File size: 2,947 Bytes
e47b08d |
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: apache-2.0
base_model: facebook/hubert-base-ls960
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: hubert-base-ls960-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: 0.86
---
<!-- 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. -->
# hubert-base-ls960-finetuned-gtzan
This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6524
- Accuracy: 0.86
## 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: 10
- eval_batch_size: 10
- seed: 42
- 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1626 | 1.0 | 90 | 2.0818 | 0.29 |
| 1.6876 | 2.0 | 180 | 1.6356 | 0.46 |
| 1.5907 | 3.0 | 270 | 1.4315 | 0.44 |
| 1.1261 | 4.0 | 360 | 1.1621 | 0.59 |
| 1.2327 | 5.0 | 450 | 1.0259 | 0.7 |
| 0.787 | 6.0 | 540 | 1.0662 | 0.68 |
| 0.9672 | 7.0 | 630 | 0.8381 | 0.77 |
| 0.626 | 8.0 | 720 | 0.7148 | 0.83 |
| 0.4198 | 9.0 | 810 | 0.8384 | 0.77 |
| 0.3601 | 10.0 | 900 | 0.5700 | 0.82 |
| 0.4672 | 11.0 | 990 | 0.8379 | 0.8 |
| 0.3303 | 12.0 | 1080 | 0.5098 | 0.86 |
| 0.2577 | 13.0 | 1170 | 0.8730 | 0.81 |
| 0.3535 | 14.0 | 1260 | 0.8539 | 0.82 |
| 0.2021 | 15.0 | 1350 | 0.8921 | 0.81 |
| 0.1995 | 16.0 | 1440 | 0.4829 | 0.88 |
| 0.3149 | 17.0 | 1530 | 0.6051 | 0.84 |
| 0.0828 | 18.0 | 1620 | 0.5581 | 0.86 |
| 0.0557 | 19.0 | 1710 | 0.5707 | 0.87 |
| 0.1019 | 20.0 | 1800 | 0.6524 | 0.86 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|