Instructions to use AndreaNotes1/xqieech with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AndreaNotes1/xqieech with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="AndreaNotes1/xqieech")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("AndreaNotes1/xqieech") model = AutoModelForTextToSpectrogram.from_pretrained("AndreaNotes1/xqieech", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download speaker_embedding.pt from AndreaNotes1/xqieech: direct link, hf CLI and curl.
- Browser
- Download file 3.7 kB
-
https://huggingface.co/AndreaNotes1/xqieech/resolve/main/speaker_embedding.pt
- Command line
-
hf download hf://AndreaNotes1/xqieech/speaker_embedding.pt
-
curl -L -o speaker_embedding.pt https://huggingface.co/AndreaNotes1/xqieech/resolve/main/speaker_embedding.pt
3.7 kB
- Xet hash:
- 3ce0b0eb0e526eadd774739612351994563ddaeff2bdcc8cb3c7e797b0d7f0eb
- Size of remote file:
- 3.7 kB
- SHA256:
- a6fdc6ed8093b62bb2497657c8f535d0bc0345fce60be5cb41f0797d77a618d9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.