Instructions to use LeBenchmark/wav2vec2-FR-3K-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LeBenchmark/wav2vec2-FR-3K-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="LeBenchmark/wav2vec2-FR-3K-base")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("LeBenchmark/wav2vec2-FR-3K-base") model = AutoModel.from_pretrained("LeBenchmark/wav2vec2-FR-3K-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 08cca41231fa082cf77e0bcf788de62fa8d24d365a0d710a16ba3da767a9889c
- Size of remote file:
- 378 MB
- SHA256:
- 30fc01e8a1d4bc1323fd93c2697731be85d22a05730bea65868df17591650840
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