Instructions to use facebook/mms-tts-dar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/mms-tts-dar with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="facebook/mms-tts-dar")# Load model directly from transformers import AutoTokenizer, AutoModelForTextToWaveform tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-dar") model = AutoModelForTextToWaveform.from_pretrained("facebook/mms-tts-dar", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 446d90f503311ea32f9359c8b65663d94724e4fbe93fb4fef5e0389780b3ffd3
- Size of remote file:
- 145 MB
- SHA256:
- 3f735a8d5695cbed73a588526bb7f6fb97576882e4bbe2e7c7766eba05658edc
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