Instructions to use MBMMurad/wav2vec2_murad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MBMMurad/wav2vec2_murad with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="MBMMurad/wav2vec2_murad")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("MBMMurad/wav2vec2_murad") model = AutoModelForCTC.from_pretrained("MBMMurad/wav2vec2_murad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from MBMMurad/wav2vec2_murad: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/MBMMurad/wav2vec2_murad/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://MBMMurad/wav2vec2_murad/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/MBMMurad/wav2vec2_murad/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- 4b3df0ddb95e13d4334439487be72fff179dd2010c8eecd01007af549d104abf
- Size of remote file:
- 1.26 GB
- SHA256:
- 9e2ba6030e74cfb64acbce6d6a75bf93bbe3c9face67bc0783af60e8acf081b5
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