ptah23/whisper-small-fleurs-bn-in

whisper-small-bn-in

This model is a fine-tuned version of openai/whisper-small on the google/fleurs dataset. It achieves the following results on the evaluation set:

• Loss: 0.1842
• Wer: 0.4568

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: 1e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: constant_with_warmup
• lr_scheduler_warmup_steps: 5
• training_steps: 2000

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.4443	0.53	100	0.3399	0.7272
0.249	1.07	200	0.2222	0.6244
0.1662	1.6	300	0.1778	0.5807
0.1221	2.14	400	0.1602	0.5397
0.0965	2.67	500	0.1484	0.5168
0.0646	3.21	600	0.1475	0.4966
0.0566	3.74	700	0.1420	0.4812
0.028	4.28	800	0.1511	0.4910
0.0325	4.81	900	0.1476	0.4766
0.0177	5.35	1000	0.1593	0.4876
0.0176	5.88	1100	0.1589	0.4715
0.0127	6.42	1200	0.1622	0.4634
0.0126	6.95	1300	0.1706	0.4673
0.0089	7.49	1400	0.1777	0.4712
0.0087	8.02	1500	0.1776	0.4666
0.0076	8.56	1600	0.1788	0.4505
0.007	9.09	1700	0.1906	0.4685
0.0057	9.63	1800	0.1840	0.4573
0.0064	10.16	1900	0.1841	0.4569
0.0057	10.7	2000	0.1842	0.4568

Framework versions

• Transformers 4.32.0.dev0
• Pytorch 1.12.1+cu116
• Datasets 2.4.0
• Tokenizers 0.12.1