#!/usr/bin/env python3
"""
Fine-tune XTTS v2 for Nigerian Languages (Yoruba, Hausa, Igbo, Pidgin).

This script uses Coqui TTS to fine-tune the XTTS model for better
Nigerian language support.
"""
import os
import sys
import json
from pathlib import Path
import torch

# Check GPU
print("=" * 60)
print("XTTS Nigerian Languages Fine-tuning")
print("=" * 60)
print(f"\nPyTorch: {torch.__version__}")
print(f"CUDA available: {torch.cuda.is_available()}")
if torch.cuda.is_available():
    print(f"GPU: {torch.cuda.get_device_name(0)}")
    mem = torch.cuda.get_device_properties(0).total_memory / 1024**3
    print(f"GPU Memory: {mem:.1f} GB")
else:
    print("WARNING: No GPU found. Training will be very slow on CPU.")

BASE_DIR = Path.home() / "voice-training"
PREPARED_DIR = BASE_DIR / "prepared_data"
OPENSLR_DIR = BASE_DIR / "datasets" / "openslr_yoruba"
OUTPUT_DIR = BASE_DIR / "output"

def check_data():
    """Check available training data."""
    print("\n=== Available Training Data ===")
    
    total_files = 0
    
    # Check Nigerian CV data
    for lang in ["yoruba", "hausa", "igbo"]:
        manifest_file = PREPARED_DIR / lang / "manifest.json"
        if manifest_file.exists():
            with open(manifest_file) as f:
                data = json.load(f)
            print(f"  {lang.upper()}: {len(data)} samples")
            total_files += len(data)
    
    # Check OpenSLR Yoruba
    if OPENSLR_DIR.exists():
        wav_count = len(list(OPENSLR_DIR.glob("*.wav")))
        print(f"  OpenSLR Yoruba: {wav_count} high-quality WAV files")
        total_files += wav_count
    
    print(f"\nTotal audio files: {total_files}")
    return total_files > 0

def prepare_xtts_dataset():
    """Prepare dataset in XTTS format."""
    print("\n=== Preparing XTTS Dataset ===")
    
    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
    
    all_samples = []
    
    # Load Nigerian CV manifests
    for lang in ["yoruba", "hausa", "igbo"]:
        manifest_file = PREPARED_DIR / lang / "manifest.json"
        if manifest_file.exists():
            with open(manifest_file) as f:
                samples = json.load(f)
            for s in samples:
                s['lang_code'] = lang[:2]  # yo, ha, ig
            all_samples.extend(samples)
    
    # Load OpenSLR Yoruba
    if OPENSLR_DIR.exists():
        tsv_file = OPENSLR_DIR / "line_index.tsv"
        if tsv_file.exists():
            with open(tsv_file, 'r', encoding='utf-8') as f:
                for line in f:
                    parts = line.strip().split('\t')
                    if len(parts) >= 2:
                        wav_file = OPENSLR_DIR / f"{parts[0]}.wav"
                        if wav_file.exists():
                            all_samples.append({
                                "audio_file": str(wav_file),
                                "text": parts[1],
                                "language": "yoruba",
                                "lang_code": "yo"
                            })
    
    # Save combined dataset
    dataset_file = OUTPUT_DIR / "nigerian_tts_dataset.json"
    with open(dataset_file, 'w', encoding='utf-8') as f:
        json.dump(all_samples, f, indent=2, ensure_ascii=False)
    
    print(f"  Created dataset with {len(all_samples)} samples")
    print(f"  Saved to: {dataset_file}")
    
    return all_samples

def run_xtts_finetuning():
    """Run XTTS fine-tuning using Coqui TTS."""
    print("\n=== Starting XTTS Fine-tuning ===")
    
    try:
        from TTS.tts.configs.xtts_config import XttsConfig
        from TTS.tts.models.xtts import Xtts
        from TTS.utils.manage import ModelManager
        
        print("  TTS modules loaded successfully")
        
        # Download base XTTS model
        print("  Downloading base XTTS v2 model...")
        model_manager = ModelManager()
        
        # The model will be downloaded to ~/.local/share/tts/
        model_path = model_manager.download_model("tts_models/multilingual/multi-dataset/xtts_v2")
        print(f"  Model path: {model_path}")
        
        print("\n  To fine-tune XTTS, use the Coqui TTS training recipes:")
        print("  https://github.com/coqui-ai/TTS/tree/dev/recipes/ljspeech/xtts_v2")
        print("\n  Or use the XTTS fine-tuning demo:")
        print("  python -m TTS.demos.xtts_ft_demo")
        
        return True
        
    except Exception as e:
        print(f"  Error: {e}")
        return False

def main():
    if not check_data():
        print("ERROR: No training data found!")
        sys.exit(1)
    
    samples = prepare_xtts_dataset()
    
    if samples:
        print("\n" + "=" * 60)
        print("Dataset prepared! Next steps:")
        print("=" * 60)
        print(f"1. Dataset: {OUTPUT_DIR / 'nigerian_tts_dataset.json'}")
        print(f"2. Total samples: {len(samples)}")
        print("\nTo start training:")
        print("  python -m TTS.demos.xtts_ft_demo")
        print("\nOr for voice cloning (no training needed):")
        print("  from TTS.api import TTS")
        print("  tts = TTS('tts_models/multilingual/multi-dataset/xtts_v2')")
        print("  tts.tts_to_file('Hello', speaker_wav='your_voice.wav', language='en')")
    
    run_xtts_finetuning()

if __name__ == "__main__":
    main()