2

app2.py

@@ -37,9 +37,17 @@ def predict_emotion(selected_audio):
 # Get the list of audio files for the dropdown
 audio_files_list = get_audio_files_list()
 
+def return_audio_clip(audio_text):
+    post_file_name = audio_text.lower() + '.wav'
+    filepath = os.path.join("pre_recoreded",post_file_name)
+    return filepath
+
+
 # Loading Gradio interface
 inputs = gr.Dropdown(label="Select Audio", choices=audio_files_list)
 audio_ui=gr.Audio()
+inputs.change(return_audio_clip,inputs,audio_ui)
+
 outputs = "text"
 title = "ML Speech Emotion Detection"
 description = "Speechbrain powered wav2vec 2.0 pretrained model on IEMOCAP dataset using Gradio."

app3.py

@@ -1,55 +0,0 @@
-import gradio as gr
-from speechbrain.pretrained.interfaces import foreign_class
-import os
-
-# 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 selected_audio(audio_file):
-    if audio_file is None:
-        return None, "Please select an audio file."
-    file_path = os.path.join("rec", audio_file)
-    audio_data = gr.Audio(file=file_path)
-    out_prob, score, index, text_lab = learner.classify_file(file_path)
-    emotion = emotion_dict[text_lab[0]]
-    return audio_data, emotion
-
-# Get the list of audio files for the dropdown
-audio_files_list = get_audio_files_list()
-
-# Define Gradio blocks
-with gr.Blocks() as blocks:
-    gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>" +
-                "Audio Emotion Detection" +
-                "</h1>")
-    with gr.Column():
-        input_audio_dropdown = gr.Dropdown(label="Select Audio", choices=audio_files_list)
-        audio_ui = gr.Audio()
-        output_text = gr.Textbox(label="Detected Emotion!")
-        detect_btn = gr.Button("Detect Emotion")
-        detect_btn.click(selected_audio, inputs=input_audio_dropdown, outputs=[audio_ui, output_text])
-
-# Launch the Gradio blocks interface
-blocks.launch()