app2

app2.py

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+from speechbrain.pretrained.interfaces import foreign_class
+import gradio as gr
+import os
+import warnings
+warnings.filterwarnings("ignore")
+
+# Function to get the list of audio files in the 'rec/' directory
+def get_audio_files_list(directory="rec"):
+    try:
+        return [f for f in os.listdir(directory) if os.path.isfile(os.path.join(directory, f))]
+    except FileNotFoundError:
+        print("The 'rec' directory does not exist. Please make sure it is the correct path.")
+        return []
+
+# Loading the speechbrain emotion detection model
+learner = foreign_class(
+    source="speechbrain/emotion-recognition-wav2vec2-IEMOCAP",
+    pymodule_file="custom_interface.py", 
+    classname="CustomEncoderWav2vec2Classifier"
+)
+
+# Building prediction function for Gradio
+emotion_dict = {
+    'sad': 'Sad', 
+    'hap': 'Happy',
+    'ang': 'Anger',
+    'fea': 'Fear',
+    'sur': 'Surprised',
+    'neu': 'Neutral'
+}
+
+def predict_emotion(selected_audio):
+    file_path = os.path.join("rec", selected_audio)
+    out_prob, score, index, text_lab = learner.classify_file(file_path)
+    return emotion_dict[text_lab[0]]
+
+# Get the list of audio files for the dropdown
+audio_files_list = get_audio_files_list()
+
+# Loading Gradio interface
+inputs = gr.Dropdown(label="Select Audio", choices=audio_files_list)
+outputs = "text"
+title = "ML Speech Emotion Detection"
+description = "Speechbrain powered wav2vec 2.0 pretrained model on IEMOCAP dataset using Gradio."
+
+interface = gr.Interface(fn=predict_emotion, inputs=inputs, outputs=outputs, title=title, description=description)
+interface.launch()