distilhubert-timit

This model is a fine-tuned version of ntu-spml/distilhubert on the TIMIT_ASR - NA dataset. It achieves the following results on the evaluation set:

• Loss: 1.3601
• Wer: 0.6776

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
5.4447	0.69	100	4.9546	1.0
2.9499	1.38	200	2.9519	1.0
2.8989	2.07	300	2.8624	1.0
2.2076	2.76	400	2.1089	1.0008
1.4186	3.45	500	1.4112	0.9165
0.9951	4.14	600	1.1378	0.7701
0.9754	4.83	700	1.0152	0.7274
0.9364	5.52	800	0.9619	0.7011
0.6557	6.21	900	0.9144	0.6868
0.5681	6.9	1000	0.8899	0.6683
0.66	7.59	1100	0.8992	0.6654
0.6144	8.28	1200	0.9299	0.6898
0.4099	8.97	1300	0.9510	0.6674
0.3384	9.66	1400	0.9598	0.6612
0.3163	10.34	1500	0.9954	0.6612
0.4204	11.03	1600	1.0164	0.6607
0.1932	11.72	1700	1.0637	0.6658
0.1449	12.41	1800	1.1190	0.6652
0.1803	13.1	1900	1.1260	0.6689
0.328	13.79	2000	1.2186	0.6751
0.0838	14.48	2100	1.2591	0.6909
0.0766	15.17	2200	1.2529	0.6780
0.0956	15.86	2300	1.2537	0.6668
0.2339	16.55	2400	1.3210	0.6797
0.0431	17.24	2500	1.3241	0.6781
0.0508	17.93	2600	1.3184	0.6683
0.0616	18.62	2700	1.3728	0.6889
0.1608	19.31	2800	1.3572	0.6771
0.0378	20.0	2900	1.3601	0.6776

Framework versions

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