Instructions to use suno/bark-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use suno/bark-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="suno/bark-small")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToWaveform processor = AutoProcessor.from_pretrained("suno/bark-small") model = AutoModelForTextToWaveform.from_pretrained("suno/bark-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download speaker_embeddings/en_speaker_0_semantic_prompt.npy from suno/bark-small: direct link, hf CLI and curl.
- Browser
- Download file 2.29 kB
-
https://huggingface.co/suno/bark-small/resolve/main/speaker_embeddings/en_speaker_0_semantic_prompt.npy
- Command line
-
hf download hf://suno/bark-small/speaker_embeddings/en_speaker_0_semantic_prompt.npy
-
curl -L -o en_speaker_0_semantic_prompt.npy https://huggingface.co/suno/bark-small/resolve/main/speaker_embeddings/en_speaker_0_semantic_prompt.npy
2.29 kB
- Xet hash:
- 2a110a557f42fcd64160fbb27aebd741be249d4d17cf967a2488582d3184b9fd
- Size of remote file:
- 2.29 kB
- SHA256:
- 01f0c8e96aee83c19514700491be4326c5a1194631cdd486e1d4b76e4d0c0de0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.