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| import streamlit as st | |
| import torch | |
| from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC, MarianMTModel, MarianTokenizer, Wav2Vec2CTCTokenizer | |
| import soundfile as sf | |
| import tempfile | |
| import numpy as np | |
| # Load models and tokenizers | |
| def load_models(): | |
| try: | |
| # Load Wav2Vec2 for ASR (Multilingual model for Urdu support) | |
| # Load the tokenizer directly using Wav2Vec2CTCTokenizer | |
| tokenizer = Wav2Vec2CTCTokenizer.from_pretrained("facebook/wav2vec2-large-xlsr-53") | |
| # Then, initialize the processor with the tokenizer | |
| asr_processor = Wav2Vec2Processor(feature_extractor=asr_processor.feature_extractor, tokenizer=tokenizer) | |
| asr_model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-xlsr-53") | |
| # Load MarianMT for translation (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 | |
| except Exception as e: | |
| st.error(f"Error loading models: {e}") | |
| return None, None, None, None | |
| # Initialize models | |
| asr_processor, asr_model, translation_tokenizer, translation_model = load_models() | |
| # ... (rest of your app.py code remains the same) | |
| # Streamlit app interface | |
| st.title("Real-Time Urdu to German Voice Translator") | |
| st.markdown("Upload an Urdu audio file in `.wav` format, and the app will transcribe and translate it.") | |
| # File uploader | |
| uploaded_file = st.file_uploader("Upload your Urdu audio file (16kHz .wav)", 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 | |
| try: | |
| # Load and validate audio file | |
| audio_input, sample_rate = sf.read(temp_file_path) | |
| if sample_rate != 16000: | |
| st.error("Audio file must have a sampling rate of 16kHz.") | |
| else: | |
| st.info("Processing the audio...") | |
| # Step 1: 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}") | |
| # Step 2: Translate Text (Urdu 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}") | |
| except Exception as e: | |
| st.error(f"An error occurred: {e}") |