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
File size: 691 Bytes
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"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
}
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