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
| { | |
| "_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" | |
| } | |