Instructions to use Bingsu/speecht5_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bingsu/speecht5_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="Bingsu/speecht5_test")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("Bingsu/speecht5_test") model = AutoModelForTextToSpectrogram.from_pretrained("Bingsu/speecht5_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Bingsu/speecht5_test: direct link, hf CLI and curl.
- Browser
- Download file 72.3 MB
-
https://huggingface.co/Bingsu/speecht5_test/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Bingsu/speecht5_test/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Bingsu/speecht5_test/resolve/main/pytorch_model.bin
72.3 MB
- Xet hash:
- 41d957760736371457ae6d6950fe0743e22527173a9962540af37b3f92676fcd
- Size of remote file:
- 72.3 MB
- SHA256:
- 319e4c805cc0303a45f68092fcf525587ca7dff3edde051dc2abb75008da0fa8
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