javadr/whisper-tiny-fa

Whisper Tiny Fa - Javad Razavian

This model is a fine-tuned version of openai/whisper-tiny on the Common Voice 16.0 dataset. It achieves the following results on the evaluation set:

• Loss: 0.9459
• Wer: 94.2810

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-06
• train_batch_size: 16
• eval_batch_size: 256
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• training_steps: 5000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
4.6309	0.08	100	4.1290	140.4220
2.5371	0.16	200	2.5264	128.3176
1.5224	0.24	300	1.7147	120.6830
1.2351	0.33	400	1.4970	112.3542
1.073	0.41	500	1.3917	103.7479
1.0077	0.49	600	1.3232	104.2199
0.9541	0.57	700	1.2781	99.6669
0.8933	0.65	800	1.2369	99.8612
0.8746	0.73	900	1.2076	99.5003
0.8306	0.81	1000	1.1809	99.8890
0.8309	0.89	1100	1.1583	96.5297
0.7982	0.98	1200	1.1370	94.2254
0.7719	1.06	1300	1.1243	96.8351
0.7799	1.14	1400	1.1065	92.6707
0.7512	1.22	1500	1.0941	93.1427
0.7212	1.3	1600	1.0838	94.6696
0.7315	1.38	1700	1.0709	96.0855
0.7002	1.46	1800	1.0595	96.0022
0.719	1.54	1900	1.0517	94.7807
0.7157	1.63	2000	1.0420	95.5303
0.7004	1.71	2100	1.0337	94.2810
0.6792	1.79	2200	1.0278	96.7518
0.6933	1.87	2300	1.0196	95.7801
0.669	1.95	2400	1.0113	98.0566
0.6627	2.03	2500	1.0063	96.8351
0.655	2.11	2600	1.0006	96.0577
0.6511	2.2	2700	0.9939	97.0572
0.6352	2.28	2800	0.9899	95.4470
0.6339	2.36	2900	0.9874	97.2238
0.6354	2.44	3000	0.9820	96.8351
0.611	2.52	3100	0.9777	94.5308
0.6143	2.6	3200	0.9752	99.0006
0.6242	2.68	3300	0.9729	98.7229
0.6324	2.76	3400	0.9681	99.1394
0.6237	2.85	3500	0.9646	96.8906
0.6285	2.93	3600	0.9621	96.1410
0.5934	3.01	3700	0.9601	97.4736
0.6129	3.09	3800	0.9575	92.9761
0.6154	3.17	3900	0.9575	97.5847
0.6334	3.25	4000	0.9555	101.0827
0.5956	3.33	4100	0.9536	94.7529
0.5956	3.41	4200	0.9507	100.3054
0.6053	3.5	4300	0.9504	94.5308
0.6199	3.58	4400	0.9491	95.0861
0.6064	3.66	4500	0.9482	91.8656
0.6154	3.74	4600	0.9478	94.1144
0.5909	3.82	4700	0.9466	91.5047
0.584	3.9	4800	0.9459	94.1144
0.5935	3.98	4900	0.9459	94.0589
0.5939	4.07	5000	0.9459	94.2810

Framework versions

• Transformers 4.37.0.dev0
• Pytorch 2.1.0+cu121
• Datasets 2.16.1
• Tokenizers 0.15.0