Instructions to use imdawon/audio_cls_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use imdawon/audio_cls_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="imdawon/audio_cls_v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("imdawon/audio_cls_v1") model = AutoModelForAudioClassification.from_pretrained("imdawon/audio_cls_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
audio_cls_v1
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.5445
- eval_accuracy: 0.8824
- eval_runtime: 17.8416
- eval_samples_per_second: 6.67
- eval_steps_per_second: 0.841
- epoch: 5.0
- step: 75
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: 10
- mixed_precision_training: Native AMP
Framework versions
- Transformers 5.18.0
- Pytorch 2.11.0+cu130
- Datasets 4.8.5
- Tokenizers 0.23.2
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