Instructions to use 7wolf/wav2ast-gender-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 7wolf/wav2ast-gender-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="7wolf/wav2ast-gender-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoFeatureExtractor, AutoModelForAudioClassification extractor = AutoFeatureExtractor.from_pretrained("7wolf/wav2ast-gender-classification") model = AutoModelForAudioClassification.from_pretrained("7wolf/wav2ast-gender-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from 7wolf/wav2ast-gender-classification: direct link, hf CLI and curl.
- Browser
- Download file 297 Bytes
-
https://huggingface.co/7wolf/wav2ast-gender-classification/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://7wolf/wav2ast-gender-classification/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/7wolf/wav2ast-gender-classification/resolve/main/preprocessor_config.json
297 Bytes
| { | |
| "do_normalize": false, | |
| "feature_extractor_type": "ASTFeatureExtractor", | |
| "feature_size": 1, | |
| "max_length": 100, | |
| "mean": -4.2677393, | |
| "num_mel_bins": 128, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "return_attention_mask": false, | |
| "sampling_rate": 16000, | |
| "std": 4.5689974 | |
| } | |