Instructions to use itriedcoding/Sol with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use itriedcoding/Sol with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="itriedcoding/Sol")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("itriedcoding/Sol") model = AutoModelForTextToSpectrogram.from_pretrained("itriedcoding/Sol", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download examples/ai.wav from itriedcoding/Sol: direct link, hf CLI and curl.
- Browser
- Download file 131 kB
-
https://huggingface.co/itriedcoding/Sol/resolve/main/examples/ai.wav
- Command line
-
hf download hf://itriedcoding/Sol/examples/ai.wav
-
curl -L -o ai.wav https://huggingface.co/itriedcoding/Sol/resolve/main/examples/ai.wav
131 kB
- Xet hash:
- 5d912c0c74f1ee67ba0bb1c72fb893303d93ff43696d7a2f5562887c7f41bb40
- Size of remote file:
- 131 kB
- SHA256:
- fbd3ae5bfbd031383ba46e0210921b6643e33b075c184ab24292a03be2456f56
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.