Instructions to use Chithekitale/PhD_tts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Chithekitale/PhD_tts with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="Chithekitale/PhD_tts")# Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("Chithekitale/PhD_tts") model = AutoModelForTextToSpectrogram.from_pretrained("Chithekitale/PhD_tts", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 105048e64194e0faa1d177ef006f7fa85f24b61592a2d49242041462ac002163
- Size of remote file:
- 5.5 kB
- SHA256:
- 3f6b2171fdd1b0b5cb1c6413311e529c753d80639653e847a33092df79320da8
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