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:
- 5fb410c922febc51dab01e415105f01883e6520429df6cd054aaa015d3862b19
- Size of remote file:
- 4.66 kB
- SHA256:
- 0e49a2ec0fa7516aec0f6c33eda0029fd69758a6a9909ae409045233f5b7f2e1
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