metadata

library_name: transformers
license: apache-2.0
base_model: miosipof/whisper-small-ft-balbus-sep28k-multiclass_v3a
tags:
  - generated_from_trainer
datasets:
  - balbus-classifier
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: >-
      whisper-small-ft-balbus-sep28k-multiclass_v3a-ft-balbus-sep28k-multiclass_v4_IT
    results:
      - task:
          name: Audio Classification
          type: audio-classification
        dataset:
          name: Apple dataset
          type: balbus-classifier
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.85924617196702
          - name: Precision
            type: precision
            value: 0.8795132394309227
          - name: Recall
            type: recall
            value: 0.8594066773532715
          - name: F1
            type: f1
            value: 0.8603528122668381

whisper-small-ft-balbus-sep28k-multiclass_v3a-ft-balbus-sep28k-multiclass_v4_IT

This model is a fine-tuned version of miosipof/whisper-small-ft-balbus-sep28k-multiclass_v3a on the Apple dataset dataset. It achieves the following results on the evaluation set:

Loss: 0.2301
Accuracy: 0.8592
Precision: 0.8795
Recall: 0.8594
F1: 0.8604
Roc-auc: None

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: 3e-06
train_batch_size: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.5
num_epochs: 2
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Roc-auc
0.6519	0.4188	100	0.6127	0.4741	0.5574	0.4759	0.4098	None
0.4774	0.8377	200	0.4080	0.7468	0.7651	0.7470	0.7497	None
0.2425	1.2565	300	0.2518	0.8439	0.8566	0.8443	0.8445	None
0.2275	1.6754	400	0.2301	0.8592	0.8795	0.8594	0.8604	None

Framework versions

Transformers 4.45.2
Pytorch 2.2.0
Datasets 3.6.0
Tokenizers 0.20.3