File size: 2,996 Bytes
aaa6c16 76835c1 aaa6c16 76835c1 aaa6c16 76835c1 aaa6c16 76835c1 aaa6c16 76835c1 aaa6c16 76835c1 aaa6c16 76835c1 aaa6c16 76835c1 aaa6c16 76835c1 aaa6c16 76835c1 aaa6c16 76835c1 aaa6c16 |
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 |
---
license: bsd-3-clause
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: ast-finetuned-audioset-10-10-0.4593-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: 0.9
---
<!-- 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-audioset-10-10-0.4593-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: 0.4717
- Accuracy: 0.9
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- 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: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7581 | 1.0 | 56 | 0.7029 | 0.78 |
| 0.3942 | 1.99 | 112 | 0.4646 | 0.86 |
| 0.3298 | 2.99 | 168 | 0.3861 | 0.88 |
| 0.1227 | 4.0 | 225 | 0.4702 | 0.86 |
| 0.0774 | 5.0 | 281 | 0.4492 | 0.9 |
| 0.0039 | 5.99 | 337 | 0.4607 | 0.9 |
| 0.0014 | 6.99 | 393 | 0.5022 | 0.9 |
| 0.0022 | 8.0 | 450 | 0.4711 | 0.9 |
| 0.0193 | 9.0 | 506 | 0.5226 | 0.86 |
| 0.0004 | 9.99 | 562 | 0.6055 | 0.82 |
| 0.0003 | 10.99 | 618 | 0.4793 | 0.89 |
| 0.0002 | 12.0 | 675 | 0.5052 | 0.9 |
| 0.0002 | 13.0 | 731 | 0.4652 | 0.89 |
| 0.0001 | 13.99 | 787 | 0.4617 | 0.9 |
| 0.0001 | 14.99 | 843 | 0.4653 | 0.9 |
| 0.0001 | 16.0 | 900 | 0.4635 | 0.91 |
| 0.0001 | 17.0 | 956 | 0.4693 | 0.9 |
| 0.0001 | 17.99 | 1012 | 0.4697 | 0.9 |
| 0.0001 | 18.99 | 1068 | 0.4715 | 0.9 |
| 0.0025 | 19.91 | 1120 | 0.4717 | 0.9 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|