Audio Classification
Transformers
Safetensors
Shadow
chucklenet
laughter-detection
wavlm
speech
humor
stand-up-comedy
kage
custom_code
Instructions to use Hayasuki/ChuckleNet-Kage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hayasuki/ChuckleNet-Kage with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Hayasuki/ChuckleNet-Kage", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForAudioClassification model = AutoModelForAudioClassification.from_pretrained("Hayasuki/ChuckleNet-Kage", trust_remote_code=True, device_map="auto") - Shadow
How to use Hayasuki/ChuckleNet-Kage with Shadow:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Download config.json from Hayasuki/ChuckleNet-Kage: direct link, hf CLI and curl.
- Browser
- Download file 481 Bytes
-
https://huggingface.co/Hayasuki/ChuckleNet-Kage/resolve/main/config.json
- Command line
-
hf download hf://Hayasuki/ChuckleNet-Kage/config.json
-
curl -L -o config.json https://huggingface.co/Hayasuki/ChuckleNet-Kage/resolve/main/config.json
481 Bytes
| { | |
| "model_type": "chucklenet", | |
| "in_dim": 768, | |
| "hidden": 256, | |
| "threshold": 0.85, | |
| "sample_rate": 16000, | |
| "base_model_name": "microsoft/wavlm-base", | |
| "max_segment_s": 6.0, | |
| "vad_threshold": 0.08, | |
| "min_segment_s": 0.4, | |
| "merge_gap_s": 0.3, | |
| "batch_size": 16, | |
| "architectures": [ | |
| "ChuckleNetDetector" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "modeling_chucklenet.ChuckleNetConfig", | |
| "AutoModelForAudioClassification": "modeling_chucklenet.ChuckleNetDetector" | |
| } | |
| } |