Audio Classification
Transformers
TensorBoard
Safetensors
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use LeaMac/final_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LeaMac/final_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="LeaMac/final_model")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("LeaMac/final_model") model = AutoModelForAudioClassification.from_pretrained("LeaMac/final_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0b5ad9482eda83be754a2001ea7dafeaa16e3d4cc54e463c04e443daa20cea32
- Size of remote file:
- 378 MB
- SHA256:
- 264962383372c0e62a1d90f670cf4fdbea506c4f4b0369db0338f65a4efcd1d8
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