Instructions to use galsenai/wav2vec2-base-waxal-keyword-spotting with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use galsenai/wav2vec2-base-waxal-keyword-spotting with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="galsenai/wav2vec2-base-waxal-keyword-spotting")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("galsenai/wav2vec2-base-waxal-keyword-spotting") model = AutoModelForAudioClassification.from_pretrained("galsenai/wav2vec2-base-waxal-keyword-spotting") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 32.0, | |
| "eval_accuracy": 0.8632057597574839, | |
| "eval_f1": 0.8953598709937869, | |
| "eval_loss": 0.7504324913024902, | |
| "eval_precision": 0.9379743242290526, | |
| "eval_runtime": 101.1391, | |
| "eval_samples_per_second": 26.093, | |
| "eval_steps_per_second": 0.87 | |
| } |