wav2vec2-wtimit-finetune – AI Model by nawta | AlphaNeural AI

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nawta/
wav2vec2-wtimit-finetune

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wav2vec2-wtimit-finetune

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: 0.0383
Wer: 0.0160

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: 64
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1000
num_epochs: 30
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
5.3743	2.82	500	2.9567	1.0
1.866	5.65	1000	0.2856	0.2580
0.2005	8.47	1500	0.0979	0.0669
0.08	11.3	2000	0.0617	0.0325
0.0497	14.12	2500	0.0578	0.0284
0.0348	16.95	3000	0.0557	0.0239
0.0269	19.77	3500	0.0447	0.0212
0.0198	22.6	4000	0.0437	0.0177
0.016	25.42	4500	0.0407	0.0164
0.014	28.25	5000	0.0383	0.0160

Framework versions

Transformers 4.11.3
Pytorch 1.9.1+cu111
Datasets 1.13.3
Tokenizers 0.10.3