whisper_input_decoder_shift_r_labels_with_force__0015

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

• Train Loss: 3.3218
• Train Accuracy: 0.0148
• Train Wermet: 0.6946
• Validation Loss: 3.1357
• Validation Accuracy: 0.0122
• Validation Wermet: 0.7760
• Epoch: 14

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:

• optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
• training_precision: float32

Training results

Train Loss	Train Accuracy	Train Wermet	Validation Loss	Validation Accuracy	Validation Wermet	Epoch
5.6249	0.0091	1.7162	4.2965	0.0094	0.9447	0
4.9223	0.0099	0.9041	4.1562	0.0097	0.9327	1
4.6814	0.0107	0.8376	3.9245	0.0103	0.8927	2
4.4407	0.0114	0.8311	3.7252	0.0107	0.8775	3
4.2445	0.0119	0.8228	3.6283	0.0108	0.8695	4
4.0889	0.0123	0.8067	3.5310	0.0110	0.8916	5
3.9575	0.0127	0.7908	3.4478	0.0113	0.8407	6
3.8547	0.0130	0.7781	3.4227	0.0113	0.8670	7
3.7599	0.0133	0.7654	3.3519	0.0115	0.8375	8
3.6763	0.0136	0.7543	3.3183	0.0116	0.8678	9
3.6006	0.0138	0.7421	3.2581	0.0117	0.8120	10
3.5300	0.0140	0.7296	3.2415	0.0118	0.8257	11
3.4554	0.0143	0.7179	3.2163	0.0119	0.8078	12
3.3930	0.0145	0.7057	3.1612	0.0121	0.7758	13
3.3218	0.0148	0.6946	3.1357	0.0122	0.7760	14

Framework versions

• Transformers 4.34.0.dev0
• TensorFlow 2.13.0
• Tokenizers 0.13.3