Instructions to use lithish2602/sample_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lithish2602/sample_data with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="lithish2602/sample_data")# Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("lithish2602/sample_data") model = AutoModelForTextToSpectrogram.from_pretrained("lithish2602/sample_data", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b256cc37687fbe2a1a4b3b2edc5dca93720adc4a0aa2f1cd5e84491eff0e2f0f
- Size of remote file:
- 578 MB
- SHA256:
- 94ef92b7fd7d6d36eccfb499efb90f153a67c72297e378d7fd8ba06f4eab27bf
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