Icelandic-finetuned-no-data-augmentation

Chenchuhui/Icelandic-finetuned-no-data-augmentation

Icelandic-finetuned-no-data-augmentation is a automatic speech recognition model from Chenchuhui. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as cc-by-4.0.

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

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At a glance

Task: Automatic Speech Recognition
Library: transformers
License: cc-by-4.0
Model type: wav2vec2
Access: Public
Created: Dec 14, 2023
Updated: Dec 18, 2023
SHA: 83a46a76

Base models

carlosdanielhernandezmena/wav2vec2-large-xlsr-53-icelandic-ep10-1000h

Model card

From the Hugging Face model README

Icelandic-finetuned-no-data-augmentation

This model is a fine-tuned version of carlosdanielhernandezmena/wav2vec2-large-xlsr-53-icelandic-ep10-1000h on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.1262
Wer: 0.1544

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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
No log	0.2	10	0.2849	0.1667
No log	0.4	20	0.3085	0.1611
No log	0.6	30	0.3264	0.1600
No log	0.8	40	0.3149	0.1577
0.3456	1.0	50	0.3221	0.1588
0.3456	1.2	60	0.3183	0.1577
0.3456	1.4	70	0.3133	0.1510
0.3456	1.6	80	0.3069	0.1521
0.3456	1.8	90	0.2970	0.1521
0.2689	2.0	100	0.2782	0.1477
0.2689	2.2	110	0.2242	0.1488
0.2689	2.4	120	0.2257	0.1454
0.2689	2.6	130	0.2722	0.1521
0.2689	2.8	140	0.2564	0.1488
0.2312	3.0	150	0.1811	0.1521
0.2312	3.2	160	0.1762	0.1454
0.2312	3.4	170	0.2154	0.1421
0.2312	3.6	180	0.1873	0.1465
0.2312	3.8	190	0.2015	0.1465
0.2153	4.0	200	0.2402	0.1499
0.2153	4.2	210	0.2366	0.1454
0.2153	4.4	220	0.1890	0.1477
0.2153	4.6	230	0.1897	0.1499
0.2153	4.8	240	0.1777	0.1488
0.1986	5.0	250	0.1659	0.1555
0.1986	5.2	260	0.1936	0.1588
0.1986	5.4	270	0.2044	0.1532
0.1986	5.6	280	0.1958	0.1555
0.1986	5.8	290	0.1760	0.1521
0.1842	6.0	300	0.2056	0.1600
0.1842	6.2	310	0.1649	0.1532
0.1842	6.4	320	0.2269	0.1532
0.1842	6.6	330	0.1572	0.1488
0.1842	6.8	340	0.1890	0.1600
0.181	7.0	350	0.1757	0.1700
0.181	7.2	360	0.2322	0.1644
0.181	7.4	370	0.2644	0.1600
0.181	7.6	380	0.2047	0.1555
0.181	7.8	390	0.2406	0.1678
0.1751	8.0	400	0.2820	0.1678
0.1751	8.2	410	0.2965	0.1655
0.1751	8.4	420	0.2841	0.1667
0.1751	8.6	430	0.2527	0.1734
0.1751	8.8	440	0.5464	0.1700
0.1836	9.0	450	0.2185	0.1622
0.1836	9.2	460	0.2129	0.1857
0.1836	9.4	470	0.3367	0.1779
0.1836	9.6	480	0.1457	0.1644
0.1836	9.8	490	0.1320	0.1667
0.2141	10.0	500	0.1262	0.1544

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.15.0
Tokenizers 0.15.0

Task: Automatic Speech Recognition
Library: transformers
Type: wav2vec2
License: cc-by-4.0
Created: Dec 14, 2023
Updated: Dec 18, 2023