Instructions to use Kibalama/urban_sounds_classification_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kibalama/urban_sounds_classification_Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Kibalama/urban_sounds_classification_Model")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Kibalama/urban_sounds_classification_Model") model = AutoModelForAudioClassification.from_pretrained("Kibalama/urban_sounds_classification_Model") - Notebooks
- Google Colab
- Kaggle
File size: 215 Bytes
c87007e | 1 2 3 4 5 6 7 8 9 10 | {
"do_normalize": true,
"feature_extractor_type": "Wav2Vec2FeatureExtractor",
"feature_size": 1,
"padding_side": "right",
"padding_value": 0.0,
"return_attention_mask": false,
"sampling_rate": 16000
}
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