Instructions to use anderloh/wav2vec2-2ClassEasyValidateMobil with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anderloh/wav2vec2-2ClassEasyValidateMobil with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="anderloh/wav2vec2-2ClassEasyValidateMobil")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("anderloh/wav2vec2-2ClassEasyValidateMobil") model = AutoModelForAudioClassification.from_pretrained("anderloh/wav2vec2-2ClassEasyValidateMobil") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 81a442f77f0b19a7762f96a72acf1f3f20f6977156a6843110b9ccb2a7fe8f9a
- Size of remote file:
- 4.98 kB
- SHA256:
- 16de80e4ee7d176355be27b1aaf557d0f61d9adc5a54e900bfc4e2205ff5fa94
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.