Audio Classification
Transformers
PyTorch
TensorBoard
Safetensors
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use pratap18/audio_classification_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pratap18/audio_classification_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="pratap18/audio_classification_model")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("pratap18/audio_classification_model") model = AutoModelForAudioClassification.from_pretrained("pratap18/audio_classification_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from pratap18/audio_classification_model: direct link, hf CLI and curl.
- Browser
- Download file 215 Bytes
-
https://huggingface.co/pratap18/audio_classification_model/resolve/refs%2Fpr%2F1/preprocessor_config.json
- Command line
-
hf download hf://pratap18/audio_classification_model@refs/pr/1/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/pratap18/audio_classification_model/resolve/refs%2Fpr%2F1/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 | |
| } | |