Instructions to use AXERA-TECH/CosyVoice2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AXERA-TECH/CosyVoice2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="AXERA-TECH/CosyVoice2")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AXERA-TECH/CosyVoice2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download token2wav-axmodels/flow.input_embedding.npy from AXERA-TECH/CosyVoice2: direct link, hf CLI and curl.
- Browser
- Download file 13.4 MB
-
https://huggingface.co/AXERA-TECH/CosyVoice2/resolve/main/token2wav-axmodels/flow.input_embedding.npy
- Command line
-
hf download hf://AXERA-TECH/CosyVoice2/token2wav-axmodels/flow.input_embedding.npy
-
curl -L -o flow.input_embedding.npy https://huggingface.co/AXERA-TECH/CosyVoice2/resolve/main/token2wav-axmodels/flow.input_embedding.npy
13.4 MB
- Xet hash:
- 3b5206ad4c1016a1302d327681883d096e8849e07caf814745f0e0b7888b73ab
- Size of remote file:
- 13.4 MB
- SHA256:
- bf492f35131077320f0b72b75979c8d5e176ea32a1900f181b3345dc00a9bd9a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.