Instructions to use KBLab/wav2vec2-large-voxrex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KBLab/wav2vec2-large-voxrex with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="KBLab/wav2vec2-large-voxrex")# Load model directly from transformers import AutoProcessor, AutoModelForPreTraining processor = AutoProcessor.from_pretrained("KBLab/wav2vec2-large-voxrex") model = AutoModelForPreTraining.from_pretrained("KBLab/wav2vec2-large-voxrex") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9551dcf2f953a6524bcb9075572de11a12cfb2204b5c3f0d2103a92ac37d3c59
- Size of remote file:
- 1.27 GB
- SHA256:
- 1114bdd459b4f730600b6e91430ed31a5d4004ffacb2f72d2675aae2c38691a6
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