Instructions to use lithish2602/speecht5_tts_ta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lithish2602/speecht5_tts_ta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="lithish2602/speecht5_tts_ta")# Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("lithish2602/speecht5_tts_ta") model = AutoModelForTextToSpectrogram.from_pretrained("lithish2602/speecht5_tts_ta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3b5948489548ad89db43921922ba1ba037838060de89ea86ea1061599211d8a2
- Size of remote file:
- 5.5 kB
- SHA256:
- 2d3c374452cd1098e6cd25e38f22ac8066675d64b2110d935e86d8419c934f2e
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