| --- |
| base_model: microsoft/wavlm-base |
| tags: |
| - audio-classification |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| 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 an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.3307 |
| - Accuracy: 0.8974 |
|
|
| ## 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: 0.0003 |
| - train_batch_size: 8 |
| - eval_batch_size: 2 |
| - seed: 0 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 32 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_ratio: 0.1 |
| - num_epochs: 10.0 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | 0.3744 | 1.0 | 793 | 0.3307 | 0.8974 | |
| | 0.3699 | 2.0 | 1586 | 0.3342 | 0.8974 | |
| | 0.2898 | 3.0 | 2379 | 0.3341 | 0.8974 | |
| | 0.3126 | 4.0 | 3173 | 0.3363 | 0.8974 | |
| | 0.3753 | 5.0 | 3966 | 0.3309 | 0.8974 | |
| | 0.3617 | 6.0 | 4759 | 0.3325 | 0.8974 | |
| | 0.3453 | 7.0 | 5552 | 0.3315 | 0.8974 | |
| | 0.3337 | 8.0 | 6346 | 0.3364 | 0.8974 | |
| | 0.2829 | 9.0 | 7139 | 0.3327 | 0.8974 | |
| | 0.3189 | 10.0 | 7930 | 0.3321 | 0.8974 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 4.34.0.dev0 |
| - Pytorch 2.0.0.post302 |
| - Datasets 2.14.5 |
| - Tokenizers 0.13.3 |
|
|
