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
Upload model
Browse files- config.json +2 -2
- model.safetensors +1 -1
config.json
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{
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"_name_or_path": "/content/drive/MyDrive/
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"activation_dropout": 0.1,
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"architectures": [
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"VitsModel"
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"speaking_rate": 1.0,
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"spectrogram_bins": 513,
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"torch_dtype": "float32",
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"transformers_version": "4.
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"upsample_initial_channel": 512,
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"upsample_kernel_sizes": [
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{
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"_name_or_path": "/content/drive/MyDrive/TRUBO-Huba/arabic/v1",
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"activation_dropout": 0.1,
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"architectures": [
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"VitsModel"
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"speaking_rate": 1.0,
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"spectrogram_bins": 513,
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"torch_dtype": "float32",
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"transformers_version": "4.42.4",
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"upsample_initial_channel": 512,
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"upsample_kernel_sizes": [
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16,
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 145231480
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version https://git-lfs.github.com/spec/v1
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oid sha256:39e3401a808a11e5a63c94624526c8aff9a9f1766e07bfdcbc2dd407a1f72928
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size 145231480
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