3

README.md

@@ -5,7 +5,7 @@ colorFrom: green
 colorTo: indigo
 sdk: gradio
 sdk_version: 3.0.19
-app_file: app4.py
+app_file: app3.py
 pinned: false
 duplicated_from: harish3110/emotion_detection
 ---

app4.py

@@ -1,56 +0,0 @@
-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)
-    emotion = emotion_dict[text_lab[0]]
-    return emotion, file_path  # Return both emotion and file path
-
-def button_click(selected_audio):
-    emotion, file_path = predict_emotion(selected_audio)
-    return emotion, gr.Interface.Play("rec/" + selected_audio)
-
-# 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 = [gr.outputs.Textbox(label="Predicted Emotion"), gr.outputs.Audio(label="Play Audio")]
-
-# Create the button
-sub_btn = gr.Interface.Button(label="Detect Emotion", elem_id="btn", onclick=button_click)
-
-title = "ML Speech Emotion Detection3"
-description = "Speechbrain powered wav2vec 2.0 pretrained model on IEMOCAP dataset using Gradio."
-
-interface = gr.Interface(fn=predict_emotion, inputs=[inputs, sub_btn], outputs=outputs, title=title, description=description)
-interface.launch()