Instructions to use facebook/mms-tts-eng with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/mms-tts-eng with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="facebook/mms-tts-eng")# Load model directly from transformers import AutoTokenizer, AutoModelForTextToWaveform tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-eng") model = AutoModelForTextToWaveform.from_pretrained("facebook/mms-tts-eng") - Notebooks
- Google Colab
- Kaggle
What is the maximum token length of the model?
#2
by mstachow - opened
I've run it so far with fairly long inputs, but what is the max token length of the model?
Hey @mstachow ! The model uses relative positional embeddings, instead of absolute positional embeddings, so the max length is not bound by the architecture. Theoretically, it has infinite max length if you have infinite compute. Instead, you'll be bound by the memory of the model, which scales with input length squared, and will depend on your hardware. However, you the performance might degrade for super large input lengths as you lose prosody over such long sequences.