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
File size: 598 Bytes
0e7f317 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | {
"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"
}
|