Instructions to use declare-lab/tango2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use declare-lab/tango2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="declare-lab/tango2")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("declare-lab/tango2", dtype="auto") - Notebooks
- Google Colab
- Kaggle
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🎵 We developed **Tango 2** building upon **Tango** for text-to-audio generation. Tango 2 was initialized with the Tango-full-ft checkpoint and underwent alignment training using DPO on audio-alpaca, a pairwise text-to-audio preference dataset. 🎶
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## Code
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🎵 We developed **Tango 2** building upon **Tango** for text-to-audio generation. Tango 2 was initialized with the Tango-full-ft checkpoint and underwent alignment training using DPO on audio-alpaca, a pairwise text-to-audio preference dataset. 🎶
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[Read the paper](https://arxiv.org/abs/2404.09956)
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## Code
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