Instructions to use nomrom/Spencer-Translator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nomrom/Spencer-Translator with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="nomrom/Spencer-Translator")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("nomrom/Spencer-Translator") model = AutoModelForAudioClassification.from_pretrained("nomrom/Spencer-Translator", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from nomrom/Spencer-Translator: direct link, hf CLI and curl.
- Browser
- Download file 4.66 kB
-
https://huggingface.co/nomrom/Spencer-Translator/resolve/main/training_args.bin
- Command line
-
hf download hf://nomrom/Spencer-Translator/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/nomrom/Spencer-Translator/resolve/main/training_args.bin
4.66 kB
- Xet hash:
- 23691aca2d57819dfee86ca16e328527b4e4494ab7ea7dfbc863199e81d905c4
- Size of remote file:
- 4.66 kB
- SHA256:
- 06868837588ba9829ac67ab89ee9702eacfcc41587b7b441031f9060c133ae30
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