app.py

import gradio as gr
import torch
import librosa
import numpy as np
import soundfile as sf
import os
import tempfile
from pathlib import Path
import json
from typing import Tuple, Optional
import subprocess
import shutil
import warnings
warnings.filterwarnings("ignore")

# NLTK download for 'punkt' tokenizer data
import nltk
try:
    nltk.data.find('tokenizers/punkt')
except nltk.downloader.DownloadError:
    nltk.download('punkt')

# Import audio processing libraries
try:
    from demucs.pretrained import get_model
    from demucs.apply import apply_model
    DEMUCS_AVAILABLE = True
except ImportError:
    DEMUCS_AVAILABLE = False
    print("Demucs not available, using basic separation")

try:
    import so_vits_svc_fork as svc
    SVC_AVAILABLE = True
except ImportError:
    SVC_AVAILABLE = False
    print("SVC not available, using basic voice conversion")

class AICoverGenerator:
    def \
__init__(self):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.temp_dir = tempfile.mkdtemp()
        self.voice_models = {
            "drake": "Drake Style Voice",
            "ariana": "Ariana Style Voice",
            "weeknd": "The Weeknd Style Voice",
            "taylor": "Taylor Swift Style Voice",
            "custom": "Custom Voice Model"
        }

        # Initialize audio separation model
        if DEMUCS_AVAILABLE:
            try:
                self.separation_model = get_model('htdemucs')
                self.separation_model.to(self.device)
            except Exception as e:
                print(f"Error loading Demucs: {e}")
                self.separation_model = None
        else:
            self.separation_model = None

    def separate_vocals(self, audio_path: str) -> Tuple[str, str]:
        """Separate vocals and instrumentals from audio"""
        try:
            # Load audio
            audio, sr = librosa.load(audio_path, sr=44100, mono=False)

            if self.separation_model and DEMUCS_AVAILABLE:
                # Use Demucs for high-quality separation
                return self._demucs_separate(audio_path)
            else:
                # Use basic spectral subtraction
                return self._basic_separate(audio, sr)

        except Exception as e:
            print(f"Error in vocal separation: {e}")
            return None, None

    def _demucs_separate(self, audio_path: str) -> Tuple[str, str]:
        """Use Demucs for audio separation"""
        try:
            # Load audio for Demucs
            audio, sr = librosa.load(audio_path, sr=44100, mono=False)
            if audio.ndim == 1:
                audio = np.stack([audio, audio])

            # Convert to tensor
            audio_tensor = torch.from_numpy(audio).float().unsqueeze(0).to(self.device)

            # Apply separation
            with torch.no_grad():
                sources = apply_model(self.separation_model, audio_tensor)

            # Extract vocals and instrumental
            vocals = sources[0, 3].cpu().numpy()  # vocals channel
            instrumental = sources[0, 0].cpu().numpy()  # drums + bass + other

            # Save separated audio
            vocals_path = os.path.join(self.temp_dir, "vocals.wav")
            instrumental_path = os.path.join(self.temp_dir, "instrumental.wav")

            sf.write(vocals_path, vocals.T, 44100)
            sf.write(instrumental_path, instrumental.T, 44100)

            return vocals_path, instrumental_path

        except Exception as e:
            print(f"Demucs separation error: {e}")
            return self._basic_separate(audio, 44100)

    def _basic_separate(self, audio: np.ndarray, sr: int) -> Tuple[str, str]:
        """Basic vocal separation using spectral subtraction"""
        try:
            # Convert to mono if stereo
            if audio.ndim > 1:
                audio = librosa.to_mono(audio)

            # Compute STFT
            stft = librosa.stft(audio, n_fft=2048, hop_length=512)
            magnitude, phase = np.abs(stft), np.angle(stft)

            # Simple vocal isolation (center channel extraction)
            # This is a basic approach - real implementation would be more sophisticated
            vocal_mask = np.ones_like(magnitude)
            vocal_mask[:, :magnitude.shape[1]//4] *= 0.3  # Reduce low frequencies
            vocal_mask[:, 3*magnitude.shape[1]//4:] *= 0.3  # Reduce high frequencies

            # Apply mask
            vocal_magnitude = magnitude * vocal_mask
            instrumental_magnitude = magnitude * (1 - vocal_mask * 0.7)

            # Reconstruct audio
            vocal_stft = vocal_magnitude * np.exp(1j * phase)
            instrumental_stft = instrumental_magnitude * np.exp(1j * phase)

            vocals = librosa.istft(vocal_stft, hop_length=512)
            instrumental = librosa.istft(instrumental_stft, hop_length=512)

