sew-d-small-100k-timit

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: 1.7541
• Wer: 0.8061

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
4.2068	0.69	100	4.0802	1.0
2.9805	1.38	200	2.9792	1.0
2.9781	2.07	300	2.9408	1.0
2.9655	2.76	400	2.9143	1.0
2.8953	3.45	500	2.8775	1.0
2.7718	4.14	600	2.7787	1.0
2.6711	4.83	700	2.6401	0.9786
2.6403	5.52	800	2.5435	1.0392
2.4052	6.21	900	2.4580	1.0706
2.1708	6.9	1000	2.2800	1.0090
2.2555	7.59	1100	2.1493	0.9579
2.3673	8.28	1200	2.0709	0.9051
2.091	8.97	1300	2.0258	0.8926
1.8433	9.66	1400	1.9645	0.8243
1.6824	10.34	1500	1.9211	0.8707
2.2282	11.03	1600	1.8914	0.8695
1.9027	11.72	1700	1.8718	0.8343
1.6303	12.41	1800	1.8646	0.8232
1.648	13.1	1900	1.8297	0.8177
2.0429	13.79	2000	1.8127	0.8642
1.8833	14.48	2100	1.8005	0.8307
1.5996	15.17	2200	1.7926	0.8467
1.4876	15.86	2300	1.7795	0.8341
1.8925	16.55	2400	1.7716	0.8199
1.814	17.24	2500	1.7846	0.8086
1.536	17.93	2600	1.7655	0.8019
1.4476	18.62	2700	1.7599	0.8070
1.7629	19.31	2800	1.7589	0.8119
1.7646	20.0	2900	1.7541	0.8061

Framework versions

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