Audio Classification
Transformers
Safetensors
English
wav2vec2
audio - wav2vec2 - deepfake-detection - synthetic-speech - tts - voice-cloning
Instructions to use Simma7/audio_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Simma7/audio_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Simma7/audio_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Simma7/audio_model") model = AutoModelForAudioClassification.from_pretrained("Simma7/audio_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from Simma7/audio_model: direct link, hf CLI and curl.
- Browser
- Download file 221 Bytes
-
https://huggingface.co/Simma7/audio_model/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://Simma7/audio_model/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/Simma7/audio_model/resolve/main/preprocessor_config.json
221 Bytes
| { | |
| "do_normalize": true, | |
| "feature_extractor_type": "Wav2Vec2FeatureExtractor", | |
| "feature_size": 1, | |
| "padding_side": "right", | |
| "padding_value": 0, | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000 | |
| } | |