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import os
import gradio as gr
import whisper
from gtts import gTTS
import io
from groq import Groq
import time
# Ensure GROQ_API_KEY is defined
GROQ_API_KEY ="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"
if not GROQ_API_KEY:
raise ValueError("GROQ_API_KEY is not set in environment variables.")
# Initialize the Groq client
client = Groq(api_key=GROQ_API_KEY)
# Load the Whisper model
model = whisper.load_model("base") # Ensure this model supports Urdu; otherwise, choose a suitable model
def process_audio(file_path):
try:
# Load the audio file
audio = whisper.load_audio(file_path)
# Transcribe the audio using Whisper (specify language if needed)
result = model.transcribe(audio, language="ur") # Specify 'ur' for Urdu
text = result["text"]
# Generate a response in Urdu using Groq
chat_completion = client.chat.completions.create(
messages=[{"role": "user", "content": text}],
model="gemma2-9b-it", # Ensure this model can handle Urdu
)
# Access the response using dot notation
response_message = chat_completion.choices[0].message.content.strip()
# Convert the response text to Urdu speech
tts = gTTS(response_message, lang='ur') # Specify language 'ur' for Urdu
response_audio_io = io.BytesIO()
tts.write_to_fp(response_audio_io) # Save the audio to the BytesIO object
response_audio_io.seek(0)
# Generate a unique filename
response_audio_path = "response_" + str(int(time.time())) + ".mp3"
# Save audio to a file
with open(response_audio_path, "wb") as audio_file:
audio_file.write(response_audio_io.getvalue())
# Return the response text and the path to the saved audio file
return response_message, response_audio_path
except Exception as e:
return f"An error occurred: {e}", None
iface = gr.Interface(
fn=process_audio,
inputs=gr.Audio(type="filepath"), # Use type="filepath"
outputs=[gr.Textbox(label="Response Text (Urdu)"), gr.Audio(label="Response Audio (Urdu)")],
live=True # Set to False if you do not need real-time updates
)
iface.launch()
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