Instructions to use fkHug/modelFromWav2vec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fkHug/modelFromWav2vec with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="fkHug/modelFromWav2vec")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("fkHug/modelFromWav2vec") model = AutoModelForCTC.from_pretrained("fkHug/modelFromWav2vec", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 19b9134cb86e25afc90855834117ffd350be33ed1e0b469a33cb6d6f6285f82f
- Size of remote file:
- 1.26 GB
- SHA256:
- 46ad2b7ef0d9691b1d1d8b3f7fa87f5305878c452fda5109d7899e0633395671
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