Instructions to use Rafeq/cry_detection_and_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rafeq/cry_detection_and_classification with Transformers:
# Load model directly from transformers import AutoProcessor, Wav2Vec2ForSpeechClassification processor = AutoProcessor.from_pretrained("Rafeq/cry_detection_and_classification") model = Wav2Vec2ForSpeechClassification.from_pretrained("Rafeq/cry_detection_and_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 214 Bytes
fe3b393 | 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": true,
"sampling_rate": 16000
}
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