unispeech-large-1500h-cv-timit

This model is a fine-tuned version of microsoft/unispeech-large-1500h-cv on the TIMIT_ASR - NA dataset. It achieves the following results on the evaluation set:

• Loss: 0.3099
• Wer: 0.2196

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
4.64	0.69	100	3.9717	0.9981
2.6793	1.38	200	2.6264	1.0
1.2221	2.07	300	0.9999	0.7167
0.9009	2.76	400	0.6509	0.5570
0.4352	3.45	500	0.4682	0.4332
0.227	4.14	600	0.3661	0.3565
0.2169	4.83	700	0.3244	0.3203
0.2687	5.52	800	0.3137	0.2981
0.127	6.21	900	0.3220	0.2828
0.0922	6.9	1000	0.3075	0.2708
0.0965	7.59	1100	0.2779	0.2576
0.1298	8.28	1200	0.3111	0.2480
0.0855	8.97	1300	0.3021	0.2421
0.0629	9.66	1400	0.3122	0.2511
0.0471	10.34	1500	0.2965	0.2368
0.0871	11.03	1600	0.3247	0.2387
0.0503	11.72	1700	0.3359	0.2363
0.0402	12.41	1800	0.2976	0.2332
0.0336	13.1	1900	0.3139	0.2321
0.0634	13.79	2000	0.3188	0.2309
0.0429	14.48	2100	0.3145	0.2335
0.028	15.17	2200	0.3244	0.2242
0.0255	15.86	2300	0.2914	0.2196
0.0406	16.55	2400	0.3249	0.2202
0.0512	17.24	2500	0.3037	0.2198
0.0269	17.93	2600	0.3218	0.2242
0.0287	18.62	2700	0.3106	0.2185
0.0319	19.31	2800	0.3124	0.2217
0.0494	20.0	2900	0.3099	0.2196

Framework versions

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