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:
- b0963e2e7ef32f3fb32f43132678c64d98eebc597c541d9b39c2a6017ae7814f
- Size of remote file:
- 4.92 kB
- SHA256:
- 570f582ff38bd8140c3457c6bcbd534d7273a651a39ca2259bf30f544650a53b
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