Instructions to use ylacombe/bark-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ylacombe/bark-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="ylacombe/bark-small")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToWaveform processor = AutoProcessor.from_pretrained("ylacombe/bark-small") model = AutoModelForTextToWaveform.from_pretrained("ylacombe/bark-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ylacombe/bark-small: direct link, hf CLI and curl.
- Browser
- Download file 1.68 GB
-
https://huggingface.co/ylacombe/bark-small/resolve/refs%2Fpr%2F14/pytorch_model.bin
- Command line
-
hf download hf://ylacombe/bark-small@refs/pr/14/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ylacombe/bark-small/resolve/refs%2Fpr%2F14/pytorch_model.bin
1.68 GB
- Xet hash:
- fdbad267848427dbb5153817e9437c1cbaa8f4ca1af8998c2b667a9cf82569cf
- Size of remote file:
- 1.68 GB
- SHA256:
- f0f7f16b24f65789ce42b3c491aa6a1cdf219f7ef425066fcd194485245e65d9
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