Instructions to use realtime-speech/s2tlarge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use realtime-speech/s2tlarge with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="realtime-speech/s2tlarge")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("realtime-speech/s2tlarge") model = AutoModel.from_pretrained("realtime-speech/s2tlarge", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 63c1b870df2ee02d6523990f426b8037e6a901ac48218bcb621033987c32f86f
- Size of remote file:
- 9.44 GB
- SHA256:
- 51e5e7909983073029a0cc73e35a20dca7e5941a2092f2e29f131ce100441110
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