| from datasets import load_dataset | |
| from huggingface_hub import hf_hub_download | |
| from sklearn.metrics import accuracy_score | |
| import importlib.util | |
| repo_id = "tlmk22/QuefrencyGuardian" | |
| model_file = "model.py" | |
| model_path = hf_hub_download(repo_id=repo_id, filename=model_file) | |
| spec = importlib.util.spec_from_file_location("model", model_path) | |
| model_module = importlib.util.module_from_spec(spec) | |
| spec.loader.exec_module(model_module) | |
| FastModelHuggingFace = model_module.FastModelHuggingFace | |
| fast_model = FastModelHuggingFace.from_pretrained(repo_id) | |
| # Perform predictions for a single WAV file | |
| map_labels = {0: "chainsaw", 1: "environment"} | |
| wav_prediction = fast_model.predict("chainsaw.wav", device="cpu") | |
| print(f"Prediction : {map_labels[wav_prediction[0]]}") | |
| # Example: predicting on a Hugging Face dataset | |
| dataset = load_dataset("rfcx/frugalai") | |
| test_dataset = dataset["test"] | |
| true_label = dataset["test"]["label"] | |
| predictions = fast_model.predict(dataset["test"]) | |
| print(accuracy_score(true_label, predictions)) | |