This model is the openai whisper small transformer adapted for Italian audio to text transcription. This model has weight decay set to 0.1 and the learning rate has been set to 1e-4 in the hyperparameter tuning process and it improved the performance on the evaluation set.

Intended uses & limitations

The model is available through its HuggingFace web app

Training and evaluation data

Data used for training is the initial 10% of train and validation of Italian Common Voice 11.0 from Mozilla Foundation. The dataset used for evaluation is the initial 10% of test of Italian Common Voice.

Training procedure

After loading the pre trained model, it has been trained on the dataset.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-04
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
training_steps: 4000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.7015	0.95	1000	0.9463	64.4689
0.3579	1.91	2000	0.8363	51.7471
0.1388	2.86	3000	0.7766	43.6425
0.0403	3.82	4000	0.7609	41.2715

Framework versions

Transformers 4.26.0.dev0
Pytorch 1.12.1+cu113
Datasets 2.7.1
Tokenizers 0.13.2

GIanlucaRub/whisper-tiny-it-5

Whisper Tiny it 5

Model description