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:
- f6815f9cf3f67437b969d932a44b8ad9756c6c3944b5de01fd4a48c77553ad32
- Size of remote file:
- 578 MB
- SHA256:
- cb00a89c76a28b411d2923694249c2d0d80f589ee79a5017b45989c2028ae31b
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