MCV_Fleurs_Combined_Irish_ASR_normalised2

This model is a fine-tuned version of openai/whisper-base on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.3052
• Wer: 19.3561

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: 0.0001
• train_batch_size: 8
• eval_batch_size: 8
• 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_steps: 500
• training_steps: 80000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.5039	2.2936	2000	0.4105	42.3127
0.2533	4.5872	4000	0.3549	32.6196
0.1690	6.8807	6000	0.3374	29.5407
0.1188	9.1743	8000	0.3489	28.4726
0.1028	11.4679	10000	0.3576	28.5729
0.0816	13.7615	12000	0.3496	26.2712
0.0524	16.0550	14000	0.3437	26.6523
0.0627	18.3486	16000	0.3598	27.2691
0.0522	20.6422	18000	0.3493	26.5520
0.0519	22.9358	20000	0.3558	27.4145
0.0285	25.2294	22000	0.3711	26.2561
0.0309	27.5229	24000	0.3646	26.3614
0.0279	29.8165	26000	0.3564	25.3736
0.0164	32.1101	28000	0.3540	26.5520
0.0311	34.4037	30000	0.3527	24.2754
0.0176	36.6972	32000	0.3549	24.8019
0.0305	38.9908	34000	0.3496	24.7668
0.0176	41.2844	36000	0.3476	24.0748
0.0281	43.5780	38000	0.3444	22.9315
0.0183	45.8716	40000	0.3499	23.3828
0.0080	48.1651	42000	0.3498	23.2976
0.0195	50.4587	44000	0.3452	23.6536
0.0152	52.7523	46000	0.3449	23.7790
0.0101	55.0459	48000	0.3399	23.0318
0.0110	57.3394	50000	0.3395	22.8713
0.0144	59.6330	52000	0.3383	22.1793
0.0111	61.9266	54000	0.3389	22.0790
0.0073	64.2202	56000	0.3268	21.9587
0.0092	66.5138	58000	0.3328	21.2667
0.0120	68.8073	60000	0.3310	21.6879
0.0078	71.1009	62000	0.3347	21.1313
0.0086	73.3945	64000	0.3267	20.2688
0.0087	75.6881	66000	0.3188	20.4844
0.0072	77.9817	68000	0.3174	20.2688
0.0052	80.2752	70000	0.3162	19.9629
0.0053	82.5688	72000	0.3130	19.9027
0.0029	84.8624	74000	0.3062	19.9077
0.0027	87.1560	76000	0.3054	19.4414
0.0034	89.4495	78000	0.3034	18.6892
0.0020	91.7431	80000	0.3052	19.3561

Framework versions

• Transformers 5.6.0.dev0
• Pytorch 2.9.0+cu126
• Datasets 4.0.0
• Tokenizers 0.22.2