Audio Classification
Transformers
ONNX
Safetensors
multilingual
eat-laughter
audio-frame-classification
audio
sound-event-detection
laughter-detection
podcast
custom_code
Instructions to use zencastr/laughter-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zencastr/laughter-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="zencastr/laughter-detection", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForAudioFrameClassification model = AutoModelForAudioFrameClassification.from_pretrained("zencastr/laughter-detection", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from zencastr/laughter-detection: direct link, hf CLI and curl.
- Browser
- Download file 251 Bytes
-
https://huggingface.co/zencastr/laughter-detection/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://zencastr/laughter-detection/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/zencastr/laughter-detection/resolve/main/preprocessor_config.json
251 Bytes
| { | |
| "feature_extractor_type": "ASTFeatureExtractor", | |
| "feature_size": 1, | |
| "sampling_rate": 16000, | |
| "padding_value": 0.0, | |
| "num_mel_bins": 128, | |
| "max_length": 398, | |
| "do_normalize": true, | |
| "mean": -4.268, | |
| "std": 4.569, | |
| "return_attention_mask": false | |
| } | |