Instructions to use omarimc/dst with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use omarimc/dst with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="omarimc/dst")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToWaveform processor = AutoProcessor.from_pretrained("omarimc/dst") model = AutoModelForTextToWaveform.from_pretrained("omarimc/dst", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download spiece.model from omarimc/dst: direct link, hf CLI and curl.
- Browser
- Download file 792 kB
-
https://huggingface.co/omarimc/dst/resolve/main/spiece.model
- Command line
-
hf download hf://omarimc/dst/spiece.model
-
curl -L -o spiece.model https://huggingface.co/omarimc/dst/resolve/main/spiece.model
792 kB
- Xet hash:
- e7463c1c6b7c46ae0de79b004f3d7318bf4307ef0bef4de3be2e38ae80fab160
- Size of remote file:
- 792 kB
- SHA256:
- d60acb128cf7b7f2536e8f38a5b18a05535c9e14c7a355904270e15b0945ea86
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