metadata
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: whisper-large-v3-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.94
whisper-large-v3-finetuned-gtzan
This model is a fine-tuned version of openai/whisper-large-v3 on the GTZAN dataset. It achieves the following results on the evaluation set:
- Loss: 0.2657
- Accuracy: 0.94
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: 4e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
2.1646 | 0.5 | 28 | 1.8012 | 0.55 |
1.0152 | 1.0 | 56 | 0.8618 | 0.79 |
1.1129 | 1.49 | 84 | 0.7426 | 0.8 |
0.8163 | 1.99 | 112 | 0.8078 | 0.75 |
0.4374 | 2.49 | 140 | 0.6259 | 0.81 |
0.4607 | 2.99 | 168 | 0.5424 | 0.83 |
0.4225 | 3.48 | 196 | 0.3723 | 0.89 |
0.1769 | 3.98 | 224 | 0.3517 | 0.9 |
0.0927 | 4.48 | 252 | 0.3385 | 0.89 |
0.0159 | 4.98 | 280 | 0.3985 | 0.88 |
0.0119 | 5.48 | 308 | 0.4626 | 0.9 |
0.029 | 5.97 | 336 | 0.4292 | 0.91 |
0.0064 | 6.47 | 364 | 0.2710 | 0.93 |
0.0057 | 6.97 | 392 | 0.2665 | 0.93 |
0.0048 | 7.47 | 420 | 0.2784 | 0.93 |
0.0049 | 7.96 | 448 | 0.2550 | 0.94 |
0.0049 | 8.46 | 476 | 0.3011 | 0.94 |
0.0044 | 8.96 | 504 | 0.2759 | 0.94 |
0.0045 | 9.46 | 532 | 0.2661 | 0.94 |
0.0048 | 9.96 | 560 | 0.2657 | 0.94 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1