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wav2vec2-base-ft-keyword-spotting
cloudwalkerw/wav2vec2-base-ft-keyword-spotting
wav2vec2-base-ft-keyword-spotting is a audio classification model from cloudwalkerw. 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
- Sep 30, 2023
- Updated
- Sep 30, 2023
- SHA
- 650dab31
Base models
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-ft-keyword-spotting
This model is a fine-tuned version of microsoft/wavlm-base on the superb dataset. It achieves the following results on the evaluation set:
- Loss: 0.2270
- Accuracy: 0.9694
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: 64
- eval_batch_size: 64
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.3203 | 1.0 | 199 | 1.2906 | 0.6328 |
| 0.9587 | 2.0 | 399 | 0.7793 | 0.7355 |
| 0.6218 | 3.0 | 599 | 0.3858 | 0.9289 |
| 0.4379 | 4.0 | 799 | 0.2581 | 0.9688 |
| 0.3779 | 4.98 | 995 | 0.2270 | 0.9694 |
Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.0.post302
- Datasets 2.14.5
- Tokenizers 0.13.3
- Task
- Audio Classification
- Library
- transformers
- Type
- wavlm
- Created
- Sep 30, 2023
- Updated
- Sep 30, 2023