Audio Classification
Transformers
TensorBoard
Safetensors
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use Hemg/dangerous-heartbeat-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hemg/dangerous-heartbeat-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Hemg/dangerous-heartbeat-detection")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Hemg/dangerous-heartbeat-detection") model = AutoModelForAudioClassification.from_pretrained("Hemg/dangerous-heartbeat-detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files- README.md +4 -4
- model.safetensors +1 -1
README.md
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This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the audiofolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Accuracy: 1.0
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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### Framework versions
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This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the audiofolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6427
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- Accuracy: 1.0
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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| 1.1853 | 1.0 | 1 | 1.5716 | 0.5556 |
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| 0.5052 | 2.0 | 3 | 0.7388 | 1.0 |
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| 0.3841 | 2.67 | 4 | 0.6427 | 1.0 |
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### Framework versions
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model.safetensors
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