Instructions to use mageec/wave2vec2_capstone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mageec/wave2vec2_capstone with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="mageec/wave2vec2_capstone")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("mageec/wave2vec2_capstone") model = AutoModelForAudioClassification.from_pretrained("mageec/wave2vec2_capstone", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Training in progress, epoch 7
Verified signature8b32908 -
Training in progress, epoch 6
Verified signature69b5d94 -
Training in progress, epoch 5
Verified signatureaeba303
-
Training in progress, epoch 4
Verified signaturee3699f5 -
Training in progress, epoch 4
Verified signature4f7d625 -
Training in progress, epoch 2
Verified signaturea90cb4c -
Training in progress, epoch 1
Verified signature7ce1d70
-
Training in progress, epoch 0
Verified signature4662d24