app.py

import torch
import torchaudio
import gradio as gr
from transformers import WhisperProcessor, WhisperForConditionalGeneration
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

device = "cuda" if torch.cuda.is_available() else "cpu"

# Load Whisper fine-tuned Odia model
whisper_model_path = "./whisper-odia-final"  # Change if needed
processor = WhisperProcessor.from_pretrained(whisper_model_path)
model = WhisperForConditionalGeneration.from_pretrained(whisper_model_path).to(device)

# Load IndicTrans2 multilingual model
trans_model_id = "ai4bharat/indictrans2-en-indic-dist-200M "
translator_tokenizer = AutoTokenizer.from_pretrained(trans_model_id, use_fast=False)
translator_model = AutoModelForSeq2SeqLM.from_pretrained(trans_model_id).to(device)

# Translation function with language tags
def translate_to_english(text):
    if not text.strip():
        return ""

    # Add source and target language tokens
    text_with_lang = f"<2en> {text.strip()}"
    inputs = translator_tokenizer(text_with_lang, return_tensors="pt", padding=True).to(device)

    output = translator_model.generate(**inputs, max_length=256)
    translated = translator_tokenizer.batch_decode(output, skip_special_tokens=True)[0]
    return translated

# ASR + Translation Pipeline
def transcribe(audio_path):
    if audio_path is None:
        return "No audio received.", ""

    speech, sr = torchaudio.load(audio_path)
    if sr != 16000:
        speech = torchaudio.functional.resample(speech, sr, 16000)

    input_features = processor(speech.squeeze().numpy(), sampling_rate=16000, return_tensors="pt").input_features.to(device)

    predicted_ids = model.generate(input_features)
    odia_text = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]

    english_text = translate_to_english(odia_text)
    return odia_text, english_text

# Gradio UI
interface = gr.Interface(
    fn=transcribe,
    inputs=gr.Audio(source="microphone", type="filepath", label="🎤 Record or Upload Odia Audio"),
    outputs=[
        gr.Textbox(label="📝 Odia Transcription"),
        gr.Textbox(label="🌐 English Translation")
    ],
    title="Whisper Odia ASR + Translation",
    description="🎙️ Speak in Odia → Get Odia transcription → Get English translation using IndicTrans2"
)

interface.launch()