Instructions to use wasmdashai/vits-ar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wasmdashai/vits-ar with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="wasmdashai/vits-ar")# Load model directly from transformers import AutoTokenizer, AutoModelForTextToWaveform tokenizer = AutoTokenizer.from_pretrained("wasmdashai/vits-ar") model = AutoModelForTextToWaveform.from_pretrained("wasmdashai/vits-ar") - Notebooks
- Google Colab
- Kaggle
ASG Models commited on
Upload model
Browse files- config.json +1 -1
config.json
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{
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"_name_or_path": "
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"activation_dropout": 0.1,
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"architectures": [
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"VitsModel"
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"_name_or_path": "/content/drive/MyDrive/vitsM/TO/TRUBO/haba/v1",
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"activation_dropout": 0.1,
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"architectures": [
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"VitsModel"
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