app.py

import gradio as gr
import torch
import os
import soundfile as sf
import numpy as np
from transformers import (
    AutoProcessor, AutoModel, # <--- CHANGED TO AutoModel
    MmsCtcTokenizer, MmsForCtc,
    VitsForConditionalGeneration, VitsTokenizer # For Bangla ViT-TTS
)
from datasets import Audio, Dataset
from huggingface_hub import HfFolder, login
import torchaudio
import librosa
import subprocess # For banglatransliterator and other CLI tools if needed
import re # For text normalization
from dataclasses import dataclass
from typing import Any, Dict, List, Union

# --- Configuration and Global Variables ---
# Set device
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {DEVICE}")

# Hugging Face Token (optional, for private models or pushing)
# It's recommended to set this as a Space Secret named HF_TOKEN
HF_TOKEN = os.environ.get("HF_TOKEN", None)
if HF_TOKEN:
    try:
        login(token=HF_TOKEN)
        print("Logged into Hugging Face Hub.")
    except Exception as e:
        print(f"Failed to log into Hugging Face Hub. Please check your token or internet connection: {e}")
else:
    print("HF_TOKEN not found. Some functionalities (like pushing models) might be limited.")

# --- Model Caching and Dynamic Loading ---
# To save memory, models are loaded on demand.
# Keep track of currently loaded models.
loaded_models = {}

# Define the path for a default reference speaker for XTTS-v2 if needed
# This file should be uploaded to your Hugging Face Space root directory.
DEFAULT_XTTS_REFERENCE_SPEAKER = "reference_speaker.wav"

def load_model(model_name):
    """Loads a specified model and its processor/tokenizer, caching it."""
    global loaded_models
    if model_name in loaded_models:
        print(f"Model {model_name} already loaded.")
        return loaded_models[model_name]

    print(f"Loading model: {model_name}...")
    model = None
    processor = None
    tokenizer = None

    try:
        if model_name == "XTTS-v2":
            # XTTS-v2 (Coqui XTTS-v2)
            # Requires `TTS` library.
            from TTS.api import TTS
            # Note: This downloads the model if not cached.
            model = TTS("tts_models/multilingual/multi-dataset/xtts_v2").to(DEVICE)
            # XTTS doesn't use a separate processor/tokenizer in the same way as other HF models
            loaded_models[model_name] = {"model": model}
        elif model_name == "StyleTTS2":
            processor = AutoProcessor.from_pretrained("yl4579/StyleTTS2")
            model = AutoModel.from_pretrained("yl4579/StyleTTS2").to(DEVICE) # <--- This line MUST have exactly 12 spaces
            # Apply 8-bit quantization if available and on GPU
            if DEVICE == "cuda":
                try:
                    model = model.half().to(DEVICE) # Convert to float16 for memory efficiency
                    print(f"Applied half-precision (float16) to {model_name}")
                except Exception as e:
                    print(f"Could not apply half-precision to {model_name}: {e}")
            loaded_models[model_name] = {"processor": processor, "model": model}
        elif model_name == "MMS-TTS":
            # MMS-TTS (facebook/mms-tts)
            # Using a general English tokenizer/model for demonstration.
            # For Bangla, you'd use `facebook/mms-tts-bn` if available or fine-tune.
            tokenizer = MmsCtcTokenizer.from_pretrained("facebook/mms-tts-eng")
            model = MmsForCtc.from_pretrained("facebook/mms-tts-eng").to(DEVICE)
            if DEVICE == "cuda":
                try:
                    model = model.half().to(DEVICE)
                    print(f"Applied half-precision (float16) to {model_name}")
                except Exception as e:
                    print(f"Could not apply half-precision to {model_name}: {e}")
            loaded_models[model_name] = {"tokenizer": tokenizer, "model": model}

