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)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("matthewagi/HeAR-s", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from matthewagi/HeAR-s: direct link, hf CLI and curl.
- Browser
- Download file 259 Bytes
-
https://huggingface.co/matthewagi/HeAR-s/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://matthewagi/HeAR-s/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/matthewagi/HeAR-s/resolve/main/preprocessor_config.json
259 Bytes
| { | |
| "clip_duration_seconds": 2.0, | |
| "do_normalize": false, | |
| "feature_extractor_type": "HearCanonFeatureExtractor", | |
| "image_size": [ | |
| 192, | |
| 128 | |
| ], | |
| "input_channels": 1, | |
| "num_audio_samples": 32000, | |
| "num_mel_bins": 128, | |
| "sampling_rate": 16000 | |
| } | |