Instructions to use Lakoc/ED_small_cv_en_deeper with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lakoc/ED_small_cv_en_deeper with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import JointCTCAttentionEncoderDecoder model = JointCTCAttentionEncoderDecoder.from_pretrained("Lakoc/ED_small_cv_en_deeper", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from Lakoc/ED_small_cv_en_deeper: direct link, hf CLI and curl.
- Browser
- Download file 301 Bytes
-
https://huggingface.co/Lakoc/ED_small_cv_en_deeper/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://Lakoc/ED_small_cv_en_deeper/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/Lakoc/ED_small_cv_en_deeper/resolve/main/preprocessor_config.json
301 Bytes
| { | |
| "do_ceptral_normalize": true, | |
| "feature_extractor_type": "Speech2TextFeatureExtractor", | |
| "feature_size": 80, | |
| "normalize_means": true, | |
| "normalize_vars": true, | |
| "num_mel_bins": 80, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000 | |
| } | |