import math
import os
import gc
import re
import sys
import time
import glob
import json
import site
import datetime
import torch
import signal
import random
import shutil
import psutil
import string
import tempfile
import platform
import argparse
import torchaudio
import torch
import traceback
import numpy as np
import gradio as gr
import pandas as pd
from word2number import w2n
from tqdm import tqdm
from pynvml import nvmlInit, nvmlShutdown, nvmlDeviceGetHandleByIndex, nvmlDeviceGetMemoryInfo
from pathlib import Path
from trainer import TrainerArgs, Trainer
from trainer_alltalk.trainer import Trainer
from TTS.config.shared_configs import BaseDatasetConfig
from TTS.tts.datasets import load_tts_samples
from TTS.tts.layers.xtts.trainer.gpt_trainer import GPTArgs, GPTTrainer, GPTTrainerConfig, XttsAudioConfig
from TTS.tts.configs.xtts_config import XttsConfig
from TTS.tts.models.xtts import Xtts
from metrics_logger import MetricsLogger
# Use a local Tokenizer to resolve Japanese support
# from TTS.tts.layers.xtts.tokenizer import multilingual_cleaners
from system.ft_tokenizer.tokenizer import multilingual_cleaners
import importlib.metadata as metadata
from packaging import version
from tokenizers import ByteLevelBPETokenizer
from tokenizers.pre_tokenizers import Whitespace
########################################################################################
# START-UP # Silence RVC warning about torch.nn.utils.weight_norm even though not used #
########################################################################################
import warnings
# Filter Flash Attention warning
warnings.filterwarnings("ignore", message="1Torch was not compiled with flash attention")
warnings.filterwarnings("ignore", message="Failed to launch Triton kernels, likely due to missing CUDA toolkit")
os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"

# Try to import whisper, install if not found
try:
    import whisper
except ImportError:
    print("[FINETUNE] OpenAI Whisper not found. Attempting to install...")
    try:
        subprocess.check_call([sys.executable, "-m", "pip", "install", "openai-whisper"])
        import whisper
        print("[FINETUNE] Successfully installed OpenAI Whisper!. Continuing.")
    except Exception as e:
        print("[FINETUNE] Failed to install OpenAI Whisper:")
        print(f"[FINETUNE] Error: {str(e)}")
        print("[FINETUNE] Please try manually installing with: pip install openai-whisper")
        sys.exit(1)

# STARTUP VARIABLES 
this_dir = Path(__file__).parent.resolve()
audio_folder = this_dir / "finetune" / "put-voice-samples-in-here"
default_path = this_dir / "finetune" / "tmp-trn"
out_path = default_path

progress = 0
theme = gr.themes.Default()
refresh_symbol = '🔄'
os.environ['TRAINER_TELEMETRY'] = '0'
pfc_status = "pass"
base_path = this_dir / "models" / "xtts"

# Set the Gradio temporary directory
gradio_temp_dir = this_dir / "finetune" / "gradio_temp"
os.environ["GRADIO_TEMP_DIR"] = str(gradio_temp_dir)

# Set validation globals to nothing
validate_train_metadata_path = None
validate_eval_metadata_path = None
validate_audio_folder = None
validate_whisper_model = None
validate_target_language = None

##############################
#### Debugging management ####
##############################
class DebugLevels:
    GPU_MEMORY = False   # GPU memory and CUDA related debugging
    MODEL_OPS = True     # Model loading, transcription, cleanup operations
    DATA_PROCESS = False # Data processing, words, sentences
    GENERAL = True       # General flow, file operations, metadata
    AUDIO = False        # Audio processing statistics and info
    SEGMENTS = False     # Detailed segment information
    DUPLICATES = False   # Duplicate handling information

def debug_print(message, level, is_error=False, is_warning=False):
    """Enhanced debug printing with categorization and formatting"""
    prefix = "[FINETUNE]"
    if is_error:
        prefix += " ERROR:"  # Was red colored emoji
    elif is_warning:
        prefix += " WARNING:"  # Was yellow colored emoji
    
    if level == "GPU_MEMORY" and DebugLevels.GPU_MEMORY:
        print(f"{prefix} [GPU] {message}")
    elif level == "MODEL_OPS" and DebugLevels.MODEL_OPS:
        print(f"{prefix} [MODEL] {message}")
    elif level == "DATA_PROCESS" and DebugLevels.DATA_PROCESS:
        print(f"{prefix} [DATA] {message}")
    elif level == "GENERAL" and DebugLevels.GENERAL:
        print(f"{prefix} [INFO] {message}")
    elif level == "AUDIO" and DebugLevels.AUDIO:
        print(f"{prefix} [AUDIO] {message}")
    elif level == "SEGMENTS" and DebugLevels.SEGMENTS:
        print(f"{prefix} [SEG] {message}")
    elif level == "DUPLICATES" and DebugLevels.DUPLICATES:
        print(f"{prefix} [DUP] {message}")

class AudioStats:
    """Track audio processing statistics"""
    def __init__(self):
        self.total_segments = 0
        self.segments_under_min = 0
        self.segments_over_max = 0
        self.total_duration = 0
        self.segment_durations = []
        
    def add_segment(self, duration):
        self.total_segments += 1
        self.total_duration += duration
        self.segment_durations.append(duration)
        
    def print_stats(self):
        if not self.segment_durations:
            return
            
        avg_duration = self.total_duration / self.total_segments
        debug_print(f"Audio Processing Statistics:", "AUDIO")
        debug_print(f"Total segments: {self.total_segments}", "AUDIO")
        debug_print(f"Average duration: {avg_duration:.2f}s", "AUDIO")
        debug_print(f"Segments under minimum: {self.segments_under_min}", "AUDIO")
        debug_print(f"Segments over maximum: {self.segments_over_max}", "AUDIO")
        
        if self.segments_under_min > 0:
            debug_print(f"{self.segments_under_min} segments are under minimum duration!", "AUDIO", is_warning=True)

def get_gpu_memory():
    """Enhanced GPU memory reporting"""
    if DebugLevels.GPU_MEMORY:
        try:
            nvmlInit()
            handle = nvmlDeviceGetHandleByIndex(0)
            info = nvmlDeviceGetMemoryInfo(handle)
            debug_print("GPU Memory Status:", "GPU_MEMORY")
            debug_print(f"Total: {info.total / 1024**2:.2f} MB", "GPU_MEMORY")
            debug_print(f"Used:  {info.used / 1024**2:.2f} MB", "GPU_MEMORY")
            debug_print(f"Free:  {info.free / 1024**2:.2f} MB", "GPU_MEMORY")
            
            # Add warning if memory is low
            if info.free / info.total < 0.1:  # Less than 10% free
                debug_print("Low GPU memory available!", "GPU_MEMORY", is_warning=True)
        except Exception as e:
            debug_print(f"NVML not available: {e}", "GPU_MEMORY", is_error=True)


########################
#### Find All Model ####
########################
base_model_detected = False
def scan_models_folder():
    global base_model_detected
    models_folder = base_path
    available_models = {}
    required_files = ["config.json", "model.pth", "mel_stats.pth", "speakers_xtts.pth", "vocab.json", "dvae.pth"]
    try:
        for subfolder in models_folder.iterdir():
            if subfolder.is_dir():
                model_name = subfolder.name
                if all(subfolder.joinpath(file).exists() for file in required_files):
                    available_models[model_name] = True
                    base_model_detected = True
                else:
                    print(f"[FINETUNE] \033[91mWarning\033[0m: Model folder '{model_name}' is missing required files")
        if not available_models:
            available_models["No Model Available"] = False
            base_model_detected = False
    except FileNotFoundError:
        print("\n[FINETUNE] \033[91mError\033[0m: No XTTS models folder found. You have not yet downloaded any models or no XTTS")
        print("[FINETUNE] \033[91mError\033[0m: models can be found. Please run AllTalk and download an XTTS model that can be")
        print("[FINETUNE] \033[91mError\033[0m: used for training. Or place a full model in the following location")
        print("[FINETUNE] \033[91mError\033[0m: \033[93m\\models\\xtts\\{modelfolderhere}\033[0m")
        sys.exit(1)  # Exit the script with an error status

    return available_models

# Get available models
available_models = scan_models_folder()

#################################################
#### Pre-Flight Checklist Functions & Gradio ####
#################################################

class SystemChecks:
    """Centralized system check management"""
    def __init__(self):
        self.status = {
            "overall": True,
            "disk_space": True,
            "ram": True,
            "gpu": True,
            "cuda": True,
            "pytorch": True,
            "tts": True,
            "base_model": True
        }
        self.results = {}
    
    def check_disk_space(self, required_gb=18):
        """Check available disk space"""
        try:
            disk_usage = shutil.disk_usage(os.getcwd())
            free_space_gb = disk_usage.free / (1 << 30)
            
            status = free_space_gb > required_gb
            self.status["disk_space"] = status
            
            self.results["disk_space"] = {
                "status": "✅ Pass" if status else "❌ Fail",
                "details": f"{free_space_gb:.2f} GB available",
                "icon": "✅" if status else "❌",
                "message": ("Sufficient disk space available" if status 
                          else f"Insufficient disk space. Need {required_gb}GB, have {free_space_gb:.2f}GB")
            }
        except Exception as e:
            self.handle_check_error("disk_space", str(e))
    
    def check_system_ram(self):
        """Check system RAM and GPU memory with refined thresholds"""
        try:
            vm = psutil.virtual_memory()
            total_ram_gb = vm.total / (1024 ** 3)
            available_ram_gb = vm.available / (1024 ** 3)
            
            # RAM check
            ram_status = available_ram_gb >= 8
            self.status["ram"] = ram_status
            
            # GPU memory check
            if torch.cuda.is_available():
                gpu_id = torch.cuda.current_device()
                gpu_name = torch.cuda.get_device_name(gpu_id)
                total_gpu_mem = torch.cuda.get_device_properties(gpu_id).total_memory / (1024 ** 3)
                used_gpu_mem = torch.cuda.memory_reserved(gpu_id) / (1024 ** 3)
                available_gpu_mem = total_gpu_mem - used_gpu_mem
                
                # New GPU status logic with better thresholds
                if total_gpu_mem >= 13:
                    gpu_status = "pass"  # More than 13GB - Clear pass
                elif total_gpu_mem >= 11.5:
                    gpu_status = "warning"  # Between 11.5GB and 13GB - Warning
                else:
                    gpu_status = "fail"  # Less than 11.5GB - Fail
                
                self.status["gpu"] = gpu_status in ["pass", "warning"]  # Both pass and warning are considered "okay"
            else:
                gpu_name = "No GPU detected"
                total_gpu_mem = 0
                available_gpu_mem = 0
                gpu_status = "fail"
                self.status["gpu"] = False
            
            self.results["memory"] = {
                "ram_status": "✅ Pass" if ram_status else "❌ Fail",
                "ram_details": f"{available_ram_gb:.2f}GB available of {total_ram_gb:.2f}GB",
                "gpu_status": {
                    "pass": "✅ Pass",
                    "warning": "⚠️ Warning",
                    "fail": "❌ Fail"
                }[gpu_status],
                "gpu_details": f"{gpu_name}: {available_gpu_mem:.2f}GB available of {total_gpu_mem:.2f}GB"
            }
        except Exception as e:
            self.handle_check_error("memory", str(e))
    
    def check_cuda_pytorch(self):
        """Check CUDA and PyTorch setup"""
        try:
            cuda_available = torch.cuda.is_available()
            pytorch_version = torch.__version__
            
            if cuda_available:
                cuda_version = torch.version.cuda
                
                # Check if CUDA is actually working
                try:
                    torch.tensor([1.0, 2.0]).cuda()
                    cuda_working = True
                except:
                    cuda_working = False
                
                pytorch_cuda_status = cuda_version in ['11.8', '12.1']
            else:
                cuda_working = False
                cuda_version = "N/A"
                pytorch_cuda_status = False
            
            self.status["cuda"] = cuda_working
            self.status["pytorch"] = pytorch_cuda_status
            
            # Separate results for CUDA and PyTorch
            self.results["cuda"] = {
                "status": "✅ Pass" if cuda_working else "❌ Fail",
                "details": f"CUDA {cuda_version}" if cuda_working else "CUDA not working"
            }
            
            self.results["pytorch"] = {
                "status": "✅ Pass" if pytorch_cuda_status else "❌ Fail",
                "details": f"PyTorch {pytorch_version} with CUDA {cuda_version}",
            }
        except Exception as e:
            self.handle_check_error("cuda_pytorch", str(e))
    
    def check_tts_version(self, required_version="0.24.0"):
        """Check TTS version"""
        try:
            installed_version = metadata.version("coqui-tts")
            status = version.parse(installed_version) >= version.parse(required_version)
            
            self.status["tts"] = status
            self.results["tts"] = {
                "status": "✅ Pass" if status else "❌ Fail",
                "details": f"TTS version {installed_version} installed",
                "meets_requirement": status
            }
        except Exception as e:
            self.handle_check_error("tts", str(e))
    
    def check_base_model(self):
        """Check XTTS base model"""
        try:
            base_model_detected = any(available_models.values()) if available_models else False
            
            self.status["base_model"] = base_model_detected
            self.results["base_model"] = {
                "status": "✅ Pass" if base_model_detected else "❌ Fail",
                "details": "Base model detected" if base_model_detected else "No base model found"
            }
        except Exception as e:
            self.handle_check_error("base_model", str(e))
    
    def handle_check_error(self, check_name: str, error_msg: str):
        """Handle errors in checks"""
        self.status[check_name] = False
        self.status["overall"] = False
        self.results[check_name] = {
            "status": "❌ Error",
            "details": f"Check failed: {error_msg}"
        }
    
    def run_all_checks(self):
        """Run all system checks"""
        self.check_disk_space()
        self.check_system_ram()
        self.check_cuda_pytorch()
        self.check_tts_version()
        self.check_base_model()
        
        # Update overall status
        self.status["overall"] = all(self.status.values())
        return self.status["overall"]
    
    def get_markdown_report(self):
        """Generate markdown report of all checks"""
        report = []
        
        # Overall Status
        overall_icon = "✅" if self.status["overall"] else "❌"
        report.append(f"## System Check Results {overall_icon}\n")
        
        # Individual Checks
        if "disk_space" in self.results:
            report.append(f"### Storage\n{self.results['disk_space']['status']} {self.results['disk_space']['details']}\n")
            
        if "memory" in self.results:
            report.append("### Memory")
            report.append(f"- RAM: {self.results['memory']['ram_status']} {self.results['memory']['ram_details']}")
            report.append(f"- GPU: {self.results['memory']['gpu_status']} {self.results['memory']['gpu_details']}\n")
            
        if "cuda_pytorch" in self.results:
            report.append(f"### CUDA & PyTorch\n{self.results['cuda_pytorch']['status']} {self.results['cuda_pytorch']['details']}\n")
            
        if "tts" in self.results:
            report.append(f"### TTS\n{self.results['tts']['status']} {self.results['tts']['details']}\n")
            
        if "base_model" in self.results:
            report.append(f"### Base Model\n{self.results['base_model']['status']} {self.results['base_model']['details']}\n")
        
        return "\n".join(report)

    def get_ui_updates(self, components):
        """Map check results to UI components"""
        # Get status for all checks
        status_updates = []
        
        # Overall status first
        overall_status = "✅ All Systems Go!" if self.status["overall"] else "⚠️ Some Checks Failed"
        status_updates.append(overall_status)
        
        # Add status for each check in the order they appear in CHECK_REQUIREMENTS
        for check_id in CHECK_REQUIREMENTS.keys():
            if check_id in self.results:
                status_updates.append(self.results[check_id]["status"])
            else:
                status_updates.append("❌ Check not run")
        
        return status_updates


def pfc_check_fail():
    global pfc_status
    if pfc_status == "fail":
        print("[FINETUNE]")
        print("[FINETUNE] \033[91m****** WARNING PRE-FLIGHT CHECKS FAILED ******* WARNING PRE-FLIGHT CHECKS FAILED *****\033[0m")
        print("[FINETUNE] \033[91m* Please refer to the \033[93mPre-flight check tab \033[91mand resolve any issues before continuing. *\033[0m")
        print("[FINETUNE] \033[91m*********** Expect errors and failures if you do not resolve these issues. ***********\033[0m")
        print("[FINETUNE]")
    return

# PFC Help Content and Requirements
HELP_CONTENT = {
    "disk_space_details": """
### Disk Space Requirements

#### Minimum Requirements
- **18GB** free disk space for training
- SSD recommended for better performance

#### Space Usage Breakdown
- Model files: ~4GB
- Training data: ~5-10GB
- Temporary files: ~4GB
- Safety margin: ~2GB

#### Troubleshooting
1. **Insufficient Space**
   - Clear space on the current partition
   - Run fine-tuning on another partition with more space
   - Keep at least 20% free space on drive

2. **Performance Issues**
   - Mechanical HDDs will be significantly slower
   - Consider using an SSD for training
""",
    
    "gpu_requirements": """
### GPU Memory Requirements

#### System Requirements
- **NVIDIA GPU Required**
- **12GB+ VRAM** recommended for optimal performance
- **8GB+ VRAM** minimum requirement

#### Platform Differences
- **Windows Systems:**
  * Can use system RAM as extended VRAM
  * 12GB VRAM or less requires 24GB+ System RAM and may fail.
  * 12GB VRAM cards will have a warning because of the above.
  * Performance may vary when using extended VRAM

- **Linux Systems:**
  * Cannot use extended VRAM
  * Limited to physical GPU VRAM only
  * Minimum 12GB VRAM recommended
""",

    "pytorch_cuda": """
### PyTorch and CUDA

- CUDA-enabled PyTorch installation required
- Compatible with CUDA 11.8/12.1
- Current PyTorch version recommended
""",

    "model_setup": """
### Base Model Requirements

#### Model Installation
- Download XTTS v2.0.3 model (recommended)
- BPE Tokenize needs a v2.0.3 model
- Use AllTalk's main interface:
  * TTS Engine Settings > XTTS > Model/Voices Download

#### Model Location
- Place in `/models/xtts/{modelname}/`
- Required files:
  * model.pth
  * config.json
  * vocab.json
  * dvae.pth
  * mel_stats.pth
  * speakers_xtts.pth
"""
}

CHECK_REQUIREMENTS = {
    "disk_space": {
        "label": "Disk Space",
        "requirement": "18GB+ Required",
        "description": "Required for temporary files and model storage",
        "priority": "high",
        "check_function": "check_disk_space"
    },
    "ram": {
        "label": "System RAM",
        "requirement": "16GB+ Recommended",
        "description": "Affects overall processing speed and stability",
        "priority": "medium",
        "check_function": "check_system_ram"
    },
    "vram": {
        "label": "GPU VRAM",
        "requirement": "12GB+ Recommended",
        "description": "Crucial for model training performance",
        "priority": "high",
        "check_function": "check_gpu_memory"
    },
    "cuda": {
        "label": "CUDA Support",
        "requirement": "CUDA 11.8/12.1",
        "description": "Required for GPU acceleration",
        "priority": "high",
        "check_function": "check_cuda_pytorch"
    },
    "pytorch": {
        "label": "PyTorch",
        "requirement": "CUDA enabled",
        "description": "Must be CUDA compatible version",
        "priority": "high",
        "check_function": "check_cuda_pytorch"
    },
    "tts": {
        "label": "TTS Version",
        "requirement": "0.24.0+",
        "description": "Required for fine-tuning",
        "priority": "high",
        "check_function": "check_tts_version"
    },
    "base_model": {
        "label": "Base Model",
        "requirement": "Must be present",
        "description": "XTTS base model required",
        "priority": "high",
        "check_function": "check_base_model"
    }
}

# Display categories for organizing checks
CHECK_CATEGORIES = {
    "hardware": {
        "title": "💻 Hardware Requirements",
        "description": "System hardware capabilities",
        "checks": ["disk_space", "ram", "vram"]
    },
    "cuda": {
        "title": "🔧 CUDA Setup",
        "description": "CUDA and PyTorch configuration",
        "checks": ["cuda", "pytorch"]
    },
    "software": {
        "title": "🤖 Software Setup",
        "description": "Required software and models",
        "checks": ["tts", "base_model"]
    }
}

class PFCComponents:
    """Store all PFC UI components"""
    def __init__(self):
        self.overall_status = None
        self.status_boxes = {}
        self.accordions = {}
        
    def create_status_box(self, check_id):
        """Create a status box for a specific check"""
        check_info = CHECK_REQUIREMENTS[check_id]
        with gr.Group():
            status = gr.Label(
                label=check_info["label"],
                value="Ready to Check",
                elem_classes="status-indicator"
            )
            gr.Markdown(f"*Required: {check_info['requirement']}*")
            self.status_boxes[check_id] = status
            return status

def create_pfc_interface():
    components = PFCComponents()
    system_checks = SystemChecks()
    
    with gr.Column():
        # Status and button at top
        with gr.Row():
            components.overall_status = gr.Label(
                value="Click 'Run System Checks' to begin",
                label="System Status",
                elem_classes="status-label", 
                scale=2
            )
        with gr.Row():                
            refresh_btn = gr.Button("Run System Checks", scale=1)    
        
        # Checks in 3x2 grid
        with gr.Row():
            for check_id in CHECK_REQUIREMENTS.keys():
                with gr.Column(scale=1):
                    components.create_status_box(check_id)
        
