metadata
library_name: transformers
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- balbus-classifier
metrics:
- accuracy
model-index:
- name: miosipof/whisper-medium-ft-balbus-sep28k-v1.4
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: 0.8006894390473206
miosipof/whisper-medium-ft-balbus-sep28k-v1.4
This model is a fine-tuned version of openai/whisper-medium on the Apple dataset dataset. It achieves the following results on the evaluation set:
- Loss: 0.1096
- Accuracy: 0.8007
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-06
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.5
- training_steps: 500
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.1827 | 0.0313 | 50 | 0.1708 | 0.5653 |
| 0.1681 | 0.0627 | 100 | 0.1632 | 0.6341 |
| 0.1603 | 0.0940 | 150 | 0.1498 | 0.6924 |
| 0.1443 | 0.1254 | 200 | 0.1371 | 0.7404 |
| 0.139 | 0.1567 | 250 | 0.1258 | 0.7640 |
| 0.1194 | 0.1880 | 300 | 0.1158 | 0.7880 |
| 0.105 | 0.2194 | 350 | 0.1173 | 0.7914 |
| 0.1114 | 0.2507 | 400 | 0.1143 | 0.7965 |
| 0.1214 | 0.2820 | 450 | 0.1109 | 0.8001 |
| 0.1178 | 0.3134 | 500 | 0.1096 | 0.8007 |
Framework versions
- Transformers 4.45.2
- Pytorch 2.2.0
- Datasets 3.2.0
- Tokenizers 0.20.3