        elif model_name == "OpenVoice-v2":
            # OpenVoice v2 (myshell-ai/OpenVoice)
            # OpenVoice is typically used by cloning its repo and running inference scripts.
            # For Gradio, we'll need to adapt its core functions.
            # Placeholder: For actual OpenVoice, you'd integrate its specific inference code.
            print("OpenVoice v2 requires specific setup. Using a placeholder for embedding extraction.")
            loaded_models[model_name] = {"model": "placeholder_openvoice_v2"}

        elif model_name == "VoiceBlender":
            # VoiceBlender (voiceblender/voiceblender)
            # This is likely a custom model or script. Placeholder.
            print("VoiceBlender requires specific setup. Using a placeholder.")
            loaded_models[model_name] = {"model": "placeholder_voiceblender"}

        elif model_name == "Bangla-ViT-TTS":
            # Bangla ViT-TTS (bangla-speech-processing/bangla_tts_female)
            processor = AutoProcessor.from_pretrained("bangla-speech-processing/bangla_tts_female")
                   model = AutoModel.from_pretrained("bangla-speech-processing/bangla_tts_female").to(DEVICE) # <--- CHANGED TO AutoModel
            if DEVICE == "cuda":
                try:
                    model = model.half().to(DEVICE)
                    print(f"Applied half-precision (float16) to {model_name}")
                except Exception as e:
                    print(f"Could not apply half-precision to {model_name}: {e}")
            loaded_models[model_name] = {"processor": processor, "model": model}

        elif model_name == "Parselmouth":
            # Parselmouth is a Python wrapper for Praat, not a Hugging Face model.
            # It's used for audio manipulation. No model to load here, just check if installed.
            try:
                import parselmouth
                print("Parselmouth library found.")
                loaded_models[model_name] = {"library": True}
            except ImportError:
                print("Parselmouth not installed. Voice FX will be disabled.")
                loaded_models[model_name] = {"library": False}

        elif model_name == "SNAC-Vocoder":
            # SNAC Vocoder (hubertsiuzdak/snac_24khz)
            # This is likely a custom model or script. Placeholder.
            print("SNAC Vocoder requires specific setup. Using a placeholder.")
            loaded_models[model_name] = {"model": "placeholder_snac_vocoder"}

        elif model_name == "Orpheus-TTS":
            # Orpheus TTS (for emotion tagging)
            # This is likely a custom model or script. Placeholder.
            print("Orpheus TTS requires specific setup. Using a placeholder for emotion tagging.")
            loaded_models[model_name] = {"model": "placeholder_orpheus_tts"}

        else:
            raise ValueError(f"Unknown model: {model_name}")

        return loaded_models[model_name]

    except Exception as e:
        print(f"Error loading {model_name}: {e}")
        return None

def unload_model(model_name):
    """Unloads a model from memory."""
    global loaded_models
    if model_name in loaded_models:
        print(f"Unloading model: {model_name}...")
        del loaded_models[model_name]
        if DEVICE == "cuda":
            torch.cuda.empty_cache() # Clear GPU memory
        print(f"Model {model_name} unloaded.")

def banglish_to_bangla(text):
    """
    Placeholder for Banglish to Bangla transliteration.
    Requires 'banglatransliterator' library.
    Example: `from banglatransliterator import BengaliTransliterator`
    `transliterator = BengaliTransliterator()`
    `return transliterator.transliterate(text)`
    """
    print(f"Applying Banglish to Bangla transliteration (placeholder): {text}")
    # For demonstration, a very simple replacement.
    # Real implementation needs a proper library and potentially a subprocess call.
    # Example using subprocess if `banglatransliterator` was a CLI tool:
    # try:
    #     result = subprocess.run(['banglatransliterator_cli', text], capture_output=True, text=True, check=True)
    #     return result.stdout.strip()
    # except FileNotFoundError:
    #     print("banglatransliterator_cli not found. Ensure it's installed and in PATH.")
    #     return text
    # except Exception as e:
    #     print(f"Error during transliteration: {e}")
    #     return text
    
