Instructions to use bosonai/higgs-audio-v2-tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bosonai/higgs-audio-v2-tokenizer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="bosonai/higgs-audio-v2-tokenizer")# pip install -U transformers accelerate # Load model directly from transformers import AutoFeatureExtractor, AutoModel extractor = AutoFeatureExtractor.from_pretrained("bosonai/higgs-audio-v2-tokenizer") model = AutoModel.from_pretrained("bosonai/higgs-audio-v2-tokenizer", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from bosonai/higgs-audio-v2-tokenizer: direct link, hf CLI and curl.
- Browser
- Download file 206 Bytes
-
https://huggingface.co/bosonai/higgs-audio-v2-tokenizer/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://bosonai/higgs-audio-v2-tokenizer/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/bosonai/higgs-audio-v2-tokenizer/resolve/main/preprocessor_config.json
206 Bytes
| { | |
| "feature_extractor_type": "DacFeatureExtractor", | |
| "feature_size": 1, | |
| "hop_length": 960, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "return_attention_mask": true, | |
| "sampling_rate": 24000 | |
| } | |