Instructions to use Matthijs/mms-tts-nld with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Matthijs/mms-tts-nld with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="Matthijs/mms-tts-nld")# Load model directly from transformers import AutoTokenizer, AutoModelForTextToWaveform tokenizer = AutoTokenizer.from_pretrained("Matthijs/mms-tts-nld") model = AutoModelForTextToWaveform.from_pretrained("Matthijs/mms-tts-nld", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e76e87ba055135e609f193edf8cb2f8d4a7e9395552e3c05afdaed9f655be775
- Size of remote file:
- 145 MB
- SHA256:
- fec191c135db0e9f085163bfa903a0dafbfc193ac5c67d265929c6f3f8cd7050
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