Create app.py

app.py

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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}")