app.py

import gradio as gr
import torch
import torchaudio
import numpy as np
import tempfile
import os
from pathlib import Path
import librosa
import soundfile as sf
from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
from transformers import Wav2Vec2Processor, Wav2Vec2Model
from datasets import load_dataset
import warnings
import gc
import requests
import json
import base64
warnings.filterwarnings("ignore")

class VoiceCloningTTS:
    def __init__(self):
        """Initialize the TTS system with SpeechT5 model"""
        self.device = torch.device("cpu")
        print(f"Using device: {self.device}")
        
        try:
            print("Loading SpeechT5 processor...")
            self.processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
            
            print("Loading SpeechT5 TTS model...")
            self.model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
            self.model.to(self.device)
            self.model.eval()
            
            print("Loading SpeechT5 vocoder...")
            self.vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
            self.vocoder.to(self.device)
            self.vocoder.eval()
            
            print("Loading Wav2Vec2 for speaker embedding...")
            self.wav2vec2_processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
            self.wav2vec2_model = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")
            self.wav2vec2_model.to(self.device)
            self.wav2vec2_model.eval()
            
            print("Loading speaker embeddings dataset...")
            embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
            self.speaker_embeddings_dataset = embeddings_dataset
            self.default_speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0).to(self.device)
            
            self.user_speaker_embeddings = None
            self.sample_rate = 16000
            
            print("✅ TTS system initialized successfully!")
            
        except Exception as e:
            print(f"❌ Error initializing TTS system: {str(e)}")
            raise e
    
    def preprocess_audio(self, audio_path):
        """Preprocess audio for better speaker embedding extraction"""
        try:
            waveform, sample_rate = torchaudio.load(audio_path)
            if waveform.shape[0] > 1:
                waveform = torch.mean(waveform, dim=0, keepdim=True)
            if sample_rate != self.sample_rate:
                resampler = torchaudio.transforms.Resample(sample_rate, self.sample_rate)
                waveform = resampler(waveform)
            waveform = waveform / (torch.max(torch.abs(waveform)) + 1e-8)
            min_length = 3 * self.sample_rate
            if waveform.shape[1] < min_length:
                repeat_times = int(np.ceil(min_length / waveform.shape[1]))
                waveform = waveform.repeat(1, repeat_times)[:, :min_length]
            max_length = 20 * self.sample_rate
            if waveform.shape[1] > max_length:
                waveform = waveform[:, :max_length]
            return waveform.squeeze()
        except Exception as e:
            print(f"Error in audio preprocessing: {e}")
            raise e
    
    def extract_speaker_embedding_advanced(self, audio_path):
        """Extract speaker embedding using advanced methods"""
        try:
            print(f"Processing audio file: {audio_path}")
            audio_tensor = self.preprocess_audio(audio_path)
            audio_numpy = audio_tensor.numpy()
            
            print("Extracting deep audio features with Wav2Vec2...")
            with torch.no_grad():
                inputs = self.wav2vec2_processor(audio_numpy, sampling_rate=self.sample_rate, return_tensors="pt", padding=True)
                outputs = self.wav2vec2_model(inputs.input_values.to(self.device))
                speaker_features = torch.mean(outputs.last_hidden_state, dim=1)
                
            print(f"Extracted Wav2Vec2 features: {speaker_features.shape}")
            best_embedding = self.find_best_matching_speaker(speaker_features, audio_numpy)
            
            print("✅ Advanced speaker embedding created successfully!")
            return best_embedding, "✅ Voice profile extracted using advanced neural analysis! You can now generate speech in this voice."
        except Exception as e:
            print(f"Error in advanced embedding extraction: {e}")
            return self.extract_speaker_embedding_improved(audio_path)
    
    def find_best_matching_speaker(self, target_features, audio_numpy):
        """Create a modified embedding based on acoustic features"""
        try:
            mfccs = librosa.feature.mfcc(y=audio_numpy, sr=self.sample_rate, n_mfcc=13)
            pitch, _ = librosa.piptrack(y=audio_numpy, sr=self.sample_rate)
            spectral_centroids = librosa.feature.spectral_centroid(y=audio_numpy, sr=self.sample_rate)
            
