File size: 3,014 Bytes
1f084cb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 |
---
library_name: transformers
license: bsd-3-clause
base_model: MIT/ast-finetuned-audioset-10-10-0.4593
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: ast-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
- name: Precision
type: precision
value: 0.946171802054155
- name: Recall
type: recall
value: 0.9379426129426129
- name: F1
type: f1
value: 0.9379839011750775
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ast-finetuned-gtzan
This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3551
- Accuracy: 0.94
- Precision: 0.9462
- Recall: 0.9379
- F1: 0.9380
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.9185 | 1.0 | 113 | 0.6489 | 0.78 | 0.8099 | 0.7976 | 0.7743 |
| 0.473 | 2.0 | 226 | 0.6660 | 0.8 | 0.8284 | 0.8208 | 0.7963 |
| 0.4124 | 3.0 | 339 | 0.6544 | 0.8 | 0.8237 | 0.8002 | 0.7880 |
| 0.1625 | 4.0 | 452 | 0.4139 | 0.86 | 0.8519 | 0.8603 | 0.8454 |
| 0.2298 | 5.0 | 565 | 0.5540 | 0.88 | 0.8689 | 0.8694 | 0.8618 |
| 0.1091 | 6.0 | 678 | 0.4291 | 0.89 | 0.8933 | 0.8935 | 0.8855 |
| 0.0208 | 7.0 | 791 | 0.4161 | 0.91 | 0.9200 | 0.9000 | 0.8977 |
| 0.0181 | 8.0 | 904 | 0.3769 | 0.92 | 0.9133 | 0.9202 | 0.9127 |
| 0.0035 | 9.0 | 1017 | 0.3431 | 0.94 | 0.9353 | 0.9424 | 0.9371 |
| 0.013 | 10.0 | 1130 | 0.3551 | 0.94 | 0.9462 | 0.9379 | 0.9380 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|