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# Install dependencies in Colab
try:
import whisper, gradio as gr
from gtts import gTTS
from groq import Groq
except:
import whisper, gradio as gr
from gtts import gTTS
from groq import Groq
import os
import tempfile
# Load Whisper model
whisper_model = whisper.load_model("base")
# Groq API Key (replace with your actual key or set as env variable)
GROQ_API_KEY = "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"
client = Groq(api_key=GROQ_API_KEY)
# Core logic
def voice_chat(audio_path):
# Step 1: Transcribe audio
result = whisper_model.transcribe(audio_path)
user_text = result["text"]
# Step 2: Groq LLM response
response = client.chat.completions.create(
messages=[{"role": "user", "content": user_text}],
model="llama3-8b-8192",
)
bot_reply = response.choices[0].message.content
# Step 3: Text to speech using gTTS
tts = gTTS(bot_reply)
with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as f:
tts.save(f.name)
audio_response_path = f.name
return user_text, bot_reply, audio_response_path
# Gradio interface
iface = gr.Interface(
fn=voice_chat,
inputs=gr.Microphone(label="🎤 Speak your question", type="filepath"),
outputs=[
gr.Text(label="📝 Transcribed Input"),
gr.Text(label="🤖 LLM Reply"),
gr.Audio(label="🔉 Spoken Reply", type="filepath")
],
title="🗣️ Real-Time Voice-to-Voice Chatbot (Whisper + Groq + gTTS)",
live=True
)
if __name__ == "__main__":
iface.launch()
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