Instructions to use VasilisAsim/wavlm-finetuned-TESS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VasilisAsim/wavlm-finetuned-TESS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="VasilisAsim/wavlm-finetuned-TESS")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("VasilisAsim/wavlm-finetuned-TESS") model = AutoModelForAudioClassification.from_pretrained("VasilisAsim/wavlm-finetuned-TESS", device_map="auto") - Notebooks
- Google Colab
- Kaggle
wavlm-finetuned-TESS
This model is a fine-tuned version of microsoft/wavlm-base-plus on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.9661
- Accuracy: 0.9982
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 |
|---|---|---|---|---|
| 9.9935 | 1.0 | 140 | 1.9379 | 0.9179 |
| 6.4561 | 2.0 | 280 | 1.3156 | 0.9786 |
| 4.6375 | 3.0 | 420 | 0.9661 | 0.9982 |
| 3.5471 | 4.0 | 560 | 0.7837 | 0.9982 |
| 3.1738 | 5.0 | 700 | 0.7290 | 0.9982 |
Framework versions
- Transformers 5.12.1
- Pytorch 2.11.0+cu128
- Datasets 5.0.0
- Tokenizers 0.22.2
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Model tree for VasilisAsim/wavlm-finetuned-TESS
Base model
microsoft/wavlm-base-plus