        # Help sections in 2x2 grid
        with gr.Group("💡 Help & Troubleshooting"):
            with gr.Row():
                with gr.Column():
                    with gr.Accordion("💻 Hardware Requirements", open=False):
                        gr.Markdown(HELP_CONTENT["gpu_requirements"])
                
                with gr.Column():
                    with gr.Accordion("🖥️ Storage Requirements", open=False):
                        gr.Markdown(HELP_CONTENT["disk_space_details"])
            
            with gr.Row():
                with gr.Column():
                    with gr.Accordion("🤖 PyTorch & CUDA", open=False):
                        gr.Markdown(HELP_CONTENT["pytorch_cuda"])
                
                with gr.Column():
                    with gr.Accordion("📦 Model Setup", open=False):
                        gr.Markdown(HELP_CONTENT["model_setup"])
                        
            def run_checks():
                system_checks.run_all_checks()
                
                # Prepare updates for all components
                updates = []
                
                # Overall status
                updates.append("✅ All Systems Go!" if system_checks.status["overall"] else "⚠️ Some Checks Failed")
                
                # Add updates for each category's checks
                for category in CHECK_CATEGORIES.values():
                    for check_id in category["checks"]:
                        if check_id == "ram":
                            updates.append(system_checks.results["memory"]["ram_status"])
                        elif check_id == "vram":
                            updates.append(system_checks.results["memory"]["gpu_status"])
                        elif check_id == "cuda":
                            updates.append(system_checks.results["cuda"]["status"])
                        elif check_id == "pytorch":
                            updates.append(system_checks.results["pytorch"]["status"])
                        else:
                            updates.append(system_checks.results.get(check_id, {}).get("status", "❌ Check not run"))
                
                return updates
            
            refresh_btn.click(
                fn=run_checks,
                outputs=[components.overall_status] + list(components.status_boxes.values())
            )
    
    return components


###########################################
#### STEP 1 Dataset Creation Functions ####
###########################################

def format_audio_list(target_language, whisper_model, max_sample_length, eval_split_number, speaker_name_input, create_bpe_tokenizer, gradio_progress=gr.Progress(), use_vad=True, precision="mixed", min_sample_length=6):
    """
    Enhanced audio processing function with VAD and precision options
    Args:
        precision: One of "mixed" (default), "float16", or "float32"
        use_vad: Whether to use SileroVAD for better speech detection
        min_sample_length: Minimum audio segment length in seconds (default 6s)
    """
    global validate_train_metadata_path, validate_eval_metadata_path, validate_audio_folder
    global validate_whisper_model, validate_target_language, out_path, torch, whisper

    # Clear down the finetune.log file
    Logger().clear_log()
    
    # Initialize statistics tracker
    stats = AudioStats()
    
    # Basic setup
    buffer = 0.3
    max_duration = float(max_sample_length)
    min_duration = float(min_sample_length)
    eval_percentage = eval_split_number / 100.0
    speaker_name = speaker_name_input
    audio_total_size = 0
    metadata = {"audio_file": [], "text": [], "speaker_name": []}
    too_long_files = []

    # Initialize directories
    if speaker_name and speaker_name != 'personsname':
        out_path = this_dir / "finetune" / speaker_name
    else:
        out_path = default_path

    debug_print(f"Initializing output directory: {out_path}", "GENERAL")
    
    os.makedirs(out_path, exist_ok=True)
    temp_folder = os.path.join(out_path, "temp")
    os.makedirs(temp_folder, exist_ok=True)
    audio_folder = os.path.join(out_path, "wavs")
    os.makedirs(audio_folder, exist_ok=True)
    original_samples_folder = os.path.join(out_path, "..", "put-voice-samples-in-here")

    # Handle language file
    lang_file_path = os.path.join(out_path, "lang.txt")
    current_language = None
    if os.path.exists(lang_file_path):
        with open(lang_file_path, 'r', encoding='utf-8') as existing_lang_file:
            current_language = existing_lang_file.read().strip()

    if current_language != target_language:
        with open(lang_file_path, 'w', encoding='utf-8') as lang_file:
            lang_file.write(target_language + '\n')
        debug_print(f"Updated language to: {target_language}", "GENERAL")
    else:
        debug_print("Using existing language setting", "GENERAL")

    # Load Whisper model with specified precision
    gradio_progress((1, 10), desc="Loading Whisper Model")
    device = "cuda" if torch.cuda.is_available() else "cpu"
    debug_print(f"Using device: {device}", "MODEL_OPS")
    
    # Monitor GPU memory before model loading
    get_gpu_memory()
    
    debug_print(f"Loading Whisper model: {whisper_model}", "MODEL_OPS")
    asr_model = whisper.load_model(whisper_model, device=device)
    
    if precision == "float16" and device == "cuda":
        debug_print("Using FP16 precision", "MODEL_OPS")
        asr_model = asr_model.half()
    elif precision == "mixed" and device == "cuda":
        debug_print("Using mixed precision", "MODEL_OPS")
    else:
        debug_print("Using FP32 precision", "MODEL_OPS")

    # Initialize SileroVAD if requested
    vad_model = None
    if use_vad:
        gradio_progress((2, 10), desc="Loading VAD Model")
        debug_print("Initializing Silero VAD", "MODEL_OPS")
        model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',
                                    model='silero_vad',
                                    force_reload=False)
        vad_model = model.to(device)
        get_speech_timestamps, collect_chunks = utils[0], utils[4]

    # Monitor GPU memory after model loading
    get_gpu_memory()

    # Load existing metadata
    gradio_progress((3, 10), desc="Checking for Existing Metadata")
    train_metadata_path = os.path.join(out_path, "metadata_train.csv")
    eval_metadata_path = os.path.join(out_path, "metadata_eval.csv")
    existing_metadata = {'train': None, 'eval': None}

    if os.path.exists(train_metadata_path):
        existing_metadata['train'] = pd.read_csv(train_metadata_path, sep="|")
        debug_print("Loaded existing training metadata", "DATA_PROCESS")

    if os.path.exists(eval_metadata_path):
        existing_metadata['eval'] = pd.read_csv(eval_metadata_path, sep="|")
        debug_print("Loaded existing evaluation metadata", "DATA_PROCESS")

    # Get audio files list
    original_audio_files = [os.path.join(original_samples_folder, file) 
                          for file in os.listdir(original_samples_folder) 
                          if file.endswith(('.mp3', '.flac', '.wav'))]

    gradio_progress((4, 10), desc="Scanning for Audio Files")
    if not original_audio_files:
        debug_print(f"No audio files found in {original_samples_folder}", "AUDIO", is_error=True)
        return None, None, 0

    debug_print(f"Found {len(original_audio_files)} audio files to process", "AUDIO")
    # Initialize processing
    whisper_words = []
    audio_steps = (0, len(original_audio_files))
    gradio_progress_duration = 0
    gradio_progress(audio_steps, desc="Processing Audio Files", unit="files")

    for audio_path in original_audio_files:
        start = datetime.datetime.now()
        audio_file_name_without_ext, _ = os.path.splitext(os.path.basename(audio_path))
        temp_audio_path = os.path.join(temp_folder, f"{audio_file_name_without_ext}.wav")

        try:
            shutil.copy2(audio_path, temp_audio_path)
            gradio_progress(audio_steps, desc=f"Processing {audio_file_name_without_ext}", unit="files")
            debug_print(f"Processing: {audio_file_name_without_ext}", "GENERAL")
        except Exception as e:
            debug_print(f"Error copying file {audio_path}: {str(e)}", "GENERAL", is_error=True)
            continue

        # Check if already processed
        prefix_check = f"wavs/{audio_file_name_without_ext}_"
        skip_processing = False
        for key in ['train', 'eval']:
            if existing_metadata[key] is not None:
                mask = existing_metadata[key]['audio_file'].str.startswith(prefix_check)
                if mask.any():
                    debug_print(f"Skipping previously processed file: {audio_file_name_without_ext}", "GENERAL")
                    skip_processing = True
                    audio_total_size = 121
                    break

        if skip_processing:
            continue

        # Load and process audio
        wav, sr = torchaudio.load(audio_path)
        if wav.size(0) != 1:
            wav = torch.mean(wav, dim=0, keepdim=True)
        wav = wav.squeeze()
        audio_total_size += (wav.size(-1) / sr)
        
        original_duration = wav.size(-1) / sr
        debug_print(f"Original audio duration: {original_duration:.2f}s", "AUDIO")

        # Process with VAD if enabled
        if use_vad and vad_model is not None:
            debug_print("Processing with VAD", "AUDIO")
            # Get VAD segments with resampling
            vad_segments = process_audio_with_vad(wav, sr, vad_model, get_speech_timestamps)
            
            # Group short segments that are close together
            merged_segments = merge_short_segments(vad_segments, min_duration, max_gap=0.3)
            debug_print(f"Merged {len(vad_segments)-len(merged_segments)} short segments", "SEGMENTS")
            
            # Convert VAD segments to audio chunks
            speech_chunks = []
            for segment in merged_segments:
                chunk = wav[segment['start']:segment['end']]
                duration = chunk.size(-1) / sr
                if duration < min_duration:
                    debug_print(f"Segment too short ({duration:.2f}s), attempting to extend", "SEGMENTS", is_warning=True)
                    # Try to extend segment if possible
                    chunk = extend_segment(wav, segment['start'], segment['end'], sr, min_duration)
                    duration = chunk.size(-1) / sr
                
                if chunk.numel() > 0:
                    speech_chunks.append((chunk, duration))
                    stats.add_segment(duration)

            # Process each speech chunk
            for chunk_idx, (chunk, duration) in enumerate(speech_chunks):
                if duration < min_duration:
                    stats.segments_under_min += 1
                    debug_print(f"Short segment: {duration:.2f}s", "SEGMENTS", is_warning=True)
                elif duration > max_duration:
                    stats.segments_over_max += 1
                    debug_print(f"Long segment: {duration:.2f}s", "SEGMENTS", is_warning=True)
                
                chunk_path = os.path.join(temp_folder, f"{audio_file_name_without_ext}_chunk_{chunk_idx}.wav")
                torchaudio.save(chunk_path, chunk.unsqueeze(0), sr)
                
                # Transcribe with appropriate precision
                if precision == "mixed" and device == "cuda":
                    with torch.cuda.amp.autocast():
                        gradio_progress((5, 10), desc="Transcribing Audio")
                        result = asr_model.transcribe(
                            chunk_path,
                            language=target_language,
                            word_timestamps=True,
                            verbose=None
                        )
                else:
                    gradio_progress((5, 10), desc="Transcribing Audio")
                    result = asr_model.transcribe(
                        chunk_path,
                        language=target_language,
                        word_timestamps=True,
                        verbose=None
                    )
                
                if not result.get("text", "").strip():
                    debug_print(f"Empty transcription for chunk {chunk_idx}", "DATA_PROCESS", is_warning=True)
                    continue

                # Process transcription result
                process_transcription_result(
                    result, chunk, sr, chunk_idx, audio_file_name_without_ext,
                    metadata, whisper_words, max_duration, buffer, speaker_name,
                    audio_folder, too_long_files, create_bpe_tokenizer, target_language
                )
                
                os.remove(chunk_path)
                debug_print(f"Processed chunk {chunk_idx} ({duration:.2f}s)", "SEGMENTS")

        else:
            # Regular processing without VAD
            debug_print("Processing without VAD", "AUDIO")
            if precision == "mixed" and device == "cuda":
                with torch.cuda.amp.autocast():
                    result = asr_model.transcribe(
                        audio_path,
                        language=target_language,
                        word_timestamps=True,
                        verbose=None
                    )
            else:
                result = asr_model.transcribe(
                    audio_path,
                    language=target_language,
                    word_timestamps=True,
                    verbose=None
                )
            
            # Process transcription result
            process_transcription_result(
                result, wav, sr, 0, audio_file_name_without_ext,
                metadata, whisper_words, max_duration, buffer, speaker_name,
                audio_folder, too_long_files, create_bpe_tokenizer, target_language
            )

        os.remove(temp_audio_path)

        # Update progress
        end = datetime.datetime.now()
        gradio_progress_duration += (end - start).total_seconds()
        audio_steps = (audio_steps[0] + 1, audio_steps[1])
        additional_data_points_needed = audio_steps[1] - audio_steps[0]
        avg_duration = gradio_progress_duration / audio_steps[0]
        gradio_estimated_duration = (avg_duration * additional_data_points_needed)
        gradio_progress(audio_steps, desc=f"Processing. Estimated Completion: {c_logger.format_duration(gradio_estimated_duration)}", unit="files")

    # Print final statistics
    stats.print_stats()

    # Verify processed files exist
    audio_files = [os.path.join(audio_folder, file) for file in os.listdir(audio_folder) if file.endswith('.wav')]
    if not audio_files:
        debug_print("No processed audio files found", "AUDIO", is_error=True)
        return None, None, 0

    # Final statistics before metadata handling
    stats.print_stats()
    
    # Handle existing metadata case
    if os.path.exists(train_metadata_path) and os.path.exists(eval_metadata_path):
        debug_print("Using existing metadata files", "DATA_PROCESS")
        _set_validation_paths(
            train_metadata_path, eval_metadata_path, audio_folder,
            whisper_model, target_language
        )
        _cleanup_resources(asr_model, existing_metadata)
        return train_metadata_path, eval_metadata_path, audio_total_size

    # Check for new metadata
    if not metadata["audio_file"]:
        debug_print("No new audio files to process", "DATA_PROCESS", is_warning=True)
        _set_validation_paths(
            train_metadata_path, eval_metadata_path, audio_folder,
            whisper_model, target_language
        )
        _cleanup_resources(asr_model, existing_metadata)
        return train_metadata_path, eval_metadata_path, audio_total_size

    # Process metadata and handle duplicates
    debug_print("Processing metadata and handling duplicates", "DATA_PROCESS")
    new_data_df = pd.DataFrame(metadata)
    
    # Duplicate detection and handling
    duplicate_files = new_data_df['audio_file'].value_counts()
    duplicates_found = duplicate_files[duplicate_files > 1]
    
    gradio_progress((6, 10), desc="Handling Duplicate Transcriptions")
    if not duplicates_found.empty:
        debug_print(f"Found {len(duplicates_found)} files with multiple transcriptions", "DUPLICATES")
        for file, count in duplicates_found.items():
            debug_print(f"{file}: {count} occurrences", "DUPLICATES")
            
        # Re-transcribe duplicates
        best_transcriptions = handle_duplicates(duplicates_found.index, audio_folder, target_language, whisper_model)
        
        # Update transcriptions and remove duplicates
        for file_path, trans_info in best_transcriptions.items():
            new_data_df.loc[new_data_df['audio_file'] == file_path, 'text'] = trans_info['text']
            debug_print(f"Updated transcription for {file_path}", "DUPLICATES")
        
        new_data_df = new_data_df.drop_duplicates(subset='audio_file', keep='first')
        debug_print(f"Cleaned up {len(duplicates_found)} duplicate entries", "DUPLICATES")
    
    # Handle evaluation split
    debug_print("Creating train/eval split", "DATA_PROCESS")
    eval_percentage = _adjust_eval_percentage(eval_split_number)
    
    # Create and validate splits
    gradio_progress((7, 10), desc="Creating Train/Eval Split")
    train_eval_split = create_dataset_splits(
        new_data_df, 
        eval_percentage, 
        random_seed=42
    )
    
    if train_eval_split is None:
        debug_print("Failed to create valid dataset splits", "DATA_PROCESS", is_error=True)
        return None, None, 0
        
    final_training_set, final_eval_set = train_eval_split
    
    # Write metadata files
    debug_print(f"Writing {len(final_training_set)} training and {len(final_eval_set)} eval samples", "DATA_PROCESS")
    try:
        gradio_progress((8, 10), desc="Saving Metadata Files")
        _write_metadata_files(
            final_training_set, final_eval_set,
            train_metadata_path, eval_metadata_path
        )
    except Exception as e:
        debug_print(f"Error writing metadata: {str(e)}", "DATA_PROCESS", is_error=True)
        raise

    # Handle BPE tokenizer
    if create_bpe_tokenizer:
        gradio_progress((9, 10), desc="Training BPE Tokenizer")
        debug_print("Training BPE Tokenizer", "MODEL_OPS")
        _create_bpe_tokenizer(whisper_words, out_path, base_path)

    debug_print("Finalizing processing", "GENERAL")
    gradio_progress((10, 10), desc="Finalizing Process")

    # Cleanup and set validation paths
    _cleanup_resources(asr_model, final_eval_set, final_training_set, new_data_df, existing_metadata)
    _set_validation_paths(
        train_metadata_path, eval_metadata_path, audio_folder,
        whisper_model, target_language
    )

    # Log final statistics
    if too_long_files:
        debug_print("Files that were split due to length:", "SEGMENTS")
        for file_name, length in too_long_files:
            debug_print(f"  {file_name}: {length:.2f} seconds", "SEGMENTS")

    return train_metadata_path, eval_metadata_path, audio_total_size

def _create_bpe_tokenizer(whisper_words, out_path, base_path):
    """Create and train BPE tokenizer"""
    vocab_path = base_path / "xttsv2_2.0.3" / "vocab.json"
    if not vocab_path.exists():
        debug_print("BPE tokenizer will not be created for this dataset creation run.", "GENERAL", is_warning=True)
        debug_print("XTTS v2.0.3 base model not found for BPE Tokenizer. Please download it first using AllTalk's", "GENERAL", is_warning=True)
        debug_print("main Gradio interface > TTS Engine Settings > XTTS > Model/Voices Downloads.", "GENERAL", is_warning=True)
        debug_print("Dataset creation will continue without the BPE tokenizer.", "GENERAL", is_warning=True)
        raise FileNotFoundError(f"Missing required file: {vocab_path}")
        
    try:
        debug_print("Initializing BPE tokenizer training", "MODEL_OPS")
        tokenizer = ByteLevelBPETokenizer(str(vocab_path))
        tokenizer.pre_tokenizer = Whitespace()
        
        # Add special tokens
        special_tokens = [
            "[PAD]", "[UNK]", "[CLS]", "[SEP]", "[MASK]", "[STOP]", "[SPACE]"
        ]
        tokenizer.add_tokens(special_tokens)
        
        debug_print(f"Training tokenizer on {len(whisper_words)} words", "MODEL_OPS")
        tokenizer.train_from_iterator(
            whisper_words,
            vocab_size=30000,
            show_progress=True,
            min_frequency=2,
            special_tokens=special_tokens
        )
        
        # Save tokenizer
        tokenizer_path = str(out_path / "bpe_tokenizer-vocab.json")
        tokenizer.save(path=tokenizer_path, pretty=True)
        debug_print(f"Saved BPE tokenizer to {tokenizer_path}", "MODEL_OPS")
        
    except Exception as e:
        debug_print(f"Failed to create BPE tokenizer: {str(e)}", "MODEL_OPS", is_error=True)
        raise

def merge_short_segments(segments, min_duration, max_gap=0.5):
    """
    More aggressive merge strategy for short segments
    - Increases max_gap to 0.5s (from 0.3s)
    - Looks ahead multiple segments for potential merges
    - Considers surrounding context
    """
    if not segments:
        return segments

    merged = []
    current_group = []
    
    for i, segment in enumerate(segments):
        current_duration = sum(s['end'] - s['start'] for s in current_group) if current_group else 0
        
        # If this is a continuation of current group
        if current_group and (segment['start'] - current_group[-1]['end']) <= max_gap:
            current_group.append(segment)
        # If starting new group but previous was too short
        elif current_group and current_duration < min_duration:
            # Look ahead for close segments
            look_ahead = min(3, len(segments) - i)  # Look up to 3 segments ahead
            for j in range(i, i + look_ahead):
                if j < len(segments) and (segments[j]['start'] - current_group[-1]['end']) <= max_gap * 2:
                    current_group.append(segments[j])
                else:
                    break
        else:
            # Save previous group if it exists
            if current_group:
                merged_segment = {
                    'start': current_group[0]['start'],
                    'end': current_group[-1]['end']
                }
                merged.append(merged_segment)
            current_group = [segment]
    
    # Handle last group
    if current_group:
        merged_segment = {
            'start': current_group[0]['start'],
            'end': current_group[-1]['end']
        }
        merged.append(merged_segment)
    
    debug_print(f"Merged {len(segments) - len(merged)} segments into {len(merged)} longer segments", "SEGMENTS")
    return merged

def extend_segment(wav, start, end, sr, min_duration, context_window=1.0):
    """
    Improved segment extension with better context handling
    - Adds context_window parameter for smoother extensions
    - More balanced extension on both sides
    - Checks audio content when extending
    """
    current_duration = (end - start) / sr
    if current_duration >= min_duration:
        return wav[start:end]
    
    samples_needed = int((min_duration - current_duration) * sr)
    
    # Try to extend equally on both sides
    extend_left = samples_needed // 2
    extend_right = samples_needed - extend_left
    
    # Add some context window
    context_samples = int(context_window * sr)
    new_start = max(0, start - extend_left - context_samples)
    new_end = min(wav.size(-1), end + extend_right + context_samples)
    
    # Check if we got enough duration
    if (new_end - new_start) / sr < min_duration:
        # If still too short, try to compensate from the other side
        if new_start == 0:
            new_end = min(wav.size(-1), end + samples_needed + context_samples)
        elif new_end == wav.size(-1):
            new_start = max(0, start - samples_needed - context_samples)
    
    debug_print(f"Extended segment from {current_duration:.2f}s to {(new_end - new_start) / sr:.2f}s", "SEGMENTS")
    return wav[new_start:new_end]

