Instructions to use facebook/mms-tts-adh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/mms-tts-adh with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="facebook/mms-tts-adh")# Load model directly from transformers import AutoTokenizer, AutoModelForTextToWaveform tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-adh") model = AutoModelForTextToWaveform.from_pretrained("facebook/mms-tts-adh", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c320fe4166d3b03ebcf79bb951325de20ca3f7bc6efda1bd5766b0217bf61ade
- Size of remote file:
- 145 MB
- SHA256:
- 039abf9be57ac8c2fbfdc7283a83d7217a8eac4fa9a6a4689a5c4b63c6c475b9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.