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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from LeBenchmark/wav2vec2-FR-7K-large: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/LeBenchmark/wav2vec2-FR-7K-large/resolve/main/model.safetensors
- Command line
-
hf download hf://LeBenchmark/wav2vec2-FR-7K-large/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/LeBenchmark/wav2vec2-FR-7K-large/resolve/main/model.safetensors
1.26 GB
- Xet hash:
- 7cb92ba8eca9af1b5b4098afba7b181ace76a7e9bc9facf0419622a32fad1f51
- Size of remote file:
- 1.26 GB
- SHA256:
- cdf58821b47253f6c3404a46970d63bbbbc180fbb79c616ebea6a9675299bbc2
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