    # Simple direct replacements for common Banglish words (very limited)
    text = text.replace("ami", "আমি").replace("tumi", "তুমি").replace("kemon", "কেমন").replace("valo", "ভালো")
    text = text.replace("ki", "কি").replace("kore", "করে").replace("na", "না").replace("hoy", "হয়")
    return text

def normalize_text(text):
    """
    Placeholder for text normalization (numbers, dates, abbreviations).
    Piper TTS uses espeak-ng and custom rules.
    """
    print(f"Applying text normalization (placeholder): {text}")
    # Example: Replace numbers with their word form (very basic)
    # For full functionality, consider `num2words` library with Bengali support
    # or a custom rule-based system.
    def num_to_bangla_words(match):
        num_str = match.group(0)
        try:
            num = int(num_str)
            # This is a very basic example. For full numbers, use a library.
            bangla_digits = ["শূন্য", "এক", "দুই", "তিন", "চার", "পাঁচ", "ছয়", "সাত", "আট", "নয়"]
            if 0 <= num <= 9:
                return bangla_digits[num]
            elif num == 10: return "দশ"
            elif num == 100: return "একশ"
            # Add more complex logic for larger numbers if needed
            return num_str # Fallback
        except ValueError:
            return num_str

    text = re.sub(r'\b\d+\b', num_to_bangla_words, text)
    return text

def get_emotion_embedding(emotion, intensity):
    """
    Placeholder for Orpheus TTS emotion embedding.
    This would typically involve loading an emotion model and generating an embedding.
    StyleTTS2 also handles contextual emotions.
    """
    print(f"Generating emotion embedding for {emotion} with intensity {intensity} (placeholder).")
    # In a real scenario, this would return a tensor embedding that can be passed to TTS models.
    # For now, we'll return None or a dummy value.
    return None # Or a dummy tensor if a model expects one

def apply_voice_blending(audio_path, speaker_embedding_1, speaker_embedding_2, blend_ratio):
    """
    Placeholder for VoiceBlender.
    This would take two speaker embeddings and blend them to generate a new voice.
    """
    print(f"Applying voice blending (placeholder) with blend ratio {blend_ratio}.")
    # For demonstration, just return the first audio.
    # In reality, this would involve complex audio processing using VoiceBlender's logic.
    return audio_path

def apply_prosody_refinement(audio_path):
    """
    Placeholder for Bangla-Speech-Emotion for syllable stress adjustment.
    """
    print("Applying prosody refinement (placeholder).")
    # This would involve analyzing the audio and text, and modifying prosody.
    return audio_path

def upscale_audio(audio_path):
    """
    Placeholder for SNAC Vocoder for 24kHz upscaling.
    """
    print("Upscaling audio to 24kHz (placeholder).")
    # In reality, this would use the SNAC Vocoder model to convert audio to 24kHz.
    # For now, we'll just resample using torchaudio if needed.
    try:
        waveform, sr = torchaudio.load(audio_path)
        if sr != 24000:
            resampler = torchaudio.transforms.Resample(orig_freq=sr, new_freq=24000)
            waveform = resampler(waveform)
            output_path = audio_path.replace(".wav", "_24khz.wav")
            torchaudio.save(output_path, waveform, 24000)
            return output_path
        return audio_path
    except Exception as e:
        print(f"Error during audio upscaling (placeholder): {e}")
        return audio_path

def extract_speaker_embedding(audio_path):
    """
    Extracts speaker embedding using OpenVoice v2 (placeholder).
    In a real OpenVoice v2 integration, this would use its `get_speaker_embedding` function.
    """
    print(f"Extracting speaker embedding from {audio_path} (placeholder).")
    # OpenVoice v2 model would be loaded and used here.
    # For now, return a dummy embedding or None.
    # A real embedding would be a torch.Tensor.
    return None

def apply_parselmouth_fx(audio_path, pitch_shift, speed_change, reverb_amount):
    """
    Applies pitch, speed, and reverb using Parselmouth.
    Requires `parselmouth` library.
    """
    print(f"Applying Parselmouth FX: Pitch={pitch_shift}, Speed={speed_change}, Reverb={reverb_amount}")
    model_info = load_model("Parselmouth")
    if not model_info or not model_info.get("library"):
        print("Parselmouth not available. Skipping FX.")
        return audio_path

    try:
        import parselmouth
        from parselmouth.praat import call

        sound = parselmouth.Sound(audio_path)