# Helper functions for better organization
def _adjust_eval_percentage(eval_split_number):
    """Adjust evaluation percentage to be within acceptable bounds"""
    eval_percentage = eval_split_number / 100.0
    min_eval_percentage = 0.1
    max_eval_percentage = 0.3
    
    if eval_percentage < min_eval_percentage:
        debug_print(f"Adjusting eval split from {eval_percentage:.1%} to {min_eval_percentage:.1%}", "DATA_PROCESS", is_warning=True)
        return min_eval_percentage
    elif eval_percentage > max_eval_percentage:
        debug_print(f"Adjusting eval split from {eval_percentage:.1%} to {max_eval_percentage:.1%}", "DATA_PROCESS", is_warning=True)
        return max_eval_percentage
    return eval_percentage

def _set_validation_paths(train_path, eval_path, audio_folder, whisper_model, target_language):
    """Set global validation paths"""
    global validate_train_metadata_path, validate_eval_metadata_path, validate_audio_folder
    global validate_whisper_model, validate_target_language
    
    validate_train_metadata_path = train_path
    validate_eval_metadata_path = eval_path
    validate_audio_folder = audio_folder
    validate_whisper_model = whisper_model
    validate_target_language = target_language

def _cleanup_resources(*resources):
    """Clean up resources and free memory"""
    for resource in resources:
        del resource
    gc.collect()
    if torch.cuda.is_available():
        torch.cuda.empty_cache()

def _write_metadata_files(train_set, eval_set, train_path, eval_path):
    """Write metadata files with error handling"""
    try:
        train_set.sort_values('audio_file').to_csv(train_path, sep='|', index=False)
        eval_set.sort_values('audio_file').to_csv(eval_path, sep='|', index=False)
        debug_print("Successfully wrote metadata files", "DATA_PROCESS")
    except Exception as e:
        debug_print(f"Failed to write metadata files: {str(e)}", "DATA_PROCESS", is_error=True)
        raise

def create_dataset_splits(df, eval_percentage, random_seed=42):
    """Create training and evaluation splits with validation"""
    if df.empty:
        debug_print("No data available for splitting", "DATA_PROCESS", is_error=True)
        return None
        
    shuffled_df = df.sample(frac=1, random_state=random_seed)
    num_val_samples = max(1, int(len(shuffled_df) * eval_percentage))
    
    if num_val_samples >= len(shuffled_df):
        debug_print("Not enough samples for valid split", "DATA_PROCESS", is_error=True)
        return None
        
    return (
        shuffled_df[num_val_samples:],  # training
        shuffled_df[:num_val_samples]   # eval
    )


def save_audio_segment(audio, sr, start_time, end_time, sentence, audio_file_name_without_ext, 
                      segment_idx, speaker_name, audio_folder, metadata, max_duration, buffer, 
                      too_long_files, target_language):
    """Helper function to save audio segments and update metadata"""
    sentence = sentence.strip()
    sentence = multilingual_cleaners(sentence, target_language)
    audio_file_name = f"{audio_file_name_without_ext}_{str(segment_idx).zfill(8)}.wav"
    
    absolute_path = os.path.join(audio_folder, audio_file_name)
    os.makedirs(os.path.dirname(absolute_path), exist_ok=True)
    
    # Extract audio segment
    audio_start = int(sr * start_time)
    audio_end = int(sr * end_time)
    audio_segment = audio[audio_start:audio_end].unsqueeze(0)
    
    # Handle long audio segments
    if audio_segment.size(-1) > max_duration * sr:
        too_long_files.append((audio_file_name, audio_segment.size(-1) / sr))
        
        while audio_segment.size(-1) > max_duration * sr:
            split_audio = audio_segment[:, :int(max_duration * sr)]
            audio_segment = audio_segment[:, int(max_duration * sr):]
            split_file_name = f"{audio_file_name_without_ext}_{str(segment_idx).zfill(8)}.wav"
            split_relative_path = os.path.join(split_file_name)
            split_absolute_path = os.path.normpath(os.path.join(audio_folder, split_relative_path))
            
            os.makedirs(os.path.dirname(split_absolute_path), exist_ok=True)
            torchaudio.save(split_absolute_path, split_audio, sr)
            
            metadata["audio_file"].append(f"wavs/{split_relative_path}")
            metadata["text"].append(sentence)
            metadata["speaker_name"].append(speaker_name)
            segment_idx += 1

    if audio_segment.size(-1) >= sr:  # Only save if segment is at least 1 second
        torchaudio.save(absolute_path, audio_segment, sr)
        metadata["audio_file"].append(f"wavs/{audio_file_name}")
        metadata["text"].append(sentence)
        metadata["speaker_name"].append(speaker_name)

def process_transcription_result(result, audio, sr, segment_idx, audio_file_name_without_ext,
                               metadata, whisper_words, max_duration, buffer, speaker_name,
                               audio_folder, too_long_files, create_bpe_tokenizer, target_language):
    """Helper function to process transcription results and save audio segments"""
    i = segment_idx + 1
    sentence = ""
    sentence_start = None
    first_word = True
    current_words = []

    for segment in result["segments"]:
        if "words" not in segment:
            continue
            
        for word_info in segment["words"]:
            word = word_info.get("word", "").strip()
            if not word:
                continue
                
            start_time = word_info.get("start", 0)
            end_time = word_info.get("end", 0)

            if create_bpe_tokenizer:
                whisper_words.append(word)

            if first_word:
                sentence_start = start_time
                if len(current_words) == 0:
                    sentence_start = max(sentence_start - buffer, 0)
                else:
                    previous_end = current_words[-1].get("end", 0) if current_words else 0
                    sentence_start = max(sentence_start - buffer, (previous_end + start_time) / 2)
                sentence = word
                first_word = False
            else:
                sentence += " " + word

            current_words.append({
                "word": word,
                "start": start_time,
                "end": end_time
            })

            # Handle sentence splitting and audio saving
            if word[-1] in ["!", ".", "?"] or (end_time - sentence_start) > max_duration:
                save_audio_segment(
                    audio, sr, sentence_start, end_time, sentence,
                    audio_file_name_without_ext, i, speaker_name,
                    audio_folder, metadata, max_duration, buffer,
                    too_long_files, target_language
                )
                i += 1
                first_word = True
                current_words = []
                sentence = ""

def process_audio_with_vad(wav, sr, vad_model, get_speech_timestamps):
    """
    Enhanced VAD processing with better end-of-speech detection
    """
    import torchaudio.transforms as T
    
    device = "cuda" if torch.cuda.is_available() else "cpu"
    wav = wav.to(device)
    
    resampler = T.Resample(sr, 16000).to(device)
    wav_16k = resampler(wav)
    
    # Adjusted VAD parameters
    vad_segments = get_speech_timestamps(
        wav_16k,
        vad_model,
        sampling_rate=16000,
        threshold=0.2,              # Lower threshold to be more sensitive to speech
        min_speech_duration_ms=200, # Shorter to catch brief utterances
        max_speech_duration_s=float('inf'),
        min_silence_duration_ms=300,# Shorter silence duration
        window_size_samples=1024,   # Smaller window for more precise detection
        speech_pad_ms=300          # Add padding to end of speech segments
    )
    
    # Scale timestamps back to original sample rate
    scale_factor = sr / 16000
    for segment in vad_segments:
        segment['start'] = int(segment['start'] * scale_factor)
        # Add extra padding at the end
        segment['end'] = int(segment['end'] * scale_factor) + int(0.2 * sr)  # Add 200ms padding
    
    merged_segments = merge_short_segments(vad_segments, min_duration=6.0, max_gap=0.5)
    
    debug_print(f"VAD processing: {len(vad_segments)} original segments, {len(merged_segments)} after merging", "SEGMENTS")
    return merged_segments

def process_audio_segments(result, wav, sr, audio_file_name_without_ext, metadata, min_duration=6.0, max_duration=None):
    """
    Process audio into segments with minimum and maximum duration constraints
    Returns list of segments with their transcriptions
    """
    segments = []
    current_segment = {
        'start': None,
        'end': None,
        'text': [],
        'words': []
    }
    
    for segment in result["segments"]:
        if "words" not in segment:
            continue
            
        for word_info in segment["words"]:
            word = word_info.get("word", "").strip()
            if not word:
                continue
                
            start_time = word_info.get("start", 0)
            end_time = word_info.get("end", 0)
            
            if current_segment['start'] is None:
                current_segment['start'] = start_time
            
            current_segment['end'] = end_time
            current_segment['text'].append(word)
            current_segment['words'].append(word_info)
            
            # Check if we should split (on punctuation or max length)
            if (word[-1] in ["!", ".", "?"] or 
                (max_duration and end_time - current_segment['start'] > max_duration)):
                
                segment_duration = current_segment['end'] - current_segment['start']
                
                # If segment is too short, try to extend it unless it's the last segment
                if segment_duration < min_duration and len(result["segments"]) > 0:
                    continue
                
                # Save the segment if it's valid
                if segment_duration >= min_duration or word[-1] in ["!", ".", "?"]:
                    segments.append(current_segment)
                    current_segment = {
                        'start': None,
                        'end': None,
                        'text': [],
                        'words': []
                    }
    
    # Handle any remaining segment
    if current_segment['start'] is not None:
        segment_duration = current_segment['end'] - current_segment['start']
        if segment_duration >= min_duration:
            segments.append(current_segment)
    
    return segments

def handle_duplicates(duplicate_files, audio_folder, target_language, whisper_model):
    """Re-transcribe duplicate files to get best transcription"""
    print("[FINETUNE] [INFO] Re-transcribing duplicate files to get best transcription")
    
    best_transcriptions = {}
    device = "cuda" if torch.cuda.is_available() else "cpu"
    asr_model = whisper.load_model(whisper_model, device=device)
    
    for file_path in duplicate_files:
        print(f"[FINETUNE] [INFO] Re-transcribing {file_path}")
        
        # Get full path
        full_path = os.path.join(audio_folder, os.path.basename(file_path.replace("wavs/", "")))
        
        # Re-transcribe with highest quality settings
        result = asr_model.transcribe(
            full_path,
            language=target_language,
            word_timestamps=True,
            verbose=None
        )
        
        # Store the new transcription
        best_transcriptions[file_path] = {
            'text': result["text"].strip(),
            'confidence': sum(s.get('confidence', 0) for s in result["segments"]) / len(result["segments"])
        }
    
    return best_transcriptions

#############################################
#### STEP 1 Dataset Validation Functions ####
#############################################
def normalize_text(text):
    # Convert text to lowercase
    text = text.lower()
    # Remove punctuation
    text = text.translate(str.maketrans('', '', string.punctuation))
    # Replace multiple spaces with a single space
    text = re.sub(r'\s+', ' ', text)
    # Convert written numbers to digits
    words = text.split()
    normalized_words = []
    for word in words:
        try:
            # Try to convert word to a number
            normalized_word = str(w2n.word_to_num(word))
        except ValueError:
            # If it fails, keep the original word
            normalized_word = word
        normalized_words.append(normalized_word)
    # Join words back into a single string
    text = ' '.join(normalized_words)
    return text

def get_audio_file_list(mismatches):
    if mismatches.empty:
        return ["No bad transcriptions"]
    else:
        return mismatches["Audio Path"].tolist()

def load_and_display_mismatches():
    def validate_audio_transcriptions(csv_paths, audio_folder, whisper_model, target_language, progress=None):
        # Load and combine metadata from CSV files
        metadata_dfs = []
        for csv_path in csv_paths:
            metadata_df = pd.read_csv(csv_path, sep='|')
            # Add source CSV tracking
            metadata_df['source_csv'] = csv_path
            metadata_df['row_index'] = metadata_df.index
            metadata_dfs.append(metadata_df)
        
        metadata_df = pd.concat(metadata_dfs, ignore_index=True)
        
        # Load Whisper model
        device = "cuda" if torch.cuda.is_available() else "cpu"
        asr_model = whisper.load_model(whisper_model, device=device)
        
        mismatches = []
        missing_files = []
        total_files = metadata_df.shape[0]
        
        if progress is not None:
            progress((0, total_files), desc="Processing files")
            
        for index, row in tqdm(metadata_df.iterrows(), total=total_files, unit="file", disable=False, leave=True):
            audio_file = row['audio_file']
            expected_text = row['text']
            audio_file_name = audio_file.replace("wavs/", "")
            audio_path = os.path.normpath(os.path.join(audio_folder, audio_file_name))
            
            if not os.path.exists(audio_path):
                missing_files.append(audio_file_name)
                if progress is not None:
                    progress((index + 1, total_files), desc="Processing files")
                continue

            # Transcribe with OpenAI Whisper
            result = asr_model.transcribe(
                audio_path,
                language=target_language,
                word_timestamps=True,
                verbose=None
            )
            
            # Get the full transcription from the result
            transcribed_text = result["text"].strip()
            
            # Normalize and compare texts
            normalized_expected_text = normalize_text(expected_text)
            normalized_transcribed_text = normalize_text(transcribed_text)
            
            if normalized_transcribed_text != normalized_expected_text:
                mismatches.append({
                    'expected_text': row['text'],
                    'transcribed_text': transcribed_text,
                    'filename': audio_file_name,
                    'full_path': audio_path,
                    'row_index': row['row_index'],
                    'source_csv': row['source_csv']
                })
                
            if progress is not None:
                progress((index + 1, total_files), desc="Processing files")

        if missing_files:
            print("[FINETUNE]")
            print("[FINETUNE] The following files are missing and should be removed from the CSV files:")
            for file_name in missing_files:
                print(f"[FINETUNE] - {file_name}")
                
        return mismatches

    progress = gr.Progress(track_tqdm=True)
    
    if validate_train_metadata_path and validate_eval_metadata_path and validate_audio_folder and validate_whisper_model and validate_target_language:
        mismatches = validate_audio_transcriptions(
            [validate_train_metadata_path, validate_eval_metadata_path],
            validate_audio_folder,
            validate_whisper_model,
            validate_target_language,
            progress
        )
        
        # Convert mismatches list to DataFrame
        df = pd.DataFrame(mismatches)
        
        # Ensure all fields are single values, not series
        for col in df.columns:
            if isinstance(df[col].iloc[0], pd.Series):
                df[col] = df[col].apply(lambda x: x.iloc[0] if isinstance(x, pd.Series) else x)
        
        # Clean all text columns
        df['expected_text'] = df['expected_text'].astype(str).apply(lambda x: x.strip())
        df['transcribed_text'] = df['transcribed_text'].astype(str).apply(lambda x: x.strip())
        df['full_path'] = df['full_path'].astype(str).apply(lambda x: x.strip())
        
        # Create display version with only visible columns
        display_df = df[['expected_text', 'transcribed_text', 'filename']].copy()
        
        return df, display_df, ""
    else:
        empty_df = pd.DataFrame(columns=["Expected Text", "Transcribed Text", "Filename"])
        return empty_df, empty_df, "Please generate your dataset first"

def save_correction_to_csv(csv_path, row_index, new_text):
    try:
        # Ensure we have single values, not Series
        if isinstance(csv_path, pd.Series):
            csv_path = str(csv_path.iloc[0])
        else:
            csv_path = str(csv_path)
            
        if isinstance(row_index, pd.Series):
            row_index = int(row_index.iloc[0])
        else:
            row_index = int(row_index)
            
        if isinstance(new_text, pd.Series):
            new_text = str(new_text.iloc[0])
        else:
            new_text = str(new_text)
       
        # Read the CSV file
        df = pd.read_csv(csv_path, sep='|')
        
        # Update the text
        df.loc[row_index, 'text'] = new_text
        
        # Save back to CSV
        df.to_csv(csv_path, sep='|', index=False)
        
        # Verify the save
        df_check = pd.read_csv(csv_path, sep='|')
        if df_check.loc[row_index, 'text'] == new_text:
            return f"Successfully updated transcription in {os.path.basename(csv_path)}"
        else:
            print(f"[FINETUNE] Save verification failed. Text mismatch.")
            return "Error: Save verification failed"
            
    except Exception as e:
        print(f"[FINETUNE] Error saving correction: {str(e)}")
        print(f"[FINETUNE] CSV path: {csv_path}")
        print(f"[FINETUNE] Row index: {row_index}")
        print(f"[FINETUNE] Full error traceback:")
        traceback.print_exc()
        return f"Error updating CSV: {str(e)}"

def save_audio_and_correction(choice, manual_text, audio_data, df, current_idx):
    """Handle both audio and transcription saves"""
    if current_idx is None:
        return {
            mismatch_table: df[['expected_text', 'transcribed_text', 'filename']],
            current_expected: "",
            save_status: "Please select a row first",
            audio_player: None
        }
    
    try:
        # Handle current_idx coming as a list from Gradio
        if isinstance(current_idx, list):
            current_idx = current_idx[0]
        
        row = df.iloc[int(current_idx)]
        audio_path = str(row['full_path']).strip()
        
        debug_print(f"Processing file: {audio_path}", "DATA_PROCESS")
        save_status_msg = []
        
        # Handle audio save if audio was edited
        if audio_data is not None and isinstance(audio_data, tuple) and len(audio_data) == 2:
            try:
                sr, audio = audio_data
                debug_print(f"Saving edited audio: {sr}Hz, length: {len(audio)}", "DATA_PROCESS")
                
                import torch
                import torchaudio
                audio_tensor = torch.tensor(audio).unsqueeze(0)
                torchaudio.save(audio_path, audio_tensor, sr)
                save_status_msg.append("Audio saved successfully")
                debug_print(f"Saved edited audio to {audio_path}", "DATA_PROCESS")
            except Exception as e:
                save_status_msg.append(f"Error saving audio: {str(e)}")
                debug_print(f"Error saving audio: {str(e)}", "DATA_PROCESS", is_error=True)

        # Handle text correction
        if choice == "Use Original":
            new_text = str(row['expected_text'])
        elif choice == "Use Whisper":
            new_text = str(row['transcribed_text'])
        elif choice == "Edit Manually":
            new_text = str(manual_text)
        
        # Save text correction to CSV
        result = save_correction_to_csv(
            str(row['source_csv']),
            int(row['row_index']),
            new_text
        )
        save_status_msg.append(result)
        
        # Update both text and expected_text in DataFrame
        if "Successfully" in result:
            df.loc[current_idx, 'text'] = new_text
            df.loc[current_idx, 'expected_text'] = new_text
            debug_print(f"Updated DataFrame with new text: {new_text}", "DATA_PROCESS")
        
        # Create updated display DataFrame
        display_df = df[['expected_text', 'transcribed_text', 'filename']].copy()
        
        return {
            mismatch_table: display_df,
            current_expected: new_text if "Successfully" in result else row['expected_text'],
            save_status: " | ".join(save_status_msg),
            audio_player: audio_path
        }
        
    except Exception as e:
        error_msg = f"Error saving correction: {str(e)}"
        debug_print(error_msg, "DATA_PROCESS", is_error=True)
        traceback.print_exc()
        return {
            mismatch_table: df[['expected_text', 'transcribed_text', 'filename']],
            current_expected: row['expected_text'] if 'row' in locals() else "",
            save_status: error_msg,
            audio_player: None
        }

#########################################
#### STEP 2 Model Training Functions ####
#########################################
def basemodel_or_finetunedmodel_choice(value):
    global basemodel_or_finetunedmodel 
    if value == "Base Model":
        basemodel_or_finetunedmodel = True
    elif value == "Existing finetuned model":
        basemodel_or_finetunedmodel = False

def train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv, learning_rate, model_to_train, continue_run, disable_shared_memory, learning_rate_scheduler, optimizer, num_workers, warm_up, max_audio_length=255995, progress=gr.Progress()):
    pfc_check_fail()

    if "No Models Available" in model_to_train:
            print("[FINETUNE] Error: No XTTS model selected for training.")
            print("[FINETUNE] Please download a model using AllTalk's main interface > TTS Engine Settings > XTTS > Model/Voices Download")
            return   

    #  Logging parameters
    RUN_NAME = "XTTS_FT"
    PROJECT_NAME = "XTTS_trainer"
    DASHBOARD_LOGGER = "tensorboard"
    LOGGER_URI = None
    model_path = this_dir / "models" / "xtts" / model_to_train

    # Set here the path that the checkpoints will be saved. Default: ./training/
    OUT_PATH = os.path.join(out_path, "training")
    print(f"[FINETUNE] [INFO] Starting Step 2 - Fine-tuning the XTTS Model")
    print(f"[FINETUNE] [INFO] Configuration Summary:")
    print(f"[FINETUNE] [INFO] - Language: {language}")
    print(f"[FINETUNE] [INFO] - Training Epochs: {num_epochs}")
    print(f"[FINETUNE] [INFO] - Batch Size: {batch_size}")
    print(f"[FINETUNE] [INFO] - Gradient Accumulation Steps: {grad_acumm}")
    print(f"[FINETUNE] [INFO] File Paths:")
    print(f"[FINETUNE] [INFO] - Training Data: {train_csv}")
    print(f"[FINETUNE] [INFO] - Evaluation Data: {eval_csv}")
    print(f"[FINETUNE] [INFO] - Base Model: {model_path}")

    if torch.cuda.is_available():
        torch.cuda.empty_cache()
        # Get the current device ID
        gpu_device_id = torch.cuda.current_device()
        gpu_available_mem_gb = (torch.cuda.get_device_properties(gpu_device_id).total_memory - torch.cuda.memory_allocated(gpu_device_id)) / (1024 ** 3)
        print(f"[FINETUNE] [INFO] GPU Memory Status:")
        print(f"[FINETUNE] [INFO] - Available VRAM: {gpu_available_mem_gb:.2f} GB")
        
        if gpu_available_mem_gb < 12:
            print("[FINETUNE] [WARNING] *** IMPORTANT MEMORY CONSIDERATION ***")
            print("[FINETUNE] [WARNING] Your available VRAM is below the recommended 12GB threshold.")
            print("[FINETUNE] [WARNING]")
            print("[FINETUNE] [WARNING] System-Specific Considerations:")
            print("[FINETUNE] [WARNING] - Windows: Will utilize system RAM as extended VRAM")
            print("[FINETUNE] [WARNING]   * Ensure sufficient system RAM is available")
            print("[FINETUNE] [WARNING]   * Recommended minimum: 24GB system RAM")
            print("[FINETUNE] [WARNING] - Linux: Limited to physical VRAM only")
            print("[FINETUNE] [WARNING]   * Training may fail with insufficient VRAM")
            print("[FINETUNE] [WARNING]   * Consider reducing batch size or using gradient accumulation")
            print("[FINETUNE] [WARNING]")
            print("[FINETUNE] [WARNING] For detailed memory management strategies and optimization tips:")
            print("[FINETUNE] [WARNING] 1. Refer to the 'Memory Management' section in the Training Guide")
            print("[FINETUNE] [WARNING] 2. Review the Pre-flight Check tab for system requirements")

