Instructions to use Bedru/wave2vec-base-Am with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bedru/wave2vec-base-Am with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Bedru/wave2vec-base-Am")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Bedru/wave2vec-base-Am") model = AutoModelForCTC.from_pretrained("Bedru/wave2vec-base-Am", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Bedru/wave2vec-base-Am: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/Bedru/wave2vec-base-Am/resolve/main/training_args.bin
- Command line
-
hf download hf://Bedru/wave2vec-base-Am/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Bedru/wave2vec-base-Am/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 8c74e00b68fb6aa167f17c3c017538a2c3cd956231a33f062d0df2727ea8a2f9
- Size of remote file:
- 5.3 kB
- SHA256:
- a7330bc6d325e0ae9b46227ba66cfa6cc815f5a186d1b7472579fc9389809ba2
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