Instructions to use facebook/mms-tts-crh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/mms-tts-crh with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="facebook/mms-tts-crh")# Load model directly from transformers import AutoTokenizer, AutoModelForTextToWaveform tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-crh") model = AutoModelForTextToWaveform.from_pretrained("facebook/mms-tts-crh") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 54ef6b932e5c7a0d3c4422afd2b25a6ecf735dc2f6b9e82bcc1d3780ca26d47f
- Size of remote file:
- 145 MB
- SHA256:
- 9368870d0fbe91a4924c621a10963ab476b5c8690360f9bbf65f962341a5c89d
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