Instructions to use yangwang825/svector-aam-aug with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yangwang825/svector-aam-aug with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="yangwang825/svector-aam-aug", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("yangwang825/svector-aam-aug", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from yangwang825/svector-aam-aug: direct link, hf CLI and curl.
- Browser
- Download file 315 Bytes
-
https://huggingface.co/yangwang825/svector-aam-aug/resolve/refs%2Fpr%2F1/preprocessor_config.json
- Command line
-
hf download hf://yangwang825/svector-aam-aug@refs/pr/1/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/yangwang825/svector-aam-aug/resolve/refs%2Fpr%2F1/preprocessor_config.json
315 Bytes
| { | |
| "auto_map": { | |
| "AutoFeatureExtractor": "feature_extraction_svector.SvectorFeatureExtractor" | |
| }, | |
| "do_normalize": true, | |
| "feature_extractor_type": "SvectorFeatureExtractor", | |
| "feature_size": 1, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "return_attention_mask": false, | |
| "sampling_rate": 16000 | |
| } |