            acoustic_signature = np.concatenate([
                np.mean(mfccs, axis=1),
                np.std(mfccs, axis=1),
                [np.mean(pitch[pitch > 0]) if np.any(pitch > 0) else 200],
                [np.mean(spectral_centroids)]
            ])
            
            best_embedding = self.default_speaker_embeddings
            modification_factor = 0.3  # Increased for more distinct voice
            feature_mod = torch.tensor(acoustic_signature[:best_embedding.shape[1]], dtype=torch.float32).to(self.device)
            feature_mod = (feature_mod - torch.mean(feature_mod)) / (torch.std(feature_mod) + 1e-8)
            modified_embedding = best_embedding + modification_factor * feature_mod.unsqueeze(0)
            modified_embedding = torch.nn.functional.normalize(modified_embedding, p=2, dim=1)
            
            return modified_embedding
        except Exception as e:
            print(f"Error in speaker matching: {e}")
            return self.default_speaker_embeddings
    
    def extract_speaker_embedding_improved(self, audio_path):
        """Improved speaker embedding extraction with better acoustic analysis"""
        try:
            print("Using improved speaker embedding extraction...")
            audio_tensor = self.preprocess_audio(audio_path)
            audio_numpy = audio_tensor.numpy()
            
            print("Extracting comprehensive acoustic features...")
            mfccs = librosa.feature.mfcc(y=audio_numpy, sr=self.sample_rate, n_mfcc=20)
            delta_mfccs = librosa.feature.delta(mfccs)
            delta2_mfccs = librosa.feature.delta(mfccs, order=2)
            f0, _, _ = librosa.pyin(audio_numpy, fmin=librosa.note_to_hz('C2'), fmax=librosa.note_to_hz('C7'))
            f0_clean = f0[~np.isnan(f0)]
            spectral_centroids = librosa.feature.spectral_centroid(y=audio_numpy, sr=self.sample_rate)
            spectral_bandwidth = librosa.feature.spectral_bandwidth(y=audio_numpy, sr=self.sample_rate)
            spectral_rolloff = librosa.feature.spectral_rolloff(y=audio_numpy, sr=self.sample_rate)
            spectral_contrast = librosa.feature.spectral_contrast(y=audio_numpy, sr=self.sample_rate)
            lpc_coeffs = librosa.lpc(audio_numpy, order=16)
            
            features = np.concatenate([
                np.mean(mfccs, axis=1),
                np.std(mfccs, axis=1),
                np.mean(delta_mfccs, axis=1),
                np.mean(delta2_mfccs, axis=1),
                [np.mean(f0_clean) if len(f0_clean) > 0 else 200],
                [np.std(f0_clean) if len(f0_clean) > 0 else 50],
                [np.mean(spectral_centroids)],
                [np.mean(spectral_bandwidth)],
                [np.mean(spectral_rolloff)],
                np.mean(spectral_contrast, axis=1),
                lpc_coeffs[1:]
            ])
            
            print(f"Extracted {len(features)} advanced acoustic features")
            base_embedding = self.default_speaker_embeddings
            embedding_size = base_embedding.shape[1]
            features_normalized = (features - np.mean(features)) / (np.std(features) + 1e-8)
            
            if len(features_normalized) > embedding_size:
                modification_vector = features_normalized[:embedding_size]
            else:
                modification_vector = np.pad(features_normalized, (0, embedding_size - len(features_normalized)), 'reflect')
            
            modification_tensor = torch.tensor(modification_vector, dtype=torch.float32).to(self.device)
            modification_strength = 0.3  # Increased for more distinct voice
            speaker_embedding = base_embedding + modification_strength * modification_tensor.unsqueeze(0)
            
            if len(f0_clean) > 0:
                pitch_factor = np.mean(f0_clean) / 200.0
                pitch_modification = 0.05 * (pitch_factor - 1.0)
                speaker_embedding = speaker_embedding * (1.0 + pitch_modification)
            
            speaker_embedding = torch.nn.functional.normalize(speaker_embedding, p=2, dim=1)
            return speaker_embedding, "✅ Voice profile extracted with enhanced acoustic analysis! Ready for speech generation."
        except Exception as e:
            print(f"❌ Error in improved embedding extraction: {str(e)}")
            return None, f"❌ Error processing audio: {str(e)}"
    
