Instructions to use hf-internal-testing/tiny-random-BarkModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-BarkModel 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-BarkModel")# Load model directly from transformers import AutoProcessor, AutoModelForTextToWaveform processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-BarkModel") model = AutoModelForTextToWaveform.from_pretrained("hf-internal-testing/tiny-random-BarkModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9c90f5a2a7d4e98b25d17281980b1a5ad7a023a450865c36e1586234e2f639c8
- Size of remote file:
- 937 kB
- SHA256:
- d505e5e033e6ccf02259ae895cc14a98364fd5d70c063dfd7831c3f8137e7354
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.