Instructions to use Marvis-AI/marvis-tts-250m-v0.1-transformers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Marvis-AI/marvis-tts-250m-v0.1-transformers with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="Marvis-AI/marvis-tts-250m-v0.1-transformers")# Load model directly from transformers import AutoProcessor, AutoModelForTextToWaveform processor = AutoProcessor.from_pretrained("Marvis-AI/marvis-tts-250m-v0.1-transformers") model = AutoModelForTextToWaveform.from_pretrained("Marvis-AI/marvis-tts-250m-v0.1-transformers", device_map="auto") - MLX
How to use Marvis-AI/marvis-tts-250m-v0.1-transformers with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir marvis-tts-250m-v0.1-transformers Marvis-AI/marvis-tts-250m-v0.1-transformers
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
- Xet hash:
- 89e20a61fe51445d6cd483b4c5a162fc83801fe0ea914d013204158f2a9c6579
- Size of remote file:
- 3.06 GB
- SHA256:
- 2ab223576583efc7d4a2a86e13aa7db6816208912ccc0783afbc0b173e6bde1e
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