Instructions to use skroed/bark with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use skroed/bark with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="skroed/bark")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToWaveform processor = AutoProcessor.from_pretrained("skroed/bark") model = AutoModelForTextToWaveform.from_pretrained("skroed/bark", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Adding `safetensors` variant of this model
This is an automated PR created with https://huggingface.co/spaces/safetensors/convert This new file is equivalent to `pytorch_model.bin` but safe in the sense that no arbitrary code can be put into it. These files also happen to load much faster than their pytorch counterpart: https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb The widgets on your model page will run using this model even if this is not merged making sure the file actually works. If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions Feel free to ignore this PR.8138dd1
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fix: just return dict
skroed09c5b6d
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Add: Code example in README
skroed71c2800
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Update: Readme
skroede6082f5
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Fix: endpoint passing speaker.
skroedc2293d7 -
Upload CustomHandler and requirements.
6eb9b90 -
Duplicate from suno/bark
Co-authored-by: Vaibhav Srivastav <reach-vb@users.noreply.huggingface.co>a48e070