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:
- 6e4e8cf88d32ba83171b0038aa8e45ecb0d111427bc057dc6e22af16753d39ba
- Size of remote file:
- 378 MB
- SHA256:
- 9e7254994a2677988660d544db2ac93a74bce0f7710d46f1fa5e6c96267383a0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.