Instructions to use Anguuuuus/laryngitis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Anguuuuus/laryngitis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Anguuuuus/laryngitis")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Anguuuuus/laryngitis") model = AutoModelForAudioClassification.from_pretrained("Anguuuuus/laryngitis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Anguuuuus/laryngitis: direct link, hf CLI and curl.
- Browser
- Download file 1.95 kB
-
https://huggingface.co/Anguuuuus/laryngitis/resolve/main/README.md
- Command line
-
hf download hf://Anguuuuus/laryngitis/README.md
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curl -L -o README.md https://huggingface.co/Anguuuuus/laryngitis/resolve/main/README.md
1.95 kB
| license: apache-2.0 | |
| base_model: facebook/wav2vec2-base | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: laryngitis | |
| 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. --> | |
| # laryngitis | |
| This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.7828 | |
| - Accuracy: 0.5455 | |
| ## 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: 1e-05 | |
| - train_batch_size: 16 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - 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 | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | 0.4888 | 1.0 | 6 | 0.7395 | 0.4091 | | |
| | 0.4714 | 2.0 | 12 | 0.7492 | 0.4545 | | |
| | 0.4298 | 3.0 | 18 | 0.7774 | 0.5 | | |
| | 0.3732 | 4.0 | 24 | 0.7864 | 0.5 | | |
| | 0.352 | 5.0 | 30 | 0.7903 | 0.5 | | |
| | 0.3147 | 6.0 | 36 | 0.8435 | 0.5 | | |
| | 0.2969 | 7.0 | 42 | 0.7719 | 0.5 | | |
| | 0.2902 | 8.0 | 48 | 0.7035 | 0.5909 | | |
| | 0.238 | 9.0 | 54 | 0.7546 | 0.5909 | | |
| | 0.2654 | 10.0 | 60 | 0.7828 | 0.5455 | | |
| ### Framework versions | |
| - Transformers 4.35.2 | |
| - Pytorch 2.1.0+cu121 | |
| - Datasets 2.16.1 | |
| - Tokenizers 0.15.1 | |