        # Pitch shifting
        if pitch_shift != 0:
            # Praat's To Manipulation requires min/max pitch. Using defaults.
            manipulation = call(sound, "To Manipulation", 0.01, 75, 600)
            pitch_tier = call(manipulation, "Extract pitch tier")
            # Shift frequencies by semitones
            call(pitch_tier, "Shift frequencies", 0, pitch_shift, "semitones")
            sound = call(manipulation, "Replace pitch tier", pitch_tier)

        # Speed control
        if speed_change != 1.0:
            # Praat's Change duration function is more complex.
            # Simple resampling changes speed and pitch. For just speed, need time-stretching.
            # For simplicity, we'll use a basic time-stretch via librosa.
            y, sr = librosa.load(sound.path, sr=None) # Load current state of sound
            y_stretched = librosa.effects.time_stretch(y, rate=speed_change)
            # Save to a temporary file
            temp_speed_path = audio_path.replace(".wav", "_temp_speed.wav")
            sf.write(temp_speed_path, y_stretched, sr)
            sound = parselmouth.Sound(temp_speed_path) # Reload for further processing

        # Reverb (Praat doesn't have direct reverb. This is a simplification/placeholder)
        # For real reverb, you'd use a dedicated audio library like `pydub` or `torchaudio.transforms`
        # or external tools. This is a conceptual placeholder for a "reverb" slider.
        if reverb_amount > 0:
            print(f"Reverb application (conceptual, not direct Praat): {reverb_amount}")
            # A simple way to simulate reverb is to add a delayed, attenuated copy of the signal.
            # This is a very basic echo, not true reverb.
            y, sr = librosa.load(sound.path, sr=None) # Load current state of sound
            delay_samples = int(0.05 * sr) # 50ms delay
            decay = 0.5 * reverb_amount # Adjust decay based on slider
            echo = np.zeros_like(y)
            if len(y) > delay_samples:
                echo[delay_samples:] = y[:-delay_samples] * decay
            y_reverb = y + echo
            y_reverb = y_reverb / np.max(np.abs(y_reverb)) * 0.9 # Normalize
            temp_reverb_path = audio_path.replace(".wav", "_temp_reverb.wav")
            sf.write(temp_reverb_path, y_reverb, sr)
            sound = parselmouth.Sound(temp_reverb_path) # Reload

        output_audio_path = audio_path.replace(".wav", f"_fx_out.wav")
        sound.save(output_audio_path, "WAV")
        return output_audio_path
    except Exception as e:
        print(f"Error applying Parselmouth FX: {e}")
        return audio_path

def text_to_speech_workflow(text, voice_preset, emotion, intensity):
    """
    Handles the full text-to-speech workflow.
    """
    print(f"TTS Request: Text='{text}', Voice='{voice_preset}', Emotion='{emotion}', Intensity={intensity}")

    # Step 1: Language Handling
    processed_text = banglish_to_bangla(text)
    processed_text = normalize_text(processed_text)

