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import os
import torch
from transformers import pipeline
from gtts import gTTS
import gradio as gr
from groq import Groq
# Load Whisper model from Hugging Face
try:
pipe = pipeline(model="openai/whisper-small", device="cuda" if torch.cuda.is_available() else "cpu")
except Exception as e:
print(f"Error loading Whisper model: {e}")
raise
GROQ_API_KEY = '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'
Client = Groq(api_key=GROQ_API_KEY)
# Function to get response from Groq LLM
def get_llm_response(transcribed_text):
try:
chat_completion = client.chat.completions.create(
messages=[{"role": "user", "content": transcribed_text}],
model="llama3-8b-8192",
)
return chat_completion.choices[0].message.content
except Exception as e:
print(f"Error getting response from LLM: {e}")
return "Sorry, I couldn't process your request."
# Function to convert text to speech
def text_to_speech(response_text):
try:
tts = gTTS(response_text, lang='en')
tts.save("response_audio.mp3")
return "response_audio.mp3" # Returning the file path
except Exception as e:
print(f"Error converting text to speech: {e}")
return "Sorry, I couldn't convert the response to audio."
# Function to handle the entire voice chat process
def voice_chat(audio_input):
try:
# Transcribe the input audio using Hugging Face Whisper model
result = pipe(audio_input)["text"]
transcribed_text = result
print(f"Transcribed Text: {transcribed_text}")
# Get the LLM response
response_text = get_llm_response(transcribed_text)
print(f"LLM Response: {response_text}")
# Convert the response text to speech and return the audio file
response_audio = text_to_speech(response_text)
return response_audio
except Exception as e:
print(f"Error in voice chat process: {e}")
return "Sorry, there was an error processing your audio."
# Create the Gradio interface
iface = gr.Interface(
fn=voice_chat,
inputs=gr.Audio(type="filepath"), # Specify input type only
outputs="audio"
)
# Launch the Gradio interface
iface.launch()
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