Feature Extraction
Transformers
Safetensors
wav2vec2
pretraining
bioacoustics
audio
self-supervised-learning
dolphin
bottlenose-dolphin
whistle
openwhistle
Instructions to use dolphinteam/OpenWhistle-BioClust with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dolphinteam/OpenWhistle-BioClust with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dolphinteam/OpenWhistle-BioClust")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForPreTraining processor = AutoProcessor.from_pretrained("dolphinteam/OpenWhistle-BioClust") model = AutoModelForPreTraining.from_pretrained("dolphinteam/OpenWhistle-BioClust", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from dolphinteam/OpenWhistle-BioClust: direct link, hf CLI and curl.
- Browser
- Download file 215 Bytes
-
https://huggingface.co/dolphinteam/OpenWhistle-BioClust/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://dolphinteam/OpenWhistle-BioClust/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/dolphinteam/OpenWhistle-BioClust/resolve/main/preprocessor_config.json
215 Bytes
| { | |
| "do_normalize": true, | |
| "feature_extractor_type": "Wav2Vec2FeatureExtractor", | |
| "feature_size": 1, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "return_attention_mask": false, | |
| "sampling_rate": 44100 | |
| } | |