wav2vec2-large-xls-r-300m-he – AI Model by mtz2110 | AlphaNeural AI

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mtz2110/
wav2vec2-large-xls-r-300m-he

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wav2vec2-large-xls-r-300m-he

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

Loss: inf
Wer: 0.5954

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: 2
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 6
total_train_batch_size: 12
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 20
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
8.8899	0.99	200	inf	1.0
3.0802	1.98	400	inf	1.0
1.4275	2.97	600	inf	0.8155
0.8737	3.96	800	inf	0.7276
0.6503	4.95	1000	inf	0.6858
0.5176	5.94	1200	inf	0.6660
0.4084	6.93	1400	inf	0.6682
0.3469	7.92	1600	inf	0.6473
3.2485	6.67	1800	inf	1.0
0.6476	7.41	2000	inf	0.6574
0.3229	8.15	2200	inf	0.6499
0.2899	8.89	2400	inf	0.6376
0.26	9.63	2600	inf	0.6405
0.2038	10.37	2800	inf	0.6409
0.2158	11.11	3000	inf	0.6313
0.1892	11.85	3200	inf	0.6185
0.1611	12.59	3400	inf	0.6271
0.1584	13.33	3600	inf	0.6101
0.1443	14.07	3800	inf	0.6121
0.1353	14.81	4000	inf	0.6194
0.1109	15.56	4200	inf	0.6321
0.1116	16.3	4400	inf	0.6025
0.1054	17.04	4600	inf	0.6029
0.0966	17.78	4800	inf	0.6069
0.0824	18.52	5000	inf	0.5998
0.0812	19.26	5200	inf	0.5972
0.0749	20.0	5400	inf	0.5954

Framework versions

Transformers 4.24.0
Pytorch 1.12.1
Datasets 2.6.1
Tokenizers 0.13.1