Instructions to use Matthijs/mms-tts-abp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Matthijs/mms-tts-abp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="Matthijs/mms-tts-abp")# Load model directly from transformers import AutoTokenizer, AutoModelForTextToWaveform tokenizer = AutoTokenizer.from_pretrained("Matthijs/mms-tts-abp") model = AutoModelForTextToWaveform.from_pretrained("Matthijs/mms-tts-abp") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d03834c7781e68339e9f2419ba9313f47ecf5d4d3c6af97cd7e52f180ef45fcd
- Size of remote file:
- 145 MB
- SHA256:
- 45e83bccaaf688a240a43422bf5b45bd155b7cee492860dd5e694fcacab299f5
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