    # Create the directory
    os.makedirs(OUT_PATH, exist_ok=True)

    # Training Parameters
    OPTIMIZER_WD_ONLY_ON_WEIGHTS = True  # for multi-gpu training please make it False
    START_WITH_EVAL = False  # if True it will star with evaluation
    BATCH_SIZE = batch_size  # set here the batch size
    GRAD_ACUMM_STEPS = grad_acumm  # set here the grad accumulation steps

    # Define here the dataset that you want to use for the fine-tuning on.
    config_dataset = BaseDatasetConfig(
        formatter="coqui",
        dataset_name="ft_dataset",
        path=os.path.dirname(train_csv),
        meta_file_train=train_csv,
        meta_file_val=eval_csv,
        language=language,
    )

    # Add here the configs of the datasets
    DATASETS_CONFIG_LIST = [config_dataset]
    TOKENIZER_FILE = str(model_path / "vocab.json")
    XTTS_CHECKPOINT = str(model_path / "model.pth")
    XTTS_CONFIG_FILE = str(model_path / "config.json")
    DVAE_CHECKPOINT = model_path / "dvae.pth"
    MEL_NORM_FILE = model_path / "mel_stats.pth"
    SPEAKERS_FILE = model_path / "speakers_xtts.pth"

    training_assets = None
    if (out_path / "bpe_tokenizer-vocab.json").exists():
        print("[FINETUNE] [INFO] Using custom BPE tokenizer")
        training_assets = {
            'Tokenizer': str(out_path / "bpe_tokenizer-vocab.json")
        }

    continue_path = None
    if continue_run:
        folders = glob.glob(os.path.join(OUT_PATH, '*/'))
        if folders:
            last_run = max(folders, key=os.path.getmtime)
            if last_run:
                checkpoints = glob.glob(os.path.join(last_run, "best_model_*.pth"))
                if checkpoints:
                    latest_checkpoint = max(checkpoints, key=os.path.getmtime)
                    if latest_checkpoint:
                        XTTS_CHECKPOINT = None
                        continue_path = last_run
                        print(f"[FINETUNE] Continuing previous fine tuning {latest_checkpoint}")


    # Copy the supporting files
    destination_dir = out_path / "chkptandnorm"
    destination_dir.mkdir(parents=True, exist_ok=True)
    shutil.copy2(DVAE_CHECKPOINT, destination_dir / DVAE_CHECKPOINT.name)
    shutil.copy2(MEL_NORM_FILE, destination_dir / MEL_NORM_FILE.name)
    shutil.copy2(SPEAKERS_FILE, destination_dir / SPEAKERS_FILE.name)

    # init args and config
    model_args = GPTArgs(
        max_conditioning_length=132300,  # 6 secs
        min_conditioning_length=66150,  # 3 secs
        debug_loading_failures=False,
        max_wav_length=max_audio_length,  # ~11.6 seconds
        max_text_length=200,
        mel_norm_file=MEL_NORM_FILE,
        dvae_checkpoint=DVAE_CHECKPOINT,
        xtts_checkpoint=XTTS_CHECKPOINT,  # checkpoint path of the model that you want to fine-tune
        tokenizer_file=TOKENIZER_FILE,
        gpt_num_audio_tokens=1026,
        gpt_start_audio_token=1024,
        gpt_stop_audio_token=1025,
        gpt_use_masking_gt_prompt_approach=True,
        gpt_use_perceiver_resampler=True,
    )
    # define audio config
    audio_config = XttsAudioConfig(sample_rate=22050, dvae_sample_rate=22050, output_sample_rate=24000)

    # Resolve Japanese threading issue
    number_of_workers = int(num_workers)
    if language == "ja":
        number_of_workers = 0

    lr_scheduler = None
    lr_scheduler_params = {}

    if learning_rate_scheduler and learning_rate_scheduler != "None":
        lr_gamma_mapping = {
            1e-6: 0.9,
            5e-6: 0.8,
            1e-5: 0.3,
            5e-5: 0.3,
            1e-4: 0.3,
            5e-4: 0.1,
            1e-3: 0.1
        }
        lr_scheduler = learning_rate_scheduler
        if lr_scheduler == "StepLR":
            lr_scheduler_params = {'step_size': 30, 'gamma': 0.1, 'last_epoch': -1}
        elif lr_scheduler == "MultiStepLR":
            exponent = 3 - int(math.log2(num_epochs) / 2)
            base = 2
            num_milestones = min(num_epochs, int(math.pow(base, exponent)))
            milestone_interval = num_epochs // (num_milestones + 1)
            milestones = [milestone_interval * (i + 1) for i in range(num_milestones)]
            lr_scheduler_params = {'milestones': milestones, 'gamma': lr_gamma_mapping[learning_rate], 'last_epoch': -1}
        elif lr_scheduler == "ExponentialLR":
            lr_scheduler_params = {'gamma': 0.5, 'last_epoch': -1}
        elif lr_scheduler == "CosineAnnealingLR":
            lr_scheduler_params = {'T_max': num_epochs, 'eta_min': 1e-6, 'last_epoch': -1}
        elif lr_scheduler == "ReduceLROnPlateau":
            lr_scheduler_params = {'mode': 'min', 'factor': 0.8, 'patience': 1, 'threshold': 0.0001,
                                   'threshold_mode': 'rel', 'cooldown': 0, 'min_lr': 1e-8, 'eps': 1e-08,}
        elif lr_scheduler == "CyclicLR":
            lr_scheduler_params = {'base_lr': learning_rate, 'max_lr': 0.1, 'step_size_up': 2000, 'step_size_down': None,
                                   'mode': 'triangular', 'gamma': 1.0, 'scale_fn': None, 'scale_mode': 'cycle',
                                   'cycle_momentum': True, 'base_momentum': 0.8, 'max_momentum': 0.9, 'last_epoch': -1}
        elif lr_scheduler == "OneCycleLR":
            #TODO: Determine total steps / epoch for this
            lr_scheduler_params = {'max_lr': learning_rate, 'total_steps': None, 'epochs_up': None, 'steps_per_epoch': None,
                                   'anneal_strategy': 'cos', 'cycle_momentum': True, 'base_momentum': 0.85,
                                   'max_momentum': 0.95, 'div_factor': 25.0, 'final_div_factor': 10000.0,
                                   'last_epoch': -1}
        elif lr_scheduler == "CosineAnnealingWarmRestarts":
            if num_epochs < 4:
                error_message = "For Cosine Annealing Warm Restarts, epochs must be at least 4. Please set a minimum of 4 epochs."
                progress(1.0, desc=f"Error: {error_message}")
                raise ValueError(error_message)
            #Set 4 learning rate restarts
            lr_scheduler_params = {'T_0': int(num_epochs / 4), 'T_mult': 1, 'eta_min': 1e-6, 'last_epoch': -1}

    optimizer_params = None
    optimizer_wd_only_on_weight = OPTIMIZER_WD_ONLY_ON_WEIGHTS

    if optimizer == "AdamW":
        optimizer_params = {
            "betas": [0.9, 0.96],
            "eps": 1e-8,
            "weight_decay": 1e-2
        }
    elif optimizer == "RMSprop":
        optimizer_params = {
            "alpha": 0.99,
            "eps": 1e-8,
            "weight_decay": 1e-4
        }
    elif optimizer == "SGD":
        optimizer_params = {
            "momentum": 0.9,
            "weight_decay": 1e-4
        }
    elif optimizer == "Adam":
        optimizer_params = {
            "betas": [0.9, 0.999],
            "eps": 1e-8,
            "weight_decay": 1e-4
        }
    elif optimizer == "Adagrad":
        optimizer_params = {
            "lr_decay": 0,
            "weight_decay": 1e-4,
            "eps": 1e-10
        }
    elif optimizer == "RAdam":
        optimizer_params = {
            "betas": [0.9, 0.999],
            "eps": 1e-8,
            "weight_decay": 1e-2
        }
    elif optimizer == "stepwisegraduallr" or optimizer == "noamlr":
        optimizer_params = { }
        # These are Hardcoded learning rate schedulers


    print(f"[FINETUNE] [INFO] Learning Scheduler {lr_scheduler}, params {lr_scheduler_params}")

    # training parameters config
    config = GPTTrainerConfig(
        epochs=num_epochs,
        output_path=OUT_PATH,
        model_args=model_args,
        run_name=RUN_NAME,
        project_name=PROJECT_NAME,
        run_description="GPT XTTS training",
        dashboard_logger=DASHBOARD_LOGGER,
        logger_uri=LOGGER_URI,
        audio=audio_config,
        batch_size=BATCH_SIZE,
        batch_group_size=48,
        eval_batch_size=BATCH_SIZE,
        num_loader_workers=number_of_workers,
        eval_split_max_size=256,
        print_step=50,
        plot_step=100,
        log_model_step=100,
        save_step=1000,
        save_n_checkpoints=1,
        save_checkpoints=True,
        # target_loss="loss",
        print_eval=False,
        # Optimizer values like tortoise, pytorch implementation with modifications to not apply WD to non-weight parameters.
        optimizer=optimizer,
        optimizer_wd_only_on_weights=OPTIMIZER_WD_ONLY_ON_WEIGHTS,
        optimizer_params=optimizer_params,
        lr=learning_rate,  # learning rate
        lr_scheduler=lr_scheduler,
        # it was adjusted accordly for the new step scheme
        lr_scheduler_params=lr_scheduler_params,
        test_sentences=[],
    )
    progress(0, desc="Model is currently training. See console for more information")
    # init the model from config
    model = GPTTrainer.init_from_config(config)
    # load training samples
    train_samples, eval_samples = load_tts_samples(
        DATASETS_CONFIG_LIST,
        eval_split=True,
        eval_split_max_size=config.eval_split_max_size,
        eval_split_size=config.eval_split_size,
    )

    global c_logger
    c_logger = MetricsLogger()

    # init the trainer
    trainer = Trainer(
        TrainerArgs(
            restore_path=None,  # xtts checkpoint is restored via xtts_checkpoint key so no need of restore it using Trainer restore_path parameter
            skip_train_epoch=False,
            continue_path=continue_path,
            start_with_eval=START_WITH_EVAL,
            grad_accum_steps=GRAD_ACUMM_STEPS,
        ),
        config,
        output_path=OUT_PATH,
        model=model,
        train_samples=train_samples,
        eval_samples=eval_samples,
        training_assets=training_assets,
        c_logger=c_logger,
        warmup=warm_up,
    )
    
    c_logger.update_model_path(OUT_PATH)

    if(disable_shared_memory):
        # Limit training to GPU memory instead of shared memory
        print("[FINETUNE] [INFO] Limiting GPU memory to 95% to prevent spillover")
        torch.cuda.set_per_process_memory_fraction(0.95)

    trainer.fit()

    # get the longest text audio file to use as speaker reference
    samples_len = [len(item["text"].split(" ")) for item in train_samples]
    longest_text_idx =  samples_len.index(max(samples_len))
    speaker_ref = train_samples[longest_text_idx]["audio_file"]

    trainer_out_path = trainer.output_path

    # deallocate VRAM and RAM
    del model, trainer, train_samples, eval_samples, config, model_args, config_dataset
    gc.collect()
    train_samples = None
    eval_samples = None
    config_dataset = None
    trainer = None
    model = None
    model_args = None
    try:
        return XTTS_CONFIG_FILE, XTTS_CHECKPOINT, TOKENIZER_FILE, trainer_out_path, speaker_ref
    except Exception as e:
        print(f"Error returning values: {e}")
        return "Error", "Error", "Error", "Error", "Error"

##########################
#### STEP 3 AND OTHER ####
##########################

def clear_gpu_cache():
    # clear the GPU cache
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
        
def find_a_speaker_file(folder_path):
    search_path = folder_path / "*" / "speakers_xtts.pth"
    files = glob.glob(str(search_path), recursive=True)
    latest_file = max(files, key=os.path.getctime, default=None)
    return latest_file

XTTS_MODEL = None
def load_model(xtts_checkpoint, xtts_config, xtts_vocab):
    pfc_check_fail()
    global XTTS_MODEL
    clear_gpu_cache()
    if not xtts_checkpoint or not xtts_config or not xtts_vocab:
        return "No Models were selected. Click the Refresh Dropdowns button and try again."
    xtts_speakers_pth = find_a_speaker_file(this_dir / "models")
    config = XttsConfig()
    config.load_json(xtts_config)
    XTTS_MODEL = Xtts.init_from_config(config)
    print("[FINETUNE] \033[94mStarting Step 3\033[0m Loading XTTS model!")
    XTTS_MODEL.load_checkpoint(config, checkpoint_path=xtts_checkpoint, vocab_path=xtts_vocab, use_deepspeed=False, speaker_file_path=xtts_speakers_pth)
    if torch.cuda.is_available():
        XTTS_MODEL.cuda()

    print("[FINETUNE] [INFO] Model Loaded!")
    return "Model Loaded!"

def run_tts(lang, tts_text, speaker_audio_file):
    if XTTS_MODEL is None or not speaker_audio_file:
        return "You need to run the previous step to load the model !!", None, None
    speaker_audio_file = str(speaker_audio_file)
    if os.path.isdir(speaker_audio_file):
        wavs_files = glob.glob(os.path.join(speaker_audio_file, "*.wav"))
        speaker_audio_file = wavs_files[0]
    else:
        wavs_files = [speaker_audio_file]

    gpt_cond_latent, speaker_embedding = XTTS_MODEL.get_conditioning_latents(audio_path=wavs_files, gpt_cond_len=XTTS_MODEL.config.gpt_cond_len, max_ref_length=XTTS_MODEL.config.max_ref_len, sound_norm_refs=XTTS_MODEL.config.sound_norm_refs)
    out = XTTS_MODEL.inference(
        text=tts_text,
        language=lang,
        gpt_cond_latent=gpt_cond_latent,
        speaker_embedding=speaker_embedding,
        temperature=XTTS_MODEL.config.temperature, # Add custom parameters here
        length_penalty=XTTS_MODEL.config.length_penalty,
        repetition_penalty=XTTS_MODEL.config.repetition_penalty,
        top_k=XTTS_MODEL.config.top_k,
        top_p=XTTS_MODEL.config.top_p,
    )

    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
        out["wav"] = torch.tensor(out["wav"]).unsqueeze(0)
        out_path = fp.name
        torchaudio.save(out_path, out["wav"], 24000)

    return "Speech generated !", out_path, speaker_audio_file

def get_available_voices(min_duration_seconds=6, speaker_name=None):
    """Get available voice files based on minimum duration.
    
    Args:
        min_duration_seconds (int): Minimum duration in seconds
        speaker_name (str, optional): Project name for custom directory
        
    Returns:
        list: Sorted list of audio file paths meeting duration requirement
    """
    directory = out_path
    if speaker_name and speaker_name != 'personsname':
        directory = this_dir / "finetune" / speaker_name
    
    valid_files = []
    wav_files = Path(f"{directory}/wavs").glob("*.wav")
    
    for voice_file in wav_files:
        try:
            # Load audio file and get duration
            waveform, sample_rate = torchaudio.load(str(voice_file))
            duration = waveform.size(1) / sample_rate  # Duration in seconds
            
            if duration >= float(min_duration_seconds):
                valid_files.append(str(voice_file))
                
        except Exception as e:
            print(f"[FINETUNE] Error processing {voice_file}: {str(e)}")
            continue
    
    return sorted(valid_files)

def find_best_models(directory, speaker_name=None):
    if speaker_name and speaker_name != 'personsname':
        directory = this_dir / "finetune" / speaker_name
    
    # Look in both the base directory and the training subdirectory
    model_files = []
    
    # Check the training directory first
    training_dir = directory / "training"
    if training_dir.exists():
        # Look in all subdirectories of training
        for subdir in training_dir.glob("*"):
            if subdir.is_dir():
                model_path = subdir / "best_model.pth"
                if model_path.exists():
                    model_files.append(str(model_path))
    
    # Also check the base directory
    for model_path in directory.glob("**/best_model.pth"):
        if "training" not in str(model_path):  # Avoid duplicates
            model_files.append(str(model_path))
            
    return sorted(model_files)

def find_jsons(directory, filename, speaker_name=None):
    if speaker_name and speaker_name != 'personsname':
        directory = this_dir / "finetune" / speaker_name

    """Find files with a specific filename in the given directory."""
    return [str(file) for file in Path(directory).rglob(filename)]


# XTTS checkpoint files (best_model.pth)
xtts_checkpoint_files = find_best_models(out_path)
# XTTS config files (config.json)
xtts_config_files = find_jsons(out_path, "config.json")
# XTTS vocab files (vocab.json)
xtts_vocab_files = find_jsons(out_path, "vocab.json")

##########################
#### STEP 4 AND OTHER ####
##########################
import shutil
from pathlib import Path
import torch

import torch
import torchaudio
import shutil
from pathlib import Path

def compact_custom_model(xtts_checkpoint_copy, folder_path, overwrite_existing):
    this_dir = Path(__file__).parent.resolve()
    if not xtts_checkpoint_copy:
        error_message = "No trained model was selected. Please click Refresh Dropdowns and try again."
        print("[FINETUNE]", error_message)
        return error_message
    
    target_dir = this_dir / "models" / "xtts" / folder_path
    if overwrite_existing == "Do not overwrite existing files" and target_dir.exists():
        error_message = "The target folder already exists. Please change folder name or allow overwrites."
        print("[FINETUNE]", error_message)
        return error_message

    xtts_checkpoint_copy = Path(xtts_checkpoint_copy)
    # Get the source directory (either tmp-trn or custom named directory)
    source_dir = xtts_checkpoint_copy.parent.parent.parent  # Go up to the base directory
    
    print(f"[FINETUNE] === File Copy Operations ===")
    print(f"[FINETUNE] Source base directory: {source_dir}")
    print(f"[FINETUNE] Target directory: {target_dir}")

    try:
        checkpoint = torch.load(xtts_checkpoint_copy, map_location=torch.device("cpu"))
    except Exception as e:
        print("[FINETUNE] Error loading checkpoint:", e)
        raise

    del checkpoint["optimizer"]
    target_dir.mkdir(parents=True, exist_ok=True)
    
    # Remove dvae-related keys from checkpoint
    for key in list(checkpoint["model"].keys()):
        if "dvae" in key:
            del checkpoint["model"][key]
    
    print(f"[FINETUNE] Processing model.pth:")
    print(f"[FINETUNE]   From: {xtts_checkpoint_copy}")
    print(f"[FINETUNE]   To: {target_dir / 'model.pth'}")
    torch.save(checkpoint, target_dir / "model.pth")

    # Copy first set of files
    folder_path_new = xtts_checkpoint_copy.parent
    print(f"[FINETUNE] Copying config files:")
    for file_name in ["vocab.json", "config.json"]:
        src_path = folder_path_new / file_name
        dest_path = target_dir / file_name
        if src_path.exists():
            shutil.copy2(src_path, dest_path)
            print(f"[FINETUNE]   {file_name}:")
            print(f"[FINETUNE]   From: {src_path}")
            print(f"[FINETUNE]   To: {dest_path}")
        else:
            print(f"[FINETUNE] Warning: {src_path} not found")

    # Copy second set of files from chkptandnorm directory
    chkptandnorm_path = source_dir / "chkptandnorm"
    print(f"[FINETUNE] Copying support files:")
    for file_name in ["speakers_xtts.pth", "mel_stats.pth", "dvae.pth"]:
        src_path = chkptandnorm_path / file_name
        dest_path = target_dir / file_name
        if src_path.exists():
            shutil.copy2(src_path, dest_path)
            print(f"[FINETUNE]   {file_name}:")
            print(f"[FINETUNE]   From: {src_path}")
            print(f"[FINETUNE]   To: {dest_path}")
        else:
            print(f"[FINETUNE] Warning: {src_path} not found")

    # Create directories for different categories of WAV files
    target_wavs_dir = target_dir / "wavs"
    target_wavs_dir.mkdir(parents=True, exist_ok=True)
    
    too_short_dir = target_wavs_dir / "too_short"
    too_long_dir = target_wavs_dir / "too_long"
    suitable_dir = target_wavs_dir / "suitable"
    
    for dir_path in [too_short_dir, too_long_dir, suitable_dir]:
        dir_path.mkdir(parents=True, exist_ok=True)

    # Process WAV files
    source_wavs_dir = source_dir / "wavs"
    print(f"[FINETUNE] Processing WAV files:")
    print(f"[FINETUNE]   From: {source_wavs_dir}")
    print(f"[FINETUNE]   To: {target_wavs_dir}")
    
    file_stats = {
        "too_short": [],
        "too_long": [],
        "suitable": []
    }

    if not source_wavs_dir.exists():
        print(f"[FINETUNE] Warning: Source WAV directory {source_wavs_dir} does not exist")
        return f"Model files copied to '/models/xtts/{folder_path}/' but no WAV files were found to process"

    for file_path in source_wavs_dir.iterdir():
        if file_path.is_file() and file_path.suffix.lower() == '.wav':
            try:
                # Load audio file and get duration
                waveform, sample_rate = torchaudio.load(file_path)
                duration = waveform.size(1) / sample_rate  # Duration in seconds
                
                # Determine category and target directory
                if duration < 6:
                    category = "too_short"
                    target_subdir = too_short_dir
                elif duration > 30:
                    category = "too_long"
                    target_subdir = too_long_dir
                else:
                    category = "suitable"
                    target_subdir = suitable_dir
                
                # Copy file to appropriate directory
                shutil.copy2(file_path, target_subdir / file_path.name)
                
                # Store file info
                file_stats[category].append({
                    "name": file_path.name,
                    "duration": round(duration, 2)
                })
                
            except Exception as e:
                print(f"[FINETUNE] Error processing {file_path.name}: {str(e)}")

    print(f"[FINETUNE] WAV File Statistics:")
    print(f"[FINETUNE]   Suitable files (6-30s): {len(file_stats['suitable'])}")
    print(f"[FINETUNE]   Too short files (<6s): {len(file_stats['too_short'])}")
    print(f"[FINETUNE]   Too long files (>30s): {len(file_stats['too_long'])}")
    print(f"[FINETUNE] === File Copy Operations Complete ===")

    # Create report file
    report_content = """WAV Files Processing Report
===========================

This folder contains WAV files categorized by their duration for use with Coqui TTS.
Suitable files for voice cloning should be between 6 and 30 seconds in length.

