Instructions to use rajat99/Fine_Tuning_XLSR_300M_testing_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rajat99/Fine_Tuning_XLSR_300M_testing_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rajat99/Fine_Tuning_XLSR_300M_testing_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("rajat99/Fine_Tuning_XLSR_300M_testing_model") model = AutoModelForCTC.from_pretrained("rajat99/Fine_Tuning_XLSR_300M_testing_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from rajat99/Fine_Tuning_XLSR_300M_testing_model: direct link, hf CLI and curl.
- Browser
- Download file 2.86 kB
-
https://huggingface.co/rajat99/Fine_Tuning_XLSR_300M_testing_model/resolve/main/training_args.bin
- Command line
-
hf download hf://rajat99/Fine_Tuning_XLSR_300M_testing_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/rajat99/Fine_Tuning_XLSR_300M_testing_model/resolve/main/training_args.bin
2.86 kB
- Xet hash:
- b42c830cab422ecb8f423bf1a144cfa898405e598e146b07066ce2c63b4619c8
- Size of remote file:
- 2.86 kB
- SHA256:
- 7cfd24e48df5c5b56bee255a2e5723e69953ae23121f955fef2b6f8ed9341c39
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