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# app.py
import streamlit as st
import torch
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC, MarianMTModel, MarianTokenizer
import soundfile as sf
import tempfile
# Load models and tokenizers
@st.cache_resource
def load_models():
# Load ASR model (Wav2Vec2 for Urdu)
asr_processor = Wav2Vec2Processor.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-ur")
asr_model = Wav2Vec2ForCTC.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-ur")
# Load translation model (Urdu to German)
translation_tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-ur-de")
translation_model = MarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-ur-de")
return asr_processor, asr_model, translation_tokenizer, translation_model
asr_processor, asr_model, translation_tokenizer, translation_model = load_models()
# Streamlit App UI
st.title("Real-Time Urdu to German Voice Translator")
st.markdown("Upload an Urdu audio file, and the app will translate it to German.")
uploaded_file = st.file_uploader("Upload an audio file (in .wav format)", type=["wav"])
if uploaded_file is not None:
with tempfile.NamedTemporaryFile(delete=False) as temp_file:
temp_file.write(uploaded_file.read())
temp_file_path = temp_file.name
# Load audio file
audio_input, sample_rate = sf.read(temp_file_path)
# Ensure proper sampling rate
if sample_rate != 16000:
st.error("Please upload a .wav file with a sampling rate of 16kHz.")
else:
st.info("Processing the audio...")
# Convert speech to text (ASR)
input_values = asr_processor(audio_input, return_tensors="pt", sampling_rate=16000).input_values
with torch.no_grad():
logits = asr_model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = asr_processor.batch_decode(predicted_ids)[0]
st.text(f"Transcribed Urdu Text: {transcription}")
# Translate Urdu text to German
translated = translation_model.generate(**translation_tokenizer(transcription, return_tensors="pt", padding=True))
german_translation = translation_tokenizer.decode(translated[0], skip_special_tokens=True)
st.success(f"Translated German Text: {german_translation}")
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