Instructions to use assoni2002/trained_model_with_zscaler_TTS_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use assoni2002/trained_model_with_zscaler_TTS_data with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="assoni2002/trained_model_with_zscaler_TTS_data")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("assoni2002/trained_model_with_zscaler_TTS_data") model = AutoModelForAudioClassification.from_pretrained("assoni2002/trained_model_with_zscaler_TTS_data") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0c452c526f38c83a72d9623e6bf9ce777eae0dcb849b01f0167d940e9674febe
- Size of remote file:
- 5.84 kB
- SHA256:
- f35a912f554d556e95395caff88a64aff2e5b2cb5bab9cf3d16339070000450a
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