Instructions to use FerhatDk/wav2vec2-base_music_speech_both_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FerhatDk/wav2vec2-base_music_speech_both_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="FerhatDk/wav2vec2-base_music_speech_both_classification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("FerhatDk/wav2vec2-base_music_speech_both_classification") model = AutoModelForAudioClassification.from_pretrained("FerhatDk/wav2vec2-base_music_speech_both_classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 26de056099bfc37d7ad2c12d3fba024f44d3cd5d4f89ea17744f57715918a4d5
- Size of remote file:
- 4.03 kB
- SHA256:
- 6c5956897f0203e0a01fb97ccc10e367becc6e1fff47b23d271a04c538c2b4c6
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