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")# 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
- Xet hash:
- e5ea6e83d08edc9ff886501a0a5983201b1c91bf1bf722f460c28a26ebd2921e
- Size of remote file:
- 1.26 GB
- SHA256:
- 3db831c2c86b9baf112dcad47a08f41d49611d4b4069c43020cfa9d5c854c6b6
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