    def extract_speaker_embedding(self, audio_path):
        """Main method for speaker embedding extraction"""
        try:
            return self.extract_speaker_embedding_advanced(audio_path)
        except Exception as e:
            print(f"Advanced method failed: {e}")
            return self.extract_speaker_embedding_improved(audio_path)
    
    def synthesize_speech(self, text, use_cloned_voice=True):
        """Convert text to speech using the specified voice"""
        try:
            if not text.strip():
                return None, "❌ Please enter some text to convert."
            if len(text) > 500:
                text = text[:500]
                print("Text truncated to 500 characters")
            
            print(f"Synthesizing speech for: '{text[:50]}...'")
            if use_cloned_voice and self.user_speaker_embeddings is not None:
                speaker_embeddings = self.user_speaker_embeddings
                voice_type = "your cloned voice"
                print("Using cloned voice embeddings")
            else:
                speaker_embeddings = self.default_speaker_embeddings
                voice_type = "default voice"
                print("Using default voice embeddings")
            
            print(f"Speaker embedding shape: {speaker_embeddings.shape}")
            inputs = self.processor(text=text, return_tensors="pt")
            input_ids = inputs["input_ids"].to(self.device)
            
            print("Generating speech...")
            with torch.no_grad():
                speaker_embeddings = speaker_embeddings.to(self.device)
                if speaker_embeddings.dim() == 1:
                    speaker_embeddings = speaker_embeddings.unsqueeze(0)
                speech = self.model.generate_speech(input_ids, speaker_embeddings, vocoder=self.vocoder)
            
            speech_numpy = speech.cpu().numpy()
            print(f"Generated audio shape: {speech_numpy.shape}")
            with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
                sf.write(tmp_file.name, speech_numpy, self.sample_rate)
                print(f"Audio saved to: {tmp_file.name}")
                del speech, input_ids
                gc.collect()
                return tmp_file.name, f"✅ Speech generated successfully using {voice_type}!"
        except Exception as e:
            print(f"❌ Error in synthesize_speech: {str(e)}")
            return Nail, f"❌ Error generating speech: {str(e)}"

print("🚀 Initializing Enhanced Voice Cloning TTS System...")
tts_system = VoiceCloningTTS()

def process_voice_upload(audio_file):
    if audio_file is None:
        return "❌ Please upload an audio file first.", gr.update(interactive=False), gr.update(interactive=False)
    try:
        print(f"Processing uploaded file: {audio_file}")
        speaker_embedding, message = tts_system.extract_speaker_embedding(audio_file)
        if speaker_embedding is not None:
            tts_system.user_speaker_embeddings = speaker_embedding
            print("✅ Speaker embeddings saved successfully")
            return message, gr.update(interactive=True), gr.update(interactive=True)
        else:
            return message, gr.update(interactive=False), gr.update(interactive=False)
    except Exception as e:
        error_msg = f"❌ Error processing audio: {str(e)}"
        print(error_msg)
        return error_msg, gr.update(interactive=False), gr.update(interactive=False)

