Instructions to use hf-tiny-model-private/tiny-random-Data2VecAudioForCTC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-Data2VecAudioForCTC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hf-tiny-model-private/tiny-random-Data2VecAudioForCTC")# Load model directly from transformers import AutoTokenizer, AutoModelForCTC tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-Data2VecAudioForCTC") model = AutoModelForCTC.from_pretrained("hf-tiny-model-private/tiny-random-Data2VecAudioForCTC") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ea0801bf45d53f21918b455b9cddae606ef8bf55d8c870d41a1a6f0cdcba6ad2
- Size of remote file:
- 272 kB
- SHA256:
- 1eacf3c400336059a496a6ada0905d5497d349a08f4db149f64af7ecefa3bfc8
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