[M] Move examples to /examples

examples/zero_shot_predictions.py

@@ -0,0 +1,51 @@
+"""
+This is an example using CLAP for zero-shot inference.
+"""
+from CLAPWrapper import CLAPWrapper
+import torch.nn.functional as F
+
+# Define classes for zero-shot
+# Should be in lower case and can be more than one word
+classes = ['coughing','sneezing','drinking sipping', 'breathing', 'brushing teeth']
+ground_truth = ['coughing']
+# Add prompt
+prompt = 'this is a sound of '
+class_prompts = [prompt + x for x in classes]
+#Load audio files
+audio_files = ['audio_file']
+
+# Load and initialize CLAP
+weights_path = "weights_path"
+# Setting use_cuda = True will load the model on a GPU using CUDA
+clap_model = CLAPWrapper(weights_path, version = '2023', use_cuda=False)
+
+# compute text embeddings from natural text
+text_embeddings = clap_model.get_text_embeddings(class_prompts)
+
+# compute the audio embeddings from an audio file
+audio_embeddings = clap_model.get_audio_embeddings(audio_files, resample=True)
+
+# compute the similarity between audio_embeddings and text_embeddings
+similarity = clap_model.compute_similarity(audio_embeddings, text_embeddings)
+
+similarity = F.softmax(similarity, dim=1)
+values, indices = similarity[0].topk(5)
+
+# Print the results
+print("Ground Truth: {}".format(ground_truth))
+print("Top predictions:\n")
+for value, index in zip(values, indices):
+    print(f"{classes[index]:>16s}: {100 * value.item():.2f}%")
+
+"""
+The output (the exact numbers may vary):
+
+Ground Truth: coughing
+Top predictions:
+
+        coughing: 98.55%
+        sneezing: 1.24%
+drinking sipping: 0.15%
+       breathing: 0.02%
+  brushing teeth: 0.01%
+"""