Directory Structure:
------------------
- 'suitable': Contains files between 6-30 seconds - ideal for voice cloning
- 'too_short': Files under 6 seconds - may not contain enough voice characteristics
- 'too_long': Files over 30 seconds - may cause processing issues

Voice Sample Usage:
-----------------
1. The 'suitable' directory contains the ideal voice samples:
   - These files are ready to use for voice cloning
   - Copy your preferred samples to '/alltalk_tts/voices/' for use in the main interface
   - Clean, clear samples with minimal background noise work best
   - Consider using multiple samples to test which gives the best results

2. Files in 'too_long':
   - Can be used but may cause issues or inconsistent results
   - Recommended: Use audio editing software (like Audacity) to:
     * Split these into 6-30 second segments
     * Remove any silence, background noise, or unwanted sounds
     * Save segments as individual WAV files
     * Consider overlap in sentences for more natural breaks

3. Files in 'too_short':
   - Not recommended for voice cloning as they lack sufficient voice characteristics
   - If most/all files are here, consider:
     * Recording longer samples of continuous speech
     * Combining multiple short segments (if they flow naturally)
     * Using audio editing software to create longer cohesive samples
     * Aim for clear, natural speech between 6-30 seconds

Best Practices:
-------------
- Choose samples with clear, consistent speech
- Avoid background noise, music, or other speakers
- Natural speaking pace and tone usually work best
- Multiple samples of varying lengths (within 6-30s) can provide better results
- Test different samples to find which produces the best voice cloning results

Summary:
--------
"""
    
    for category, files in file_stats.items():
        report_content += f"\n{category.replace('_', ' ').title()} files ({len(files)}):\n"
        for file_info in files:
            report_content += f"- {file_info['name']}: {file_info['duration']} seconds\n"
    
    report_content += """
Notes:
------
- Files in 'suitable' are ready for use with Coqui TTS
- Files in 'too_short' are under 6 seconds and may need to be checked or excluded
- Files in 'too_long' are over 30 seconds and may need to be split or excluded

Please review the files in 'too_short' and 'too_long' directories."""

    with open(target_wavs_dir / "audio_report.txt", "w") as f:
        f.write(report_content)

    print(f"[FINETUNE] Model & WAV samples processed and copied to '/models/xtts/{folder_path}/'")
    return f"Model & WAV samples processed and copied to '/models/xtts/{folder_path}/'"

def delete_training_data():
    # Define the folder to be deleted
    folder_to_delete = Path(out_path)

    # Check if the folder exists before deleting
    if folder_to_delete.exists():
        # Iterate over all files and subdirectories
        for item in folder_to_delete.iterdir():
            # Exclude trainer_0_log.txt from deletion
            if item.name != "trainer_0_log.txt":
                try:
                    if item.is_file():
                        item.unlink()
                    elif item.is_dir():
                        shutil.rmtree(item)
                except PermissionError:
                    print(f"[FINETUNE] PermissionError: Could not delete {item}. Skipping.")

        print(f"[FINETUNE] Project Name folder > {folder_to_delete} < was deleted successfully.")
        return "Specified Project Name folder & tmp data was deleted successfully."
    else:
        print(f"[FINETUNE] Project Name folder > {folder_to_delete} < does not exist.")
        return "Specified Project Name folder could not be found."

def clear_folder_contents(folder_path):
    # Check if the folder exists before clearing its contents
    if folder_path.exists() and folder_path.is_dir():
        # List all files and subdirectories in the folder
        for item in os.listdir(folder_path):
            item_path = folder_path / item
            if item_path.is_file():
                # If it's a file, remove it
                os.remove(item_path)
            elif item_path.is_dir():
                # If it's a subdirectory, remove it recursively
                shutil.rmtree(item_path)

        print(f"[FINETUNE] Contents of {folder_path} deleted successfully.")
        return f"Contents of '{folder_path}' deleted successfully."
    else:
        print(f"[FINETUNE] Folder {folder_path} does not exist.")
        return f"Folder '{folder_path}' does not exist."

def delete_voice_sample_contents():
    # Define the folders to be cleared
    voice_samples_folder = this_dir / "finetune" / "put-voice-samples-in-here"
    gradio_temp_folder = this_dir / "finetune" / "gradio_temp"
    # Clear the contents of the gradio_temp folder
    gradio_temp_message = clear_folder_contents(gradio_temp_folder)
    # Clear the contents of the voice samples folder
    voice_samples_message = clear_folder_contents(voice_samples_folder)
    return voice_samples_message

#######################
#### OTHER Generic ####
#######################
# define a logger to redirect 
import logging
class Logger:
    _instance = None
    
    def __new__(cls, *args, **kwargs):
        if cls._instance is None:
            cls._instance = super(Logger, cls).__new__(cls)
            cls._instance.log_file = "finetune.log"
            cls._instance.terminal = sys.stdout
            cls._instance.current_model_path = None  # To store current training path
            
            # Open in append mode
            cls._instance.log = open(cls._instance.log_file, "a")
        return cls._instance

    def __init__(self, *args, **kwargs):
        pass

    def set_model_path(self, path):
        self.current_model_path = path

    def write(self, message):
        filtered_message = ''.join(char for char in message 
                                 if char.isprintable() or char in '\n\r\t')
        self.terminal.write(filtered_message)
        try:
            self.log.write(filtered_message)
            self.log.flush()
        except:
            pass

    def flush(self):
        self.terminal.flush()
        try:
            self.log.flush()
        except:
            pass

    def isatty(self):
        return False
    
    def clear_log(self):
        """Delete and recreate the log file to clear its contents."""
        try:
            # Close the current append-only file handle
            self.log.close()
            # Delete the log file
            os.remove(self.log_file)
            # Reopen the file in append mode for further logging
            self.log = open(self.log_file, "a")
        except Exception as e:
            print(f"Failed to delete and recreate log file: {e}")

_logging_setup_done = False

def setup_logging():
    global _logging_setup_done
    if _logging_setup_done:
        return
        
    # redirect stdout and stderr to a file
    sys.stdout = Logger()
    sys.stderr = sys.stdout

    logging.basicConfig(
        level=logging.INFO,
        format="[FINETUNE] %(message)s",
        handlers=[
            logging.StreamHandler(sys.stdout)
        ]
    )
    
    _logging_setup_done = True

# Call setup at module level
setup_logging()

c_logger = MetricsLogger()
def load_metrics():
    return c_logger.plot_metrics(), f"Running Time: {c_logger.format_duration(c_logger.total_duration)} - Estimated Completion: {c_logger.format_duration(c_logger.estimated_duration)}"
def read_logs():
    sys.stdout.flush()
    with open(sys.stdout.log_file, "r") as f:
        content = f.read()
        # Additional filtering when reading the file
        return ''.join(char for char in content 
                      if char.isprintable() or char in '\n\r\t')

def cleanup_before_exit(signum, frame):
    print("[FINETUNE] Received interrupt signal. Cleaning up and exiting...")
    # Perform cleanup operations here if necessary
    sys.exit(0)

def create_refresh_button(refresh_components, refresh_methods, elem_class, interactive=True):
    def refresh(speaker_name, min_duration_seconds):
        updates = {}
        for component, method in zip(refresh_components, refresh_methods):
            # Pass both speaker_name and min_duration_seconds to the method
            args = method(speaker_name=speaker_name, min_duration_seconds=min_duration_seconds) if callable(method) else method
            if args and 'choices' in args:
                args['value'] = args['choices'][-1] if args['choices'] else ""
            for k, v in args.items():
                setattr(component, k, v)
            updates[component] = gr.update(**(args or {}))
        return updates

    refresh_button = gr.Button("Refresh Dropdowns", elem_classes=elem_class, interactive=interactive)
    refresh_button.click(
        fn=refresh,
        inputs=[speaker_name_input_testing, min_audio_length],
        outputs=refresh_components
    )

    return refresh_button

def create_refresh_button_next(refresh_components, refresh_methods, elem_class, interactive=True):
    def refresh_export(speaker_name):
        global out_path
        if speaker_name and speaker_name != 'personsname':
            out_path = this_dir / "finetune" / speaker_name
        else:
            out_path = this_dir / "finetune" / "tmp-trn"
        
        updates = {}
        for component, method in zip(refresh_components, refresh_methods):
            args = method(speaker_name=speaker_name) if callable(method) else method
            if args and 'choices' in args:
                args['value'] = args['choices'][-1] if args['choices'] else ""
            for k, v in args.items():
                setattr(component, k, v)
            updates[component] = gr.update(**(args or {}))
        return updates

    refresh_button = gr.Button("Refresh Dropdowns", elem_classes=elem_class, interactive=interactive)
    refresh_button.click(
        fn=refresh_export,
        inputs=[speaker_name_input_export],
        outputs=refresh_components
    )
    return refresh_button

################################
#### Gradio CSS for Styling ####
################################
custom_css = """
/* Status indicators and labels */
.status-label {
    font-size: 1.2em;
    font-weight: bold;
    padding: 10px;
    border-radius: 5px;
    margin: 10px 0;
}

.status-indicator {
    padding: 5px 10px;
    border-radius: 3px;
    font-weight: bold;
}

/* Layout components */
.status-box {
    display: flex;
    align-items: center;
    padding: 0.5rem;
    margin-bottom: 0.5rem;
    border-radius: 4px;
    background: var(--background-fill-secondary);
}

.status-group {
    margin-bottom: 1rem;
    padding: 1rem;
    border-radius: 8px;
    background: var(--background-fill-primary);
}

/* Help section styling */
.help-section {
    margin-top: 2rem;
    border-top: 1px solid var(--border-color-primary);
    padding-top: 1rem;
}

.help-accordion {
    margin-bottom: 0.5rem;
}

/* Ensure consistent font size in accordions */
.gradio-container .prose,
.gradio-container .prose p,
.gradio-container .prose ul,
.gradio-container .prose ol,
.gradio-container .prose ul li,
.gradio-container .prose ol li {
    font-size: 1.02em !important; /* Adjust to the preferred size, e.g., 1em or 14px */
    line-height: 1.3; /* Improves readability */
}

/* Grid layout */
.check-grid {
    display: grid;
    grid-template-columns: repeat(auto-fit, minmax(250px, 1fr));
    gap: 1rem;
    margin-bottom: 1rem;
}
"""

if __name__ == "__main__":
    # Register the signal handler
    signal.signal(signal.SIGINT, cleanup_before_exit)

################
#### GRADIO ####
################

    parser = argparse.ArgumentParser(
        description="""XTTS fine-tuning demo\n\n"""
        """
        Example runs:
        python3 TTS/demos/xtts_ft_demo/xtts_demo.py --port 
        """,
        formatter_class=argparse.RawTextHelpFormatter,
    )
    parser.add_argument(
        "--num_epochs",
        type=int,
        help="Number of epochs to train. Default: 10",
        default=10,
    )
    parser.add_argument(
        "--batch_size",
        type=int,
        help="Batch size. Default: 4",
        default=4,
    )
    parser.add_argument(
        "--grad_acumm",
        type=int,
        help="Grad accumulation steps. Default: 1",
        default=1,
    )
    parser.add_argument(
        "--max_audio_length",
        type=int,
        help="Max permitted audio size in seconds. Default: 11",
        default=11,
    )

    args = parser.parse_args()

#####################
#### GRADIO INFO ####
#####################

    with gr.Blocks(theme=gr.themes.Base(), css=custom_css) as demo:
        with gr.Row():
            gr.Markdown("## XTTS Models Finetuning")
            gr.Markdown("")
            gr.Markdown("")
            dark_mode_btn = gr.Button("Light/Dark Mode", variant="primary", size="sm")
            dark_mode_btn.click(None, None, None,
            js="""() => {
                if (document.querySelectorAll('.dark').length) {
                    document.querySelectorAll('.dark').forEach(el => el.classList.remove('dark'));
                    // localStorage.setItem('darkMode', 'disabled');
                } else {
                    document.querySelector('body').classList.add('dark');
                    // localStorage.setItem('darkMode', 'enabled');
                }
            }""", show_api=False)
        with gr.Tab("🚀 Pre-flight Checklist"):
            create_pfc_interface() 

#######################
#### GRADIO STEP 1 ####
#######################
        with gr.Tab("📁 Step 1 - Generating the dataset"):
            with gr.Tab("Generate Dataset"):  
                # Define directories
                this_dir = Path(__file__).parent.resolve()
                voice_samples_dir = this_dir / "finetune" / "put-voice-samples-in-here"
                training_data_dir = this_dir / "finetune" / "tmp-trn" / "wavs"
                metadata_files = [
                    this_dir / "finetune" / "tmp-trn" / "metadata_eval.csv",
                    this_dir / "finetune" / "tmp-trn" / "metadata_train.csv",
                    this_dir / "finetune" / "tmp-trn" / "lang.txt"
                ]

                # Ensure directories exist
                voice_samples_dir.mkdir(parents=True, exist_ok=True)
                training_data_dir.mkdir(parents=True, exist_ok=True)

                def upload_audio(files):
                    for file in files:
                        shutil.copy(file.name, voice_samples_dir)
                    return f"Uploaded {len(files)} files to {voice_samples_dir}"

                def delete_existing_audio():
                    for file in voice_samples_dir.iterdir():
                        if file.is_file():
                            file.unlink()
                    return f"Deleted all files in {voice_samples_dir}"

                def delete_existing_training_data():
                    if training_data_dir.exists():
                        for file in training_data_dir.iterdir():
                            if file.is_file():
                                file.unlink()
                    for metadata_file in metadata_files:
                        if metadata_file.exists():
                            metadata_file.unlink()
                    return f"Deleted all files in {training_data_dir} and related metadata files"

                with gr.Row():
                    with gr.Column(scale=1):
                        audio_files_upload = gr.Files(label="Upload Audio Files")
                    with gr.Column(scale=3):
                        with gr.Row():                                        
                            with gr.Column(scale=1):                        
                                audio_upload_button = gr.Button("Upload New Audio Samples")
                                delete_audio_button = gr.Button("Delete Existing Audio Samples")
                                delete_dataset_button = gr.Button("Delete Existing Training Dataset")                        
                            with gr.Column(scale=2):
                                gr.Markdown("""
                                You can manually copy your audio files to `/finetune/put-voice-samples-in-here/` or use the upload to the left and click "Upload New Audio Samples". Once you have uploaded files, you can start creating your dataset.

                                - If you wish to delete previously uploaded audio samples files then use 'Delete Existing Audio Samples'.
                                - If you wish to delete previously generated training datasets, please use 'Delete Existing Training Dataset'.
                                - If you wish to re-use your previously created training data, fill in the `Training Project Name` corectly and click 'Create Dataset'.
                                """)
                        with gr.Row():
                            output_text = gr.Textbox(label="Audio File Management Result", interactive=False) 

                # Define actions for buttons
                audio_upload_button.click(upload_audio, inputs=audio_files_upload, outputs=output_text)
                delete_audio_button.click(delete_existing_audio, outputs=output_text)
                delete_dataset_button.click(delete_existing_training_data, outputs=output_text)

                def update_language_options(model):
                    # English-only models
                    if model in ["tiny.en", "base.en", "small.en", "medium.en"]:
                        languages = ["en"]
                    else:
                        # Multilingual models
                        languages = [
                            "en", "es", "fr", "de", "it", "pt", "pl", "tr", "ru", "nl", "cs",
                            "ar", "zh", "hu", "ko", "ja"
                        ]
                    return gr.Dropdown(choices=languages, value=languages[0])
                
                with gr.Row():
                    speaker_name_input = gr.Textbox(
                        label="Training Project Name",
                        value="personsname",
                        visible=True,
                        scale=2,
                    )
                    whisper_model = gr.Dropdown(
                        label="Whisper Model",
                        value="large-v3",
                        choices=[
                            ("tiny.en", "tiny.en"),
                            ("tiny", "tiny"),
                            ("base.en", "base.en"),
                            ("base", "base"),
                            ("small.en", "small.en"),
                            ("small", "small"),
                            ("medium.en", "medium.en"),
                            ("medium", "medium"),
                            ("large-v1", "large-v1"),
                            ("large-v2", "large-v2"),
                            ("large-v3", "large-v3"),
                            ("large", "large"),
                            ("large-v3-turbo", "large-v3-turbo"),
                            ("turbo", "turbo")
                        ],
                        scale=2,
                    )
                    lang = gr.Dropdown(
                        label="Dataset Language",
                        value="en",
                        choices=[
                            "en",
                            "es",
                            "fr",
                            "de",
                            "it",
                            "pt",
                            "pl",
                            "tr",
                            "ru",
                            "nl",
                            "cs",
                            "ar",
                            "zh",
                            "hu",
                            "ko",
                            "ja"
                        ],
                        scale=1,
                    )
                    max_sample_length = gr.Dropdown(
                        label="Max Audio Length (in seconds)",
                        value="30",
                        choices=["10", "15", "20", "25", "30", "35", "40", "45", "50", "55", "60", "65", "70", "75", "80", "85", "90"],
                        scale=2
                    )
                    eval_split_number = gr.Number(
                        label="Evaluation Data Split",
                        value=15,  # Default value
                        minimum=5,  # Minimum value
                        maximum=95,  # Maximum value
                        step=1,  # Increment step
                        scale=1,
                    )
                    create_bpe_tokenizer = gr.Checkbox(
                        label='BPE Tokenizer',
                        value=False,
                        info='Custom Tokenizer for training'
                    )
                    use_vad = gr.Checkbox(
                    label="VAD",
                    value=True,
                    info="Enable Silero VAD for better speech detection"
                    )
                    precision = gr.Dropdown(
                        label="Model Precision",
                        value="mixed",
                        choices=[
                            ("Mixed", "mixed"),
                            ("FP16", "float16"),
                            ("FP32", "float32")
                        ],
                    )
                    
                   
                with gr.Accordion("🔍 Dataset Creation Debug Settings", open=False):
                    def update_debug_levels(gpu, model, data, general, audio, segments, duplicates):
                        DebugLevels.GPU_MEMORY = gpu
                        DebugLevels.MODEL_OPS = model
                        DebugLevels.DATA_PROCESS = data
                        DebugLevels.GENERAL = general
                        DebugLevels.AUDIO = audio
                        DebugLevels.SEGMENTS = segments
                        DebugLevels.DUPLICATES = duplicates
                        return "Debug settings updated"

                    def select_all_debug():
                        return {
                            debug_gpu: True,
                            debug_model: True,
                            debug_data: True,
                            debug_general: True,
                            debug_audio: True,
                            debug_segments: True,
                            debug_duplicates: True
                        }

                    def clear_all_debug():
                        return {
                            debug_gpu: False,
                            debug_model: False,
                            debug_data: False,
                            debug_general: False,
                            debug_audio: False,
                            debug_segments: False,
                            debug_duplicates: False
                        }                    

                    with gr.Row():
                        gr.Markdown("""
                        Enable or disable different types of debug messages during dataset creation.
                        These settings will apply to the current session only.
                        """)
                    
                    with gr.Row():
                        with gr.Column(scale=1):
                            debug_gpu = gr.Checkbox(
                                label="GPU Memory",
                                value=DebugLevels.GPU_MEMORY,
                                info="GPU memory and CUDA related debugging"
                            )
                            debug_model = gr.Checkbox(
                                label="Model Operations",
                                value=DebugLevels.MODEL_OPS,
                                info="Model loading, transcription, cleanup operations"
                            )
                            debug_data = gr.Checkbox(
                                label="Data Processing",
                                value=DebugLevels.DATA_PROCESS,
                                info="Data processing, words, sentences"
                            )
                        
