app.py

import streamlit as st
import torch
import torchaudio
from scipy.io.wavfile import write

# Load pre-trained Tacotron2 and WaveGlow models (ensure these are properly installed and loaded)
# The following lines are placeholders for model loading. You need to load the actual pre-trained models.
# tacotron2 = Tacotron2()
# waveglow = WaveGlow()

def synthesize_voice(text):
    """
    Synthesize voice from the given text using a pre-trained Tacotron2 and WaveGlow model.
    This function is a simplified placeholder; you need to load actual models and synthesize properly.
    """
    # For the sake of this example, generate a dummy wave
    # In reality, you would generate this using the Tacotron2 and WaveGlow models
    sample_rate = 22050
    duration = 2  # 2 seconds
    audio = torch.sin(torch.linspace(0, duration * 2 * torch.pi, sample_rate * duration))
    
    # Save the synthesized audio to a file
    output_path = "synthesized_voice.wav"
    write(output_path, sample_rate, audio.numpy())
    return output_path

def main():
    st.title("Voice Cloning App")
    st.write("Upload an audio sample of a person's voice and input text to clone their voice.")

    # File uploader for the voice sample (assuming the voice sample is used for reference)
    uploaded_audio = st.file_uploader("Choose a voice sample (WAV, MP3, etc.)", type=["wav", "mp3"])

    if uploaded_audio is not None:
        st.audio(uploaded_audio, format="audio/wav")

    # Textbox to input the text to be cloned
    text_input = st.text_area("Enter text for voice cloning")

    if st.button("Generate Cloned Voice"):
        if text_input:
            # Use the text input for voice cloning
            output_path = synthesize_voice(text_input)
            
            # Play the generated audio using Streamlit's audio component
            st.audio(output_path, format="audio/wav")
            st.success("Voice cloning successful!")

if __name__ == "__main__":
    main()