Instructions to use kamilakesbi/ms_clap with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kamilakesbi/ms_clap with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("kamilakesbi/ms_clap", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from kamilakesbi/ms_clap: direct link, hf CLI and curl.
- Browser
- Download file 473 Bytes
-
https://huggingface.co/kamilakesbi/ms_clap/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://kamilakesbi/ms_clap/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/kamilakesbi/ms_clap/resolve/main/preprocessor_config.json
473 Bytes
| { | |
| "feature_extractor_type": "ClapFeatureExtractor", | |
| "feature_size": 64, | |
| "fft_window_size": 1024, | |
| "frequency_max": 14000, | |
| "frequency_min": 50, | |
| "hop_length": 320, | |
| "max_length_s": 7, | |
| "nb_frequency_bins": 513, | |
| "nb_max_samples": 480000, | |
| "padding": "repeat", | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "processor_class": "MSClapProcessor", | |
| "return_attention_mask": false, | |
| "sampling_rate": 44100, | |
| "top_db": null, | |
| "truncation": "rand_trunc" | |
| } | |