Instructions to use microsoft/VibeVoice-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/VibeVoice-1.5B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="microsoft/VibeVoice-1.5B")# Load model directly from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/VibeVoice-1.5B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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➡️ **Code:** [microsoft/VibeVoice-Code](https://github.com/microsoft/VibeVoice)
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## Training details
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Transformer-based Large Language Model (LLM) integrated with specialized acoustic and semantic tokenizers and a diffusion-based decoding head.
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- LLM: [Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B) for this release.
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➡️ **Code:** [microsoft/VibeVoice-Code](https://github.com/microsoft/VibeVoice)
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<img src="figures/VibeVoice.jpg" alt="VibeVoice Overview" height="250px" style="margin-right: 10px;">
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<img src="figures/MOS-preference.png" alt="MOS Preference Results" height="260px">
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</p>
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## Training details
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Transformer-based Large Language Model (LLM) integrated with specialized acoustic and semantic tokenizers and a diffusion-based decoding head.
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- LLM: [Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B) for this release.
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