Instructions to use LeBenchmark/wav2vec2-FR-2.6K-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LeBenchmark/wav2vec2-FR-2.6K-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="LeBenchmark/wav2vec2-FR-2.6K-base")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("LeBenchmark/wav2vec2-FR-2.6K-base") model = AutoModel.from_pretrained("LeBenchmark/wav2vec2-FR-2.6K-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ec26d5478d2d19b0816756eb101b76fd2a37ee79bafe30672b767cddb6488334
- Size of remote file:
- 378 MB
- SHA256:
- 2b3c55904ae44300cb2f6e0e63c4396745dcda981f8dab035ad05fac875ac612
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