Instructions to use facebook/mms-tts-kab with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/mms-tts-kab with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="facebook/mms-tts-kab")# Load model directly from transformers import AutoTokenizer, AutoModelForTextToWaveform tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-kab") model = AutoModelForTextToWaveform.from_pretrained("facebook/mms-tts-kab", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f37c6085475ccf510fae5e644e2767e8d9d92dcaf5af49e02f9ccfbe342ce49c
- Size of remote file:
- 145 MB
- SHA256:
- 9081b9a1c97eaea071efa9763485a5aef8bfc0234fcf89a1da0304489db4b94f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.