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 speaker_embedding.pt from Bingsu/speecht5_test: direct link, hf CLI and curl.
- Browser
- Download file 7.5 kB
-
https://huggingface.co/Bingsu/speecht5_test/resolve/main/speaker_embedding.pt
- Command line
-
hf download hf://Bingsu/speecht5_test/speaker_embedding.pt
-
curl -L -o speaker_embedding.pt https://huggingface.co/Bingsu/speecht5_test/resolve/main/speaker_embedding.pt
7.5 kB
- Xet hash:
- 413045400973c38bbae5f6f09a82c78f63715f3b31dc2ea3b06867dfb4043941
- Size of remote file:
- 7.5 kB
- SHA256:
- 3b82296aacd6654b57bf4c7f0f1ea1080e7f4242af34543ec8af7628cb427f49
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