Instructions to use bezzam/neucodec-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bezzam/neucodec-hf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="bezzam/neucodec-hf")# pip install -U transformers accelerate # Load model directly from transformers import AutoFeatureExtractor, AutoModel extractor = AutoFeatureExtractor.from_pretrained("bezzam/neucodec-hf") model = AutoModel.from_pretrained("bezzam/neucodec-hf", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from bezzam/neucodec-hf: direct link, hf CLI and curl.
- Browser
- Download file 292 Bytes
-
https://huggingface.co/bezzam/neucodec-hf/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://bezzam/neucodec-hf/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/bezzam/neucodec-hf/resolve/main/preprocessor_config.json
292 Bytes
| { | |
| "feature_extractor_type": "NeuCodecFeatureExtractor", | |
| "feature_size": 80, | |
| "frame_length": 400, | |
| "frame_shift": 160, | |
| "hop_length": 320, | |
| "num_mel_bins": 80, | |
| "padding_side": "right", | |
| "padding_value": 1, | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000, | |
| "stride": 2 | |
| } | |