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")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Simma7/audio_model") model = AutoModelForAudioClassification.from_pretrained("Simma7/audio_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f839023e3996be1a882b7367a1320e157387aa66f0915d1646ed7567b41f63aa
- Size of remote file:
- 1.26 GB
- SHA256:
- 905e330265c40a76955911ce86691fb4c8f2ed9c8085f4f046cac1383592a0ac
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