Spaces:
Sleeping
Sleeping
File size: 2,024 Bytes
6c0f485 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 |
# -*- coding: utf-8 -*-
import os
import gradio as gr
import whisper
from gtts import gTTS
from groq import Groq
# Set up Groq API client
client = Groq(
api_key="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",
)
# Load Whisper model
model = whisper.load_model("base")
def chatbot(audio):
# Transcribe the audio input using Whisper
transcription = model.transcribe(audio)
user_input = transcription["text"]
# Generate a response using Llama 8B via Groq API
chat_completion = client.chat.completions.create(
messages=[
{
"role": "user",
"content": user_input,
}
],
model="llama3-8b-8192",
)
response_text = chat_completion.choices[0].message.content
# Convert the response text to speech using gTTS
tts = gTTS(text=response_text, lang='en')
tts.save("response.mp3")
return response_text, "response.mp3"
# Create a custom interface
def build_interface():
with gr.Blocks() as demo:
gr.Markdown(
"""
<h1 style="text-align: center; color: #4CAF50;">Voice-to-Voice Chatbot</h1>
<h3 style="text-align: center;">Powered by OpenAI Whisper, Llama 8B, and gTTS</h3>
<p style="text-align: center;">Talk to the AI-powered chatbot and get responses in real-time. Start by recording your voice.</p>
"""
)
with gr.Row():
with gr.Column(scale=1):
audio_input = gr.Audio(type="filepath", label="Record Your Voice")
with gr.Column(scale=2):
chatbot_output_text = gr.Textbox(label="Chatbot Response")
chatbot_output_audio = gr.Audio(label="Audio Response")
submit_button = gr.Button("Submit")
submit_button.click(chatbot, inputs=audio_input, outputs=[chatbot_output_text, chatbot_output_audio])
return demo
# Launch the interface
if __name__ == "__main__":
interface = build_interface()
interface.launch()
|