Instructions to use hf-internal-testing/tiny-random-ClapModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ClapModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-ClapModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-ClapModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-ClapModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from hf-internal-testing/tiny-random-ClapModel: direct link, hf CLI and curl.
- Browser
- Download file 537 Bytes
-
https://huggingface.co/hf-internal-testing/tiny-random-ClapModel/resolve/1560fbdfd73377fb7d9be13d43066e8dcb9a2dbd/preprocessor_config.json
- Command line
-
hf download hf://hf-internal-testing/tiny-random-ClapModel@1560fbdfd73377fb7d9be13d43066e8dcb9a2dbd/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/hf-internal-testing/tiny-random-ClapModel/resolve/1560fbdfd73377fb7d9be13d43066e8dcb9a2dbd/preprocessor_config.json
537 Bytes
| { | |
| "chunk_length_s": 10, | |
| "feature_extractor_type": "ClapFeatureExtractor", | |
| "feature_size": 64, | |
| "fft_window_size": 1024, | |
| "frequency_max": 14000, | |
| "frequency_min": 50, | |
| "hop_length": 480, | |
| "max_length_s": 10, | |
| "n_fft": 1024, | |
| "nb_frequency_bins": 513, | |
| "nb_max_frames": 1000, | |
| "nb_max_samples": 480000, | |
| "padding": "repeatpad", | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "processor_class": "ClapProcessor", | |
| "return_attention_mask": false, | |
| "sampling_rate": 48000, | |
| "top_db": null, | |
| "truncation": "fusion" | |
| } | |