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# Import libraries
import whisper
import os
from gtts import gTTS
import gradio as gr
from groq import Groq
# Load Whisper model for transcription
model = whisper.load_model("base")
# Set up Groq API client (ensure GROQ_API_KEY is set in your environment)
#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
client = Groq(api_key=os.getenv("voiceTOvoice"))
#GROQ_API_KEY = "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"
#client = Groq(api_key=GROQ_API_KEY)
# Function to get the LLM response from Groq
def get_llm_response(user_input):
chat_completion = client.chat.completions.create(
messages=[{"role": "user", "content": user_input}],
model="llama3-8b-8192", # Replace with your desired model
)
return chat_completion.choices[0].message.content
# Function to convert text to speech using gTTS
def text_to_speech(text, output_audio="output_audio.mp3"):
tts = gTTS(text)
tts.save(output_audio)
return output_audio
# Main chatbot function to handle audio input and output
def chatbot(audio):
# Step 1: Transcribe the audio using Whisper
result = model.transcribe(audio)
user_text = result["text"]
# Step 2: Get LLM response from Groq
response_text = get_llm_response(user_text)
# Step 3: Convert the response text to speech
output_audio = text_to_speech(response_text)
return response_text, output_audio
# Gradio interface for real-time interaction
iface = gr.Interface(
fn=chatbot,
inputs=gr.Audio("microphone", type="filepath"), # Input from mic
outputs=[gr.Textbox(), gr.Audio(type="filepath")], # Output: response text and audio
live=True
)
# Launch the Gradio app
iface.launch()
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