Instructions to use wrice/wav2vec2-conformer-rope-large-weight-norm-fix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wrice/wav2vec2-conformer-rope-large-weight-norm-fix with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="wrice/wav2vec2-conformer-rope-large-weight-norm-fix")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("wrice/wav2vec2-conformer-rope-large-weight-norm-fix") model = AutoModel.from_pretrained("wrice/wav2vec2-conformer-rope-large-weight-norm-fix", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7a00d58768a062f135d352f56002e09e6c067187295d08a688bad32a349e4e7d
- Size of remote file:
- 2.37 GB
- SHA256:
- 384c49204f63ce981ba1d24adef55aed9de00cf67be0ed490d21072468703c8c
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