                        with gr.Column(scale=1):
                            debug_general = gr.Checkbox(
                                label="General",
                                value=DebugLevels.GENERAL,
                                info="General flow, file operations, metadata"
                            )
                            debug_audio = gr.Checkbox(
                                label="Audio",
                                value=DebugLevels.AUDIO,
                                info="Audio processing statistics and info"
                            )
                            debug_segments = gr.Checkbox(
                                label="Segments",
                                value=DebugLevels.SEGMENTS,
                                info="Detailed segment information"
                            )
                            debug_duplicates = gr.Checkbox(
                                label="Duplicates",
                                value=DebugLevels.DUPLICATES,
                                info="Duplicate handling information"
                            )

                    with gr.Row():
                        debug_select_all = gr.Button("Select All")
                        debug_clear_all = gr.Button("Clear All")

                    # Debug update functions
                    for checkbox in [debug_gpu, debug_model, debug_data, debug_general, 
                                    debug_audio, debug_segments, debug_duplicates]:
                        checkbox.change(
                            fn=update_debug_levels,
                            inputs=[debug_gpu, debug_model, debug_data, debug_general,
                                debug_audio, debug_segments, debug_duplicates],
                            outputs=[gr.Textbox(visible=False)]  # Hidden status output
                        )

                    debug_select_all.click(
                        fn=select_all_debug,
                        inputs=[],
                        outputs=[debug_gpu, debug_model, debug_data, debug_general,
                                debug_audio, debug_segments, debug_duplicates]
                    )

                    debug_clear_all.click(
                        fn=clear_all_debug,
                        inputs=[],
                        outputs=[debug_gpu, debug_model, debug_data, debug_general,
                                debug_audio, debug_segments, debug_duplicates]
                    )                    
                                

                progress_data = gr.Label(
                    label="Progress:"
                )
                logs = gr.Textbox(
                    label="Logs:",
                    interactive=False,
                    lines=10,
                )
                demo.load(read_logs, None, logs, every=1)
                # Update `lang` options when the `whisper_model` changes
                whisper_model.change(fn=update_language_options, inputs=whisper_model, outputs=lang)
                prompt_compute_btn = gr.Button(value="Step 1 - Create dataset")
            
                def preprocess_dataset(language, whisper_model, max_sample_length, eval_split_number, 
                                    speaker_name_input, create_bpe_tokenizer, use_vad, precision, 
                                    progress=gr.Progress()):
                    clear_gpu_cache()
                    test_for_audio_files = [file for file in os.listdir(audio_folder) 
                                        if any(file.lower().endswith(ext) for ext in ['.wav', '.mp3', '.flac'])]
                    if not test_for_audio_files:
                        return "I cannot find any mp3, wav or flac files in the folder called 'put-voice-samples-in-here'", "", ""
                    else:
                        try:
                            train_meta, eval_meta, audio_total_size = format_audio_list(
                                target_language=language,
                                whisper_model=whisper_model,
                                max_sample_length=max_sample_length,
                                eval_split_number=eval_split_number,
                                speaker_name_input=speaker_name_input,
                                create_bpe_tokenizer=create_bpe_tokenizer,
                                gradio_progress=progress,
                                use_vad=use_vad,
                                precision=precision
                            )
                        except:
                            traceback.print_exc()
                            error = traceback.format_exc()
                            return f"The data processing was interrupted due an error !! Please check the console to verify the full error message! \n Error summary: {error}", "", ""

                    clear_gpu_cache()

                    if audio_total_size < 120:
                        message = "The total duration of the audio file or files you provided, was less than 2 minutes in length. Please add more audio samples."
                        print("[FINETUNE] ", message)
                        return message, "", ""

                    # Final GPU after full cleanup
                    get_gpu_memory()

                    print("[FINETUNE] [INFO] Dataset Generated. Either run Dataset Validation or move to Step 2")
                    # Return 3x speaker_name_input's at the end to satisfy `prompt_compute_btn.click` populating all the `personname` Project Name boxes across the Gradio interface
                    return "Dataset Generated. Either run Dataset Validation or move to Step 2", train_meta, eval_meta, speaker_name_input, speaker_name_input, speaker_name_input
            
           
            with gr.Tab("Dataset Generation Guide"):
                gr.Markdown("""
                # Dataset Generation Guide
                Below you'll find comprehensive instructions and information about generating your dataset. 
                Click each section to expand its contents.
                """)
                
                with gr.Accordion("🎯 Quick Start Guide", open=False):
                    gr.Markdown(f"""
                    1. Place audio files (MP3, WAV, or FLAC) in folder `/alltalk_tts/finetune/put-voice-samples-in-here` or upload through the interface
                    2. Provide 2+ minutes of clear audio (more is better and 5+ recommended). Variation in cadence and speech patterns recommended
                    3. Set your project name and adjust settings as needed
                    4. Generate the dataset
                    5. When your dataset it generated, you can either validate the dataset to confirm transcriptions or move to Step 2
                    
                    **Minimum Requirements:**
                    - 2 minutes of clear audio
                    - Consistent audio quality
                    - Minimal background noise
                    """)
                    
                with gr.Accordion("📋 Detailed Instructions", open=False):
                    gr.Markdown(f"""
                    #### Step-by-Step Process
                    1. Review [Coqui's guide on creating a good dataset](https://docs.coqui.ai/en/latest/what_makes_a_good_dataset.html)
                    2. Place your audio files in `{str(audio_folder)}` or upload through interface
                    3. Ensure at least 2 minutes of audio (5+ minutes recommended)
                    4. Set Maximum Audio Length (controls segment size per epoch)
                    5. Enter unique Project Name for dedicated training folder
                    6. Select appropriate Whisper model (see Model Selection guide below)
                    7. Choose Dataset Language (en Wisper models only support English)
                    8. Set Evaluation Split percentage (default 15% recommended)
                    9. Consider enabling BPE Tokenizer for unique speech patterns
                    """)
                    
                with gr.Accordion("🔧 Process Overview", open=False):
                    gr.Markdown("""
                    #### What Happens During Generation
                    1. Whisper transcribes and segments your audio
                    2. System creates smaller clips with transcriptions
                    3. Generates training and evaluation datasets
                    4. Organizes files in project directory
                    
                    #### Generated Files:
                    - `/finetune/[project_name]/lang.txt`: Language code
                    - `/finetune/[project_name]/metadata_train.csv`: Training data
                    - `/finetune/[project_name]/metadata_eval.csv`: Evaluation data
                    - `/finetune/[project_name]/wavs/`: Processed audio clips
                    """)
                    
                with gr.Accordion("🔍 Whisper Model Selection", open=False):
                    gr.Markdown("""
                    ### Choose Your Model
                    
                    #### Quick Selection Guide:
                    - **English Only, Limited Resources:** `tiny.en` or `base.en`
                    - **Multilingual, Limited Resources:** `tiny` or `base`
                    - **Balanced Performance:** `small` or `medium`
                    - **Highest Quality:** `large-v3`
                    - **Speed Priority:** `large-v3-turbo` or `turbo`
                    
                    ### Detailed Model Comparison
                    | Model            | Language | Speed         | VRAM  | Best For                   |
                    |------------------|----------|---------------|-------|-----------------------------|
                    | `tiny.en`        | English  | ⚡️⚡️⚡️⚡️⚡️     | 0.5GB | Quick English tests         |
                    | `tiny`           | All      | ⚡️⚡️⚡️⚡️⚡️     | 0.5GB | Quick multilingual tests    |
                    | `base.en`        | English  | ⚡️⚡️⚡️⚡️       | 1GB   | Basic English use           |
                    | `base`           | All      | ⚡️⚡️⚡️⚡️       | 1GB   | Basic multilingual use      |
                    | `small.en`       | English  | ⚡️⚡️⚡️         | 2GB   | Daily English use           |
                    | `small`          | All      | ⚡️⚡️⚡️         | 2GB   | Daily multilingual use      |
                    | `medium.en`      | English  | ⚡️⚡️           | 4GB   | Better English quality      |
                    | `medium`         | All      | ⚡️⚡️           | 4GB   | Better multilingual quality |
                    | `large-v1`       | All      | ⚡️             | 8GB   | Legacy support              |
                    | `large-v2`       | All      | ⚡️             | 8GB   | Improved legacy             |
                    | `large-v3`       | All      | ⚡️             | 10GB  | Best overall quality        |
                    | `large`          | All      | ⚡️             | 10GB  | Standard high quality       |
                    | `large-v3-turbo` | All      | ⚡️⚡️⚡️         | 10GB  | Fast high quality           |
                    | `turbo`          | All      | ⚡️⚡️⚡️⚡️        | 10GB  | Fastest high quality        |
                    """)
                    
                with gr.Accordion("⚙️ Advanced Settings", open=False):
                    gr.Markdown("""
                    ### Voice Activity Detection (VAD)
                    - Recommended: Enabled
                    - Detects and isolates speech segments
                    - Removes silence and non-speech audio
                    - Improves accuracy and processing speed
                    
                    ### Model Precision
                    #### Mixed Precision (Recommended for Modern GPUs)
                    - Balances speed and accuracy
                    - Best for NVIDIA GPUs Pascal architecture (GTX 10 series) and newer
                    - Automatically switches between FP16 and FP32 as needed
                    - Can significantly reduce VRAM usage

                    #### FP16 (Half Precision)
                    - Fastest processing and lowest memory usage
                    - Only supported on NVIDIA GPUs Pascal architecture (GTX 10 series) and newer
                    - May have slightly lower accuracy
                    - Not recommended for older GPUs as they lack hardware support

                    #### FP32 (Full Precision)
                    - Universal compatibility - works on all GPU models
                    - Required for older NVIDIA GPUs (pre-GTX 10 series)
                    - Default option for CPU processing
                    - Highest accuracy but uses more VRAM
                    - Slower processing speed compared to FP16/Mixed
                    - Use if experiencing stability issues with other precision modes

                    ### GPU Compatibility Guide
                                      
                    | Precision Mode   | Description                                   | Supported GPU Architectures                      | Examples of Compatible GPUs                        |
                    |------------------|-----------------------------------------------|--------------------------------------------------|----------------------------------------------------|
                    | **FP32 (Full Precision)** | Universal compatibility, highest accuracy  | All GPUs, including older models               | GTX 900 series and older, all AMD GPUs, all laptop GPUs before 2016 |
                    | **Mixed Precision (Recommended)** | Balances speed and accuracy, auto-switches between FP16 and FP32 | NVIDIA Turing (RTX 20 series) and newer       | RTX 20 series, RTX 30 series, RTX 40 series, Tesla V100, A100        |
                    | **FP16 (Half Precision)** | Fastest processing, lowest memory usage     | NVIDIA Turing and newer (with Tensor Cores)    | RTX 20 series, RTX 30 series, RTX 40 series, Tesla V100, A100        |
                    """)

                with gr.Accordion("🔍 Dataset Creation Debug Settings", open=False):
                    gr.Markdown("""
                    Each debug category serves a specific purpose in helping you understand what's happening during the dataset creation process:

                    #### GPU Memory Monitoring
                    - **What it shows:** Detailed GPU VRAM usage and allocation
                    - **When it's useful:**
                        * Troubleshooting out-of-memory errors
                        * Monitoring VRAM consumption during processing
                        * Understanding memory spikes during model loading
                        * Tracking memory cleanup effectiveness
                    - **Example messages:**
                        * "GPU Memory Status: Total: 8192 MB, Used: 3584 MB, Free: 4608 MB"
                        * "Low GPU memory available!"

                    #### Model Operations
                    - **What it shows:** Model loading, inference, and cleanup steps
                    - **When it's useful:**
                        * Tracking model initialization progress
                        * Monitoring transcription operations
                        * Debugging model-related errors
                        * Verifying proper model cleanup
                    - **Example messages:**
                        * "Loading Whisper model: large-v3"
                        * "Using FP16 precision"
                        * "Model successfully unloaded from GPU"

                    #### Data Processing
                    - **What it shows:** Text and dataset handling operations
                    - **When it's useful:**
                        * Understanding how text is being processed
                        * Tracking dataset creation progress
                        * Monitoring CSV file operations
                        * Debugging transcription issues
                    - **Example messages:**
                        * "Loaded existing training metadata"
                        * "Processing metadata and handling duplicates"
                        * "Created train/eval split with ratio 0.15"

                    #### General Flow
                    - **What it shows:** Overall process status and file operations
                    - **When it's useful:**
                        * Following the general progress
                        * Tracking file system operations
                        * Understanding process flow
                        * Identifying process bottlenecks
                    - **Example messages:**
                        * "Initializing output directory"
                        * "Dataset Generated. Move to Dataset Validation"
                        * "Successfully wrote metadata files"

                    #### Audio Processing
                    - **What it shows:** Audio file statistics and processing details
                    - **When it's useful:**
                        * Understanding audio segmentation
                        * Tracking audio duration statistics
                        * Identifying problematic audio files
                        * Monitoring audio quality issues
                    - **Example messages:**
                        * "Total segments: 157"
                        * "Average duration: 8.3s"
                        * "Found 3 segments under minimum duration"

                    #### Segments
                    - **What it shows:** Detailed information about audio segmentation
                    - **When it's useful:**
                        * Understanding how audio is being split
                        * Debugging segment length issues
                        * Tracking segment merging operations
                        * Identifying problematic splits
                    - **Example messages:**
                        * "Extended segment from 4.2s to 6.0s"
                        * "Merged 5 segments into 2 longer segments"
                        * "Segment too short (2.1s)"

                    #### Duplicates
                    - **What it shows:** Information about duplicate handling
                    - **When it's useful:**
                        * Tracking duplicate detection
                        * Understanding how duplicates are resolved
                        * Monitoring transcription quality
                        * Verifying duplicate cleanup
                    - **Example messages:**
                        * "Found 2 files with multiple transcriptions"
                        * "Re-transcribing duplicate files"
                        * "Updated transcription for file.wav"

                    ### Usage Tips
                    - Enable specific categories based on what you're trying to troubleshoot
                    - Use GPU Memory when dealing with VRAM issues
                    - Enable Segments when audio splitting seems incorrect
                    - Turn on Data Process when checking transcription quality
                    - Enable Duplicates when investigating repeated content
                    - Use General for overall process monitoring
                    - Enable Audio when investigating quality issues
                    - Turn on Model Ops when dealing with model-related problems
                    """)                 
       
                with gr.Accordion("❗ Troubleshooting", open=False):
                    gr.Markdown("""
                    ### Common Issues
                    
                    #### Windows Users
                    - If you see `UserWarning: huggingface_hub cache-system uses symlinks` you can safely ignore it or alternatively:
                    1. Close the application
                    2. Restart command prompt as Administrator
                    3. Relaunch the finetuning process
                    
                    ### Managing Whisper Model Files

                    #### Why Delete Model Files?
                    - Free up disk space (each model is ~3GB)
                    - Troubleshoot model-related issues
                    - Switch to different model versions
                    - Clear corrupted downloads

                    #### How to Find and Delete Whisper Models

                    Each Whisper model is downloaded automatically when first used and stored in your system's cache. Here's how to manage them:

                    **Windows:**
                    1. Open File Explorer
                    2. Copy and paste this path into the address bar:
                    ```
                    C:\\Users\\<YourUsername>\\.cache\\whisper
                    ```
                    (Replace `<YourUsername>` with your actual Windows username)
                    3. You'll see folders named after each model you've used (e.g., "base", "large-v3")

                    **Linux:**
                    1. Open your terminal or file manager
                    2. Navigate to:
                    ```
                    /home/<YourUsername>/.cache/whisper
                    ```
                    (Replace `<YourUsername>` with your Linux username)
                    3. Model files will be in this directory

                    **macOS:**
                    1. Open Finder
                    2. Press `Cmd+Shift+G` to open "Go to Folder"
                    3. Enter this path:
                    ```
                    /Users/<YourUsername>/.cache/whisper
                    ```
                    (Replace `<YourUsername>` with your macOS username)

                    #### To Delete Models:
                    1. **Individual Models:** Delete specific model folders to free up space
                    2. **All Models:** Delete the entire `whisper` folder to remove all cached models
                    3. Don't worry - models will automatically redownload when needed

                    #### Important Notes:
                    - Deleting models won't affect your training data or results
                    - New model downloads will occur next time you use a model
                    - Consider keeping frequently used models to avoid redownloading
                    """)

        with gr.TabItem("📊 Dataset Validation"):
            with gr.Row():
                gr.Markdown("""# Audio Transcription Validation""")
            with gr.Row():
                with gr.Accordion("🎯 Audio Transcription Validation Help", open=False):
                    with gr.Row():              
                        with gr.Column():
                            gr.Markdown("""
                            # Dataset Validation and Correction Tool

                            This tool helps you ensure the quality of your training data by comparing:
                            - Original transcriptions from your CSV dataset files
                            - Running new Whisper transcriptions of the same audio files
                            
                            Only mismatched transcriptions are shown for review, helping you focus on potential problems.

                            ### Understanding the Display
                            
                            #### Table Contents:
                            - **Original Text**: Current transcription from metadata files
                            - **Whisper Text**: New transcription from Whisper
                            - **Filename**: The audio file being transcribed

                            ### Common Mismatch Causes:
                            - Background noise interference
                            - Multiple speakers in audio
                            - Unclear pronunciation
                            - Audio quality issues
                            - Whisper confidence variations
                            - Punctuation differences
                            
                            ### Interactive Audio Editor

                            The built-in audio editor provides several powerful features:

                            #### Playback Controls:
                            - Play/Pause: Standard audio playback
                            - Seek Bar: Navigate through audio
                            - Volume Control: Adjust playback volume
                            - Speed Control: Adjust playback speed

                            #### Audio Editing Features:
                            - **Trim Start/End**: Remove silence or unwanted audio
                                * Drag handles on waveform edges
                                * Fine-tune start/end points
                                * Preview trimmed audio                            
                            """)

                        with gr.Column():
                            gr.Markdown("""
                            ...continued
                            
                            #### Tips for Audio Editing:
                            - Trim silence from start/end
                            - Remove background noises
                            - Keep only clear speech
                            - Maintain natural pauses
                            - Ensure clean cut points

                            ### Correction Workflow:

                            1. **Select an Entry**:
                                * Click any row in the mismatch table
                                * Audio loads automatically
                                * Both transcriptions display

                            2. **Review Audio**:
                                * Listen to original
                                * Edit if necessary
                                * Preview changes

                            3. **Choose Text Correction Method**:
                                * **Use Original**: Keep current transcription
                                * **Use Whisper**: Accept new transcription
                                * **Edit Manually**: Write custom transcription

                            4. **Save Changes**:
                                * Click "Save Audio and Correction"
                                * Both audio and text will be saved
                                * The table will reflect the change

                            ### Best Practices:
                            - Listen before editing
                            - Make minimal necessary cuts
                            - Verify transcription accuracy
                            - Check edited audio playback

                            Changes automatically update the appropriate CSV file and refresh the display.
                            """)                  
            
            with gr.Row():
                progress_box = gr.Textbox(label="Progress", interactive=False)                
                load_button = gr.Button("Run Validation")

            with gr.Row():
                with gr.Column(scale=2):
                    # Store full DataFrame in state
                    state = gr.State()
                    # Display DataFrame shows only visible columns
                    mismatch_table = gr.DataFrame(
                        headers=["Original Text", "Whisper Text", "Filename"],
                        datatype=["str", "str", "str"],
                        interactive=False,
                        wrap=True
                    )
                
                with gr.Column(scale=1):
                    audio_player = gr.Audio(
                        label="Audio Player (Edit enabled)", 
                        interactive=True
                    )
                    current_expected = gr.Textbox(label="Original Text", interactive=False)
                    current_transcribed = gr.Textbox(label="Whisper Text", interactive=False)
                    text_choice = gr.Radio(
                        choices=["Use Original", "Use Whisper", "Edit Manually"],
                        label="Choose Transcription",
                        value="Use Original"
                    )
                    manual_edit = gr.Textbox(
                        label="Manual Edit",
                        interactive=True,
                        visible=False
                    )
                    current_index = gr.Number(visible=False)
                    save_button = gr.Button("Save Audio and Correction")
                    save_status = gr.Textbox(label="Save Status", interactive=False)

            def update_audio_player(evt: gr.SelectData, df):
                try:
                    selected_row = df.iloc[evt.index]
                    audio_path = selected_row['full_path']
                    
                    # If it's a Series, get the first value
                    if isinstance(audio_path, pd.Series):
                        audio_path = audio_path.iloc[0]
                    
                    # Clean the path string
                    audio_path = str(audio_path).strip()
                    # print(f"[FINETUNE] Selected audio path: {audio_path}")
                    
                    # Get the text values directly from the row, ensuring we get strings not Series
                    expected = str(selected_row['expected_text']).strip() if isinstance(selected_row['expected_text'], str) else selected_row['expected_text'].iloc[0]
                    transcribed = str(selected_row['transcribed_text']).strip() if isinstance(selected_row['transcribed_text'], str) else selected_row['transcribed_text'].iloc[0]
                    
                    if not os.path.exists(audio_path):
                        print(f"[FINETUNE] Warning: Audio file not found: {audio_path}")
                        return {
                            audio_player: None,
                            current_expected: "",
                            current_transcribed: "",
                            current_index: None,
                            save_status: f"Error: Audio file not found"
                        }
                    
                    return {
                        audio_player: audio_path,
                        current_expected: expected,
                        current_transcribed: transcribed,
                        current_index: evt.index,
                        save_status: "Ready to save correction"
                    }
                except Exception as e:
                    print(f"[FINETUNE] Error in update_audio_player: {str(e)}")
                    print(f"[FINETUNE] Selected row data: {selected_row if 'selected_row' in locals() else 'Not available'}")
                    return {
                        audio_player: None,
                        current_expected: "",
                        current_transcribed: "",
                        current_index: None,
                        save_status: f"Error: {str(e)}"
                    }

