Instructions to use dkurt/wav2vec2-base-ft-keyword-spotting-int8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dkurt/wav2vec2-base-ft-keyword-spotting-int8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="dkurt/wav2vec2-base-ft-keyword-spotting-int8")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("dkurt/wav2vec2-base-ft-keyword-spotting-int8") model = AutoModelForAudioClassification.from_pretrained("dkurt/wav2vec2-base-ft-keyword-spotting-int8") - Notebooks
- Google Colab
- Kaggle
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
anton-l/wav2vec2-base-ft-keyword-spotting model quantized with Optimum OpenVINO.
| Accuracy on eval (baseline) | Accuracy on eval (quantized) |
|---|---|
| 0.9828 | 0.9553 (-0.0274) |
- Downloads last month
- 12