Instructions to use LeBenchmark/wav2vec2-FR-1K-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LeBenchmark/wav2vec2-FR-1K-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="LeBenchmark/wav2vec2-FR-1K-large")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("LeBenchmark/wav2vec2-FR-1K-large") model = AutoModel.from_pretrained("LeBenchmark/wav2vec2-FR-1K-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 103193ee55f0b70346b7d783d8fc334560654457bab2a464dbc393d330591640
- Size of remote file:
- 1.26 GB
- SHA256:
- 8ec789a4d3741a0f78fd4845cd0b6ffb2deb0a0b5523df61888181c25defaeec
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