            # Event handlers
            text_choice.change(
                lambda x: gr.update(visible=x=="Edit Manually"),
                text_choice,
                manual_edit
            )
            
            mismatch_table.select(
                update_audio_player,
                [state],  # Use full DataFrame from state
                [audio_player, current_expected, current_transcribed, current_index, save_status]
            )
            
            save_button.click(
                save_audio_and_correction,
                inputs=[text_choice, manual_edit, audio_player, state, current_index],
                outputs=[mismatch_table, current_expected, save_status, audio_player]
            )
            
            # Store both display and full DataFrame
            load_button.click(
                load_and_display_mismatches,
                outputs=[state, mismatch_table, progress_box]  # state gets full df, mismatch_table gets display_df
            )


#######################
#### GRADIO STEP 2 ####
#######################
        with gr.Tab("💻 Step 2 - Training"):
            with gr.Tab("Training the model"):
                with gr.Row():
                    speaker_name_input_training = gr.Textbox(
                        label="Project Name",
                        value="personsname",
                        visible=True,
                        scale=1,
                    )
                    with gr.Group():
                        continue_run = gr.Checkbox(
                            value=False,
                            label="Continue Previous Project",
                            scale=1,
                            visible=False,
                        )
                        # Continue Run has been set to invisible from the interface as its setting a 1000 epoch run when used. Have been unable to track down where this is coming from.
                        disable_shared_memory = gr.Checkbox(
                            value=False,
                            label="Disable Shared Memory Use",
                            scale=1,
                        )
                        warm_up = gr.Checkbox(
                            value=False,
                            label="Perform Warmup Learning",
                            scale=1,
                        )
                    train_csv = gr.Textbox(
                        label="Train CSV file path:",
                        scale=2,
                    )
                    eval_csv = gr.Textbox(
                        label="Eval CSV file path:",
                        scale=2,
                    )
                with gr.Row():
                    model_to_train_choices = list(available_models.keys())
                    model_to_train = gr.Dropdown(
                        choices=model_to_train_choices,
                        label="Select the Model to train",
                        value=model_to_train_choices[0] if model_to_train_choices else None,
                        scale=2,
                    )                  
  
                    learning_rates = gr.Dropdown(
                        value=5e-6,
                        label="Learning Rate",
                        choices=[
                            ("1e-6", 1e-6),
                            ("5e-6", 5e-6),
                            ("1e-5", 1e-5),
                            ("5e-5", 5e-5),
                            ("1e-4", 1e-4),
                            ("5e-4", 5e-4),
                            ("1e-3", 1e-3),
                        ],
                        type="value",
                        allow_custom_value=True,
                        scale=1,
                    )
                    learning_rate_scheduler = gr.Dropdown(
                        value="CosineAnnealingWarmRestarts",
                        label="Learning Rate Scheduler(s)",
                        choices=[
                            ("None", "None"),
                            ("Cosine Annealing", "CosineAnnealingLR"),
                            ("Cosine Annealing Warm Restarts", "CosineAnnealingWarmRestarts"),
                            ("Cyclic", "CyclicLR"),
                            ("Exponential", "ExponentialLR"),
                            ("Multi Step", "MultiStepLR"),
                            ("Reduce on Plateau", "ReduceLROnPlateau"),
                            ("Step", "StepLR")
                            #("OneCycleLR", "OneCycleLR"),
                        ],
                        type="value",
                        allow_custom_value=False,
                        scale=2,
                    )
                    optimizer = gr.Dropdown(
                        value="AdamW",
                        label="Optimizer",
                        choices=[
                            ("AdamW", "AdamW"),
                            ("SGD with Momentum", "SGD"),
                            ("RMSprop", "RMSprop"),
                            ("Adagrad", "Adagrad"),
                            ("Adam", "Adam"),
                            ("Rectified Adam", "RAdam"),
                            ("Step Wise", "stepwisegraduallr"),
                            ("Noam", "noamlr")
                        ],
                        type="value",
                        allow_custom_value=False,
                        scale=2,
                    )
                    num_workers = gr.Dropdown(
                        value="8",
                        label="Workers/Threads",
                        choices=[
                            "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"
                        ],
                        allow_custom_value=False,
                        interactive=True,                     
                        scale=0,
                    )
                with gr.Row():
                    num_epochs =  gr.Slider(
                        label="Number of epochs:",
                        minimum=1,
                        maximum=100,
                        step=1,
                        value=args.num_epochs,
                    )
                    batch_size = gr.Slider(
                        label="Batch size:",
                        minimum=2,
                        maximum=512,
                        step=1,
                        value=args.batch_size,
                    )
                    grad_acumm = gr.Slider(
                        label="Grad accumulation steps:",
                        minimum=1,
                        maximum=128,
                        step=1,
                        value=args.grad_acumm,
                    )
                    max_audio_length = gr.Slider(
                        label="Max permitted audio size in seconds:",
                        minimum=2,
                        maximum=20,
                        step=1,
                        value=args.max_audio_length,
                    )

                progress_train = gr.Label(
                    label="Progress:"
                )

                with gr.Row():
                    train_time = gr.Label("Estimated Total Training Time", show_label=False, scale=2)
                    train_btn = gr.Button(value="Step 2 - Run the training", scale=1)

                with gr.Row():
                    model_data = gr.Image(c_logger.plot_metrics(), show_label=False)

                logs_tts_train = gr.Textbox(
                    label="Logs:",
                    interactive=False,
                    lines=10,
                )
                demo.load(load_metrics, None, [model_data, train_time], every=1)
                demo.load(read_logs, None, logs_tts_train, every=1)

                def train_model(language, train_csv, eval_csv, learning_rates, model_to_train, num_epochs, batch_size, grad_acumm, max_audio_length, speaker_name_input_training, continue_run, disable_shared_memory, learning_rate_scheduler, optimizer, num_workers, warm_up, progress=gr.Progress()):
                    clear_gpu_cache()
                    global out_path
                    if speaker_name_input_training and speaker_name_input_training != 'personsname':
                        out_path = this_dir / "finetune" / speaker_name_input_training
                    else:
                        out_path = default_path

                    if not train_csv or not eval_csv:
                        if (out_path / "metadata_eval.csv").exists() and (out_path / "metadata_train.csv").exists():
                            train_csv = out_path / "metadata_train.csv"
                            eval_csv = out_path / "metadata_eval.csv"
                            print("Using existing metadata and training csv.")
                        else:
                            return "You need to run the data processing step or manually set `Train CSV` and `Eval CSV` fields !", "", "", "", ""
                    try:
                        # convert seconds to waveform frames
                        max_audio_length = int(max_audio_length * 22050)
                        learning_rate = float(learning_rates)  # Convert the learning rate value to a float
                        progress(0, "Initializing training...")
                        config_path, original_xtts_checkpoint, vocab_file, exp_path, speaker_wav = train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv, learning_rate, model_to_train, continue_run, disable_shared_memory, learning_rate_scheduler, optimizer, num_workers, warm_up, max_audio_length=max_audio_length, progress=gr.Progress())
                    
                        # copy original files to avoid parameters changes issues
                        shutil.copy(config_path, exp_path)
                        shutil.copy(vocab_file, exp_path)

                        ft_xtts_checkpoint = os.path.join(exp_path, "best_model.pth")
                        print("[FINETUNE] Model training done. Move to Step 3")
                        clear_gpu_cache()
                        return "Model training done. Move to Step 3", config_path, vocab_file, ft_xtts_checkpoint, speaker_wav, speaker_name_input_training

                    except ValueError as ve:
                        # This will catch the specific ValueError we raised in train_gpt
                        error_message = str(ve)
                        print(f"[FINETUNE] Error: {error_message}")
                        return f"Training error: {error_message}", "", "", "", "", ""
                    except Exception as e:
                        # This will catch any other unexpected errors
                        error_message = f"An unexpected error occurred: {str(e)}"
                        print(f"[FINETUNE] Error: {error_message}")
                        return f"Training error: {error_message}", "", "", "", "", ""
                
            with gr.Tab("Training Guide"):
                gr.Markdown("""
                # Comprehensive Training Guide
                Below you'll find detailed explanations of all training parameters and processes. 
                Each section contains both conceptual explanations and specific configuration guidance.
                """)
                
                with gr.Accordion("🎯 Quick Start Training Guide", open=False):
                    gr.Markdown("""
                    Before diving into specific settings, it's important to understand the basic workflow of training your model. 
                    This section provides an overview of the essential parameters you'll need to get started with training.
                    
                    The training process involves feeding your prepared dataset through the model multiple times (epochs) while adjusting
                    the model's parameters using specific optimization strategies. The goal is to teach the model to replicate your voice
                    accurately while maintaining natural speech patterns.

                    #### Required Settings:
                    - **Project Name:** Name for your training session
                    - **Train/Eval CSV:** Should be auto-populated from Step 1
                    - **Model Selection:** Choose your base model
                    - **Basic Parameters:**
                        * Epochs: How many training cycles (default: 10)
                        * Batch Size: Samples per training step (default: 4)
                        * Grad Accumulation: Steps between updates (default: 1)
                        * Learning Rate: Training speed (default: 5e-6)

                    #### Learning Settings
                    - **Learning Rate:** 5e-6 (recommended start)
                    - **Optimizer:** AdamW (default)
                    - **Scheduler:** CosineAnnealingWarmRestarts (default)
                    
                    These default settings are carefully chosen to provide a good starting point for most training scenarios. They
                    balance training speed with stability and are suitable for most consumer-grade GPUs.
                    """)

                with gr.Accordion("📁 Using Your Own Training Dataset", open=False):
                    gr.Markdown("""
                    If you are **not** using a dataset built on Step 1 and wish to use your own custom pre-built dataset, you can do so with the following instructions.
                    
                    To train the model with your custom dataset, follow these steps carefully to ensure your files and folder structure are set up correctly.

                    #### Folder and File Structure:
                    1. **Create a project folder:** Navigate to `/alltalk_tts/finetune/` and create a new folder with the name of your project (e.g., `myproject`).
                    2. **Prepare Required Files:**
                        - Inside your project folder, add a file named `lang.txt` containing the two-letter language code for your dataset (e.g. `en` for English, `de` for German).
                        - Place your `metadata_train.csv` and `metadata_eval.csv` files in the project folder. These files must follow the dataset formatting guidelines provided by [Coqui AI documentation](https://docs.coqui.ai/en/latest/formatting_your_dataset.html).
                    3. **Add Your Audio Files:**
                        - Within your project folder, create a subfolder named `wavs` and add your audio files here. The audio files should be in `.wav` format.

                    #### CSV Files: Training vs. Evaluation
                    - **Training Data (`metadata_train.csv`):** This CSV file contains the majority of your dataset. The model uses this data to learn and adapt to the patterns in your audio. It’s recommended to have about 85% of your audio files in the training dataset, though this ratio can be adjusted based on your needs.
                    - **Evaluation Data (`metadata_eval.csv`):** This CSV file contains a smaller subset of your audio (typically around 15%). During training, the model periodically tests itself on this evaluation data to gauge how well it is learning. This evaluation step helps prevent overfitting, where the model might learn training data too closely and struggle with new data.

                    **Guideline:** The `metadata_train.csv` and `metadata_eval.csv` files must follow the formatting guidelines provided by [Coqui AI documentation](https://docs.coqui.ai/en/latest/formatting_your_dataset.html).

                    #### Updating the Gradio Interface:
                    - In the `Project Name` field, enter the name of your project folder (e.g., `myproject`).
                    - Populate the `Train CSV file path` and `Eval CSV file path` fields with the full paths to your `metadata_train.csv` and `metadata_eval.csv` files, respectively.
                    - Once set up, you are ready to begin training with your custom dataset.

                    #### Directory Structure Example:
                    ```plaintext
                    finetune/
                    └── {projectfolder}/ 
                        ├── metadata_train.csv
                        ├── metadata_eval.csv
                        ├── lang.txt
                        └── wavs/
                            ├── audio1.wav
                            ├── audio2.wav
                            └── ...
                    ```
                    """)

                with gr.Accordion("📊 Training Metrics and Logs", open=False):
                    gr.Markdown("""
                    ### Understanding Training Metrics

                    The training progress is visualized through six different graphs, each showing different aspects of the training process:

                    #### 1. Epoch Metrics (Avg Losses)
                    - Shows average losses across full epochs
                    - Lower values indicate better model performance
                    - Three metrics tracked:
                        * Avg Loss (Green): Overall model loss (may be hidden behind the blue line)
                        * Avg Loss Text CE (Red): Text cross-entropy loss
                        * Avg Loss MEL CE (Blue): Audio MEL spectrogram loss
                    
                    #### 2. Step-wise Loss Metrics
                    - Shows individual training step losses
                    - More granular view than epoch metrics
                    - Same color coding as epoch metrics
                    - Helps identify specific problematic training steps

                    #### 3. Learning Rate Schedule
                    - Shows learning rate changes over training steps
                    - Yellow line indicates current learning rate
                    - Useful for verifying scheduler behavior
                    - Cosine annealing shows cyclical pattern

                    #### 4. Gradient Norm over Steps
                    - Indicates how dramatically weights are changing
                    - Helps monitor training stability
                    - Sudden spikes may indicate training issues
                    - Should generally remain stable

                    #### 5. Step and Loader Times
                    - Orange: Time taken for each training step
                    - Cyan: Time taken to load data
                    - Helps identify performance bottlenecks
                    - Higher loader times may indicate I/O issues

                    #### 6. Training vs Validation Loss
                    - Compares training and validation performance
                    - Green: Training loss
                    - Red: Validation loss
                    - Growing gap may indicate overfitting

                    ### Detailed Training Logs
                    
                    A complete record of your training run is stored in `trainer_0_log.txt`, located in your training folder:
                    ```
                    finetune/[project_name]/training/XTTS_FT-[timestamp]/trainer_0_log.txt
                    ```
                    
                    This log contains:
                    - All training metrics
                    - Detailed step information
                    - Model configuration
                    - Performance statistics
                    - Error messages (if any)

                    **Note:** This log file will be deleted when you use the "Delete Training Data" option in the Model Export step. If you want to keep a record of your training run, make sure to save a copy of this file before deleting the training data.

                    ### Reading the Graphs

                    - **Downward Trends** in loss values indicate improving model performance
                    - **Smooth Lines** suggest stable training
                    - **Jagged Lines** might indicate instability or need for hyperparameter adjustment
                    - **Plateaus** in loss curves might indicate learning has stalled
                    - **Diverging** validation and training losses might indicate overfitting

                    ### Common Patterns

                    - **Good Training Run:**
                        * Steadily decreasing losses
                        * Stable gradient norms
                        * Consistent step times
                        * Close training/validation losses

                    - **Potential Issues:**
                        * Rapidly increasing losses
                        * Highly erratic gradient norms
                        * Growing gap between training/validation loss
                        * Inconsistent step times
                    """)
                
                with gr.Accordion("💾 Memory Management", open=False):
                    gr.Markdown("""
                    Memory management is crucial for successful training. Understanding how different parameters affect memory usage
                    can help you optimize your training process and avoid out-of-memory errors. The two main types of memory to 
                    consider are GPU VRAM (Video RAM) and system RAM.

                    Your GPU's VRAM is the primary resource that will limit what you can do during training. The more VRAM you have,
                    the larger batches you can process and the faster your training can progress. However, even with limited VRAM,
                    there are several strategies you can use to successfully train your model.

                    #### VRAM Requirements:
                    - **12GB+ VRAM (Recommended)**
                        * Optimal performance
                        * Fewer memory constraints
                        * Better batch size options
                        * Suitable for larger models
                    - **8GB VRAM (Minimum)**
                        * Windows: Requires 24GB+ System RAM
                        * Linux: May encounter limitations
                        * Requires careful parameter tuning
                        * May need smaller batch sizes

                    #### Memory Optimization Strategies:
                    - **Batch Size Reduction**
                        * Smaller batches use less VRAM
                        * May increase training time
                        * Start small, increase if stable
                        * Monitor memory usage while adjusting
                    
                    - **Gradient Accumulation**
                        * Simulates larger batches
                        * Uses less peak memory
                        * Helps with limited VRAM
                        * Maintains training stability
                    
                    - **Precision Settings**
                        * Mixed precision for modern GPUs
                        * FP32 for older GPUs
                        * FP16 for maximum efficiency
                        * Balance accuracy vs. memory
                    
                    - **Worker Threads**
                        * Fewer workers = less memory
                        * Balance between speed and RAM usage
                        * Adjust based on system resources
                        * Start with 4-8 workers

                    #### Shared Memory Option:
                    The shared memory option affects how your system handles memory overflow:

                    - **Disable Shared Memory Use:**
                        * Limits GPU to 95% VRAM usage
                        * Prevents system RAM spillover
                        * Recommended for high-VRAM GPUs
                        * Clearer error messages
                    
                    - **Keep Enabled for:**
                        * Windows with low VRAM GPUs
                        * Systems with plenty of RAM
                        * When encountering OOM errors
                        * Better fallback options
                    """)

                with gr.Accordion("⚙️ Batch Size & Gradient Accumulation", open=False):
                    gr.Markdown("""
                    Understanding batch size and gradient accumulation is crucial for successful training. These two parameters work
                    together to determine how your model processes data and updates its weights during training.

                    #### Batch Size Explained
                    Batch size determines how many audio samples are processed simultaneously during training. Think of it like
                    cooking multiple meals at once - while it's more efficient to cook several dishes simultaneously, your kitchen
                    (GPU memory) needs to be large enough to handle it.

                    - **What It Controls:**
                        * Number of samples processed together
                        * Memory usage per step
                        * Training stability
                        * Learning effectiveness
                    
                    - **Guidelines:**
                        * Larger = faster but more VRAM
                        * Smaller = stable but slower
                        * Find balance for your GPU
                        * Monitor training stability
                    
                    - **Recommended Ranges:**
                        * 12GB+ VRAM: 4-8 batch size
                        * 8GB VRAM: 2-4 batch size
                        * Adjust based on performance
                        * Start small and increase if stable

                    #### Gradient Accumulation Deep Dive
                    Gradient accumulation is like keeping a running tally of changes before updating your recipe. Instead of
                    adjusting your cooking technique after each meal, you gather feedback from several attempts before making
                    changes.

                    - **Purpose:**
                        * Simulates larger batch sizes
                        * Reduces memory usage
                        * Maintains training stability
                        * Improves learning quality
                    
                    - **How It Works:**
                        * Accumulates gradients over steps
                        * Updates model less frequently
                        * Allows larger effective batches
                        * Maintains learning quality
                    
                    - **Setting Guidelines:**
                        * Start with 1 (default)
                        * Increase if OOM errors occur
                        * Multiply with batch size for true batch
                        * Monitor training progress
                    
                    - **Common Values:**
                        * 1-4: Standard usage
                        * 4-8: Memory constrained
                        * 8+: Severe memory limitations
                        * Adjust based on stability

                    By carefully balancing batch size and gradient accumulation, you can achieve good training results even
                    with limited GPU resources. Start conservative and adjust based on your system's performance.
                    """)

                with gr.Accordion("📊 Learning Rate & Schedulers", open=False):
                    gr.Markdown("""
                    The learning rate and its scheduling are perhaps the most critical aspects of training. Think of the learning
                    rate as the size of the steps your model takes when learning - too large and it might overshoot the best
                    solution, too small and it might take too long to get there.

                    #### Understanding Learning Rates
                    The learning rate determines how much the model changes in response to each batch of training data. Finding
                    the right learning rate is crucial for successful training.

                    #### Learning Rate Options:
                    | Learning Rate Range        | Description                               | Best Use Cases                | Additional Notes                  |
                    |----------------------------|-------------------------------------------|-------------------------------|------------------------------------|
                    | **1e-6 (Very Conservative)** | Extremely stable, very slow progress      | Fine adjustments              | Use when stability is crucial      |
                    | **5e-6 (Recommended Start)** | Good balance of speed and stability       | Most training scenarios       | Best starting point, stable        |
                    | **1e-5 to 5e-5 (Moderate)**  | Faster learning, can be unstable          | Experienced users             | Requires monitoring                |
                    | **1e-4 to 1e-3 (Aggressive)**| Very fast, high risk of instability       | Advanced users                | Caution required, may diverge      |
                    | **5e-4 to 1e-3 (Very Aggressive)** | Rapid rate, often too large            | Experimental only             | High risk of divergence, high regularization needed |

                    #### Learning Rate Schedulers Explained
                    Schedulers automatically adjust the learning rate during training. This can help fine-tune the learning
                    process and avoid getting stuck in suboptimal solutions.

