Instructions to use LeBenchmark/wav2vec2-FR-7K-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LeBenchmark/wav2vec2-FR-7K-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="LeBenchmark/wav2vec2-FR-7K-large")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("LeBenchmark/wav2vec2-FR-7K-large") model = AutoModel.from_pretrained("LeBenchmark/wav2vec2-FR-7K-large") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c2715f0693a822855f7a2a38f0391d1e42233b0a4971255556f5eec16e0bbe18
- Size of remote file:
- 1.26 GB
- SHA256:
- 419e0a322a43382fb891f68f95bf36b51f91e90d6f44adbc1ce1968a73707de3
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