Feature Extraction
Transformers
PyTorch
hear_canon_vit
audio
medical
embeddings
vision-transformer
distillation
canon
custom_code
Instructions to use matthewagi/HeAR-s with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use matthewagi/HeAR-s with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="matthewagi/HeAR-s", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("matthewagi/HeAR-s", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 987 Bytes
73629b7 108888b 73629b7 | 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 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | {
"_name_or_path": "distilled-hear-vit-s-canon",
"architectures": [
"HearCanonViTModel"
],
"auto_map": {
"AutoConfig": "configuration_hear_canon.HearCanonViTConfig",
"AutoModel": "modeling_hear_canon.HearCanonViTModel"
},
"clip_seconds": 2.0,
"canon": true,
"canon_2d": true,
"canon_a": true,
"canon_abcd": true,
"canon_b": true,
"canon_b_qkv": false,
"canon_c": true,
"canon_causal": false,
"canon_d": true,
"canon_kernel": 4,
"canon_no_pos_enc": true,
"hidden_act": "gelu",
"hidden_size": 384,
"image_size": [
192,
128
],
"intermediate_size": 1536,
"layer_norm_eps": 1e-06,
"model_type": "hear_canon_vit",
"num_attention_heads": 6,
"num_audio_samples": 32000,
"num_channels": 1,
"num_hidden_layers": 12,
"patch_size": 16,
"pooled_dim": 384,
"pooler_output_size": 384,
"sample_rate": 16000,
"timm_model_name": "vit_small_patch16_224",
"torch_dtype": "float32",
"transformers_version": "4.50.3"
}
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