metadata

library_name: transformers
license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
datasets:
  - balbus-classifier
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: miosipof/whisper-small-ft-balbus-sep28k-v1.6
    results:
      - task:
          name: Audio Classification
          type: audio-classification
        dataset:
          name: Apple dataset
          type: balbus-classifier
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8100908806016922
          - name: Precision
            type: precision
            value: 0.8183656957928802
          - name: Recall
            type: recall
            value: 0.7261306532663316
          - name: F1
            type: f1
            value: 0.7694941042221377

miosipof/whisper-small-ft-balbus-sep28k-v1.6

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

Loss: 0.1091
Accuracy: 0.8101
Precision: 0.8184
Recall: 0.7261
F1: 0.7695
Roc-auc: 0.8006

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: 2e-06
train_batch_size: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.5
training_steps: 1200
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Roc-auc
0.1683	0.2506	200	0.1682	0.5730	0.7364	0.0341	0.0652	0.5123
0.1494	0.5013	400	0.1446	0.7084	0.6603	0.6838	0.6718	0.7056
0.1212	0.7519	600	0.1236	0.7629	0.6917	0.8245	0.7523	0.7699
0.1088	1.0025	800	0.1107	0.8062	0.8337	0.6945	0.7578	0.7936
0.0955	1.2531	1000	0.1106	0.8081	0.8036	0.7416	0.7713	0.8006
0.0997	1.5038	1200	0.1091	0.8101	0.8184	0.7261	0.7695	0.8006

Framework versions

Transformers 4.45.2
Pytorch 2.2.0
Datasets 3.2.0
Tokenizers 0.20.3