Instructions to use LeBenchmark/wav2vec2-FR-1K-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LeBenchmark/wav2vec2-FR-1K-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="LeBenchmark/wav2vec2-FR-1K-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("LeBenchmark/wav2vec2-FR-1K-base") model = AutoModel.from_pretrained("LeBenchmark/wav2vec2-FR-1K-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from LeBenchmark/wav2vec2-FR-1K-base: direct link, hf CLI and curl.
- Browser
- Download file 159 Bytes
-
https://huggingface.co/LeBenchmark/wav2vec2-FR-1K-base/resolve/main/preprocessor_config.json
- Command line
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hf download hf://LeBenchmark/wav2vec2-FR-1K-base/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/LeBenchmark/wav2vec2-FR-1K-base/resolve/main/preprocessor_config.json
159 Bytes
| { | |
| "do_normalize": false, | |
| "feature_size": 1, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000 | |
| } | |