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
- Xet hash:
- f3ae5659672d30329cc4f97c3a173e340e472c37a7ef89da84ccb1314b9d2ef3
- Size of remote file:
- 4.92 kB
- SHA256:
- 16cf32ba02fa196928fa6be2a38430cf761ce7f0b6a85a07392e24e812db0359
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