Update main.py

main.py

@@ -5,10 +5,9 @@ drive.mount('/content/drive')
 
 pip install transformers librosa torch soundfile numba numpy TTS datasets gradio protobuf==3.20.3
 
-"""Emotion Detection (Using Text Dataset)
+"""Emotion Detection (Using Text Dataset)""""
 
 """
-
 !pip install --upgrade numpy tensorflow transformers TTS
 
 !pip freeze > requirements.txt"""
@@ -77,28 +76,6 @@ emotion = "happy"
 output_audio = generate_emotional_speech("Welcome to the smart library!", emotion)
 print(f"Generated Speech Saved At: {output_audio}")
 
-#
-import librosa
-import soundfile as sf
-
-def adjust_pitch(audio_path, pitch_factor):
-    # Load audio
-    y, sr = librosa.load(audio_path)
-    # Adjust pitch
-    y_shifted = librosa.effects.pitch_shift(y, sr, n_steps=pitch_factor)
-    # Save adjusted audio
-    sf.write(audio_path, y_shifted, sr)
-
-def adjust_speed(audio_path, speed_factor):
-    # Load the audio file
-    y, sr = librosa.load(audio_path)
-
-    # Adjust the speed (this alters the duration of the audio)
-    y_speeded = librosa.effects.time_stretch(y, speed_factor)
-
-    # Save the adjusted audio
-    sf.write(audio_path, y_speeded, sr)
-
 
 """Integrating the Workflow"""
 
@@ -274,6 +251,28 @@ save_path = "/content/drive/My Drive/fine_tuned_tacotron2.pth"
 torch.save(model.state_dict(), save_path)
 
 
+import librosa
+import soundfile as sf
+
+def adjust_pitch(audio_path, pitch_factor):
+    # Load audio
+    y, sr = librosa.load(audio_path)
+    # Adjust pitch
+    y_shifted = librosa.effects.pitch_shift(y, sr, n_steps=pitch_factor)
+    # Save adjusted audio
+    sf.write(audio_path, y_shifted, sr)
+
+def adjust_speed(audio_path, speed_factor):
+    # Load the audio file
+    y, sr = librosa.load(audio_path)
+
+    # Adjust the speed (this alters the duration of the audio)
+    y_speeded = librosa.effects.time_stretch(y, speed_factor)
+
+    # Save the adjusted audio
+    sf.write(audio_path, y_speeded, sr)
+
+
 """Set up the Gradio interface"""
 
 import gradio as gr