Instructions to use junbeom2/audio_cls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use junbeom2/audio_cls with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="junbeom2/audio_cls")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("junbeom2/audio_cls") model = AutoModelForAudioClassification.from_pretrained("junbeom2/audio_cls", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from junbeom2/audio_cls: direct link, hf CLI and curl.
- Browser
- Download file 1.25 kB
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https://huggingface.co/junbeom2/audio_cls/resolve/main/README.md
- Command line
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hf download hf://junbeom2/audio_cls/README.md
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curl -L -o README.md https://huggingface.co/junbeom2/audio_cls/resolve/main/README.md
1.25 kB
| library_name: transformers | |
| base_model: Kkonjeong/wav2vec2-base-korean | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: audio_cls | |
| results: [] | |
| <!-- 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 is a fine-tuned version of [Kkonjeong/wav2vec2-base-korean](https://huggingface.co/Kkonjeong/wav2vec2-base-korean) on the None dataset. | |
| ## 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 4.57.3 | |
| - Pytorch 2.9.0+cu126 | |
| - Datasets 4.0.0 | |
| - Tokenizers 0.22.1 | |