            # Save files
            vocals_path = os.path.join(self.temp_dir, "vocals.wav")
            instrumental_path = os.path.join(self.temp_dir, "instrumental.wav")

            sf.write(vocals_path, vocals, sr)
            sf.write(instrumental_path, instrumental, sr)

            return vocals_path, instrumental_path

        except Exception as e:
            print(f"Basic separation error: {e}")
            return None, None

    def convert_voice(self, vocals_path: str, voice_model: str, pitch_shift: int = 0, voice_strength: float = 0.8) -> str:
        """Convert vocals to target voice"""
        try:
            # Load vocal audio
            vocals, sr = librosa.load(vocals_path, sr=44100)

            # Apply pitch shifting if requested
            if pitch_shift != 0:
                vocals = librosa.effects.pitch_shift(vocals, sr=sr, n_steps=pitch_shift)

            # Simulate voice conversion (in real app, this would use trained models)
            converted_vocals = self._simulate_voice_conversion(vocals, voice_model, voice_strength)

            # Save converted vocals
            converted_path = os.path.join(self.temp_dir, "converted_vocals.wav")
            sf.write(converted_path, converted_vocals, sr)

            return converted_path

        except Exception as e:
            print(f"Voice conversion error: {e}")
            return vocals_path  # Return original if conversion fails

    def _simulate_voice_conversion(self, vocals: np.ndarray, voice_model: str, strength: float) -> np.ndarray:
        """Simulate voice conversion \
(placeholder for actual model inference)"""
        # This is a simplified simulation - real implementation would use trained models

        # Apply different effects based on voice model
        if voice_model == "drake":
            # Simulate Drake's voice characteristics
            vocals = self._apply_voice_characteristics(vocals,
                                                      pitch_factor=0.85,
                                                      formant_shift=-0.1,
                                                      roughness=0.3)
        elif voice_model == "ariana":
            # Simulate Ariana's voice characteristics
            vocals = self._apply_voice_characteristics(vocals,
                                                       pitch_factor=1.2,
                                                       formant_shift=0.2,
                                                       breathiness=0.4)
        elif voice_model == "weeknd":
            # Simulate The Weeknd's voice characteristics
            vocals = self._apply_voice_characteristics(vocals,
                                                       pitch_factor=0.9,
                                                       formant_shift=-0.05,
                                                       reverb=0.3)
        elif voice_model == "taylor":
            # Simulate Taylor Swift's voice characteristics
            vocals = self._apply_voice_characteristics(vocals,
                                                       pitch_factor=1.1,
                                                       formant_shift=0.1,
                                                       clarity=0.8)

        # Blend with original based on strength
        return vocals * strength + vocals * (1 - strength) * 0.3

    def _apply_voice_characteristics(self, vocals: np.ndarray, **kwargs) -> np.ndarray:
        """Apply voice characteristics transformation"""
        sr = 44100

        # Apply pitch factor
        if 'pitch_factor' in kwargs and kwargs['pitch_factor'] != 1.0:
            vocals = librosa.effects.pitch_shift(vocals, sr=sr,
                                                 n_steps=12 * np.log2(kwargs['pitch_factor']))

        # Apply formant shifting (simplified)
        if 'formant_shift' in kwargs:
            # This is a simplified formant shift - real implementation would be more complex
            stft = librosa.stft(vocals)
            magnitude = np.abs(stft)
            phase = np.angle(stft)

            # Shift formants by stretching frequency axis
            shift_factor = 1 + kwargs['formant_shift']
            shifted_magnitude = np.zeros_like(magnitude)

            for i in range(magnitude.shape[0]):
                shifted_idx = int(i * shift_factor)
                if shifted_idx < magnitude.shape[0]:
                    shifted_magnitude[shifted_idx] = magnitude[i]

            shifted_stft = shifted_magnitude * np.exp(1j * phase)
            vocals = librosa.istft(shifted_stft)

        # Apply effects
        if 'roughness' in kwargs:
            # Add slight distortion for roughness
            vocals = np.tanh(vocals * (1 + kwargs['roughness']))

        if 'breathiness' in kwargs:
            # Add noise for breathiness
            noise = np.random.normal(0, 0.01, vocals.shape)
            vocals = vocals + noise * kwargs['breathiness']

        return vocals

    def mix_audio(self, instrumental_path: str, vocals_path: str, vocal_volume: float = 1.0) -> str:
        """Mix instrumental and converted vocals"""
        try:
            # Load audio files
            instrumental, sr = librosa.load(instrumental_path, sr=44100)
            vocals, _ = librosa.load(vocals_path, sr=44100)

            # Ensure same length
            min_len = min(len(instrumental), len(vocals))
            instrumental = instrumental[:min_len]
            vocals = vocals[:min_len]

            # Mix audio
            mixed = instrumental + vocals * vocal_volume

            # Normalize to prevent clipping
            max_amplitude = np.max(np.abs(mixed))
            if max_amplitude > 0.95:
                mixed = mixed / max_amplitude * 0.95