    # Step 2: Emotion Tagging & Contextual Emotions
    emotion_embedding = get_emotion_embedding(emotion, intensity) # Placeholder for Orpheus TTS

    audio_output_path = "output_tts.wav"
    speaker_embedding = None # Will be set based on voice_preset or reference audio

    try:
        if voice_preset == "Nabanita" or voice_preset == "Pradeep":
            # For Nabanita/Pradeep, we'll use Bangla-ViT-TTS.
            # Note: Bangla-ViT-TTS is typically a female voice. For Pradeep (male),
            # you'd ideally need a male Bangla TTS model or apply voice conversion.
            # This implementation uses the base model.
            model_info = load_model("Bangla-ViT-TTS")
            if not model_info: raise Exception("Bangla-ViT-TTS model not loaded.")
            processor = model_info["processor"]
            model = model_info["model"]

            inputs = processor(text=processed_text, return_tensors="pt").to(DEVICE)
            # Bangla-ViT-TTS doesn't directly take emotion embeddings in this simple API.
            # If it did, you'd pass `speaker_embeddings=emotion_embedding` or similar.
            speech = model.generate_speech(inputs["input_ids"], speaker_embeddings=None).cpu().numpy()
            sf.write(audio_output_path, speech, model.config.sampling_rate)

        elif voice_preset == "Hybrid":
            # This implies blending Nabanita and Pradeep.
            # Requires OpenVoice v2 for embeddings and VoiceBlender.
            # Placeholder: Synthesize with Bangla-ViT-TTS, then conceptually blend.
            model_info = load_model("Bangla-ViT-TTS")
            if not model_info: raise Exception("Bangla-ViT-TTS model not loaded.")
            processor = model_info["processor"]
            model = model_info["model"]
            inputs = processor(text=processed_text, return_tensors="pt").to(DEVICE)
            speech = model.generate_speech(inputs["input_ids"], speaker_embeddings=None).cpu().numpy()
            sf.write(audio_output_path, speech, model.config.sampling_rate)

            # Conceptual blending step (requires actual VoiceBlender integration)
            # speaker_embedding_nabanita = extract_speaker_embedding("path/to/nabanita_sample.wav")
            # speaker_embedding_pradeep = extract_speaker_embedding("path/to/pradeep_sample.wav")
            # audio_output_path = apply_voice_blending(audio_output_path, speaker_embedding_nabanita, speaker_embedding_pradeep, 0.6)
            print("Hybrid voice blending is a placeholder. Output is from Bangla-ViT-TTS.")

        elif voice_preset == "Custom":
            # This would use a user-trained model (e.g., from the Colab notebook).
            # For demonstration, we'll use XTTS-v2 with a reference speaker.
            # In a real scenario, you'd load the specific fine-tuned model from HF Hub.
            model_info = load_model("XTTS-v2")
            if not model_info: raise Exception("XTTS-v2 model not loaded.")
            xtts_model = model_info["model"]

            # XTTS-v2 requires a speaker reference audio.
            # For a custom voice, this would be a sample from the fine-tuned voice.
            # Ensure DEFAULT_XTTS_REFERENCE_SPEAKER exists in your Space.
            reference_audio_path = DEFAULT_XTTS_REFERENCE_SPEAKER
            if not os.path.exists(reference_audio_path):
                # Create a dummy reference if not found for testing
                dummy_audio = np.random.rand(16000 * 3).astype(np.float32) # 3 seconds of noise
                sf.write(reference_audio_path, dummy_audio, 16000)
                print(f"Created dummy reference audio at {reference_audio_path}")

            # Generate speech with XTTS-v2
            xtts_model.tts_to_file(
                text=processed_text,
                file_path=audio_output_path,
                speaker_wav=reference_audio_path,
                language="bn" # Or "en" for English, or "auto"
            )
            print(f"Custom voice generated using XTTS-v2 with reference: {reference_audio_path}")

        else:
            raise ValueError("Invalid voice preset selected.")

