Instructions to use facebook/mms-tts-hay with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/mms-tts-hay with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="facebook/mms-tts-hay")# Load model directly from transformers import AutoTokenizer, AutoModelForTextToWaveform tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-hay") model = AutoModelForTextToWaveform.from_pretrained("facebook/mms-tts-hay", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3771bb4a8e7c0128d45d92c8175e02b4ee7c8f85d5938a80e3d9e45bcdc3feed
- Size of remote file:
- 145 MB
- SHA256:
- c1f0c1813c72dddbabc42678a0c66e81d5385b3c28f9ee746dbf1f74118522cc
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