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# Install required libraries
import os
from io import BytesIO
import gradio as gr
from gtts import gTTS
from pydub import AudioSegment
import whisper
import openai
# Set up OpenAI API key
os.environ["OPENAI_API_KEY"] = "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"
openai.api_key = os.environ["OPENAI_API_KEY"]
# Initialize models
whisper_model = whisper.load_model("base") # Load Whisper model
# Define the voice-to-voice workflow
def voice_to_voice(audio):
# 1. Transcribe audio using Whisper
transcription_result = whisper_model.transcribe(audio, fp16=False)
user_input = transcription_result["text"]
# 2. Get response from OpenAI's GPT
response = openai.ChatCompletion.create(
model="gpt-4",
messages=[{"role": "user", "content": user_input}],
)
response_text = response.choices[0].message["content"]
# 3. Convert LLM response to audio using gTTS
tts = gTTS(text=response_text, lang="en")
audio_fp = BytesIO()
tts.write_to_fp(audio_fp)
audio_fp.seek(0)
# Convert gTTS output to a playable format using pydub
audio_segment = AudioSegment.from_file(audio_fp, format="mp3")
output_fp = BytesIO()
audio_segment.export(output_fp, format="mp3")
output_fp.seek(0)
return response_text, output_fp
# Gradio interface
iface = gr.Interface(
fn=voice_to_voice,
inputs=gr.Audio(type="filepath"),
outputs=[gr.Textbox(label="Transcription"), gr.Audio(label="Response Audio")],
live=True,
title="Real-Time Voice-to-Voice Chatbot",
description="Speak into the microphone and get a spoken response from the chatbot.",
)
# Launch Gradio app
if __name__ == "__main__":
iface.launch()
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