Sample app code with OpenVINO inference

app.py

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+from speechbrain.inference.interfaces import foreign_class
+from custom_interface import CustomEncoderWav2vec2Classifier
+from speechbrain.pretrained import EncoderClassifier
+
+# Function in SpeechBrain to load and use custom PyTorch models 
+classifier = foreign_class(
+   source="speechbrain/emotion-recognition-wav2vec2-IEMOCAP",
+   pymodule_file="custom_interface.py",
+   classname="CustomEncoderWav2vec2Classifier"
+)
+
+# Model checkpoint files
+checkpoint = EncoderClassifier.from_hparams(
+    source="speechbrain/emotion-recognition-wav2vec2-IEMOCAP",
+    savedir="./"  # Directory to save the model
+)
+
+# Convert hparams to a dictionary
+hparams_dict = vars(checkpoint.hparams)
+
+# OpenVINO inference optimization parameters
+device = "cpu"
+ov_opts = {"device_name": device, "PERFORMANCE_HINT": "LATENCY"}
+
+instance = CustomEncoderWav2vec2Classifier(modules=checkpoint.mods,
+                                           hparams=hparams_dict, model=classifier.mods["wav2vec2"].model,
+                                           audio_file_path="speechbrain/emotion-recognition-wav2vec2-IEMOCAP/anger.wav",
+                                           backend="openvino",
+                                           ov_opts=ov_opts,
+                                           save_ov_model=False)
+
+
+# OpenVINO inference
+print("=" * 30)
+print(f"[INFO] Inference Device: {ov_opts['device_name']}")
+print("=" * 30)
+print("\n[INFO] Performing OpenVINO inference...")
+out_prob, score, index, text_lab = instance.classify_file("speechbrain/emotion-recognition-wav2vec2-IEMOCAP/anger.wav")
+print(f"[RESULT] OpenVINO Inference Output: {text_lab[index-1]}")