Audio Classification
Transformers
TensorBoard
Safetensors
audio-spectrogram-transformer
Generated from Trainer
Eval Results (legacy)
Instructions to use Hemg/dangerous-heartbeat-MIT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hemg/dangerous-heartbeat-MIT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Hemg/dangerous-heartbeat-MIT")# pip install -U transformers accelerate # Load model directly from transformers import AutoFeatureExtractor, AutoModelForAudioClassification extractor = AutoFeatureExtractor.from_pretrained("Hemg/dangerous-heartbeat-MIT") model = AutoModelForAudioClassification.from_pretrained("Hemg/dangerous-heartbeat-MIT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from Hemg/dangerous-heartbeat-MIT: direct link, hf CLI and curl.
- Browser
- Download file 296 Bytes
-
https://huggingface.co/Hemg/dangerous-heartbeat-MIT/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://Hemg/dangerous-heartbeat-MIT/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/Hemg/dangerous-heartbeat-MIT/resolve/main/preprocessor_config.json
296 Bytes
| { | |
| "do_normalize": true, | |
| "feature_extractor_type": "ASTFeatureExtractor", | |
| "feature_size": 1, | |
| "max_length": 1024, | |
| "mean": -4.2677393, | |
| "num_mel_bins": 128, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000, | |
| "std": 4.5689974 | |
| } | |