Instructions to use labhamlet/wavjepa-nat-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use labhamlet/wavjepa-nat-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="labhamlet/wavjepa-nat-base", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("labhamlet/wavjepa-nat-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from labhamlet/wavjepa-nat-base: direct link, hf CLI and curl.
- Browser
- Download file 301 Bytes
-
https://huggingface.co/labhamlet/wavjepa-nat-base/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://labhamlet/wavjepa-nat-base/preprocessor_config.json
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curl -L -o preprocessor_config.json https://huggingface.co/labhamlet/wavjepa-nat-base/resolve/main/preprocessor_config.json
301 Bytes
| { | |
| "auto_map": { | |
| "AutoFeatureExtractor": "feature_extraction_wavjepa_nat.WavJEPANatFeatureExtractor" | |
| }, | |
| "feature_extractor_type": "WavJEPANatFeatureExtractor", | |
| "feature_size": 1, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000 | |
| } | |