sarpba/whisper-large-v2-CV18-hu-cleaned

Whisper large-v2 CV18 Hu

This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whispe r-large-v2) on the fsicoli/common_voice_18_0 dataset. It achieves the following results on the evaluation google/fleurs set:

• Loss: 0.3493
• Wer Ortho: 21.9936
• Wer: 16.0057

Összesített Metrikák

google/fleurs_hu_hu_test:

• Átlagos WER: 21.75%
• Átlagos CER: 6.10%
• Átlagos Normalizált WER: 14.73%
• Átlagos Normalizált CER: 4.73%

common_voice_17_0_hu_test (it's a fals test (test split was in training)):

• Átlagos WER: 1.16%
• Átlagos CER: 0.22%
• Átlagos Normalizált WER: 0.79%
• Átlagos Normalizált CER: 0.16%

Kvantált modellek eredményei:

Model	WER	CER	Normalized_WER	Normalized_CER	Database	Split	Runtime
int8_bfloat16	21.49	5.93	16.04	6.21	google/fleurs	test	550.18
bfloat16	21.33	5.87	15.91	6.15	google/fleurs	test	593.96
int8	21.01	5.63	15.38	5.88	google/fleurs	test	668.91
int8_float32	21.01	5.63	15.38	5.88	google/fleurs	test	669.81
int8_float16	20.96	5.65	15.31	5.91	google/fleurs	test	570.11
float16	20.92	5.64	15.24	5.9	google/fleurs	test	589.29

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-06
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 250
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Wer Ortho	Wer
0.1543	0.1	500	0.3619	25.7695	21.5802
0.1336	0.2	1000	0.3661	26.4197	21.9212
0.1358	0.3	1500	0.3516	25.4414	20.7548
0.1165	0.39	2000	0.3431	25.3937	20.3601
0.0959	0.49	2500	0.3581	26.6345	20.4438
0.1045	0.59	3000	0.3427	25.9127	19.9653
0.099	0.69	3500	0.3380	25.3937	19.6902
0.1034	0.79	4000	0.3412	24.5765	19.0083
0.0919	0.89	4500	0.3370	25.0119	19.3672
0.077	0.99	5000	0.3295	24.5884	19.3433
0.0447	1.09	5500	0.3405	23.6220	17.5668
0.0435	1.18	6000	0.3364	23.2999	17.4353
0.0383	1.28	6500	0.3370	22.9957	17.4831
0.0388	1.38	7000	0.3391	22.9838	17.1123
0.0436	1.48	7500	0.3345	22.7332	17.6745
0.0466	1.58	8000	0.3327	23.6101	17.3994
0.0357	1.68	8500	0.3477	24.2961	17.8121
0.0417	1.78	9000	0.3259	22.8883	16.7115
0.0383	1.88	9500	0.3206	22.0055	16.5859
0.0381	1.97	10000	0.3425	23.1508	16.8192
0.0153	2.07	10500	0.3461	22.5304	16.9807
0.0158	2.17	11000	0.3467	22.8227	16.7115
0.0228	2.27	11500	0.3439	22.3276	16.4244
0.0231	2.37	12000	0.3581	23.3954	16.6756
0.0171	2.47	12500	0.3537	22.7094	16.4304
0.0188	2.57	13000	0.3503	22.4588	16.8072
0.0157	2.67	13500	0.3518	22.5245	16.3826
0.0154	2.76	14000	0.3534	22.2739	16.0715
0.0205	2.86	14500	0.3479	21.9399	16.0237
0.0164	2.96	15000	0.3493	21.9936	16.0057

Framework versions

• Transformers 4.34.1
• Pytorch 2.3.0+cu121
• Datasets 2.14.6
• Tokenizers 0.14.1