app.py

import gradio as gr
import torch
import numpy as np
from transformers import pipeline

# 1. Setup device (Use GPU if available on the Space, otherwise CPU)
device = "cuda:0" if torch.cuda.is_available() else "cpu"

print(f"Loading aoxo/swaram model on {device}...")

# 2. Load the TTS pipeline globally so it only loads once when the Space starts
try:
    synthesizer = pipeline("text-to-speech", model="aoxo/swaram", device=device)
    print("Model loaded successfully!")
except Exception as e:
    print(f"Error loading model: {e}")
    synthesizer = None

# 3. Define the prediction function
def generate_audio(text):
    if not text.strip():
        return None, "Please enter some text."
        
    if synthesizer is None:
        return None, "Error: Model failed to load. Check Space logs."

    try:
        # Generate speech
        speech = synthesizer(text)
        
        # The transformers pipeline returns a dictionary: 
        # {'audio': numpy array, 'sampling_rate': int}
        audio_data = speech["audio"]
        sample_rate = speech["sampling_rate"]
        
        # Gradio expects audio in (sample_rate, numpy_1D_array) format
        # Pipeline audio is usually shape (1, N). We squeeze it to (N,)
        if len(audio_data.shape) > 1:
            audio_data = np.squeeze(audio_data)
            
        return (sample_rate, audio_data), "Success!"
        
    except Exception as e:
        return None, f"Generation Error: {str(e)}"

# 4. Build the Gradio Interface
with gr.Blocks(title="Swaram Malayalam TTS", theme=gr.themes.Soft()) as demo:
    gr.Markdown(
        """
        # 🗣️ Swaram Malayalam Text-to-Speech
        Enter Malayalam text below to generate speech using the `aoxo/swaram` model.
        """
    )
    
    with gr.Row():
        with gr.Column():
            text_input = gr.Textbox(
                label="Enter Malayalam Text",
                placeholder="മലയാളം ടൈപ്പ് ചെയ്യുക...",
                lines=5
            )
            
            with gr.Row():
                clear_btn = gr.Button("Clear")
                generate_btn = gr.Button("Generate Speech", variant="primary")
            
            gr.Examples(
                examples=[
                    ["നമസ്കാരം, ഇതെന്റെ പുതിയ ശബ്ദമാണ്."],
                    ["കേരളം ദൈവത്തിന്റെ സ്വന്തം നാടാണ്."],
                    ["കള്ളാ കടയാടി മോനെ"]
                ],
                inputs=[text_input],
                label="Examples"
            )
            
        with gr.Column():
            audio_output = gr.Audio(label="Generated Audio", type="numpy", interactive=False)
            status_output = gr.Textbox(label="Status", interactive=False)
            
    # Event Listeners
    generate_btn.click(
        fn=generate_audio,
        inputs=[text_input],
        outputs=[audio_output, status_output],
        api_name="synthesize" # Allows this Space to be used as an API later
    )
    
    clear_btn.click(
        fn=lambda: (None, None, ""),
        inputs=[],
        outputs=[text_input, audio_output, status_output]
    )

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