Create app.py

app.py

@@ -0,0 +1,172 @@
+import streamlit as st
+import torch
+from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
+import soundfile as sf
+import io
+import numpy as np
+import base64
+
+# Set page config
+st.set_page_config(
+    page_title="Nepali Text-to-Speech Converter",
+    page_icon="🎧",
+    layout="centered"
+)
+
+# Custom CSS
+st.markdown("""
+    <style>
+    .main {
+        padding: 2rem;
+    }
+    .stTextInput > div > div > input {
+        min-height: 100px;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+@st.cache_resource
+def load_model():
+    """Load and cache the model and processor"""
+    try:
+        processor = SpeechT5Processor.from_pretrained("aryamanstha/speecht5_nepali_oslr43_oslr143")
+        model = SpeechT5ForTextToSpeech.from_pretrained("aryamanstha/speecht5_nepali_oslr43_oslr143")
+        vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
+        
+        # Move to GPU if available
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        model = model.to(device)
+        vocoder = vocoder.to(device)
+        
+        return processor, model, vocoder, device
+    except Exception as e:
+        st.error(f"Error loading model: {str(e)}")
+        return None, None, None, None
+
+def create_speaker_embedding():
+    """Create a default speaker embedding"""
+    speaker_embedding = torch.zeros(512)
+    return speaker_embedding.unsqueeze(0)
+
+def generate_speech(text, processor, model, vocoder, speaker_embeddings, device):
+    """Generate speech from text"""
+    try:
+        # Prepare input
+        inputs = processor(text=text, return_tensors="pt").to(device)
+        speaker_embeddings = speaker_embeddings.to(device)
+        
+        # Generate speech
+        speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
+        
+        # Convert to numpy
+        speech = speech.cpu().numpy()
+        
+        # Save to BytesIO
+        audio_buffer = io.BytesIO()
+        sf.write(audio_buffer, speech, samplerate=16000, format='WAV')
+        audio_buffer.seek(0)
+        
+        return audio_buffer
+    except Exception as e:
+        st.error(f"Error generating speech: {str(e)}")
+        return None
+
+def get_audio_player_html(audio_bytes):
+    """Create an HTML audio player with the audio data"""
+    audio_base64 = base64.b64encode(audio_bytes.read()).decode()
+    return f"""
+        <audio controls autoplay>
+            <source src="data:audio/wav;base64,{audio_base64}" type="audio/wav">
+            Your browser does not support the audio element.
+        </audio>
+    """
+
+def main():
+    st.title("🎤 Nepali Text-to-Speech Converter")
+    
+    # Add introduction
+    st.markdown("""
+    Convert Nepali text to speech using SpeechT5 model. Simply enter your text below and click 'Generate Speech'.
+    """)
+    
+    # Initialize session state for tracking model loading
+    if 'model_loaded' not in st.session_state:
+        st.session_state.model_loaded = False
+    
+    # Load model
+    if not st.session_state.model_loaded:
+        with st.spinner("Loading model... This may take a few minutes..."):
+            processor, model, vocoder, device = load_model()
+            if None not in (processor, model, vocoder):
+                st.session_state.model_loaded = True
+                st.session_state.processor = processor
+                st.session_state.model = model
+                st.session_state.vocoder = vocoder
+                st.session_state.device = device
+                st.success("Model loaded successfully! 🚀")
+            else:
+                st.error("Failed to load model. Please refresh the page to try again.")
+                return
+    
+    # Create text input area
+    text_input = st.text_area(
+        "Enter Nepali Text:",
+        height=100,
+        placeholder="तपाईंको नेपाली पाठ यहाँ लेख्नुहोस्..."
+    )
+    
+    # Create speaker embedding
+    speaker_embeddings = create_speaker_embedding()
+    
+    # Add generate button
+    col1, col2 = st.columns([1, 2])
+    with col1:
+        generate_button = st.button("🔊 Generate Speech")
+    
+    # Generate speech when button is clicked
+    if generate_button and text_input:
+        with st.spinner("Generating speech..."):
+            audio_buffer = generate_speech(
+                text_input,
+                st.session_state.processor,
+                st.session_state.model,
+                st.session_state.vocoder,
+                speaker_embeddings,
+                st.session_state.device
+            )
+            
+            if audio_buffer:
+                # Display audio player
+                st.markdown("### Generated Speech:")
+                st.markdown(get_audio_player_html(audio_buffer), unsafe_allow_html=True)
+                
+                # Add download button
+                audio_buffer.seek(0)
+                st.download_button(
+                    label="📥 Download Audio",
+                    data=audio_buffer,
+                    file_name="generated_speech.wav",
+                    mime="audio/wav"
+                )
+    
+    # Add usage instructions
+    with st.expander("ℹ️ Usage Instructions"):
+        st.markdown("""
+        1. Enter your Nepali text in the text area above
+        2. Click the 'Generate Speech' button
+        3. Wait for the audio to be generated
+        4. Use the audio player to listen to the generated speech
+        5. Download the audio file if desired
+        
+        **Note**: For best results, enter clear and grammatically correct Nepali text.
+        """)
+    
+    # Add footer
+    st.markdown("---")
+    st.markdown(
+        "Made with ❤️ using Streamlit and SpeechT5 | "
+        "Model: [aryamanstha/speecht5_nepali_oslr43_oslr143](https://huggingface.co/aryamanstha/speecht5_nepali_oslr43_oslr143)"
+    )
+
+if __name__ == "__main__":
+    main()