Function 1: Tamil Audio to Tamil Text (Transcription)
def transcribe_audio(audio_path):
if not audio_path:
return "Please upload an audio file."
try:
with open(audio_path, "rb") as file:
transcription = client.audio.transcriptions.create(
file=(os.path.basename(audio_path), file.read()),
model="whisper-large-v3",
language="ta", # Tamil
response_format="verbose_json",
)
return transcription.text
except Exception as e:
return f"Error in transcription: {str(e)}"
Function 2: Tamil Text to English Translation
def translate_tamil_to_english(tamil_text):
if not tamil_text:
return "Please enter Tamil text for translation."
prompt = f"""Translate the below Tamil text to English:\n
Tamil Text: {tamil_text}\n
Give only the translated part as the output without any extra words."""
try:
response = client.chat.completions.create(
model="gemma2-9b-it",
messages=[{"role": "user", "content": prompt}],
)
return response.choices[0].message.content.strip()
except Exception as e:
return f"Error in translation: {str(e)}"
Function 3: English Text to Image Generation
def generate_image(english_text):
if not english_text:
return "Please enter a description for image generation."
try:
payload = {"inputs": english_text}
response = requests.post(f"https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-schnell", json=payload)
response.raise_for_status()
image_bytes = response.content
image = Image.open(io.BytesIO(image_bytes))
return image
except Exception as e:
return f"Error in image generation: {str(e)}"
Function 4: English Text to Further Text Generation
def generate_text(english_text):
if not english_text:
return "Please enter a prompt."
try:
response = client.chat.completions.create(
model="deepseek-r1-distill-llama-70b",
messages=[{"role": "user", "content": english_text}],
)
return response.choices[0].message.content.strip()
except Exception as e:
return f"Error in text generation: {str(e)}"
Combined Function to Process All Steps Sequentially
def process_audio(audio_path):
# Step 1: Tamil Audio → Tamil Text
tamil_text = transcribe_audio(audio_path)
if "Error" in tamil_text:
return tamil_text, None, None, None
# Step 2: Tamil Text → English Text
english_text = translate_tamil_to_english(tamil_text)
if "Error" in english_text:
return tamil_text, english_text, None, None
# Step 3: English Text → Image
image = generate_image(english_text)
if "Error" in str(image):
return tamil_text, english_text, None, None
# Step 4: English Text → Generated Text
generated_text = generate_text(english_text)
return tamil_text, english_text, image, generated_text
Create Gradio Interface
iface = gr.Interface(
fn=process_audio,
inputs=gr.Audio(type="filepath", label="Upload Tamil Audio"),
outputs=[
gr.Textbox(label="Transcribed Tamil Text"),
gr.Textbox(label="Translated English Text"),
gr.Image(label="Generated Image"),
gr.Textbox(label="Generated Text from English Prompt"),
],
title="TransArt: A Multimodal Application for Vernacular Language Translation and Image Synthesis",
description="""Upload a Tamil audio file or live voice record Tamil audio and
get transcription, translation, image generation, and further text generation."""
)
