Instructions to use Trimux/basqueModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Trimux/basqueModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="Trimux/basqueModel")# Load model directly from transformers import AutoTokenizer, AutoModelForTextToWaveform tokenizer = AutoTokenizer.from_pretrained("Trimux/basqueModel") model = AutoModelForTextToWaveform.from_pretrained("Trimux/basqueModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 675da06714e72c7fdda6549282891444d96928886973b40b9ca0946d8fecf107
- Size of remote file:
- 145 MB
- SHA256:
- a4da81d404159ba4023dd470863ea43b7fdab61be5a8ecee8c2c1e7ea69dc698
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