Instructions to use Devishri1/finetuned_speecht5_50Kdata with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Devishri1/finetuned_speecht5_50Kdata with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="Devishri1/finetuned_speecht5_50Kdata")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("Devishri1/finetuned_speecht5_50Kdata") model = AutoModelForTextToSpectrogram.from_pretrained("Devishri1/finetuned_speecht5_50Kdata", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Devishri1/finetuned_speecht5_50Kdata: direct link, hf CLI and curl.
- Browser
- Download file 5.43 kB
-
https://huggingface.co/Devishri1/finetuned_speecht5_50Kdata/resolve/main/training_args.bin
- Command line
-
hf download hf://Devishri1/finetuned_speecht5_50Kdata/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Devishri1/finetuned_speecht5_50Kdata/resolve/main/training_args.bin
5.43 kB
- Xet hash:
- 55f102a1c62f81a8618459740535474d4cc256c079a33dedb707b5b647bf5f91
- Size of remote file:
- 5.43 kB
- SHA256:
- 88b685d6c774eadf637c3b043af947278d8cf3586d32edd9d3e01ad9ec9713e3
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