diff --git a/requirements.txt b/requirements.txt
index 8ee2b256..3621e915 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -40,3 +40,4 @@ torch==2.3.0
 ultralytics==8.3.3
 diffusers==0.30.3
 transformers==4.45.2
+PyAudio==0.2.14
diff --git a/src/apps/pages/programs/ImageGenerators/speech_textToImageGenerator.py b/src/apps/pages/programs/ImageGenerators/speech_textToImageGenerator.py
new file mode 100644
index 00000000..bb9c8bc2
--- /dev/null
+++ b/src/apps/pages/programs/ImageGenerators/speech_textToImageGenerator.py
@@ -0,0 +1,77 @@
+import streamlit as st
+from PIL import Image
+import torch
+from diffusers import StableDiffusionPipeline
+import speech_recognition as sr
+
+# Function to generate image
+@st.cache_resource
+def load_pipeline():
+    model_id = "CompVis/stable-diffusion-v1-4"
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    
+    # Load the pipeline without float16 if using CPU
+    if device == "cpu":
+        pipe = StableDiffusionPipeline.from_pretrained(model_id)
+    else:
+        pipe = StableDiffusionPipeline.from_pretrained(model_id, revision="fp16", torch_dtype=torch.float16)
+        
+    pipe.to(device)
+    return pipe
+
+# Function to recognize speech
+def recognize_speech():
+    recognizer = sr.Recognizer()
+    with sr.Microphone() as source:
+        st.info("Listening for speech...")
+        recognizer.adjust_for_ambient_noise(source, duration=1)
+        audio = recognizer.listen(source)
+        
+        try:
+            st.info("Recognizing speech...")
+            text = recognizer.recognize_google(audio)
+            st.success(f"Recognized: {text}")
+            return text
+        except sr.UnknownValueError:
+            st.error("Could not understand audio")
+        except sr.RequestError as e:
+            st.error(f"Could not request results; {e}")
+        return None
+
+# Define the main function
+def speech_textToImageGenerator():
+    
+    # Button to use speech recognition outside the form
+    if st.button("Use Speech Recognition"):
+        recognized_text = recognize_speech()
+        if recognized_text:
+            st.session_state.input_text = recognized_text
+
+    # Text input form
+    with st.form("Text_input_form"):
+        prompt = st.text_input("Enter a prompt to generate Image:", st.session_state.get("input_text", ""))
+        submit_button = st.form_submit_button("Generate Image")
+
+    # Generate image if prompt is available
+    if prompt and submit_button:
+        try:
+            pipe = load_pipeline()
+            
+            # Generate the image
+            if torch.cuda.is_available():
+                with torch.autocast("cuda"):
+                    output = pipe(prompt + " 4k, High Resolution", guidance_scale=8.5)
+            else:
+                output = pipe(prompt + " 4k, High Resolution", guidance_scale=8.5)
+
+            image = output.images[0]
+            st.image(image, caption="Generated Image", use_column_width=True)
+
+            # Optionally, save the image
+            image.save('src/apps/pages/programs/ImageGenerators/generated_image.png')
+            st.success("Image generated successfully!")
+        except Exception as e:
+            st.error(f"An error occurred: {str(e)}")
+    elif submit_button:
+        st.warning("Please enter a prompt first.")
+
diff --git a/src/apps/pages/programs/ImageGenerators/textToImageGenerator.py b/src/apps/pages/programs/ImageGenerators/textToImageGenerator.py
deleted file mode 100644
index cc782885..00000000
--- a/src/apps/pages/programs/ImageGenerators/textToImageGenerator.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import streamlit as st
-from PIL import Image
-import torch
-from diffusers import StableDiffusionPipeline
-
-# Function to generate image
-@st.cache_resource
-def load_pipeline():
-    model_id = "CompVis/stable-diffusion-v1-4"
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    
-    # Load the pipeline without float16 if using CPU
-    if device == "cpu":
-        pipe = StableDiffusionPipeline.from_pretrained(model_id)
-    else:
-        pipe = StableDiffusionPipeline.from_pretrained(model_id, revision="fp16", torch_dtype=torch.float16)
-        
-    pipe.to(device)
-    return pipe
-
-# Define the main function
-def textToImageGenerator():
-    with st.form("Text_input_form"):
-        st.session_state.input_text = st.text_input("Enter a prompt to generate Image:")
-        
-        start_button = st.form_submit_button("Generate Image")
-        if start_button:
-            st.session_state.running = True
-    
-    if st.session_state.input_text:
-        try:
-            pipe = load_pipeline()
-
-            if torch.cuda.is_available():
-                with torch.autocast("cuda"):
-                    output = pipe(st.session_state.input_text + " 4k, High Resolution", guidance_scale=8.5)
-            else:
-                output = pipe(st.session_state.input_text + " 4k, High Resolution", guidance_scale=8.5)
-
-            image = output.images[0]
-            st.image(image, caption="Generated Image", use_column_width=True)
-
-            # Optionally, save the image
-            image.save('src/apps/pages/programs/ImageGenerators/generated_image.png')
-            st.success("Image generated successfully!")
-        except Exception as e:
-            st.error(f"An error occurred: {str(e)}")
-    else:
-        st.warning("Please enter a prompt first.")
-
-