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_1_fine_prompt.npy from reach-vb/bark-endpoint: direct link, hf CLI and curl.
- Browser
- Download file 18.8 kB
-
https://huggingface.co/reach-vb/bark-endpoint/resolve/main/speaker_embeddings/de_speaker_1_fine_prompt.npy
- Command line
-
hf download hf://reach-vb/bark-endpoint/speaker_embeddings/de_speaker_1_fine_prompt.npy
-
curl -L -o de_speaker_1_fine_prompt.npy https://huggingface.co/reach-vb/bark-endpoint/resolve/main/speaker_embeddings/de_speaker_1_fine_prompt.npy
18.8 kB
- Xet hash:
- f4b54702a9a94ea1cc2db0320fc1e8cae68ed657369f1bd571b36d8169219079
- Size of remote file:
- 18.8 kB
- SHA256:
- 9f8eaca4b732dd3264213ac387f4c91f83a574563b0d72368b237df03fe92b4d
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