# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("audio-classification", model="sulaimank/waxal-lid")# Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("sulaimank/waxal-lid")
model = AutoModelForAudioClassification.from_pretrained("sulaimank/waxal-lid")Quick Links
waxal-lid
This model is a fine-tuned version of sulaimank/w2v-bert-waxal on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0535
- Acc: 0.9917
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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Acc |
|---|---|---|---|---|
| 1.1406 | 0.2667 | 100 | 0.4611 | 0.99 |
| 0.3675 | 0.5333 | 200 | 0.1759 | 0.99 |
| 0.1697 | 0.8 | 300 | 0.0989 | 0.9917 |
| 0.1199 | 1.0667 | 400 | 0.0739 | 0.9917 |
| 0.1036 | 1.3333 | 500 | 0.0632 | 0.9917 |
| 0.1042 | 1.6 | 600 | 0.0548 | 0.99 |
| 0.0707 | 1.8667 | 700 | 0.0535 | 0.9917 |
| 0.0593 | 2.0 | 750 | 0.0535 | 0.9917 |
Framework versions
- Transformers 5.13.0
- Pytorch 2.12.1+cu130
- Datasets 3.6.0
- Tokenizers 0.22.2
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