wav2vec2-final-1-lm-2 – AI Model by chrisvinsen | AlphaNeural AI

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chrisvinsen/
wav2vec2-final-1-lm-2

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wav2vec2-19

WER 0.283

WER 0.126 with 3-Gram

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: 0.6305
Wer: 0.4499

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: 32
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: 800
num_epochs: 60

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
3.4816	2.74	400	1.0717	0.8927
0.751	5.48	800	0.7155	0.7533
0.517	8.22	1200	0.7039	0.6675
0.3988	10.96	1600	0.5935	0.6149
0.3179	13.7	2000	0.6477	0.5999
0.2755	16.44	2400	0.5549	0.5798
0.2343	19.18	2800	0.6626	0.5798
0.2103	21.92	3200	0.6488	0.5674
0.1877	24.66	3600	0.5874	0.5339
0.1719	27.4	4000	0.6354	0.5389
0.1603	30.14	4400	0.6612	0.5210
0.1401	32.88	4800	0.6676	0.5131
0.1286	35.62	5200	0.6366	0.5075
0.1159	38.36	5600	0.6064	0.4977
0.1084	41.1	6000	0.6530	0.4835
0.0974	43.84	6400	0.6118	0.4853
0.0879	46.58	6800	0.6316	0.4770
0.0815	49.32	7200	0.6125	0.4664
0.0708	52.05	7600	0.6449	0.4683
0.0651	54.79	8000	0.6068	0.4571
0.0555	57.53	8400	0.6305	0.4499

Framework versions

Transformers 4.19.2
Pytorch 1.11.0+cu113
Datasets 2.2.2
Tokenizers 0.12.1