Instructions to use eustlb/higgs-v2-archive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eustlb/higgs-v2-archive with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="eustlb/higgs-v2-archive")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToWaveform processor = AutoProcessor.from_pretrained("eustlb/higgs-v2-archive") model = AutoModelForTextToWaveform.from_pretrained("eustlb/higgs-v2-archive", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from eustlb/higgs-v2-archive: direct link, hf CLI and curl.
- Browser
- Download file 250 Bytes
-
https://huggingface.co/eustlb/higgs-v2-archive/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://eustlb/higgs-v2-archive/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/eustlb/higgs-v2-archive/resolve/main/preprocessor_config.json
250 Bytes
| { | |
| "feature_extractor_type": "DacFeatureExtractor", | |
| "feature_size": 1, | |
| "hop_length": 1, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "processor_class": "HiggsAudioV2Processor", | |
| "return_attention_mask": true, | |
| "sampling_rate": 24000 | |
| } | |