[M] Move examples to /examples

examples/zero_shot_classification.py

@@ -0,0 +1,46 @@
+"""
+This is an example using CLAP to perform zeroshot
+    classification on ESC50 (https://github.com/karolpiczak/ESC-50).
+"""
+
+from CLAPWrapper import CLAPWrapper
+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 dataset
+root_path = "root_path" # Folder with ESC-50-master/
+dataset = ESC50(root=root_path, download=True) #If download=False code assumes base_folder='ESC-50-master' in esc50_dataset.py
+prompt = 'this is the sound of '
+y = [prompt + x for x in dataset.classes]
+
+# Load and initialize CLAP
+weights_path = "weights_path"
+clap_model = CLAPWrapper(weights_path, version = '2023', use_cuda=False)
+
+# 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))
+
+"""
+The output:
+
+ESC50 Accuracy: 93.9%
+
+"""