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 config.json from labhamlet/bearings: direct link, hf CLI and curl.
- Browser
- Download file 691 Bytes
-
https://huggingface.co/labhamlet/bearings/resolve/main/config.json
- Command line
-
hf download hf://labhamlet/bearings/config.json
-
curl -L -o config.json https://huggingface.co/labhamlet/bearings/resolve/main/config.json
691 Bytes
| { | |
| "architectures": [ | |
| "BearingsModel" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_bearings.BearingsConfig", | |
| "AutoFeatureExtractor": "feature_extraction_bearings.BearingsFeatureExtractor", | |
| "AutoModel": "modeling_bearings.BearingsModel" | |
| }, | |
| "dtype": "float32", | |
| "encoder_depth": 6, | |
| "encoder_embedding_dim": 384, | |
| "f_max": 14000.0, | |
| "f_min": 50.0, | |
| "fshape": 16, | |
| "hop_length": 320, | |
| "in_channels": 7, | |
| "layer_norm_eps": 1e-05, | |
| "mlp_ratio": 4.0, | |
| "model_type": "bearings", | |
| "n_fft": 1024, | |
| "num_heads": 12, | |
| "num_mel_bins": 128, | |
| "sampling_rate": 32000, | |
| "target_length": 200, | |
| "transformers_version": "4.57.6", | |
| "tshape": 8, | |
| "win_length": 1024 | |
| } | |