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wavlm-large-finetuned-iemocap2
Zahra99/wavlm-large-finetuned-iemocap2
wavlm-large-finetuned-iemocap2 is a audio classification model from Zahra99. Use it for the audio classification task on the model card, and read the license before you ship it in a product. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Audio Classification
- Library
- transformers
- Model type
- wavlm
- Access
- Public
- Created
- Feb 15, 2023
- Updated
- Feb 15, 2023
- SHA
- 110622a5
Model card
From the Hugging Face model README
<!-- 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-large-finetuned-iemocap2
This model is a fine-tuned version of microsoft/wavlm-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.0935
- Accuracy: 0.5335
- F1: 0.5005
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 1.3826 | 0.98 | 25 | 1.3815 | 0.2502 | 0.1003 |
| 1.3263 | 1.98 | 50 | 1.3663 | 0.2502 | 0.1002 |
| 1.2563 | 2.98 | 75 | 1.2589 | 0.3870 | 0.3051 |
| 1.1869 | 3.98 | 100 | 1.2042 | 0.3977 | 0.3428 |
| 1.1291 | 4.98 | 125 | 1.1768 | 0.4539 | 0.4557 |
| 1.1171 | 5.98 | 150 | 1.1425 | 0.4888 | 0.4799 |
| 1.0811 | 6.98 | 175 | 1.1316 | 0.4956 | 0.4851 |
| 1.0627 | 7.98 | 200 | 1.1241 | 0.5044 | 0.4859 |
| 1.079 | 8.98 | 225 | 1.1026 | 0.5228 | 0.5031 |
| 1.0294 | 9.98 | 250 | 1.1018 | 0.5199 | 0.4959 |
| 1.0088 | 10.98 | 275 | 1.0903 | 0.5325 | 0.5046 |
| 1.0217 | 11.98 | 300 | 1.0966 | 0.5296 | 0.5015 |
| 1.0034 | 12.98 | 325 | 1.1012 | 0.5296 | 0.4990 |
| 1.0024 | 13.98 | 350 | 1.0832 | 0.5393 | 0.5127 |
| 1.0047 | 14.98 | 375 | 1.0902 | 0.5315 | 0.4986 |
| 0.9436 | 15.98 | 400 | 1.0896 | 0.5373 | 0.5085 |
| 0.9584 | 16.98 | 425 | 1.0859 | 0.5412 | 0.5114 |
| 0.9859 | 17.98 | 450 | 1.0865 | 0.5412 | 0.5120 |
| 0.9679 | 18.98 | 475 | 1.0926 | 0.5335 | 0.4999 |
| 0.9468 | 19.98 | 500 | 1.0935 | 0.5335 | 0.5005 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
- Task
- Audio Classification
- Library
- transformers
- Type
- wavlm
- Created
- Feb 15, 2023
- Updated
- Feb 15, 2023