Instructions to use VasilisAsim/hubert-finetuned-SAVEE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VasilisAsim/hubert-finetuned-SAVEE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="VasilisAsim/hubert-finetuned-SAVEE")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("VasilisAsim/hubert-finetuned-SAVEE") model = AutoModelForAudioClassification.from_pretrained("VasilisAsim/hubert-finetuned-SAVEE") - Notebooks
- Google Colab
- Kaggle
hubert-finetuned-SAVEE
This model is a fine-tuned version of facebook/hubert-base-ls960 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.4798
- Accuracy: 0.4167
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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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_steps: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 24 | 1.8901 | 0.3125 |
| No log | 2.0 | 48 | 1.7471 | 0.3333 |
| 7.6156 | 3.0 | 72 | 1.5627 | 0.375 |
| 7.6156 | 4.0 | 96 | 1.4798 | 0.4167 |
| 6.7035 | 5.0 | 120 | 1.5339 | 0.3958 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for VasilisAsim/hubert-finetuned-SAVEE
Base model
facebook/hubert-base-ls960