Instructions to use Bazzar/bark-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bazzar/bark-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="Bazzar/bark-small")# Load model directly from transformers import AutoProcessor, AutoModelForTextToWaveform processor = AutoProcessor.from_pretrained("Bazzar/bark-small") model = AutoModelForTextToWaveform.from_pretrained("Bazzar/bark-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| from typing import Dict, List, Any | |
| from transformers import pipeline | |
| class EndpointHandler(): | |
| def __init__(self, path=""): | |
| self.pipeline = pipeline("text-to-speech",model=path) | |
| def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]: | |
| """ | |
| data args: | |
| inputs (:obj: `str` | `PIL.Image` | `np.array`) | |
| kwargs | |
| Return: | |
| A :obj:`list` | `dict`: will be serialized and returned | |
| """ | |
| inputs = data.pop("inputs",data) | |
| prediction = self.pipeline(inputs) | |
| return prediction |