metadata

library_name: transformers
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
  - generated_from_trainer
datasets:
  - fleurs
metrics:
  - wer
model-index:
  - name: whisper-large-v3-turbo-serbian-serbia
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: fleurs
          type: fleurs
          config: sr_rs
          split: None
          args: sr_rs
        metrics:
          - name: Wer
            type: wer
            value: 18.343921455385505

whisper-large-v3-turbo-serbian-serbia

This model is a fine-tuned version of openai/whisper-large-v3-turbo on the fleurs dataset. It achieves the following results on the evaluation set:

Loss: 0.5289
Model Preparation Time: 0.0069
Wer Ortho: 34.0354
Wer: 18.3439

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: 64
eval_batch_size: 64
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 128
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_ratio: 0.06
num_epochs: 1
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time	Wer Ortho	Wer
0.1827	0.0263	32	0.8340	0.0069	36.3524	21.6142
0.1531	0.0527	64	0.6341	0.0069	36.4759	21.7225
0.1551	0.0790	96	0.6893	0.0069	36.0038	21.6647
0.1495	0.1054	128	0.5745	0.0069	35.4518	21.2388
0.1388	0.1317	160	0.5572	0.0069	35.5680	20.5241
0.1359	0.1581	192	0.6313	0.0069	36.2217	20.6974
0.138	0.1844	224	0.5451	0.0069	35.9384	20.4014
0.1332	0.2108	256	0.6109	0.0069	35.5171	20.2209
0.131	0.2371	288	0.5748	0.0069	35.7859	20.6324
0.1316	0.2635	320	0.5705	0.0069	34.9942	20.1559
0.1296	0.2898	352	0.6122	0.0069	35.3791	20.0332
0.1274	0.3162	384	0.5848	0.0069	35.1322	19.8960
0.1185	0.3425	416	0.5458	0.0069	35.4227	19.8239
0.1207	0.3689	448	0.5714	0.0069	34.8053	19.6289
0.1193	0.3952	480	0.6553	0.0069	34.7327	19.3546
0.119	0.4216	512	0.5913	0.0069	35.0523	19.4629
0.122	0.4479	544	0.5890	0.0069	34.8489	19.4846
0.1203	0.4743	576	0.5554	0.0069	35.0886	19.5206
0.1164	0.5006	608	0.5637	0.0069	34.7690	19.4124
0.1173	0.5270	640	0.5492	0.0069	34.6601	19.2319
0.1175	0.5533	672	0.5847	0.0069	34.4785	19.3474
0.1154	0.5797	704	0.5900	0.0069	34.7618	18.8926
0.1136	0.6060	736	0.6104	0.0069	34.6092	19.0586
0.1142	0.6324	768	0.5393	0.0069	34.2533	18.8276
0.1156	0.6587	800	0.5473	0.0069	34.5221	18.8926
0.1125	0.6851	832	0.5747	0.0069	34.4858	18.8276
0.1107	0.7114	864	0.5470	0.0069	34.1880	18.8709
0.1088	0.7378	896	0.5620	0.0069	34.4785	18.8926
0.1111	0.7641	928	0.5832	0.0069	34.4059	18.8493
0.1081	0.7904	960	0.5430	0.0069	34.3623	18.8204
0.108	0.8168	992	0.5560	0.0069	34.4567	18.7626
0.106	0.8431	1024	0.5344	0.0069	34.3550	18.8637
0.1051	0.8695	1056	0.5496	0.0069	34.4567	18.5894
0.1096	0.8958	1088	0.5341	0.0069	34.1008	18.4811
0.1098	0.9222	1120	0.5339	0.0069	34.2025	18.4306
0.1045	0.9485	1152	0.5289	0.0069	34.0354	18.3439
0.1072	0.9749	1184	0.5265	0.0069	34.0863	18.4089

Framework versions

Transformers 4.51.3
Pytorch 2.5.1
Datasets 3.6.0
Tokenizers 0.21.1