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_9_semantic_prompt.npy from reach-vb/bark-endpoint: direct link, hf CLI and curl.
- Browser
- Download file 2.26 kB
-
https://huggingface.co/reach-vb/bark-endpoint/resolve/main/speaker_embeddings/de_speaker_9_semantic_prompt.npy
- Command line
-
hf download hf://reach-vb/bark-endpoint/speaker_embeddings/de_speaker_9_semantic_prompt.npy
-
curl -L -o de_speaker_9_semantic_prompt.npy https://huggingface.co/reach-vb/bark-endpoint/resolve/main/speaker_embeddings/de_speaker_9_semantic_prompt.npy
2.26 kB
- Xet hash:
- c637f0fded1d543fd2545f8fe854cb36d7263d66431d169a0be0400264376978
- Size of remote file:
- 2.26 kB
- SHA256:
- 8efede4bec02ec98fc470dc53ca00154fea57f895a931ef6f44b1e9621b18ad2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.