Instructions to use skroed/bark with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use skroed/bark with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="skroed/bark")# Load model directly from transformers import AutoProcessor, AutoModelForTextToWaveform processor = AutoProcessor.from_pretrained("skroed/bark") model = AutoModelForTextToWaveform.from_pretrained("skroed/bark") - Notebooks
- Google Colab
- Kaggle
skroed commited on
Commit ·
09c5b6d
1
Parent(s): 71c2800
fix: just return dict
Browse files- handler.py +1 -1
handler.py
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@@ -39,4 +39,4 @@ class EndpointHandler:
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# postprocess the prediction
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prediction = outputs.cpu().numpy().tolist()
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return
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# postprocess the prediction
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prediction = outputs.cpu().numpy().tolist()
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return {"generated_audio": prediction}
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