Instructions to use LeBenchmark/wav2vec2-FR-3K-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LeBenchmark/wav2vec2-FR-3K-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="LeBenchmark/wav2vec2-FR-3K-large")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("LeBenchmark/wav2vec2-FR-3K-large") model = AutoModel.from_pretrained("LeBenchmark/wav2vec2-FR-3K-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a4ab2e858ef742a6f3f19bbffba695ed6e501fddccbd6c36bf4274b5a1e54708
- Size of remote file:
- 1.27 GB
- SHA256:
- c5a9f6739f5b4f5d808656b4e05e594e1717de2a841440a833eda93d6967faa8
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