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")# pip install -U transformers accelerate # 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
Download flax_model.msgpack from LeBenchmark/wav2vec2-FR-3K-large: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/LeBenchmark/wav2vec2-FR-3K-large/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://LeBenchmark/wav2vec2-FR-3K-large/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/LeBenchmark/wav2vec2-FR-3K-large/resolve/main/flax_model.msgpack
1.26 GB
- Xet hash:
- 38f2e5d7be760f14fd293f8f5823238379edd8e8df11a38fd642df42a1ee3160
- Size of remote file:
- 1.26 GB
- SHA256:
- b373b452fe40d0712e3446c236860f4792b9a8b2c02f3dc50fb2224c8c885d9d
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