onnx-community/ast-finetuned-gtzan-ONNX
By onnx-community · ↓ 12 · ♥ 1 · Updated 2026-02-12
What's in this repo
This page lists the files in onnx-community/ast-finetuned-gtzan-ONNX, including model.onnx, model_bnb4.onnx, model_fp16.onnx and more.
- 8 ONNX models (.onnx files)
- 13 files, including 8 .onnx, 3 .json, 1 .md
- Last updated 2026-02-12
ONNX files
| File | Size | Download |
|---|---|---|
| onnx/model.onnx | — | Download |
| onnx/model_bnb4.onnx | — | Download |
| onnx/model_fp16.onnx | — | Download |
| onnx/model_int8.onnx | — | Download |
| onnx/model_q4.onnx | — | Download |
| onnx/model_q4f16.onnx | — | Download |
| onnx/model_quantized.onnx | — | Download |
| onnx/model_uint8.onnx | — | Download |
How to use
These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.
README
ast-finetuned-gtzan (ONNX)
This is an ONNX version of ramsri818/ast-finetuned-gtzan. It was automatically converted and uploaded using this Hugging Face Space.
Usage with Transformers.js
See the pipeline documentation for audio-classification: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.AudioClassificationPipeline
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: 0.4436
- Accuracy: 0.93
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
- 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 |
|---|---|---|---|---|
| 0.0001 | 1.0 | 225 | 0.5546 | 0.89 |
| 1.204 | 2.0 | 450 | 0.9484 | 0.81 |
| 0.4719 | 3.0 | 675 | 0.7417 | 0.85 |
| 0.0132 | 4.0 | 900 | 0.7101 | 0.9 |
| 0.0527 | 5.0 | 1125 | 0.8170 | 0.86 |
| 0.0 | 6.0 | 1350 | 0.6406 | 0.93 |
| 0.3099 | 7.0 | 1575 | 0.8426 | 0.84 |
| 0.0 | 8.0 | 1800 | 0.9173 | 0.89 |
| 0.0 | 9.0 | 2025 | 0.7142 | 0.9 |
| 0.0602 | 10.0 | 2250 | 0.4718 | 0.92 |
| 0.0003 | 11.0 | 2475 | 0.9860 | 0.9 |
| 0.0001 | 12.0 | 2700 | 0.5918 | 0.91 |
| 0.0 | 13.0 | 2925 | 0.4886 | 0.92 |
| 0.0 | 14.0 | 3150 | 0.4562 | 0.93 |
| 0.0 | 15.0 | 3375 | 0.4360 | 0.94 |
| 0.0 | 16.0 | 3600 | 0.4433 | 0.94 |
| 0.0 | 17.0 | 3825 | 0.4454 | 0.94 |
| 0.0 | 18.0 | 4050 | 0.4454 | 0.94 |
| 0.0 | 19.0 | 4275 | 0.4434 | 0.93 |
| 0.0 | 20.0 | 4500 | 0.4436 | 0.93 |
Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3
Model info
Created: 2026-02-12
Updated: 2026-02-12
View on Hugging Face