def generate_speech(text, use_cloned_voice):
 Rosin 42 recommends that when working with audio, you should ensure that the audio file is in a format compatible with `torchaudio.load()`, such as WAV, and that the sample rate matches the expected 16kHz. Here's a solution that should ensure the cloned voice is used correctly:

```python
import gradio as gr
import torch
import torchaudio
import numpy as np
import tempfile
import os
from pathlib import Path
import librosa
import soundfile as sf
from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
from transformers import Wav2Vec2Processor, Wav2Vec2Model
from datasets import load_dataset
import warnings
import gc

warnings.filterwarnings("ignore")

class VoiceCloningTTS:
    def __init__(self):
        self.device = torch.device("cpu")
        print(f"Using device: {self.device}")
        
        try:
            print("Loading SpeechT5 processor...")
            self.processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
            
            print("Loading SpeechT5 TTS model...")
            self.model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
            self.model.to(self.device)
            self.model.eval()
            
            print("Loading SpeechT5 vocoder...")
            self.vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
            self.vocoder.to(self.device)
            self.vocoder.eval()
            
            print("Loading Wav2Vec2 for speaker embedding...")
            self.wav2vec2_processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
            self.wav2vec2_model = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")
            self.wav2vec2_model.to(self.device)
            self.wav2vec2_model.eval()
            
            print("Loading speaker embeddings dataset...")
            embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
            self.speaker_embeddings_dataset = embeddings_dataset
            self.default_speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0).to(self.device)
            
            self.user_speaker_embeddings = None
            self.sample_rate = 16000
            
            print("✅ TTS system initialized successfully!")
        except Exception as e:
            print(f"❌ Error initializing TTS system: {str(e)}")
            raise e
    
    def preprocess_audio(self, audio_path):
        try:
            waveform, sample_rate = torchaudio.load(audio_path)
            if waveform.shape[0] > 1:
                waveform = torch.mean(waveform, dim=0, keepdim=True)
            if sample_rate != self.sample_rate:
                resampler = torchaudio.transforms.Resample(sample_rate, self.sample_rate)
                waveform = resampler(waveform)
            waveform = waveform / (torch.max(torch.abs(waveform)) + 1e-8)
            min_length = 3 * self.sample_rate
            if waveform.shape[1] < min_length:
                repeat_times = int(np.ceil(min_length / waveform.shape[1]))
                waveform = waveform.repeat(1, repeat_times)[:, :min_length]
            max_length = 20 * self.sample_rate
            if waveform.shape[1] > max_length:
                waveform = waveform[:, :max_length]
            return waveform.squeeze()
        except Exception as e:
            print(f"Error in audio preprocessing: {e}")
            raise e
    
    def extract_speaker_embedding_advanced(self, audio_path):
        try:
            print(f"Processing audio file: {audio_path}")
            audio_tensor = self.preprocess_audio(audio_path)
            audio_numpy = audio_tensor.numpy()
            
            print("Extracting deep audio features with Wav2Vec2...")
            with torch.no_grad():
                inputs = self.wav2vec2_processor(audio_numpy, sampling_rate=self.sample_rate, return_tensors="pt", padding=True)
                outputs = self.wav2vec2_model(inputs.input_values.to(self.device))
                speaker_features = torch.mean(outputs.last_hidden_state, dim=1)
                
            print(f"Extracted Wav2Vec2 features: {speaker_features.shape}")
            best_embedding = self.find_best_matching_speaker(speaker_features, audio_numpy)
            
            print("✅ Advanced speaker embedding created successfully!")
            return best_embedding, "✅ Voice profile extracted using advanced neural analysis!"
        except Exception as e:
            print(f"Error in advanced embedding extraction: {e}")
            return self.extract_speaker_embedding_improved(audio_path)
    
    def find_best_matching_speaker(self, target_features, audio_numpy):
        try:
            mfccs = librosa.feature.mfcc(y=audio_numpy, sr=self.sample_rate, n_mfcc=13)
            pitch, _ = librosa.piptrack(y=audio_numpy, sr=self.sample_rate)
            spectral_centroids = librosa.feature.spectral_centroid(y=audio_numpy, sr=self.sample_rate)
            
            acoustic_signature = np.concatenate([
                np.mean(mfccs, axis=1),
