: wav2vec2
License: apache-2.0
Created: May 26, 2022
Updated: May 27, 2022