Instructions to use vuiseng9/wav2vec2-base-100h with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vuiseng9/wav2vec2-base-100h with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="vuiseng9/wav2vec2-base-100h")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("vuiseng9/wav2vec2-base-100h") model = AutoModelForCTC.from_pretrained("vuiseng9/wav2vec2-base-100h") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1f3e59414e4de97f022077fc97405bc3c3a3f31dc5cf8c719ae009b289445b61
- Size of remote file:
- 378 MB
- SHA256:
- 0a445d205b6dc42bca27c0c52b7bb3607fd58a720f0d02a757893cff37ee3ab7
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