        # Step 5: Prosody Refinement & Upscaling
        audio_output_path = apply_prosody_refinement(audio_output_path) # Placeholder
        audio_output_path = upscale_audio(audio_output_path) # Placeholder for SNAC Vocoder

        return audio_output_path

    except Exception as e:
        print(f"Error in TTS workflow: {e}")
        return None

def voice_changer_workflow(source_audio_tuple, target_audio_tuple, pitch_shift, speed_change, reverb_amount):
    """
    Handles the voice conversion workflow.
    """
    if source_audio_tuple is None:
        return None, "Please upload a source audio file."

    source_sr, source_audio_np = source_audio_tuple

    # Save source audio temporarily
    source_audio_path = "source_audio_temp.wav"
    sf.write(source_audio_path, source_audio_np, source_sr)

    target_audio_path = None
    if target_audio_tuple is not None:
        target_sr, target_audio_np = target_audio_tuple
        target_audio_path = "target_audio_temp.wav"
        sf.write(target_audio_path, target_audio_np, target_sr)

    print(f"Voice Changer Request: Source='{source_audio_path}', Target='{target_audio_path}', Pitch={pitch_shift}, Speed={speed_change}, Reverb={reverb_amount}")

    try:
        # Step 2: Extract speaker embeddings (conceptual for OpenVoice v2)
        # In a real OpenVoice v2 setup, you'd use its voice conversion capabilities here.
        # If target_audio_path is provided, you'd extract its embedding and use OpenVoice to convert
        # the source audio to the target speaker's voice.
        # For now, we'll apply FX directly to the source audio.

        converted_audio_path = source_audio_path # Start with source audio

        if target_audio_path:
            # This is where OpenVoice v2 voice conversion would happen.
            # It would take source_audio_path and the speaker embedding from target_audio_path
            # and produce a new audio.
            # For now, this is a placeholder.
            print("OpenVoice v2 voice conversion is a placeholder. Applying FX only.")
            # Example:
            # openvoice_model_info = load_model("OpenVoice-v2")
            # if openvoice_model_info:
            #     target_speaker_embedding = extract_speaker_embedding(target_audio_path)
            #     if target_speaker_embedding:
            #         # This is a conceptual call to OpenVoice's conversion function
            #         converted_audio_path = openvoice_model_info["model"].convert_voice(
            #             source_audio_path, target_speaker_embedding, output_path="converted_openvoice.wav"
            #         )
            #     else:
            #         print("Could not extract target speaker embedding.")
            # else:
            #     print("OpenVoice-v2 model not loaded.")

        # Apply FX using Parselmouth to the current `converted_audio_path` (which is source_audio_path initially)
        final_audio_path = apply_parselmouth_fx(converted_audio_path, pitch_shift, speed_change, reverb_amount)

        return final_audio_path, "Voice conversion successful!"

    except Exception as e:
        print(f"Error in Voice Changer workflow: {e}")
        return None, f"Error: {e}"

def custom_voice_training_placeholder(audio_file, voice_name):
    """
    Placeholder for custom voice training.
    This process is typically done offline in a Colab notebook due to GPU memory/time constraints.
    """
    if audio_file is None:
        return "Please upload an audio file for training.", None
    if not voice_name:
        return "Please provide a name for your custom voice.", None

    print(f"Received request to train custom voice '{voice_name}' with audio: {audio_file.name}")
    
    # In a real scenario, you would:
    # 1. Save the uploaded audio file.
    # 2. Trigger an asynchronous training job (e.g., on a separate GPU instance, or queue it).
    # 3. Provide a link or mechanism for the user to check the training status.
    # 4. Once trained, the model would be pushed to Hugging Face Hub.

    # For this Gradio app, we'll just simulate the process.
    message = ( # <--- This line MUST have exactly 8 spaces before 'message'
        f"Custom voice training for '{voice_name}' initiated with {audio_file.name}. "
        "This process typically takes a long time (minutes to hours) on a dedicated GPU "
        "and is best done in a separate environment like a Google Colab notebook. "
        "Once trained, your model would be available on Hugging Face Hub and could be "
        "loaded under the 'Custom' voice preset in the Text-to-Speech tab."
    )
    return message, None # Return None for audio output as no audio is generated here

