Feature Extraction
Transformers
Safetensors
wav2vec2
bioacoustics
audio
self-supervised-learning
dolphin
bottlenose-dolphin
whistle
openwhistle
Instructions to use OpenWhistleNeurIPS26/OpenWhistle-Wav2Vec2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenWhistleNeurIPS26/OpenWhistle-Wav2Vec2.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="OpenWhistleNeurIPS26/OpenWhistle-Wav2Vec2.0")# Load model directly from transformers import AutoProcessor, Wav2vec2ForPreTraining_randommask processor = AutoProcessor.from_pretrained("OpenWhistleNeurIPS26/OpenWhistle-Wav2Vec2.0") model = Wav2vec2ForPreTraining_randommask.from_pretrained("OpenWhistleNeurIPS26/OpenWhistle-Wav2Vec2.0") - Notebooks
- Google Colab
- Kaggle
| { | |
| "do_normalize": true, | |
| "feature_size": 1, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "return_attention_mask": false, | |
| "sampling_rate": 44100 | |
| } | |