Instructions to use fordb/bird2vec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fordb/bird2vec with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="fordb/bird2vec")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("fordb/bird2vec") model = AutoModelForAudioClassification.from_pretrained("fordb/bird2vec", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 90c2ee0afcfa84bc6a2291e8f4cc6aec5e3362d1211e49e88cb3dc31ebb85be4
- Size of remote file:
- 378 MB
- SHA256:
- 750bde43c81860b84fc94b2373f4ea6421fe56a043d714d4e23a54a78ecee74d
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