src/train.py.save

# 📁 Estructura del proyecto generado para 'voice'

# 📦 voice/
# ├── README.md
# ├── requirements.txt
# ├── src/
# │   ├── model.py
# │   ├── train.py
# │   └── inference.py
# ├── data/
# │   └── samples/
# └── app_gradio.py

# ========================================
# ✅ README.md (inicial)

readme = '''
# 🐽 Kuchiuya Voice Synthesizer

Este proyecto permite entrenar e inferir voces sintéticas al estilo Kuchiuya.

## Requisitos
```bash
pip install -r requirements.txt
```

## Entrenamiento
```bash
python src/train.py
```

## Inferencia
```bash
python src/inference.py --text "El barro no miente."
```

## Demo Web
```bash
python app_gradio.py
```
'''

# ========================================
# ✅ requirements.txt

requirements = '''
torch
torchaudio
numpy
scipy
librosa
soundfile
gradio
pyttsx3
'''

# ========================================
# ✅ model.py

model_py = '''
import torch.nn as nn

class SimpleTTSModel(nn.Module):
    def __init__(self, input_dim=256, hidden_dim=512, output_dim=80):
        super(SimpleTTSModel, self).__init__()
        self.model = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, output_dim)
        )

    def forward(self, x):
        return self.model(x)
'''

# ========================================
# ✅ train.py

train_py = '''
import torch
import torch.nn as nn
import torch.optim as optim
from src.model import SimpleTTSModel
import numpy as np

print("🐷 Entrenando modelo Kuchiuya...")

# Datos simulados (para demostrar el flujo)
x = torch.randn((100, 256))
y = torch.randn((100, 80))

model = SimpleTTSModel()
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

for epoch in range(10):
    optimizer.zero_grad()
    outputs = model(x)
    loss = criterion(outputs, y)
    loss.backward()
    optimizer.step()
    print(f"Epoch {epoch+1}/10, Pérdida: {loss.item():.4f}")

# Guardar modelo
torch.save(model.state_dict(), "tts_model.pth")
print("✅ Modelo guardado como 'tts_model.pth'")
'''

# ========================================
# ✅ inference.py

inference_py = '''
import argparse
import pyttsx3

parser = argparse.ArgumentParser()
parser.add_argument('--text', type=str, required=True, help='Texto a sintetizar')
args = parser.parse_args()

engine = pyttsx3.init()
engine.setProperty('rate', 150)
engine.save_to_file(args.text, 'output.wav')
engine.runAndWait()

print(f"🔊 Texto sintetizado: '{args.text}' → guardado como 'output.wav'")
'''

# ========================================
# ✅ app_gradio.py

app_gradio_py = '''
import gradio as gr
import pyttsx3
import os

def sintetizar(texto):
    engine = pyttsx3.init()
    engine.setProperty('rate', 150)
    output_path = "voz_kuchiuya.wav"
    engine.save_to_file(texto, output_path)
    engine.runAndWait()
    return output_path

demo = gr.Interface(
    fn=sintetizar,
    inputs=gr.Textbox(lines=2, placeholder="Escribe tu frase Kuchiuya aquí..."),
    outputs=gr.Audio(type="filepath"),
    title="Voz Kuchiuya",
    description="Convierte texto en una voz ritual cyberpunk del universo Ariplanteater."
)
demo.launch()
'''