app.py

import os
import sys
import gradio as gr
import numpy as np
import torch
import subprocess
import shutil
from pathlib import Path

# Model repository information
REPO_URL = "https://github.com/hpbyte/myanmar-tts.git"
MODEL_DIR = "trained_model"
REPO_DIR = "myanmar-tts"

# Check and install the package if not already installed
def setup_environment():
    status_msg = ""
    
    # Clone the repository if it doesn't exist
    if not os.path.exists(REPO_DIR):
        status_msg += "Cloning repository...\n"
        subprocess.run(["git", "clone", REPO_URL], check=True)
    
    # Add the repository to Python path
    repo_path = os.path.abspath(REPO_DIR)
    if repo_path not in sys.path:
        sys.path.append(repo_path)
        status_msg += f"Added {repo_path} to Python path\n"
    
    # Create model directory if it doesn't exist
    if not os.path.exists(MODEL_DIR):
        os.makedirs(MODEL_DIR)
        status_msg += f"Created {MODEL_DIR} directory\n"
    
    return status_msg + "Environment setup complete"

# Function to synthesize speech
def synthesize_speech(text):
    try:
        # Import necessary modules from the repository
        sys.path.append(REPO_DIR)
        from myanmar_tts.text import text_to_sequence
        from myanmar_tts.utils.hparams import create_hparams
        from myanmar_tts.train import load_model
        from myanmar_tts.synthesis import generate_speech
        import scipy.io.wavfile
        
        # Check if model exists, if not provide instructions
        checkpoint_path = os.path.join(MODEL_DIR, "checkpoint_latest.pth.tar")
        config_path = os.path.join(MODEL_DIR, "hparams.yml")
        
        if not os.path.exists(checkpoint_path) or not os.path.exists(config_path):
            return None, f"""Model files not found. Please upload:
1. The checkpoint file at: {checkpoint_path}
2. The hparams.yml file at: {config_path}

You can obtain these files from the original repository or by training the model."""
        
        # Load the model and hyperparameters
        hparams = create_hparams(config_path)
        model = load_model(hparams)
        model.load_state_dict(torch.load(checkpoint_path, map_location=torch.device('cpu'))['state_dict'])
        model.eval()
        
        # Process text input
        sequence = np.array(text_to_sequence(text, ['burmese_cleaners']))[None, :]
        sequence = torch.autograd.Variable(torch.from_numpy(sequence)).cpu().long()
        
        # Generate mel spectrograms
        mel_outputs, mel_outputs_postnet, _, alignments = model.inference(sequence)
        
        # Generate waveform
        with torch.no_grad():
            waveform = generate_speech(mel_outputs_postnet, hparams)
        
        # Save and return the audio
        output_path = "output.wav"
        scipy.io.wavfile.write(output_path, hparams.sampling_rate, waveform)
        
        return output_path, "Speech generated successfully!"
    
    except Exception as e:
        return None, f"Error: {str(e)}\n\nMake sure you have uploaded the model files to the {MODEL_DIR} directory."

# Function for the Gradio interface
def tts_interface(text):
    if not text.strip():
        return None, "Please enter some text."
    
    return synthesize_speech(text)

# Set up the environment
setup_message = setup_environment()
print(setup_message)

# Create the Gradio interface
demo = gr.Interface(
    fn=tts_interface,
    inputs=[
        gr.Textbox(
            lines=3, 
            placeholder="Enter Burmese text here...", 
            label="Text"
        )
    ],
    outputs=[
        gr.Audio(label="Generated Speech"),
        gr.Textbox(label="Status")
    ],
    title="Myanmar (Burmese) Text-to-Speech",
    description="""
    This is a demo of the Myanmar Text-to-Speech system developed by hpbyte.
    Enter Burmese text in the box below and click 'Submit' to generate speech.
    
    **Note:** You need to upload the model files to the 'trained_model' directory:
    - checkpoint_latest.pth.tar
    - hparams.yml
    
    GitHub Repository: https://github.com/hpbyte/myanmar-tts
    """,
    examples=[
        ["မင်္ဂလာပါ"],
        ["မြန်မာစကားပြောစနစ်ကို ကြိုဆိုပါတယ်"],
        ["ဒီစနစ်ဟာ မြန်မာစာကို အသံအဖြစ် ပြောင်းပေးနိုင်ပါတယ်"],
    ]
)

# Launch the app
if __name__ == "__main__":
    demo.launch()