Instructions to use dima806/bird_sounds_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/bird_sounds_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="dima806/bird_sounds_classification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("dima806/bird_sounds_classification") model = AutoModelForAudioClassification.from_pretrained("dima806/bird_sounds_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7bdeb9ad2e2a2e0d51a1c893da63660e526299ff4566941d7f27d4bcd92e8052
- Size of remote file:
- 378 MB
- SHA256:
- 3b716bdda7f787e0c86c156c910df36458503a3869d30134eb76798955d08d94
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