                    #### Scheduler Types:
                    | Scheduler                       | Description                                    | Best Use Cases                             | Additional Notes              |
                    |---------------------------------|------------------------------------------------|--------------------------------------------|--------------------------------|
                    | **None**                        | Fixed learning rate throughout                 | Testing                                    | Simple, predictable behavior; may miss optimal points |
                    | **CosineAnnealingWarmRestarts** | Cyclical learning rate changes                 | General training, exploration             | Requires 4+ epochs; best all-around choice |
                    | **CosineAnnealingLR**           | Smooth cosine decay, no restarts               | Continuous decay needs, stable convergence | Suitable for gradual reduction |
                    | **StepLR**                      | Step-wise reduction                            | Planned schedules, predictable decay       | Allows manual step control |
                    | **MultiStepLR**                 | Step reductions at multiple intervals          | Customized training schedules              | Requires precise tuning |
                    | **ReduceLROnPlateau**           | Adapts to training performance                 | Avoiding plateaus, unknown scenarios       | Reduces rate when improvement slows |
                    | **ExponentialLR**               | Exponential decay by a fixed factor            | Rapid convergence                          | Requires monitoring |
                    | **CyclicLR**                    | Cycles between min and max rates               | Early-stage convergence, high variability  | Advanced usage |
                    | **OneCycleLR**                  | Peaks and returns over single cycle            | Large datasets, fast single-cycle training | Efficient but requires setup |

                    The choice of scheduler can significantly impact your training results. The recommended
                    CosineAnnealingWarmRestarts provides a good balance of exploration and exploitation during training.
                    
                    #### Perform Warmup Learning
                    The warmup learning phase is a useful feature for stabilizing early training. During warmup, the learning rate gradually increases from a low initial value to the target learning rate over a set number of steps or epochs. This helps the model adjust gradually, especially beneficial for high learning rates or large batch sizes.

                    - **What Warmup Does:**
                        * Prevents large early adjustments
                        * Improves initial stability
                        * Gradually increases learning rate
                        * Helps avoid gradient explosion

                    - **When to Use:**
                        * Recommended with high learning rates
                        * Useful for models that diverge early
                        * Ideal for large batch sizes
                        * Suitable for most cases

                    - **Guidelines:**
                        * Enable to improve training stability
                        * Monitor initial learning curve
                        * Adjust warmup steps as needed

                    The warmup feature, labeled as **Perform Warmup Learning**, provides a smoother start to the training process, making it ideal for ensuring stability and allowing the model to settle into a balanced learning pattern.
                    """)

                with gr.Accordion("🔧 Optimizers", open=False):
                    gr.Markdown("""
                    Optimizers are the algorithms that actually update your model's weights during training. Different optimizers
                    have different characteristics and can be better suited for certain types of training tasks.

                    Think of optimizers as different strategies for adjusting your model's parameters - some are more aggressive,
                    some more conservative, and some try to be more adaptive to the specific characteristics of your training data.

                    #### Available Optimizers:
                    | Optimizer                 | Description                                     | Best Use Cases                      | Additional Notes                             |
                    |---------------------------|-------------------------------------------------|-------------------------------------|----------------------------------------------|
                    | **AdamW (Recommended)**   | Best all-around performance with weight decay   | General training, modern standard  | Stable convergence, handles most scenarios well |
                    | **Adam**                  | Similar to AdamW, less regularization           | Small datasets, testing            | Reliable, well-established optimizer        |
                    | **SGD with Momentum**     | Traditional approach with momentum              | Learning optimization              | Requires more tuning, good for manual control |
                    | **RMSprop**               | Adaptive learning rates, good for RNNs          | Specialized use cases              | Handles varying gradients, alternative to Adam |
                    | **RAdam**                 | Rectified Adam with stable start                | Large batches, advanced setups     | Improved convergence, newer variant         |
                    | **Adagrad**               | Adjusts learning rates for sparse data          | Tasks with rare features           | Slower convergence, handles infrequent updates well |
                    | **Step Wise (StepLR)**    | Reduces learning rate in predefined steps       | Long training schedules            | Requires careful setup, effective for staged training |
                    | **Noam (NoamLR)**         | Uses warmup and decay, designed for NLP         | Transformer architectures          | Good for gradual adaptation, specialized for certain tasks |

                    For most users, AdamW provides the best combination of performance and stability. It includes good defaults
                    that work well across a wide range of scenarios and includes proper weight decay regularization.
                    """)

                with gr.Accordion("🔄 Training Epochs", open=False):
                    gr.Markdown("""
                    An epoch represents one complete pass through your entire training dataset. Understanding how many epochs to
                    use and how to monitor training progress is crucial for achieving good results.

                    The number of epochs needed can vary significantly depending on your dataset size, model complexity, and
                    desired output quality. Too few epochs might result in underfitting, while too many could lead to overfitting.

                    #### Understanding Epochs:
                    - **What Is An Epoch**
                        * Complete dataset pass
                        * Training milestone
                        * Progress measurement
                        * Learning cycle
                        * Quality checkpoint
                        * Resource consideration
                    
                    - **How Many to Use:**
                        * Minimum: 4-5 epochs
                        * Standard: 10-15 epochs
                        * Maximum: Based on monitoring
                        * Dataset dependent
                        * Quality dependent
                        * Resource dependent
                    
                    - **Monitoring Factors:**
                        * Loss curves
                        * Evaluation metrics
                        * Generated audio quality
                        * Training stability
                        * Resource usage
                        * Time constraints
                    
                    - **Warning Signs:**
                        * Increasing eval loss
                        * Degrading quality
                        * Plateauing metrics
                        * Unstable metrics
                        * Resource exhaustion
                        * Diminishing returns

                    Training for the right number of epochs is crucial. Start with the standard range of 10-15 epochs and adjust
                    based on your results. Pay attention to both quantitative metrics and qualitative audio output quality.
                    """)

                with gr.Accordion("📈 Max Audio Length", open=False):
                    gr.Markdown("""
                    The maximum audio length setting controls how long individual audio segments can be during training. This
                    setting has important implications for both memory usage and training effectiveness.

                    This parameter needs to be balanced carefully - longer segments can capture more context but require more
                    memory and processing power. Shorter segments are more memory-efficient but might miss important long-term
                    patterns in speech.

                    #### Configuration Impact:
                    - **Purpose:**
                        * Controls maximum clip length
                        * Affects memory usage
                        * Influences training stability
                        * Determines context window
                        * Impacts training speed
                        * Memory management tool
                    
                    - **Setting Guidelines:**
                        * Default: 11 seconds
                        * Minimum: 2 seconds
                        * Maximum: 20 seconds
                        * System dependent
                        * Quality dependent
                        * Resource balanced
                    
                    - **Considerations:**
                        * Longer = more VRAM needed
                        * Shorter = more segments
                        * Balance quality vs resources
                        * Context preservation
                        * Memory limitations
                        * Training stability

                    #### Optimization Tips:
                    - Start with default (11s)
                    - Reduce if encountering OOM
                    - Increase if audio quality suffers
                    - Monitor system resources
                    - Check segment distribution
                    - Balance with batch size

                    The default setting of 11 seconds works well for most cases, providing a good balance between context
                    preservation and resource usage. Adjust based on your specific needs and system capabilities.
                    """)

                
#######################
#### GRADIO STEP 3 ####
#######################
            
        with gr.Tab("✅ Step 3 - Testing"):
            with gr.Tab("Testing"):
                with gr.Row():
                    with gr.Column() as col1:
                        xtts_checkpoint = gr.Dropdown(
                            [str(file) for file in xtts_checkpoint_files],
                            label="XTTS checkpoint path (best_model.pth):",
                            value="",
                            allow_custom_value=True,
                        )

                        xtts_config = gr.Dropdown(
                            [str(file) for file in xtts_config_files],
                            label="XTTS config path (config.json):",
                            value="",
                            allow_custom_value=True,
                        )

                        xtts_vocab = gr.Dropdown(
                            [str(file) for file in xtts_vocab_files],
                            label="XTTS vocab path (vocab.json):",
                            value="",
                            allow_custom_value=True,
                        )
                        progress_load = gr.Label(
                            label="Progress:"
                        )
                        load_btn = gr.Button(value="Step 3 - Load Fine-tuned XTTS model")

                    with gr.Column() as col2:
                        with gr.Row():
                            # Gather the voice files
                            available_speaker_audios = get_available_voices()

                            # Create Dropdown for speaker reference audio
                            speaker_reference_audio = gr.Dropdown(
                                available_speaker_audios,
                                label="Speaker reference audio (Press Refresh Dropdowns):",
                                value="",  # Set the default value if needed
                                allow_custom_value=True,  # Allow custom values
                                scale=2,
                            )
                            min_audio_length = gr.Dropdown(
                                label="Min Audio Length (seconds)",
                                value="6",
                                choices=["3", "4", "5", "6", "7", "8", "9", "10"],
                                type="value",
                                allow_custom_value=False,
                                scale=1,
                            )                            
                        with gr.Row():
                            speaker_name_input_testing = gr.Textbox(
                                label="Project Name",
                                value="personsname",
                                visible=True,
                                scale=1,
                            )                            
                            tts_language = gr.Dropdown(
                                label="Language",
                                value="en",
                                choices=[
                                    "en",
                                    "es",
                                    "fr",
                                    "de",
                                    "it",
                                    "pt",
                                    "pl",
                                    "tr",
                                    "ru",
                                    "nl",
                                    "cs",
                                    "ar",
                                    "zh",
                                    "hu",
                                    "ko",
                                    "ja",
                                ]
                            )
                            # Create refresh button
                            refresh_button = create_refresh_button(
                                [xtts_checkpoint, xtts_config, xtts_vocab, speaker_reference_audio,
                                speaker_name_input_testing],
                                [
                                    lambda speaker_name, min_duration_seconds: {"choices": find_best_models(out_path, speaker_name=speaker_name), "value": ""},
                                    lambda speaker_name, min_duration_seconds: {"choices": find_jsons(out_path, "config.json", speaker_name=speaker_name), "value": ""},
                                    lambda speaker_name, min_duration_seconds: {"choices": find_jsons(out_path, "vocab.json", speaker_name=speaker_name), "value": ""},
                                    lambda speaker_name, min_duration_seconds: {"choices": get_available_voices(min_duration_seconds=int(min_duration_seconds), speaker_name=speaker_name), "value": ""},
                                ],
                                elem_class="refresh-button-class"
                            )
                        tts_text = gr.Textbox(
                            label="Input Text:",
                            value="I've just fine tuned a text to speech language model and this is how it sounds. If it doesn't sound right, I will try a different Speaker Reference Audio file.",
                            lines=5,
                        )
                        tts_btn = gr.Button(value="Step 4 - Inference (Generate TTS)")

                with gr.Row():
                    progress_gen = gr.Label(
                        label="Progress:"
                    )
                with gr.Row():
                    tts_output_audio = gr.Audio(label="TTS Generated Speech.")
                    reference_audio = gr.Audio(label="Speaker Reference Audio Sample.")
                        
            with gr.Tab("Testing Guide"):
                gr.Markdown("""
                # Testing Your Fine-tuned Model 
                This section guides you through evaluating the quality of your fine-tuned model using various reference audios. Testing ensures your model produces high-quality TTS that matches your target speaker characteristics. Follow the steps below for effective testing and validation.
                """)

                with gr.Accordion("🎯 Testing Overview", open=False):
                    gr.Markdown("""
                    The testing phase allows you to assess your model's TTS performance by loading a fine-tuned XTTS model, selecting a speaker reference audio, and generating sample speech. This helps verify that your model closely matches the desired speaker's voice, tone, and inflection.

                    - **Select a Speaker Reference**: Reference audios 8 seconds or longer are available for testing to ensure a meaningful TTS quality check.
                    - **Testing Multiple References**: Use various speaker reference audios to identify which one provides the most accurate TTS results for your intended use.
                    - **Exporting Your Model**: Once satisfied with your model's performance, you can proceed to export it. All reference audio files will be packaged with your model export, along with a text document explaining the available audio files, their durations, and the relevance of each for TTS generation.

                    After testing, you'll be ready to export your model and prepare it for integration into your TTS applications.
                    """)

                with gr.Accordion("⚠️ Important Notes", open=False):
                    gr.Markdown("""
                    Before beginning the testing phase, please review these important considerations to ensure a smooth experience.

                    - **Refreshing Dropdowns**: If dropdown lists do not seem fully populated or correct, use the **Refresh Dropdowns** button. This rescans the finetuning folder path and repopulates the lists with updated reference files. You can also manually edit paths if necessary.
                    - **Compatible Configurations**: You may use any `config.json` or `vocab.json` files as long as they match the model version used during training. The key file is the XTTS checkpoint, which is essential for model loading.
                    - **Reference File Length Requirement**: Any audio clips under 7 seconds are excluded from the reference list by default, as shorter clips generally do not provide sufficient information for quality TTS generation.
                    - **Unexpected Reference Counts**: If the number of reference files is lower than anticipated, it may be due to Whisper's initial segmentation step, which sometimes results in shorter or longer-than-ideal clips. If needed, try manually segmenting your audio files to ensure optimal segments for Whisper processing and training.
                    """)

                with gr.Accordion("📝 Testing Instructions", open=False):
                    gr.Markdown("""
                    Follow these instructions to test and validate your fine-tuned model effectively.

                    1. **Refresh Dropdowns**: Begin by clicking **Refresh Dropdowns** to ensure all speaker reference WAV files are listed correctly in the dropdown. This step ensures that any new files added are included.
                    2. **Load the Fine-tuned Model**: Load the XTTS model from your training. The dropdowns should automatically populate with available reference options.
                    3. **Select a Speaker Reference Audio**: Choose from the available speaker references in the dropdown list. 
                    - **Experiment with Multiple References**: Test with different reference audios to identify which best captures your target speaker's characteristics.
                    - **Reference Length Considerations**: Reference audios longer than 8 seconds are prioritized here, as they generally provide more reliable TTS quality.
                    4. **Troubleshooting Long Reference Files**: Some references may be too lengthy, potentially causing issues in generating TTS output. If this occurs, try a different reference sample from the list.
                    5. **Evaluate Results**: Listen to the generated TTS outputs. Consider both the voice quality and consistency with your intended speaker's tone, as well as clarity and naturalness of speech.

                    Once satisfied with the TTS performance, you can move to the next stage for exporting your model and managing your training data.
                    """)

                with gr.Accordion("🔍 What the Testing Step Does", open=False):
                    gr.Markdown("""
                    The testing step serves as the final stage for validating your fine-tuned model's performance.

                    - **Load Model and Configuration**: This step loads the fine-tuned XTTS model into memory, along with any compatible configuration files specified.
                    - **Generate TTS Output**: Using the selected speaker reference audio, the model generates TTS to assess its quality and voice accuracy. This allows you to evaluate how well the model aligns with the desired voice characteristics and whether further refinement is needed.
                    - **Evaluate Model Effectiveness**: Listen carefully to the TTS samples generated. This step is essential to confirm that the model is effectively fine-tuned and ready for deployment.

                    The testing stage allows you to confirm that your model accurately matches the intended voice characteristics before final export.
                    """)
                        
        with gr.Tab("📦 Model Export"):
            with gr.Accordion("🎯 Export Overview", open=False):
                gr.Markdown("""
                The model export process compresses your trained model, organizes your voice samples, and prepares everything for production use. This phase helps you:
                
                - Package your fine-tuned model efficiently
                - Organize voice samples by quality
                - Clean up training data
                - Manage disk space effectively
                
                Before proceeding with export, ensure you've completed testing and are satisfied with your model's performance.
                
                Your model will be moved to `/alltalk_tts/models/xtts/{your-chosen-folder-name}`
                """)

            with gr.Accordion("📊 Voice Sample Organization", open=False):
                gr.Markdown("""
                During export, your voice samples are automatically analyzed and organized into three categories:

                #### Directory Structure
                - **suitable/**: Contains files between 6-30 seconds - ideal for voice cloning
                - **too_short/**: Files under 6 seconds - may lack sufficient voice characteristics
                - **too_long/**: Files over 30 seconds - may cause processing issues

                #### Audio Report
                The system generates an `audio_report.txt` file in your export folder `/alltalk_tts/models/xtts/{your-chosen-folder-name}/wavs/` containing:
                - Complete list of processed audio files
                - Duration of each sample
                - Quality categorization
                - Recommendations for best samples to use
                - Usage guidelines for each category

                #### Using Voice Samples
                To use your exported voice samples:
                1. Review the `audio_report.txt` file in your export folder
                2. Select preferred samples from the 'suitable' directory
                3. Manually copy chosen samples to the AllTalk 'voices' folder
                4. Use these samples for TTS generation in the main interface
                """)

            with gr.Accordion("🔄 Export Options", open=False):
                gr.Markdown("""
                The system provides several export and cleanup options:

                #### Compact & Move Model
                - Compresses the fine-tuned model for efficient storage
                - Organizes voice samples into quality-based directories
                - Creates detailed audio report
                - Moves everything to your specified folder in the models directory
                - Preserves all necessary files for future use

                #### Cleanup Options
                1. **Delete Generated Training Data**
                - Removes intermediate training files
                - Clears temporary data
                - Frees up significant disk space
                - Should be done after successful model export

                2. **Delete Original Voice Samples**
                - Removes files from 'put-voice-samples-in-here' folder
                - Optional cleanup step
                - Consider keeping originals for future training
                """)

            with gr.Accordion("💾 Storage Management", open=False):
                gr.Markdown("""
                After export, you can free up additional disk space:

                #### Whisper Model Cleanup
                If you don't plan to train more models, you can remove the Whisper model from your cache (approximately 3GB):

                **Linux Users:**
                ```
                ~/.cache/huggingface/hub/(whisper-folder)
                ```

                **Windows Users:**
                ```
                C:\\Users\\(your-username)\\.cache\\huggingface\\hub\\(whisper-folder)
                ```

                #### Storage Tips
                - Keep exported models and selected voice samples
                - Remove training data after successful export
                - Consider archiving original audio files
                - Maintain backup of successful exports
                
                Remember to test your exported model and voice samples in the main AllTalk interface before removing any files.
                """)
            final_progress_data = gr.Label(
                label="Progress:"
            )
            with gr.Row():
                xtts_checkpoint_copy = gr.Dropdown(
                    [str(file) for file in xtts_checkpoint_files],
                    label="XTTS checkpoint path (Select the model you want to copy over):",
                    value="",
                    allow_custom_value=True,
                    scale=2,
                )
                speaker_name_input_export = gr.Textbox(
                    label="Project Name (Refresh Dropdowns on change)",
                    value="personsname",
                    visible=True,
                    scale=1,
                )
                # Create refresh button
                refresh_button = create_refresh_button_next(
                    [xtts_checkpoint_copy,speaker_name_input_export],
                    [
                        lambda speaker_name: {"choices": find_best_models(out_path, speaker_name), "value": ""},
                    ],
                    elem_class="refresh-button-class")
            with gr.Row():
                overwrite_existing = gr.Dropdown(value="Do not overwrite existing files", choices=["Overwrite existing files", "Do not overwrite existing files"], label="File Overwrite Options",)
                folder_path = gr.Textbox(label="Enter a new folder name (will be sub the models folder)", lines=1, value="mycustomfolder")
                compact_custom_btn = gr.Button(value="Compact and move model to a folder name of your choosing")
            with gr.Row():
                gr.Textbox(value="This will DELETE your training data and the raw finetuned model from the specified Project Name (above)", scale=2, show_label=False, interactive=False)
                delete_training_btn = gr.Button(value="Delete generated training data")
            with gr.Row():
                gr.Textbox(value="This will DELETE your original voice samples from /finetune/put-voice-samples-in-here/.", scale=2, show_label=False, interactive=False)
                delete_voicesamples_btn = gr.Button(value="Delete original voice samples")

                prompt_compute_btn.click(
                    fn=preprocess_dataset,
                    inputs=[
                        lang,
                        whisper_model,
                        max_sample_length,
                        eval_split_number,
                        speaker_name_input,
                        create_bpe_tokenizer,
                        use_vad,
                        precision
                    ],
                    outputs=[
                        progress_data,
                        train_csv,
                        eval_csv,
                        speaker_name_input_training,
                        speaker_name_input_testing,
                        speaker_name_input_export
                    ],
                )


            train_btn.click(
                fn=train_model,
                inputs=[
                    lang,
                    train_csv,
                    eval_csv,
                    learning_rates,
                    model_to_train, 
                    num_epochs,
                    batch_size,
                    grad_acumm,
                    max_audio_length,
                    speaker_name_input_training,
                    continue_run,
                    disable_shared_memory,
                    learning_rate_scheduler,
                    optimizer,
                    num_workers,
                    warm_up
                ],
                outputs=[progress_train, xtts_config, xtts_vocab, xtts_checkpoint, speaker_reference_audio, speaker_name_input_testing],
            )
            
            load_btn.click(
                fn=load_model,
                inputs=[
                    xtts_checkpoint,
                    xtts_config,
                    xtts_vocab
                ],
                outputs=[progress_load],
            )

            tts_btn.click(
                fn=run_tts,
                inputs=[
                    tts_language,
                    tts_text,
                    speaker_reference_audio,
                ],
                outputs=[progress_gen, tts_output_audio, reference_audio],
            )    
            compact_custom_btn.click(
                fn=compact_custom_model,
                inputs=[xtts_checkpoint_copy, folder_path, overwrite_existing],
                outputs=[final_progress_data],
            )
            delete_training_btn.click(
                fn=delete_training_data,
                outputs=[final_progress_data],
            )
            delete_voicesamples_btn.click(
                fn=delete_voice_sample_contents,
                outputs=[final_progress_data],
            )
            model_to_train.change(basemodel_or_finetunedmodel_choice, model_to_train, None)

    demo.queue().launch(
        show_api=False,
        inbrowser=True,
        share=False,
        debug=False,
        server_port=7052,
        server_name="127.0.0.1",
    )