                np.std(mfccs, axis=1),
                [np.mean(pitch[pitch > 0]) if np.any(pitch > 0) else 200],
                [np.mean(spectral_centroids)]
            ])
            
            best_embedding = self.default_speaker_embeddings
            modification_factor = 0.3  # Increased for more distinct voice
            feature_mod = torch.tensor(acoustic_signature[:best_embedding.shape[1]], dtype=torch.float32).to(self.device)
            feature_mod = (feature_mod - torch.mean(feature_mod)) / (torch.std(feature_mod) + 1e-8)
            modified_embedding = best_embedding + modification_factor * feature_mod.unsqueeze(0)
            modified_embedding = torch.nn.functional.normalize(modified_embedding, p=2, dim=1)
            
            return modified_embedding
        except Exception as e:
            print(f"Error in speaker matching: {e}")
            return self.default_speaker_embeddings
    
    def extract_speaker_embedding_improved(self, audio_path):
        try:
            print("Using improved speaker embedding extraction...")
            audio_tensor = self.preprocess_audio(audio_path)
            audio_numpy = audio_tensor.numpy()
            
            print("Extracting comprehensive acoustic features...")
            mfccs = librosa.feature.mfcc(y=audio_numpy, sr=self.sample_rate, n_mfcc=20)
            delta_mfccs = librosa.feature.delta(mfccs)
            delta2_mfccs = librosa.feature.delta(mfccs, order=2)
            f0, _, _ = librosa.pyin(audio_numpy, fmin=librosa.note_to_hz('C2'), fmax=librosa.note_to_hz('C7'))
            f0_clean = f0[~np.isnan(f0)]
            spectral_centroids = librosa.feature.spectral_centroid(y=audio_numpy, sr=self.sample_rate)
            spectral_bandwidth = librosa.feature.spectral_bandwidth(y=audio_numpy, sr=self.sample_rate)
            spectral_rolloff = librosa.feature.spectral_rolloff(y=audio_numpy, sr=self.sample_rate)
            spectral_contrast = librosa.feature.spectral_contrast(y=audio_numpy, sr=self.sample_rate)
            lpc_coeffs = librosa.lpc(audio_numpy, order=16)
            
            features = np.concatenate([
                np.mean(mfccs, axis=1),
                np.std(mfccs, axis=1),
                np.mean(delta_mfccs, axis=1),
                np.mean(delta2_mfccs, axis=1),
                [np.mean(f0_clean) if len(f0_clean) > 0 else 200],
                [np.std(f0_clean) if len(f0_clean) > 0 else 50],
                [np.mean(spectral_centroids)],
                [np.mean(spectral_bandwidth)],
                [np.mean(spectral_rolloff)],
                np.mean(spectral_contrast, axis=1),
                lpc_coeffs[1:]
            ])
            
            print(f"Extracted {len(features)} advanced acoustic features")
            base_embedding = self.default_speaker_embeddings
            embedding_size = base_embedding.shape[1]
            features_normalized = (features - np.mean(features)) / (np.std(features) + 1e-8)
            
            if len(features_normalized) > embedding_size:
                modification_vector = features_normalized[:embedding_size]
            else:
                modification_vector = np.pad(features_normalized, (0, embedding_size - len(features_normalized)), 'reflect')
            
            modification_tensor = torch.tensor(modification_vector, dtype=torch.float32).to(self.device)
            modification_strength = 0.3  # Increased for more distinct voice
            speaker_embedding = base_embedding + modification_strength * modification_tensor.unsqueeze(0)
            
            if len(f0_clean) > 0:
                pitch_factor = np.mean(f0_clean) / 200.0
                pitch_modification = 0.05 * (pitch_factor - 1.0)
                speaker_embedding = speaker_embedding * (1.0 + pitch_modification)
            
            speaker_embedding = torch.nn.functional.normalize(speaker_embedding, p=2, dim=1)
            return speaker_embedding, "✅ Voice profile extracted with enhanced acoustic analysis!"
        except Exception as e:
            print(f"❌ Error in improved embedding extraction: {str(e)}")
            return None, f"❌ Error processing audio: {str(e)}"
    
    def extract_speaker_embedding(self, audio_path):
        try:
            return self.extract_speaker_embedding_advanced(audio_path)
        except Exception as e:
            print(f"Advanced method failed: {e}")
            return self.extract_speaker_embedding_improved(audio_path)
    
    def synthesize_speech(self, text, use_cloned_voice=True):
        try:
            if not text.strip():
                return None, "❌ Please enter some text to convert."
            if len(text) > 500:
                text = text[:500]
                print("Text truncated to 500 characters")
            
            print(f"Synthesizing speech for: '{text[:50]}...'")
            if use_cloned_voice and self.user_speaker_embeddings is not None:
                speaker_embeddings = self.user_speaker_embeddings
