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---
tags:
- audio-classification
- generated_from_trainer
- wolof
metrics:
- accuracy
- precision
- f1
model-index:
- name: wavlm-base
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wavlm-base
This model is a fine-tuned version of [microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-base) on the galsenai/waxal_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1345
- Accuracy: 0.6783
- Precision: 0.8774
- F1: 0.7615
## 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: 30
- eval_batch_size: 30
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 120
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 32.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|
| 4.4506 | 2.53 | 500 | 4.8601 | 0.0224 | 0.0136 | 0.0066 |
| 3.0523 | 5.05 | 1000 | 4.6674 | 0.0720 | 0.0460 | 0.0394 |
| 1.949 | 7.58 | 1500 | 4.1533 | 0.1156 | 0.1847 | 0.1064 |
| 1.3427 | 10.1 | 2000 | 3.8173 | 0.1448 | 0.2382 | 0.1347 |
| 1.0064 | 12.63 | 2500 | 3.5546 | 0.2183 | 0.4464 | 0.2385 |
| 0.7985 | 15.15 | 3000 | 3.1172 | 0.3842 | 0.6336 | 0.4258 |
| 0.6505 | 17.68 | 3500 | 2.9231 | 0.5165 | 0.7677 | 0.5995 |
| 0.5367 | 20.2 | 4000 | 2.4935 | 0.5961 | 0.8182 | 0.6755 |
| 0.465 | 22.73 | 4500 | 2.2411 | 0.6412 | 0.8624 | 0.7272 |
| 0.4075 | 25.25 | 5000 | 2.1345 | 0.6783 | 0.8774 | 0.7615 |
| 0.3793 | 27.78 | 5500 | 2.2535 | 0.6681 | 0.8792 | 0.7543 |
| 0.3418 | 30.3 | 6000 | 2.3390 | 0.6662 | 0.8905 | 0.7576 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2
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