Feature Extraction
Transformers
Safetensors
bearings
audio
spatial-audio
ambisonics
self-supervised
sound-event-localization
custom_code
Instructions to use labhamlet/bearings with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use labhamlet/bearings with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="labhamlet/bearings", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("labhamlet/bearings", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from labhamlet/bearings: direct link, hf CLI and curl.
- Browser
- Download file 214 Bytes
-
https://huggingface.co/labhamlet/bearings/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://labhamlet/bearings/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/labhamlet/bearings/resolve/main/preprocessor_config.json
214 Bytes
| { | |
| "auto_map": { | |
| "AutoFeatureExtractor": "feature_extraction_bearings.BearingsFeatureExtractor" | |
| }, | |
| "feature_extractor_type": "BearingsFeatureExtractor", | |
| "sampling_rate": 32000, | |
| "window_seconds": 2.0 | |
| } | |