Instructions to use hf-internal-testing/tiny-random-EncodecModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-EncodecModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-EncodecModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoFeatureExtractor, AutoModel extractor = AutoFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-EncodecModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-EncodecModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from hf-internal-testing/tiny-random-EncodecModel: direct link, hf CLI and curl.
- Browser
- Download file 234 Bytes
-
https://huggingface.co/hf-internal-testing/tiny-random-EncodecModel/resolve/ccc7ec0382d71464988d186ae801a7b144e72007/preprocessor_config.json
- Command line
-
hf download hf://hf-internal-testing/tiny-random-EncodecModel@ccc7ec0382d71464988d186ae801a7b144e72007/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/hf-internal-testing/tiny-random-EncodecModel/resolve/ccc7ec0382d71464988d186ae801a7b144e72007/preprocessor_config.json
234 Bytes
| { | |
| "chunk_length_s": null, | |
| "feature_extractor_type": "EncodecFeatureExtractor", | |
| "feature_size": 1, | |
| "overlap": null, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "return_attention_mask": true, | |
| "sampling_rate": 24000 | |
| } | |