Instructions to use rdsmaia/wav2vec2_class with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rdsmaia/wav2vec2_class with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="rdsmaia/wav2vec2_class")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("rdsmaia/wav2vec2_class") model = AutoModelForAudioClassification.from_pretrained("rdsmaia/wav2vec2_class", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from rdsmaia/wav2vec2_class: direct link, hf CLI and curl.
- Browser
- Download file 215 Bytes
-
https://huggingface.co/rdsmaia/wav2vec2_class/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://rdsmaia/wav2vec2_class/preprocessor_config.json
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curl -L -o preprocessor_config.json https://huggingface.co/rdsmaia/wav2vec2_class/resolve/main/preprocessor_config.json
215 Bytes
| { | |
| "do_normalize": true, | |
| "feature_extractor_type": "Wav2Vec2FeatureExtractor", | |
| "feature_size": 1, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "return_attention_mask": false, | |
| "sampling_rate": 16000 | |
| } | |