Text-to-Speech
Transformers
PyTorch
TensorBoard
Safetensors
speecht5
text-to-audio
Generated from Trainer
Instructions to use Sagicc/speecht5_finetuned_multilingual_librispeech_pl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sagicc/speecht5_finetuned_multilingual_librispeech_pl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="Sagicc/speecht5_finetuned_multilingual_librispeech_pl")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("Sagicc/speecht5_finetuned_multilingual_librispeech_pl") model = AutoModelForTextToSpectrogram.from_pretrained("Sagicc/speecht5_finetuned_multilingual_librispeech_pl", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Sagicc/speecht5_finetuned_multilingual_librispeech_pl: direct link, hf CLI and curl.
- Browser
- Download file 4.22 kB
-
https://huggingface.co/Sagicc/speecht5_finetuned_multilingual_librispeech_pl/resolve/main/training_args.bin
- Command line
-
hf download hf://Sagicc/speecht5_finetuned_multilingual_librispeech_pl/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Sagicc/speecht5_finetuned_multilingual_librispeech_pl/resolve/main/training_args.bin
4.22 kB
- Xet hash:
- 6e806e687dd1f22f9f8cffed921563f94db0fd22b08f8e8b7f697d9e28b5213e
- Size of remote file:
- 4.22 kB
- SHA256:
- adfe553d64f6f5484e699b93ad1cbfd5999003c48d3963a6b1fad341fca9cbf6
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