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")# pip install -U transformers accelerate # 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
Download sentencepiece.bpe.model from realtime-speech/s2tlarge: direct link, hf CLI and curl.
- Browser
- Download file 5.17 MB
-
https://huggingface.co/realtime-speech/s2tlarge/resolve/main/sentencepiece.bpe.model
- Command line
-
hf download hf://realtime-speech/s2tlarge/sentencepiece.bpe.model
-
curl -L -o sentencepiece.bpe.model https://huggingface.co/realtime-speech/s2tlarge/resolve/main/sentencepiece.bpe.model
5.17 MB
- Xet hash:
- c22a976c6568dda67e86f5639734224438a1846684faaa2ac3bae4f8fa01005f
- Size of remote file:
- 5.17 MB
- SHA256:
- 026a76827537db9f1348e4d5aaa127bb10a2f2ff633243f3a52d16be82d73f9d
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