Instructions to use Jurabek/Audio_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jurabek/Audio_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Jurabek/Audio_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Jurabek/Audio_classification") model = AutoModelForAudioClassification.from_pretrained("Jurabek/Audio_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Jurabek/Audio_classification: direct link, hf CLI and curl.
- Browser
- Download file 2.23 kB
-
https://huggingface.co/Jurabek/Audio_classification/resolve/refs%2Fpr%2F1/README.md
- Command line
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hf download hf://Jurabek/Audio_classification@refs/pr/1/README.md
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curl -L -o README.md https://huggingface.co/Jurabek/Audio_classification/resolve/refs%2Fpr%2F1/README.md
2.23 kB
| license: apache-2.0 | |
| base_model: stevhliu/my_awesome_minds_model | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - minds14 | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: Audio_classification | |
| results: | |
| - task: | |
| name: Audio Classification | |
| type: audio-classification | |
| dataset: | |
| name: minds14 | |
| type: minds14 | |
| config: en-US | |
| split: train | |
| args: en-US | |
| metrics: | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.04424778761061947 | |
| <!-- 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_classification | |
| This model is a fine-tuned version of [stevhliu/my_awesome_minds_model](https://huggingface.co/stevhliu/my_awesome_minds_model) on the minds14 dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 2.6589 | |
| - Accuracy: 0.0442 | |
| ## 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: 32 | |
| - eval_batch_size: 32 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 4 | |
| - total_train_batch_size: 128 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_ratio: 0.1 | |
| - num_epochs: 10 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | No log | 0.8 | 3 | 2.6369 | 0.0708 | | |
| | No log | 1.87 | 7 | 2.6416 | 0.0531 | | |
| | 2.5939 | 2.93 | 11 | 2.6429 | 0.0619 | | |
| | 2.5939 | 4.0 | 15 | 2.6646 | 0.0531 | | |
| | 2.5939 | 4.8 | 18 | 2.6581 | 0.0442 | | |
| | 2.5818 | 5.87 | 22 | 2.6622 | 0.0442 | | |
| | 2.5818 | 6.93 | 26 | 2.6591 | 0.0442 | | |
| | 2.5708 | 8.0 | 30 | 2.6589 | 0.0442 | | |
| ### Framework versions | |
| - Transformers 4.34.1 | |
| - Pytorch 2.1.0+cu118 | |
| - Datasets 2.14.6 | |
| - Tokenizers 0.14.1 | |