Instructions to use BasketTechologies/bark with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BasketTechologies/bark with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="BasketTechologies/bark")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToWaveform processor = AutoProcessor.from_pretrained("BasketTechologies/bark") model = AutoModelForTextToWaveform.from_pretrained("BasketTechologies/bark", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update handler.py
Browse files- handler.py +1 -1
handler.py
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@@ -4,7 +4,7 @@ from transformers import pipeline
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class EndpointHandler:
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def __init__(self, path=""):
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self.model = pipeline("text-
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def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
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"""
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class EndpointHandler:
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def __init__(self, path=""):
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self.model = pipeline("text-classification", "suno/bark")
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def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
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"""
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