Instructions to use LeBenchmark/wav2vec2-FR-1K-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LeBenchmark/wav2vec2-FR-1K-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="LeBenchmark/wav2vec2-FR-1K-base")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("LeBenchmark/wav2vec2-FR-1K-base") model = AutoModel.from_pretrained("LeBenchmark/wav2vec2-FR-1K-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d0e9c28ab710ccd970ac3f7a0b73392c7802848820a2915bbe45518999ac5eae
- Size of remote file:
- 378 MB
- SHA256:
- 15e9fca6a0fbadeaf9e213214dd0f5f81785f7573fe83fc9c3c09ebe62534756
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