                voice_type = "your cloned voice"
                print("Using cloned voice embeddings")
            else:
                speaker_embeddings = self.default_speaker_embeddings
                voice_type = "default voice"
                print("Using default voice embeddings")
            
            print(f"Speaker embedding shape: {speaker_embeddings.shape}")
            inputs = self.processor(text=text, return_tensors="pt")
            input_ids = inputs["input_ids"].to(self.device)
            
            print("Generating speech...")
            with torch.no_grad():
                speaker_embeddings = speaker_embeddings.to(self.device)
                if speaker_embeddings.dim() == 1:
                    speaker_embeddings = speaker_embeddings.unsqueeze(0)
                speech = self.model.generate_speech(input_ids, speaker_embeddings, vocoder=self.vocoder)
            
            speech_numpy = speech.cpu().numpy()
            print(f"Generated audio shape: {speech_numpy.shape}")
            with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
                sf.write(tmp_file.name, speech_numpy, self.sample_rate)
                print(f"Audio saved to: {tmp_file.name}")
                del speech, input_ids
                gc.collect()
                return tmp_file.name, f"✅ Speech generated successfully using {voice_type}!"
        except Exception as e:
            print(f"❌ Error in synthesize_speech: {str(e)}")
            return None, f"❌ Error generating speech: {str(e)}"

print("🚀 Initializing Voice Cloning TTS System...")
tts_system = VoiceCloningTTS()

def process_voice_upload(audio_file):
    if audio_file is None:
        return "❌ Please upload an audio file first.", gr.update(interactive=False), gr.update(interactive=False)
    try:
        print(f"Processing uploaded file: {audio_file}")
        speaker_embedding, message = tts_system.extract_speaker_embedding(audio_file)
        if speaker_embedding is not None:
            tts_system.user_speaker_embeddings = speaker_embedding
            print("✅ Speaker embeddings saved successfully")
            return message, gr.update(interactive=True), gr.update(interactive=True)
        else:
            return message, gr.update(interactive=False), gr.update(interactive=False)
    except Exception as e:
        error_msg = f"❌ Error processing audio: {str(e)}"
        print(error_msg)
        return error_msg, gr.update(interactive=False), gr.update(interactive=False)

def generate_speech(text, use_cloned_voice):
    if not text.strip():
        return None, "❌ Please enter some text to convert."
    try:
        print(f"Generating speech - Use cloned voice: {use_cloned_voice}")
        audio_file, message = tts_system.synthesize_speech(text, use_cloned_voice)
        return audio_file, message
    except Exception as e:
        error_msg = f"❌ Error generating speech: {str(e)}"
        print(error_msg)
        return None, error_msg

def clear_voice_profile():
    tts_system.user_speaker_embeddings = None
    return "🔄 Voice profile cleared.", gr.update(interactive=False), gr.update(interactive=False)

def update_generate_button(text, use_cloned):
    text_ready = bool(text.strip())
    voice_ready = (not use_cloned) or (tts_system.user_speaker_embeddings is not None)
    return gr.update(interactive=text_ready and voice_ready)

with gr.Blocks(title="Voice Cloning TTS System") as demo:
    gr.Markdown("# Voice Cloning TTS System")
    gr.Markdown("Upload an audio file to clone your voice and generate speech.")
    
    with gr.Row():
        with gr.Column():
            voice_upload = gr.Audio(label="Upload Voice Sample", type="filepath", sources=["upload", "microphone"])
            upload_status = gr.Textbox(label="Status", interactive=False)
            clear_btn = gr.Button("Clear Voice Profile")
        
        with gr.Column():
            text_input = gr.Textbox(label="Text to Convert", lines=5)
            use_cloned_voice = gr.Checkbox(label="Use Cloned Voice", value=True, interactive=False)
            generate_btn = gr.Button("Generate Speech", interactive=False)
    
    output_audio = gr.Audio(label="Generated Speech", type="filepath")
    generation_status = gr.Textbox(label="Generation Status", interactive=False)
    
    voice_upload.change(fn=process_voice_upload, inputs=[voice_upload], outputs=[upload_status, use_cloned_voice, generate_btn])
    text_input.change(fn=update_generate_button, inputs=[text_input, use_cloned_voice], outputs=[generate_btn])
    use_cloned_voice.change(fn=update_generate_button, inputs=[text_input, use_cloned_voice], outputs=[generate_btn])
    generate_btn.click(fn=generate_speech, inputs=[text_input, use_cloned_voice], outputs=[output_audio, generation_status])
    clear_btn.click(fn=clear_voice_profile, outputs=[upload_status, use_cloned_voice, generate_btn])

if __name__ == "__main__":
    print("🌟 Starting Voice Cloning TTS System...")
    demo.launch()