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")# pip install -U transformers accelerate # 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] hf download Marvis-AI/marvis-tts-250m-v0.1-transformers --local-dir marvis-tts-250m-v0.1-transformers
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download preprocessor_config.json from Marvis-AI/marvis-tts-250m-v0.1-transformers: direct link, hf CLI and curl.
- Browser
- Download file 270 Bytes
-
https://huggingface.co/Marvis-AI/marvis-tts-250m-v0.1-transformers/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://Marvis-AI/marvis-tts-250m-v0.1-transformers/preprocessor_config.json
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curl -L -o preprocessor_config.json https://huggingface.co/Marvis-AI/marvis-tts-250m-v0.1-transformers/resolve/main/preprocessor_config.json
270 Bytes
| { | |
| "chunk_length_s": null, | |
| "feature_extractor_type": "EncodecFeatureExtractor", | |
| "feature_size": 1, | |
| "overlap": null, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "processor_class": "CsmProcessor", | |
| "return_attention_mask": true, | |
| "sampling_rate": 24000 | |
| } |