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wav2vec2-base-finetuned-ks
jso1/wav2vec2-base-finetuned-ks
wav2vec2-base-finetuned-ks is a audio classification model from jso1. 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. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Audio Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- wav2vec2
- Access
- Public
- Created
- Nov 17, 2022
- Updated
- Nov 17, 2022
- SHA
- dd530598
Datasets
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. -->
wav2vec2-base-finetuned-ks
This model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset. It achieves the following results on the evaluation set:
- Loss: 0.0862
- Accuracy: 0.9835
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6609 | 1.0 | 399 | 0.5366 | 0.9662 |
| 0.29 | 2.0 | 798 | 0.1719 | 0.9776 |
| 0.184 | 3.0 | 1197 | 0.1134 | 0.9794 |
| 0.1763 | 4.0 | 1596 | 0.0935 | 0.9809 |
| 0.1266 | 5.0 | 1995 | 0.0862 | 0.9835 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.3.2
- Tokenizers 0.12.1
- Task
- Audio Classification
- Library
- transformers
- Type
- wav2vec2
- License
- apache-2.0
- Created
- Nov 17, 2022
- Updated
- Nov 17, 2022