Instructions to use facebook/mms-tts-ame with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/mms-tts-ame with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="facebook/mms-tts-ame")# Load model directly from transformers import AutoTokenizer, AutoModelForTextToWaveform tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-ame") model = AutoModelForTextToWaveform.from_pretrained("facebook/mms-tts-ame") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1d67f1b293be729c164b1412482b154b56f12e627b9fcde218b746f91875ba35
- Size of remote file:
- 145 MB
- SHA256:
- 6a0f2ca1d5607286d89de8f0a7b7ee62aa998ff922cc3b931281ed2515cb9f4a
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