    # --- Gradio Interface ---

    with gr.Blocks(title="Ultra Pro Max Bangla TTS System") as demo:
        gr.Markdown(
            """
            # 🌟 Ultra Pro Max Detailed Plan: Free Bangla TTS System with Voice Cloning & Conversion 🌟
            Welcome to the advanced Bangla Text-to-Speech and Voice Changer system!
            """
        )

        with gr.Tab("Text-to-Speech"):
            with gr.Row():
                text_input = gr.Textbox(
                    label="Enter Text (Bangla, English, or Banglish)",
                    placeholder="আপনার টেক্সট এখানে লিখুন অথবা Write your text here...",
                    lines=5
                )
            with gr.Row():
                voice_preset_dropdown = gr.Dropdown(
                    choices=["Nabanita", "Pradeep", "Hybrid", "Custom"],
                    label="Voice Preset",
                    value="Nabanita"
                )
                emotion_dropdown = gr.Dropdown(
                    choices=["Neutral", "Happy", "Sad", "Angry", "Excited", "Whisper", "Sarcastic", "Fear", "Disgust", "Surprise", "Calm", "Confused", "Bored", "Shouting", "Questioning", "Annoyed", "Tired", "Hopeful"],
                    label="Emotion",
                    value="Neutral"
                )
                intensity_slider = gr.Slider(
                    minimum=0.0, maximum=1.0, step=0.1, value=0.5, label="Emotion Intensity"
                )
            tts_button = gr.Button("Generate Speech")
            tts_output = gr.Audio(label="Generated Speech", type="filepath")

            tts_button.click(
                fn=text_to_speech_workflow,
                inputs=[text_input, voice_preset_dropdown, emotion_dropdown, intensity_slider],
                outputs=tts_output
            )

        with gr.Tab("Voice Changer"):
            gr.Markdown("### Convert the voice of an audio file or apply effects.")
            with gr.Row():
                source_audio_input = gr.Audio(label="Upload Source Audio", type="numpy")
                target_audio_input = gr.Audio(label="Upload Target Audio for Voice Conversion (Optional, for cloning)", type="numpy")
            with gr.Row():
                pitch_slider = gr.Slider(minimum=-12, maximum=12, step=1, value=0, label="Pitch Shift (semitones)")
                speed_slider = gr.Slider(minimum=0.5, maximum=2.0, step=0.1, value=1.0, label="Speed (0.5x - 2x)")
                reverb_slider = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=0.0, label="Reverb Amount (0-1)")
            voice_changer_button = gr.Button("Apply Voice Change / FX")
            voice_changer_output = gr.Audio(label="Processed Audio", type="filepath")
            voice_changer_message = gr.Textbox(label="Status", interactive=False)

            voice_changer_button.click(
                fn=voice_changer_workflow,
                inputs=[source_audio_input, target_audio_input, pitch_slider, speed_slider, reverb_slider],
                outputs=[voice_changer_output, voice_changer_message]
            )

        with gr.Tab("Custom Voice Training"):
            gr.Markdown("### Train a custom voice using your own audio data.")
            gr.Markdown(
                """
                **Note:** This process is computationally intensive and is best performed offline
                using a dedicated GPU environment like a Google Colab notebook.
                The previous step provided a Colab notebook for fine-tuning Bangla ViT-TTS.
                Once trained, you can use your custom voice by selecting 'Custom' in the Text-to-Speech tab.
                """
            )
            with gr.Row():
                training_audio_input = gr.File(label="Upload Audio for Training (5 min - 1 hr WAV/FLAC)", type="file")
                voice_name_input = gr.Textbox(label="Name for Custom Voice", placeholder="e.g., MyBanglaVoice")
            train_button = gr.Button("Simulate Training (See Note Above)")
            training_status_output = gr.Textbox(label="Training Status", interactive=False)
            training_audio_output = gr.Audio(label="Output (N/A for training)", type="filepath", visible=False) # Not used for training output

            train_button.click(
                fn=custom_voice_training_placeholder,
                inputs=[training_audio_input, voice_name_input],
                outputs=[training_status_output, training_audio_output]
            )

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