sew-d-small-100k-ft-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: 1.7482
• Wer: 0.7987

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
2.7719	4.14	600	2.7815	0.9999
2.6531	4.83	700	2.6375	1.0065
2.6425	5.52	800	2.5602	1.0210
2.3963	6.21	900	2.4665	1.0591
2.1447	6.9	1000	2.2792	0.9848
2.2719	7.59	1100	2.2237	0.9465
2.3629	8.28	1200	2.1058	0.8907
2.0913	8.97	1300	2.0113	0.9070
1.8334	9.66	1400	1.9466	0.8177
1.6608	10.34	1500	1.9217	0.8698
2.2194	11.03	1600	1.9091	0.8727
1.9002	11.72	1700	1.8746	0.8332
1.6268	12.41	1800	1.8782	0.7951
1.6455	13.1	1900	1.8230	0.8225
2.0308	13.79	2000	1.8067	0.8560
1.855	14.48	2100	1.8129	0.8177
1.5901	15.17	2200	1.7891	0.8367
1.4848	15.86	2300	1.7821	0.8201
1.8754	16.55	2400	1.7700	0.8137
1.7975	17.24	2500	1.7795	0.8171
1.5194	17.93	2600	1.7605	0.7977
1.4374	18.62	2700	1.7529	0.7978
1.7498	19.31	2800	1.7522	0.8023
1.7452	20.0	2900	1.7482	0.7987

Framework versions

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