whisper-tiny-luganda-final

This model is a fine-tuned version of openai/whisper-tiny on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.4549
• Wer: 0.4660

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: 5e-05
• train_batch_size: 8
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.05
• training_steps: 21000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
2.0498	0.1088	500	0.9947	0.8351
1.5134	0.2175	1000	0.7854	0.7198
1.2494	0.3263	1500	0.6915	0.7817
1.1587	0.4351	2000	0.6377	0.7916
1.0453	0.5438	2500	0.5958	0.7276
1.0065	0.6526	3000	0.5708	0.5803
0.935	0.7614	3500	0.5478	0.5882
0.8838	0.8701	4000	0.5256	0.5876
0.8956	0.9789	4500	0.5123	0.5380
0.6998	1.0877	5000	0.5078	0.5332
0.6735	1.1964	5500	0.4976	0.5400
0.7159	1.3052	6000	0.4934	0.5097
0.6693	1.4140	6500	0.4843	0.5043
0.6513	1.5227	7000	0.4774	0.5038
0.6478	1.6315	7500	0.4736	0.5015
0.6554	1.7403	8000	0.4634	0.5042
0.6491	1.8490	8500	0.4608	0.4941
0.6636	1.9578	9000	0.4526	0.4774
0.4392	2.0666	9500	0.4581	0.4788
0.4567	2.1753	10000	0.4575	0.4842
0.4383	2.2841	10500	0.4562	0.4787
0.4479	2.3929	11000	0.4546	0.4747
0.4431	2.5016	11500	0.4517	0.4820
0.4354	2.6104	12000	0.4498	0.4612
0.4956	2.7192	12500	0.4442	0.4825
0.4427	2.8279	13000	0.4454	0.4693
0.4371	2.9367	13500	0.4430	0.4573
0.2794	3.0455	14000	0.4429	0.4544
0.2826	3.1542	14500	0.4491	0.4694
0.2914	3.2630	15000	0.4497	0.4536
0.3065	3.3718	15500	0.4501	0.4557
0.2879	3.4805	16000	0.4492	0.4532
0.2703	3.5893	16500	0.4495	0.4535
0.269	3.6981	17000	0.4466	0.4681
0.2834	3.8068	17500	0.4445	0.4686
0.2758	3.9156	18000	0.4470	0.4660
0.1819	4.0244	18500	0.4497	0.4640
0.1855	4.1331	19000	0.4510	0.4611
0.1832	4.2419	19500	0.4543	0.4693
0.1826	4.3507	20000	0.4558	0.4640
0.1779	4.4594	20500	0.4553	0.4672
0.1821	4.5682	21000	0.4549	0.4660

Framework versions

• Transformers 4.47.1
• Pytorch 2.5.1+cu124
• Datasets 3.2.0
• Tokenizers 0.21.0