XLSR-300M-bokmaal

NbAiLab/XLSR-300M-bokmaal

XLSR-300M-bokmaal 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 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: Mar 2, 2022
Updated: Mar 24, 2022
SHA: 1941e5ac

Datasets

NbAiLab/NPSC

Model card

From the Hugging Face model README

XLSR-300M-bokmaal

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the NBAILAB/NPSC - 16K_MP3_BOKMAAL dataset. It achieves the following results on the evaluation set:

Loss: 0.1635
Wer: 0.1005

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
3.0307	0.32	500	3.0026	1.0
2.7865	0.64	1000	2.4849	0.9926
0.7522	0.95	1500	0.4567	0.3594
0.5703	1.27	2000	0.3440	0.2586
0.4762	1.59	2500	0.2925	0.2178
0.4585	1.91	3000	0.2442	0.1981
0.4013	2.23	3500	0.2495	0.1818
0.449	2.54	4000	0.2152	0.1808
0.355	2.86	4500	0.2179	0.1670
0.3142	3.18	5000	0.1953	0.1542
0.3242	3.5	5500	0.2103	0.1526
0.3016	3.82	6000	0.1911	0.1477
0.2713	4.13	6500	0.1836	0.1422
0.2807	4.45	7000	0.1924	0.1447
0.2929	4.77	7500	0.1848	0.1402
0.2595	5.09	8000	0.1783	0.1330
0.2289	5.41	8500	0.1901	0.1313
0.2567	5.72	9000	0.1784	0.1298
0.2401	6.04	9500	0.1956	0.1298
0.2098	6.36	10000	0.1748	0.1277
0.2246	6.68	10500	0.1777	0.1254
0.2197	7.0	11000	0.1703	0.1222
0.2122	7.32	11500	0.1917	0.1221
0.2746	7.63	12000	0.1769	0.1215
0.2148	7.95	12500	0.1736	0.1193
0.1915	8.27	13000	0.1814	0.1161
0.2462	8.59	13500	0.1748	0.1166
0.1872	8.91	14000	0.1769	0.1133
0.1886	9.22	14500	0.1852	0.1143
0.1789	9.54	15000	0.1696	0.1126
0.1692	9.86	15500	0.1817	0.1122
0.1765	10.18	16000	0.1769	0.1093
0.1699	10.5	16500	0.1604	0.1084
0.1591	10.81	17000	0.1777	0.1080
0.1499	11.13	17500	0.1645	0.1074
0.163	11.45	18000	0.1704	0.1065
0.1597	11.77	18500	0.1576	0.1064
0.1484	12.09	19000	0.1637	0.1041
0.1464	12.4	19500	0.1631	0.1047
0.156	12.72	20000	0.1686	0.1029
0.1625	13.04	20500	0.1648	0.1023
0.1395	13.36	21000	0.1688	0.1027
0.1387	13.68	21500	0.1670	0.1013
0.1434	13.99	22000	0.1677	0.1017
0.1442	14.31	22500	0.1688	0.1008
0.1439	14.63	23000	0.1647	0.1004
0.137	14.95	23500	0.1636	0.1006

Framework versions

Transformers 4.17.0.dev0
Pytorch 1.10.1+cu102
Datasets 1.18.2.dev0
Tokenizers 0.11.0

Task: Automatic Speech Recognition
Library: transformers
Type: wav2vec2
License: apache-2.0
Created: Mar 2, 2022
Updated: Mar 24, 2022