Instructions to use yangwang825/svector-aam-aug with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yangwang825/svector-aam-aug with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="yangwang825/svector-aam-aug", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("yangwang825/svector-aam-aug", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 643 Bytes
3c65bbc 354768f 3c65bbc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | {
"architectures": [
"SvectorModel"
],
"auto_map": {
"AutoConfig": "configuration_svector.SvectorConfig",
"AutoModel": "modeling_svector.SvectorModel",
"AutoModelForAudioClassification": "modeling_svector.SvectorModelForSequenceClassification"
},
"hidden_size": 512,
"hop_length": 10,
"initializer_range": 0.02,
"loss_fn": "aam",
"mean_norm": true,
"model_type": "svector",
"n_mels": 80,
"norm_type": "sentence",
"num_classes": 5994,
"num_heads": 8,
"num_layers": 5,
"sample_rate": 16000,
"std_norm": false,
"torch_dtype": "float32",
"transformers_version": "4.31.0",
"win_length": 25
}
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