Create sample_inference_script.py

sample_inference_script.py

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+from huggingface_hub import hf_hub_download
+import pandas as pd
+import numpy as np
+import ai_edge_litert as litert
+
+# Download model
+repo_id = "sammlapp/perch2-tflite"
+model_path = hf_hub_download(repo_id=repo_id, filename="Perch2.tflite")
+
+# Download and read labels
+labels_path = hf_hub_download(repo_id=repo_id, filename="perch2_class_labels.txt")
+labels = pd.read_csv(labels_path, header=None).iloc[:, 0].tolist()
+
+print("Model:", model_path)
+print("Labels:", len(labels))
+
+# path = "./Perch2.tflite"
+interpreter = litert.interpreter.Interpreter(model_path=model_path)
+interpreter.allocate_tensors()
+
+sig = interpreter.get_signature_runner("serving_default")
+
+input_details = interpreter.get_input_details()
+signature_list = interpreter.get_signature_list()
+
+input_name = signature_list["serving_default"]["inputs"][0]
+output_names = signature_list["serving_default"]["outputs"]
+input_shape = input_details[0]["shape"]
+input_dtype = input_details[0]["dtype"]
+
+sample_input = np.random.uniform(-1, 1, size=(10, 160000)).astype(input_dtype)
+
+# Run via signature
+result = sig(**{input_name: sample_input})
+
+# Each output key in the signature dict will hold a numpy array (safe to access)
+print("Output keys:", result.keys())
+for k, v in result.items():
+    print(k, v.shape)