Instructions to use facebook/mms-tts-ceb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/mms-tts-ceb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="facebook/mms-tts-ceb")# Load model directly from transformers import AutoTokenizer, AutoModelForTextToWaveform tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-ceb") model = AutoModelForTextToWaveform.from_pretrained("facebook/mms-tts-ceb") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7cc56a4eb326152c8d351b7cb17e5fc8e11b38f296d1b92e971dd3ea3e1f1267
- Size of remote file:
- 145 MB
- SHA256:
- fef1aa56cd88c87794c6128ae73c4129eb7809bc255ea412e4b1fc5e8e8d9a3e
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