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wav2vec2-base-960h-finetuned-ks
faizandigi009/wav2vec2-base-960h-finetuned-ks
wav2vec2-base-960h-finetuned-ks is a audio classification model from faizandigi009. 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
- wav2vec2
- Access
- Public
- Created
- Apr 28, 2025
- Updated
- May 5, 2025
- SHA
- 0b98c214
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-960h-finetuned-ks
This model is a fine-tuned version of facebook/wav2vec2-base-960h on the audiofolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.2908
- Accuracy: 0.8929
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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.5519 | 1.0 | 70 | 0.4880 | 0.8571 |
| 0.8835 | 2.0 | 140 | 0.6964 | 0.7286 |
| 0.3766 | 3.0 | 210 | 0.3114 | 0.8714 |
| 0.2251 | 4.0 | 280 | 0.2908 | 0.8929 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cpu
- Datasets 3.5.1
- Tokenizers 0.21.1
- Task
- Audio Classification
- Library
- transformers
- Type
- wav2vec2
- Created
- Apr 28, 2025
- Updated
- May 5, 2025