This model is a fine-tuned version of openai/whisper-medium on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:

Loss: 0.1937
Wer Ortho: 6.9198
Wer: 377.5100

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: 2
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 32
total_train_batch_size: 64
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
training_steps: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer Ortho	Wer
No log	0.2	1	3.3895	994.8779	63403.2129
No log	0.4	2	1.5156	976.0929	62244.9799
No log	0.6	3	0.6959	810.9695	51722.0884
No log	0.8	4	0.2952	39.7212	2477.5100
No log	1.0	5	0.1937	6.9198	377.5100

Framework versions

Transformers 4.53.2
Pytorch 2.9.1+cu128
Datasets 2.14.4
Tokenizers 0.21.4

mmarron14/whisper-medium-cv17-es-5-steps_proc1-def3

Whisper Medium CV17 Es 5 steps with processing of text using method 1; with same configuration as 5-steps-sin_proc-def3: with filtering 30sec, and with customised optimizer- María Marrón

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

mmarron14/
whisper-medium-cv17-es-5-steps_proc1-def3