Delete dataset_inference.py

dataset_inference.py

@@ -1,32 +0,0 @@
-from CLAP_API import CLAP
-from esc50_dataset import ESC50
-import torch.nn.functional as F
-import numpy as np
-from tqdm import tqdm
-from sklearn.metrics import accuracy_score
-
-# Load CLAP
-weights_path = 'C:\\Users\\sdeshmukh\\Desktop\\CLAP_package\\model\\new\\best.pth' # Add weight path here
-clap_model = CLAP(weights_path, use_cuda=False)
-
-# Load dataset
-dataset = ESC50(root='data', download=True)
-prompt = 'this is a sound of '
-Y = [prompt + x for x in dataset.classes]
-
-# Computing text embeddings
-text_embeddings = clap_model.get_text_embeddings(Y)
-
-# Computing audio embeddings
-y_preds, y_labels = [], []
-for i in tqdm(range(len(dataset))):
-    x, _, one_hot_target = dataset.__getitem__(i)
-    audio_embeddings = clap_model.get_audio_embeddings([x], resample=True)
-    similarity = clap_model.compute_similarity(audio_embeddings, text_embeddings)
-    y_pred = F.softmax(similarity.detach().cpu(), dim=1).numpy()
-    y_preds.append(y_pred)
-    y_labels.append(one_hot_target.detach().cpu().numpy())
-
-y_labels, y_preds = np.concatenate(y_labels, axis=0), np.concatenate(y_preds, axis=0)
-acc = accuracy_score(np.argmax(y_labels, axis=1), np.argmax(y_preds, axis=1))
-print('ESC50 Accuracy {}'.format(acc))