sew-d-small-100k-ft-timit-2

This model is a fine-tuned version of asapp/sew-d-small-100k on the TIMIT_ASR - NA dataset. It achieves the following results on the evaluation set:

• Loss: 1.7357
• Wer: 0.7935

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: 32
• eval_batch_size: 1
• 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: 20.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
4.1554	0.69	100	4.0531	1.0
2.9584	1.38	200	2.9775	1.0
2.9355	2.07	300	2.9412	1.0
2.9048	2.76	400	2.9143	1.0
2.8568	3.45	500	2.8786	1.0
2.7248	4.14	600	2.7553	0.9833
2.6124	4.83	700	2.5874	1.0511
2.5463	5.52	800	2.4630	1.0883
2.3302	6.21	900	2.3948	1.0651
2.0669	6.9	1000	2.2228	0.9920
2.1991	7.59	1100	2.0815	0.9185
2.293	8.28	1200	2.0229	0.8674
2.0366	8.97	1300	1.9590	0.9165
1.767	9.66	1400	1.9129	0.8125
1.6222	10.34	1500	1.8868	0.8259
2.173	11.03	1600	1.8691	0.8661
1.8614	11.72	1700	1.8388	0.8250
1.5928	12.41	1800	1.8528	0.7772
1.5978	13.1	1900	1.8002	0.7892
1.9886	13.79	2000	1.7848	0.8448
1.8042	14.48	2100	1.7819	0.8156
1.5488	15.17	2200	1.7615	0.8228
1.4468	15.86	2300	1.7565	0.7946
1.8153	16.55	2400	1.7537	0.8341
1.77	17.24	2500	1.7527	0.7958
1.4742	17.93	2600	1.7592	0.7850
1.4088	18.62	2700	1.7421	0.8149
1.7066	19.31	2800	1.7382	0.7977
1.7068	20.0	2900	1.7357	0.7935

Framework versions

• Transformers 4.12.0.dev0
• Pytorch 1.8.1
• Datasets 1.14.1.dev0
• Tokenizers 0.10.3