Whisper small vi - Ox

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

Model description

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The following hyperparameters were used during training:

learning_rate: 1.25e-05
train_batch_size: 8
eval_batch_size: 2
seed: 42
optimizer: Use 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_steps: 500
num_epochs: 3.0
mixed_precision_training: Native AMP

Training Loss	Epoch	Step	Validation Loss	Wer
0.469	0.0156	1000	0.4203	24.3421
0.347	0.0311	2000	0.3729	22.5519
0.3173	0.0467	3000	0.3462	21.2276
0.3297	0.0623	4000	0.3402	19.7556
0.3225	0.0779	5000	0.3223	19.5226
0.2993	0.0934	6000	0.3068	17.3288
0.3226	0.1090	7000	0.2958	17.3288
0.2842	0.1246	8000	0.2881	16.8116
0.287	0.1401	9000	0.2847	16.3569
0.3101	0.1557	10000	0.2757	15.9477
0.2852	0.1713	11000	0.2680	15.7715
0.2617	0.1868	12000	0.2659	15.9136
0.2534	0.2024	13000	0.2596	15.5499
0.2388	0.2180	14000	0.2577	15.1520
0.227	0.2336	15000	0.2548	14.7258
0.2418	0.2491	16000	0.2515	14.5325
0.2521	0.2647	17000	0.2518	14.5212
0.2768	0.2803	18000	0.2473	14.3677
0.2552	0.2958	19000	0.2432	13.8221
0.2395	0.3114	20000	0.2370	14.6519
0.2189	0.3270	21000	0.2336	13.6232
0.2226	0.3426	22000	0.2314	13.2197
0.2273	0.3581	23000	0.2328	13.5607
0.2571	0.3737	24000	0.2287	13.0889
0.2629	0.3893	25000	0.2315	13.0946
0.2181	0.4048	26000	0.2267	12.8275
0.2191	0.4204	27000	0.2296	13.6516
0.2189	0.4360	28000	0.2248	13.0605
0.2471	0.4515	29000	0.2235	12.5718
0.2037	0.4671	30000	0.2189	12.7593
0.2077	0.4827	31000	0.2187	13.2822
0.1795	0.4983	32000	0.2188	12.7138
0.2084	0.5138	33000	0.2177	12.7707
0.2355	0.5294	34000	0.2083	12.0318
0.2105	0.5450	35000	0.2082	12.1512
0.2059	0.5605	36000	0.2122	12.3330
0.1918	0.5761	37000	0.2076	11.8045
0.1812	0.5917	38000	0.2068	11.9636
0.1909	0.6072	39000	0.2083	13.4527
0.2454	0.6228	40000	0.2075	11.8272
0.2087	0.6384	41000	0.2031	11.6965
0.1988	0.6540	42000	0.2026	11.5374
0.1917	0.6695	43000	0.2025	11.5203
0.1688	0.6851	44000	0.2023	12.5320