Instructions to use elzara/audio_class with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use elzara/audio_class with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, Wav2Vec2ForSpeechClassification processor = AutoProcessor.from_pretrained("elzara/audio_class") model = Wav2Vec2ForSpeechClassification.from_pretrained("elzara/audio_class", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from elzara/audio_class: direct link, hf CLI and curl.
- Browser
- Download file 257 Bytes
-
https://huggingface.co/elzara/audio_class/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://elzara/audio_class/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/elzara/audio_class/resolve/main/preprocessor_config.json
257 Bytes
| { | |
| "do_normalize": true, | |
| "feature_extractor_type": "Wav2Vec2FeatureExtractor", | |
| "feature_size": 1, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "processor_class": "Wav2Vec2Processor", | |
| "return_attention_mask": false, | |
| "sampling_rate": 16000 | |
| } | |