README.md

metadata

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
model-index:
  - name: ast-finetuned-gtzan
    results:
      - task:
          name: Audio Classification
          type: audio-classification
        dataset:
          name: GTZAN
          type: marsyas/gtzan
          config: all
          split: None
          args: all
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9

ast-finetuned-gtzan

This model is a fine-tuned version of MIT/ast-finetuned-audioset-10-10-0.4593 on the GTZAN dataset. It achieves the following results on the evaluation set:

• Loss: 0.3410
• 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: 3e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 25
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.627	1.0	50	0.8714	0.795
0.4145	2.0	100	0.5660	0.825
0.2344	3.0	150	0.4988	0.85
0.1334	4.0	200	0.3726	0.87
0.0341	5.0	250	0.3637	0.895
0.0172	6.0	300	0.4197	0.87
0.0338	7.0	350	0.5035	0.87
0.002	8.0	400	0.5825	0.86
0.001	9.0	450	0.4126	0.895
0.0093	10.0	500	0.4564	0.89
0.0056	11.0	550	0.4783	0.84
0.0162	12.0	600	0.3161	0.89
0.0019	13.0	650	0.4062	0.875
0.0005	14.0	700	0.3630	0.895
0.0098	15.0	750	0.3410	0.9
0.008	16.0	800	0.3385	0.89
0.0001	17.0	850	0.3434	0.895
0.0067	18.0	900	0.3414	0.885
0.0064	19.0	950	0.3453	0.895
0.0001	20.0	1000	0.3422	0.885
0.0001	21.0	1050	0.3520	0.89
0.0036	22.0	1100	0.3403	0.89
0.0001	23.0	1150	0.3394	0.89
0.0001	24.0	1200	0.3407	0.89
0.0026	25.0	1250	0.3417	0.89

Framework versions

• Transformers 4.57.3
• Pytorch 2.9.0+cu128
• Datasets 2.18.0
• Tokenizers 0.22.1