Instructions to use reach-vb/bark-endpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use reach-vb/bark-endpoint with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="reach-vb/bark-endpoint")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToWaveform processor = AutoProcessor.from_pretrained("reach-vb/bark-endpoint") model = AutoModelForTextToWaveform.from_pretrained("reach-vb/bark-endpoint", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download speaker_embeddings/de_speaker_0_semantic_prompt.npy from reach-vb/bark-endpoint: direct link, hf CLI and curl.
- Browser
- Download file 3.24 kB
-
https://huggingface.co/reach-vb/bark-endpoint/resolve/main/speaker_embeddings/de_speaker_0_semantic_prompt.npy
- Command line
-
hf download hf://reach-vb/bark-endpoint/speaker_embeddings/de_speaker_0_semantic_prompt.npy
-
curl -L -o de_speaker_0_semantic_prompt.npy https://huggingface.co/reach-vb/bark-endpoint/resolve/main/speaker_embeddings/de_speaker_0_semantic_prompt.npy
3.24 kB
- Xet hash:
- 7042c2ba1db77d3334f9a77a78c0b14c65abf9aa381b94c86c1c61eec10fa8ba
- Size of remote file:
- 3.24 kB
- SHA256:
- 44e5539dbcd158a2471c59b46cde7f0564d108cd2fd935e3664b2ec733ad4393
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