Instructions to use voidful/mhubert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use voidful/mhubert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="voidful/mhubert-base")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("voidful/mhubert-base") model = AutoModel.from_pretrained("voidful/mhubert-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from voidful/mhubert-base: direct link, hf CLI and curl.
- Browser
- Download file 212 Bytes
-
https://huggingface.co/voidful/mhubert-base/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://voidful/mhubert-base/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/voidful/mhubert-base/resolve/main/preprocessor_config.json
212 Bytes
| { | |
| "do_normalize": true, | |
| "feature_extractor_type": "Wav2Vec2FeatureExtractor", | |
| "feature_size": 1, | |
| "padding_side": "right", | |
| "padding_value": 0, | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000 | |
| } | |