Instructions to use facebook/mms-tts-kin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/mms-tts-kin with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="facebook/mms-tts-kin")# Load model directly from transformers import AutoTokenizer, AutoModelForTextToWaveform tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-kin") model = AutoModelForTextToWaveform.from_pretrained("facebook/mms-tts-kin") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 02b58ea65ea9d5b0070df28f1ef9ba7eda509c1e51112a1424aecefe564edf98
- Size of remote file:
- 145 MB
- SHA256:
- 33ac99db4e303d9e520eb970f6d795f245713fabaa6002140a418e576e5e650b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.