metadata

license: apache-2.0
base_model: openai/whisper-medium
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: whisper-og-audio-abuse-feature
    results: []

whisper-og-audio-abuse-feature

This model is a fine-tuned version of openai/whisper-medium on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.4737
Accuracy: 0.8922
Macro Precision: 0.8730
Macro Recall: 0.8547
Macro F1-score: 0.8631

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: 5e-05
train_batch_size: 16
eval_batch_size: 16
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: 0.01
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Macro Precision	Macro Recall	Macro F1-score
0.4515	0.4367	50	0.4025	0.8069	0.8192	0.8136	0.8066
0.3406	0.8734	100	0.3144	0.8807	0.8875	0.8760	0.8787
0.2698	1.3100	150	0.3496	0.8487	0.8667	0.8408	0.8441
0.2478	1.7467	200	0.2916	0.8942	0.8938	0.8935	0.8937
0.1959	2.1834	250	0.3660	0.8721	0.8795	0.8671	0.8698
0.1286	2.6201	300	0.3699	0.8881	0.8902	0.8853	0.8869
0.1314	3.0568	350	0.3474	0.8905	0.8898	0.8906	0.8901
0.0632	3.4934	400	0.4238	0.8844	0.8843	0.8831	0.8836
0.0761	3.9301	450	0.3957	0.8905	0.8927	0.8878	0.8894
0.0344	4.3668	500	0.4951	0.8918	0.8932	0.8895	0.8908
0.0318	4.8035	550	0.5091	0.8967	0.8969	0.8953	0.8960

Framework versions

Transformers 4.41.2
Pytorch 2.1.2
Datasets 2.19.2
Tokenizers 0.19.1