Instructions to use Sajid030/text-to-speech with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sajid030/text-to-speech with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="Sajid030/text-to-speech")# Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("Sajid030/text-to-speech") model = AutoModelForTextToSpectrogram.from_pretrained("Sajid030/text-to-speech", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3ca6cc332af3955b296a8a38750db8830d663953ff8daf9fc7f25143a02d1ab3
- Size of remote file:
- 578 MB
- SHA256:
- 2f7ac99cb01a810bea42635fd92b1dc80b625fb06885b6efb3e3ebab517297f9
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