app.py

import torch
from transformers import AutoModel, AutoTokenizer
import gradio as gr
import soundfile as sf
import numpy as np
import tempfile

# Load model and tokenizer
device = "cpu"  # Change to "cuda" if you have GPU
model = AutoModel.from_pretrained("ai4bharat/vits_rasa_13", trust_remote_code=True).to(device)
tokenizer = AutoTokenizer.from_pretrained("ai4bharat/vits_rasa_13", trust_remote_code=True)

# Speaker IDs for languages
LANG_SPEAKER_MAP = {
    "mar": 13,  # Marathi Male
    "hin": 13,  # Reuse Marathi Male for Hindi
    "san": 17   # Sanskrit Male
    "tam": 18
}

DEFAULT_STYLE_ID = 0  # ALEXA

def generate_audio(text, language):
    if not text.strip():
        return "Error: Text cannot be empty."

    speaker_id = LANG_SPEAKER_MAP.get(language.lower())
    if speaker_id is None:
        return f"Unsupported language: {language}"

    inputs = tokenizer(text=text, return_tensors="pt").to(device)
    with torch.no_grad():
        outputs = model(inputs['input_ids'], speaker_id=speaker_id, emotion_id=DEFAULT_STYLE_ID)
    waveform = outputs.waveform.squeeze().cpu().numpy()
    sample_rate = model.config.sampling_rate

    # Save temp audio
    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
        sf.write(f.name, waveform, sample_rate)
        return sample_rate, waveform

# Gradio Interface with clean inputs
iface = gr.Interface(
    fn=generate_audio,
    inputs=[
        gr.Textbox(label="Enter Text"),
        gr.Dropdown(["mar", "hin", "san"], label="Select Language")
    ],
    outputs=gr.Audio(label="Generated Audio"),
    title="VITS TTS for Indian Languages (Marathi, Hindi, Sanskrit)",
    description="Uses ai4bharat/vits_rasa_13. Enter text and select a language."
)

iface.launch()