app-new.py

import torch
from transformers import VitsModel, AutoTokenizer, VitsConfig
import soundfile as sf
import numpy as np
import gradio as gr
import os
from thaicleantext import clean_thai_text

def load_tts_model(pth_path, speed=1.0):
    """Load the TTS model from a .pth file"""
    try:
        loaded_dict = torch.load(pth_path, map_location=torch.device('cpu'))
        config = VitsConfig(**loaded_dict['config'])
        model = VitsModel(config)
        model.load_state_dict(loaded_dict['model_state'])
        model.eval()
        model.speaking_rate = speed
        tokenizer = AutoTokenizer.from_pretrained("VIZINTZOR/tts-tha-vits")
        return model, tokenizer, None
    except Exception as e:
        return None, None, f"Error loading model: {str(e)}"

def generate_speech(model, tokenizer, text, speed, volume, output_file="output.wav"):
    """Generate speech from text and save to file"""
    try:
        model.speaking_rate = speed
        inputs = tokenizer(text, return_tensors="pt")
        with torch.no_grad():
            waveform = model(**inputs).waveform
        waveform = waveform.squeeze().cpu().numpy()
        waveform = waveform / np.max(np.abs(waveform))  # Normalize to [-1, 1]
        waveform = waveform * volume  # Apply volume adjustment
        sample_rate = model.config.sampling_rate
        sf.write(output_file, waveform, sample_rate)
        return output_file, None
    except Exception as e:
        return None, f"Error generating speech: {str(e)}"

def get_available_models(model_dir="./models"):
    """Get list of .pth files in the models directory"""
    if not os.path.exists(model_dir):
        return []
    return [os.path.join(model_dir, f) for f in os.listdir(model_dir) if f.endswith('.pth')]

def tts_interface(text, model_path, speed, volume):
    """Gradio interface function"""
    model, tokenizer, error = load_tts_model(model_path, speed)
    if model is None or tokenizer is None:
        return None, error
    
    output_file = "output.wav"
    text = clean_thai_text(text)
    audio_file, error = generate_speech(model, tokenizer, text, speed, volume, output_file)
    if audio_file:
        return audio_file, "Audio generated successfully!"
    return None, error

# Create Gradio interface
with gr.Blocks(title="Text-to-Speech Generator", theme=gr.themes.Soft()) as demo:
    gr.Markdown("# Text-to-Speech Generator")
    gr.Markdown("Enter text, select a model, adjust speed and volume, and generate audio!")
    
    with gr.Row():
        with gr.Column(scale=2):
            text_input = gr.Textbox(
                label="Input Text",
                placeholder="Enter your text here...",
                lines=5
            )
            model_dropdown = gr.Dropdown(
                label="Select Model",
                choices=get_available_models(),
                value=get_available_models()[0] if get_available_models() else None
            )
            
        with gr.Column(scale=1):
            speed_slider = gr.Slider(
                minimum=0.5,
                maximum=2.0,
                value=1.0,
                step=0.05,
                label="Speaking Speed",
                info="1.0 is normal speed"
            )
            volume_slider = gr.Slider(
                minimum=0.1,
                maximum=1.0,
                value=1,
                step=0.05,
                label="Volume",
                info="Adjust output volume"
            )
            generate_btn = gr.Button("Generate Audio", variant="primary")
    
    with gr.Row():
        audio_output = gr.Audio(label="Generated Audio")
        status_output = gr.Textbox(label="Status", interactive=False)
    
    # Connect the button to the function
    generate_btn.click(
        fn=tts_interface,
        inputs=[text_input, model_dropdown, speed_slider, volume_slider],
        outputs=[audio_output, status_output]
    )

# Launch the interface
demo.launch()