Audio Classification
Transformers
PyTorch
laughter_detection
laughter-detection
wavlm
speech
humor
stand-up-comedy
genki
Instructions to use Hayasuki/ChuckleNet-Genki with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hayasuki/ChuckleNet-Genki with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Hayasuki/ChuckleNet-Genki")# pip install -U transformers accelerate # Load model directly from transformers import WavLMForLaughterDetection model = WavLMForLaughterDetection.from_pretrained("Hayasuki/ChuckleNet-Genki", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from Hayasuki/ChuckleNet-Genki: direct link, hf CLI and curl.
- Browser
- Download file 1.54 kB
-
https://huggingface.co/Hayasuki/ChuckleNet-Genki/resolve/main/config.json
- Command line
-
hf download hf://Hayasuki/ChuckleNet-Genki/config.json
-
curl -L -o config.json https://huggingface.co/Hayasuki/ChuckleNet-Genki/resolve/main/config.json
1.54 kB
| { | |
| "model_type": " laughter_detection", | |
| "architectures": ["WavLMForLaughterDetection"], | |
| "task": "audio_classification", | |
| "num_labels": 2, | |
| "label_mapping": {"0": "no_laugh", "1": "laughter"}, | |
| "dataset": { | |
| "name": "standup4ai", | |
| "num_videos": 221, | |
| "total_duration_minutes": 540, | |
| "language": "en", | |
| "class_balance": { | |
| "positive": 0.132, | |
| "negative": 0.868 | |
| } | |
| }, | |
| "audio_config": { | |
| "sample_rate": 16000, | |
| "hop_length": 512, | |
| "n_fft": 2048, | |
| "n_mels": 128, | |
| "fmin": 80, | |
| "fmax": 8000 | |
| }, | |
| "prosody_features": { | |
| "dimension": 23, | |
| "source": "eGeMAPS v02", | |
| "extraction": "librosa + openSMILE", | |
| "features": [ | |
| "F0_mean", "F0_std", "F0_slope", "F0_median", "F0_range", | |
| "pause_duration", | |
| "speech_rate", "word_duration_mean", "word_duration_std", "syllable_rate", "phonation_time", "total_time", | |
| "RMS_energy_mean", "RMS_energy_std", "RMS_energy_range", | |
| "MFCC1", "MFCC2", "MFCC3", | |
| "spectral_centroid", "spectral_bandwidth", "spectral_rolloff", | |
| "voice_activity_ratio", | |
| "formant_emphasis" | |
| ] | |
| }, | |
| "validation": { | |
| "method": "5-fold_stratified_cv", | |
| "n_folds": 5, | |
| "prosody_only_f1": "0.960 ± 0.021", | |
| "fusion_f1": "0.879 ± 0.034", | |
| "delta_f1": "-0.081", | |
| "p_value": 0.024, | |
| "confidence_interval_95": [-0.152, -0.010] | |
| }, | |
| "fusion_analysis": { | |
| "finding": "NEGATIVE_RESULT", | |
| "cause": "frozen_WavLM_mean_pool_destoys_temporal_cues", | |
| "regression": "prosody_only_better_than_fusion" | |
| } | |
| } |