Instructions to use mohammadh128/wav2vec_fa_v01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mohammadh128/wav2vec_fa_v01 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mohammadh128/wav2vec_fa_v01")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("mohammadh128/wav2vec_fa_v01") model = AutoModelForCTC.from_pretrained("mohammadh128/wav2vec_fa_v01") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8e0542c3e809e1a8ca27fb6e38229c8a68eee8e710908d6441a15cd99b79b845
- Size of remote file:
- 1.26 GB
- SHA256:
- 930ce463c5b4d165eac2ba17fdc1896dac629431531ea326d191351e17bffc9e
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