Instructions to use hf-internal-testing/tiny-random-VitsModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-VitsModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="hf-internal-testing/tiny-random-VitsModel")# Load model directly from transformers import AutoTokenizer, AutoModelForTextToWaveform tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-VitsModel") model = AutoModelForTextToWaveform.from_pretrained("hf-internal-testing/tiny-random-VitsModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3d40513fe50fca2f7ffc3694f8f70999d67bd1a7cc45fd62e334298c8e160bfe
- Size of remote file:
- 380 kB
- SHA256:
- c0c306c453b645a512e815205ed60f75d0497844c4fa891bfbd440d26b22ff1a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.