Instructions to use lysandre/text-to-speech-pipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lysandre/text-to-speech-pipeline with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="lysandre/text-to-speech-pipeline")# Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("lysandre/text-to-speech-pipeline") model = AutoModelForTextToSpectrogram.from_pretrained("lysandre/text-to-speech-pipeline", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 80e06ad724ddf05ebf03b2f3bbd9ba387b1e1aa4c01f943ae45360a1387fffcb
- Size of remote file:
- 585 MB
- SHA256:
- eccce95745831a1915292135e542097cc4d6bf9cb966331c1e99d6da40aa2330
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