Create app.py import os import whisper from gtts import gTTS from groq import Groq import gradio as gr # Initialize Whisper model model = whisper.load_model("base") # Initialize Groq API (set your GROQ_API_KEY in the environment) client = Groq(api_key=os.environ.get("GROQ_API_KEY")) # Step 1: Transcribe Audio (Speech-to-Text using Whisper) def transcribe_audio(audio_path): result = model.transcribe(audio_path) return result['text'] # Step 2: Interact with LLM (Groq API) def interact_with_llm(user_input): chat_completion = client.chat.completions.create( messages=[ { "role": "user", "content": user_input, } ], model="llama3-8b-8192", stream=False, ) response = chat_completion.choices[0].message.content return response # Step 3: Convert Text to Speech using gTTS def text_to_speech(text): tts = gTTS(text, lang="en") audio_file = "response.mp3" tts.save(audio_file) return audio_file # Combined workflow: Transcribe -> Interact with LLM -> Convert to Speech def chatbot(audio): # Step 1: Transcribe Audio to Text transcription = transcribe_audio(audio) # Step 2: Get LLM response based on transcription llm_response = interact_with_llm(transcription) # Step 3: Convert LLM response to audio (text-to-speech) audio_output = text_to_speech(llm_response) return transcription, llm_response, audio_output # Gradio Interface setup interface = gr.Interface( fn=chatbot, inputs=gr.Audio(type="filepath", label="Speak into the microphone"), outputs=[ "text", # Transcription output "text", # LLM response output gr.Audio(type="filepath", label="Response Audio") # Final audio output ], live=True, title="Real-Time Voice-to-Voice Chatbot", description="Talk to an AI in real-time! Speak into the microphone, get a response, and hear it back.", ) # Launch Gradio app interface.launch()

app.py import os import whisper from gtts import gTTS from groq import Groq import gradio as gr # Initialize Whisper model model = whisper.load_model(/"base/") # Initialize Groq API (set your GROQ_API_KEY in the environment) client = Groq(api_key=os.environ.get(/"GROQ_API_KEY/")) # Step 1: Transcribe Audio (Speech-to-Text using Whisper) def transcribe_audio(audio_path): result = model.transcribe(audio_path) return result['text'] # Step 2: Interact with LLM (Groq API) def interact_with_llm(user_input): chat_completion = client.chat.completions.create( messages=[ { /"role/": /"user/", /"content/": user_input, } ], model=/"llama3-8b-8192/", stream=False, ) response = chat_completion.choices[0].message.content return response # Step 3: Convert Text to Speech using gTTS def text_to_speech(text): tts = gTTS(text, lang=/"en/") audio_file = /"response.mp3/" tts.save(audio_file) return audio_file # Combined workflow: Transcribe -> Interact with LLM -> Convert to Speech def chatbot(audio): # Step 1: Transcribe Audio to Text transcription = transcribe_audio(audio) # Step 2: Get LLM response based on transcription llm_response = interact_with_llm(transcription) # Step 3: Convert LLM response to audio (text-to-speech) audio_output = text_to_speech(llm_response) return transcription, llm_response, audio_output # Gradio Interface setup interface = gr.Interface( fn=chatbot, inputs=gr.Audio(type=/"filepath/", label=/"Speak into the microphone/"), outputs=[ /"text/", # Transcription output /"text/", # LLM response output gr.Audio(type=/"filepath/", label=/"Response Audio/") # Final audio output ], live=True, title=/"Real-Time Voice-to-Voice Chatbot/", description=/"Talk to an AI in real-time! Speak into the microphone, get a response, and hear it back./", ) # Launch Gradio app interface.launch()

@@ -0,0 +1,68 @@
+import os
+import whisper
+from gtts import gTTS
+from groq import Groq
+import gradio as gr
+
+# Initialize Whisper model
+model = whisper.load_model("base")
+
+ GROQ_API_KEY = "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"
+
+client = Groq(api_key=GROQ_API_KEY)
+# Step 1: Transcribe Audio (Speech-to-Text using Whisper)
+def transcribe_audio(audio_path):
+    result = model.transcribe(audio_path)
+    return result['text']
+
+# Step 2: Interact with LLM (Groq API)
+def interact_with_llm(user_input):
+    chat_completion = client.chat.completions.create(
+        messages=[
+            {
+                "role": "user",
+                "content": user_input,
+            }
+        ],
+        model="llama3-8b-8192",
+        stream=False,
+    )
+    response = chat_completion.choices[0].message.content
+    return response
+
+# Step 3: Convert Text to Speech using gTTS
+def text_to_speech(text):
+    tts = gTTS(text, lang="en")
+    audio_file = "response.mp3"
+    tts.save(audio_file)
+    return audio_file
+
+# Combined workflow: Transcribe -> Interact with LLM -> Convert to Speech
+def chatbot(audio):
+    # Step 1: Transcribe Audio to Text
+    transcription = transcribe_audio(audio)
+    
+    # Step 2: Get LLM response based on transcription
+    llm_response = interact_with_llm(transcription)
+    
+    # Step 3: Convert LLM response to audio (text-to-speech)
+    audio_output = text_to_speech(llm_response)
+    
+    return transcription, llm_response, audio_output
+
+# Gradio Interface setup
+interface = gr.Interface(
+    fn=chatbot,
+    inputs=gr.Audio(type="filepath", label="Speak into the microphone"),
+    outputs=[
+        "text",  # Transcription output
+        "text",  # LLM response output
+        gr.Audio(type="filepath", label="Response Audio")  # Final audio output
+    ],
+    live=True,
+    title="Real-Time Voice-to-Voice Chatbot",
+    description="Talk to an AI in real-time! Speak into the microphone, get a response, and hear it back.",
+)
+
+# Launch Gradio app
+interface.launch()