app.py

import torch
import gradio as gr
import json
import numpy as np
import soundfile as sf

# Import your Glow-TTS model and related utilities
from glow_tts.models import GlowTTS
from glow_tts.utils import text_to_sequence, sequence_to_mel  # Replace with actual functions if different

# Define paths to your model and configuration (relative paths)
MODEL_PATH = 'best_model.pth'
CONFIG_PATH = 'config.json'

# Load configuration and model
def load_model(model_path, config_path):
    # Load the model configuration
    with open(config_path, 'r') as f:
        config = json.load(f)
    
    # Initialize the Glow-TTS model
    model = GlowTTS(config)
    
    # Load the trained model weights
    model.load_state_dict(torch.load(model_path))
    model.eval()
    
    return model

# Load the model
model = load_model(MODEL_PATH, CONFIG_PATH)

# Define the function to generate speech
def generate_speech(text):
    # Convert text to sequence
    sequence = text_to_sequence(text)
    inputs = torch.tensor(sequence).unsqueeze(0)  # Add batch dimension
    
    with torch.no_grad():
        # Generate mel spectrogram from text sequence
        mel_output = model(inputs)
    
    # Convert mel spectrogram to waveform
    # This step might require a vocoder (e.g., HiFi-GAN) to convert mel spectrograms to audio
    audio_waveform = mel_to_audio(mel_output)  # Replace with actual conversion if needed

    # Save the waveform to a temporary file
    temp_file = 'temp.wav'
    sf.write(temp_file, audio_waveform, 22050)  # Adjust sample rate if necessary
    
    return temp_file

# Define Gradio interface
interface = gr.Interface(
    fn=generate_speech,
    inputs="text",
    outputs="audio",
    title="Glow-TTS Model",
    description="Generate speech from text using the Glow-TTS model."
)

# Launch the Gradio interface
if __name__ == "__main__":
    interface.launch()