Audio Classification
Transformers
Safetensors
custom
laughter-detection
laughter-anticipation
wavlm
speech
humor
stand-up-comedy
ten
human-verified
Instructions to use Hayasuki/ChuckleNet-Ten with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hayasuki/ChuckleNet-Ten with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Hayasuki/ChuckleNet-Ten")# pip install -U transformers accelerate # Load model directly from transformers import ChuckleNetVerified model = ChuckleNetVerified.from_pretrained("Hayasuki/ChuckleNet-Ten", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from Hayasuki/ChuckleNet-Ten: direct link, hf CLI and curl.
- Browser
- Download file 598 Bytes
-
https://huggingface.co/Hayasuki/ChuckleNet-Ten/resolve/main/config.json
- Command line
-
hf download hf://Hayasuki/ChuckleNet-Ten/config.json
-
curl -L -o config.json https://huggingface.co/Hayasuki/ChuckleNet-Ten/resolve/main/config.json
598 Bytes
| { | |
| "model_type": "custom", | |
| "architectures": ["ChuckleNetVerified"], | |
| "base_model": "microsoft/wavlm-base", | |
| "task": "audio-classification", | |
| "num_labels": 2, | |
| "label2id": {"not_laugh": 0, "laugh": 1}, | |
| "id2label": {"0": "not_laugh", "1": "laugh"}, | |
| "prosody_dim": 10, | |
| "hidden_dim": 256, | |
| "classifier_layers": [256, 128, 64], | |
| "dropout": 0.3, | |
| "activation": "relu", | |
| "training_f1": 0.975, | |
| "iou_f1_118v": 0.3302, | |
| "average_precision": 0.2290, | |
| "dataset": "Gillick-87 (human-verified)", | |
| "paper": "https://github.com/Das-rebel/autonomous_laughter_prediction", | |
| "license": "mit" | |
| } | |