Instructions to use facebook/mms-tts-mxv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/mms-tts-mxv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="facebook/mms-tts-mxv")# Load model directly from transformers import AutoTokenizer, AutoModelForTextToWaveform tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-mxv") model = AutoModelForTextToWaveform.from_pretrained("facebook/mms-tts-mxv", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e49128a88744eef3f61da7e20fdf494315cb759d40cf8e48489718b99b9dd4ab
- Size of remote file:
- 145 MB
- SHA256:
- 227dbd3d9837f80356658a2723829dcbaf2b14a53f94cfc0c8ef19e7e1dbb9b3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.