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audio_cls
organbird/audio_cls
audio_cls is a audio classification model from organbird. 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
- Dec 22, 2025
- Updated
- Dec 22, 2025
- SHA
- 3922be9c
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. -->
audio_cls
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.7095
- Accuracy: 0.0252
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 15 | 2.6587 | 0.0588 |
| No log | 2.0 | 30 | 2.7004 | 0.0168 |
| No log | 3.0 | 45 | 2.6831 | 0.0672 |
| No log | 4.0 | 60 | 2.7172 | 0.0168 |
| No log | 5.0 | 75 | 2.7095 | 0.0252 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
- Task
- Audio Classification
- Library
- transformers
- Type
- wav2vec2
- Created
- Dec 22, 2025
- Updated
- Dec 22, 2025