wav2vec2-large-voxrex-npsc

NbAiLab/wav2vec2-large-voxrex-npsc

wav2vec2-large-voxrex-npsc is a automatic speech recognition model from NbAiLab. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as cc0-1.0.

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

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At a glance

Task: Automatic Speech Recognition
Library: transformers
License: cc0-1.0
Model type: wav2vec2
Access: Public
Created: Mar 2, 2022
Updated: Sep 13, 2023
SHA: 168dd72f

Base models

KBLab/wav2vec2-large-voxrex

Datasets

NbAiLab/NPSC

Model card

From the Hugging Face model README

wav2vec2-large-voxrex-npsc

This model is a fine-tuned version of KBLab/wav2vec2-large-voxrex on the NBAILAB/NPSC - 16K_MP3 dataset. It achieves the following results on the evaluation set:

Loss: nan
Wer: 1.0

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: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 2000
num_epochs: 15.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
2.9728	0.32	500	2.9449	1.0
2.5099	0.64	1000	1.8492	0.9910
0.7872	0.97	1500	0.4467	0.3774
0.5993	1.29	2000	0.3181	0.2819
0.5134	1.61	2500	0.2638	0.2401
0.4544	1.93	3000	0.2287	0.2091
0.4085	2.26	3500	0.2153	0.1918
0.3921	2.58	4000	0.2004	0.1804
0.4613	2.9	4500	0.1905	0.1732
0.3402	3.22	5000	0.1778	0.1659
0.3258	3.55	5500	0.1732	0.1571
0.3044	3.87	6000	0.1677	0.1497
0.2914	4.19	6500	0.1597	0.1420
0.278	4.51	7000	0.1574	0.1386
0.2858	4.84	7500	0.1552	0.1300
0.2585	5.16	8000	0.1523	0.1276
0.2827	5.48	8500	0.1448	0.1265
0.3365	5.8	9000	0.1411	0.1232
0.2488	6.13	9500	0.1456	0.1195
0.2406	6.45	10000	0.1414	0.1194
0.2488	6.77	10500	0.1393	0.1173
0.3084	7.09	11000	0.1379	0.1164
0.2365	7.41	11500	0.1387	0.1165
0.2217	7.74	12000	0.1381	0.1132
0.2381	8.06	12500	0.1360	0.1126
0.2329	8.38	13000	0.1357	0.1124
0.2103	8.7	13500	0.1335	0.1087
0.2366	9.03	14000	0.1388	0.1105
0.2289	9.35	14500	0.1383	0.1098
0.2486	9.67	15000	0.1386	0.1087
0.2772	9.99	15500	0.1598	0.1093
0.2728	10.32	16000	0.1814	0.1110
0.3437	10.64	16500	0.2505	0.1124
0.431	10.96	17000	0.2828	0.1143
0.3929	11.28	17500	0.2977	0.1149
0.4396	11.61	18000	0.3198	0.1170
0.59	11.93	18500	0.4158	0.1315
0.7813	12.25	19000	0.6123	0.2208
0.9345	12.57	19500	0.6815	0.2885
0.998	12.89	20000	0.7587	0.1991
1.0493	13.22	20500	0.7583	0.1996
1.438	13.54	21000	nan	1.0
0.0	13.86	21500	nan	1.0
0.0	14.18	22000	nan	1.0
0.0	14.51	22500	nan	1.0
0.0	14.83	23000	nan	1.0

Framework versions

Transformers 4.17.0.dev0
Pytorch 1.10.2+cu113
Datasets 1.18.3.dev0
Tokenizers 0.11.0

Task: Automatic Speech Recognition
Library: transformers
Type: wav2vec2
License: cc0-1.0
Created: Mar 2, 2022
Updated: Sep 13, 2023