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
File size: 251 Bytes
849623f | 1 2 3 4 5 6 7 8 9 10 11 12 13 | {
"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
}
|