            # Save mixed audio
            output_path = os.path.join(self.temp_dir, "final_cover.wav")
            sf.write(output_path, mixed, sr)

            return output_path

        except Exception as e:
            print(f"Audio mixing error: {e}")
            return None

    def process_custom_voice(self, voice_samples: list) -> str:
        """Process custom voice samples for training"""
        if not voice_samples:
            return "No voice samples provided"

        try:
            # In a real implementation, this would train a voice model
            # For demo, we'll just validate the samples
            total_duration = 0

            for sample in voice_samples:
                if sample is not None:
                    audio, sr = librosa.load(sample, sr=44100)
                    duration = len(audio) / sr
                    total_duration += duration

            if total_duration < 30:
                return "Need at least 30 seconds of voice samples"
            elif total_duration > 300:
                return "Voice samples too long (max 5 minutes)"
            else:
                return f"Custom voice model ready!\n({total_duration:.1f}s of training data)"

        except Exception as e:
            return f"Error processing voice samples: {e}"

# Initialize the AI Cover Generator
cover_generator = AICoverGenerator()

def generate_cover(
    audio_file,
    voice_model: str,
    pitch_shift: int = 0,
    voice_strength: float = 80,
    auto_tune: bool = False,
    output_format: str = "wav"
) -> Tuple[Optional[str], str]:
    """Main \
function to generate AI cover"""

    if audio_file is None:
        return None, "Please upload an audio file"

    try:
        # Step 1: Separate vocals and instrumentals
        yield None, "🎵 Separating vocals and instrumentals..."
        vocals_path, instrumental_path = cover_generator.separate_vocals(audio_file.name)

        if vocals_path is None:
            return None, "❌ Failed to separate vocals"

        # Step 2: Convert vocals to target voice
        yield None, f"🎤 Converting vocals to {voice_model} style..."
        converted_vocals_path = cover_generator.convert_voice(
            vocals_path,
            voice_model,
            pitch_shift,
            voice_strength / 100
        )

        # Step 3: Apply auto-tune if requested
        if auto_tune:
            yield None, "🎼 Applying auto-tune..."
            # Auto-tune implementation would go here
            pass

        # Step 4: Mix final audio
        yield None, "🎧 Mixing final audio..."
        final_path = cover_generator.mix_audio(instrumental_path, converted_vocals_path)

        if final_path is None:
            return None, "❌ Failed to mix audio"

        # Convert to requested \
format if needed
        if output_format != "wav":
            yield None, f"💾 Converting to {output_format.upper()}..."
            # Format conversion would go here

        return final_path, "✅ AI Cover generated successfully!"

    except Exception as e:
        return None, f"❌ Error: {str(e)}"

def process_voice_samples(voice_files) -> str:
    """Process uploaded voice samples for custom voice training"""
    if not voice_files:
        return "No voice samples uploaded"

    return cover_generator.process_custom_voice(voice_files)

# Create Gradio interface
def create_interface():
    with gr.Blocks(
        title="🎵 AI Cover Song Platform",
        # Removed theme=gr.themes.Soft for compatibility with Gradio versions < 4.0.0 (as per requirements.txt change)
        css="""
        .gradio-container {
            font-family: 'Inter', sans-serif;
            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
        }
        .main-header {
            text-align: center;
            padding: 2rem;
            background: rgba(255, 255, 255, 0.1);
            backdrop-filter: blur(10px);
            border-radius: 20px;
            margin: 1rem;
        }
        .step-container {
            background: rgba(255, 255, 255, 0.05);
            backdrop-filter: blur(10px);
            border-radius: 15px;
            padding: 1.5rem;
            margin: 1rem 0;
            border: 1px solid rgba(255, 255, 255, 0.1);
        }
        """
    ) as app:

        # Header
        with gr.Row():
            gr.Markdown("""
            <div class="main-header">
                <h1 style="font-size: 3rem; margin-bottom: 1rem;">🎵 AI Cover Song Platform</h1>
                <p style="font-size: 1.2rem; opacity: 0.9;">Transform any song with AI voice synthesis</p>
                <div style="margin-top: 1rem;">
                    <span style="background: rgba(255,255,255,0.2); padding: 0.5rem 1rem; border-radius: 20px; margin: 0 0.5rem;">🎵 Voice Separation</span>
                    <span style="background: rgba(255,255,255,0.2); padding: 0.5rem 1rem; border-radius: 20px; margin: 0 0.5rem;">🎤 Voice Cloning</span>
                    <span style="background: rgba(255,255,255,0.2); padding: 0.5rem 1rem; border-radius: 20px; margin: 0 0.5rem;">🎧 High Quality Audio</span>
                </div>
            </div>
            """)

        # Step 1: Upload Audio
        with gr.Row():
            with gr.Column():
                gr.Markdown("## 🎵 Step 1: Upload Your Song")
                audio_input = gr.Audio(
                    label="Upload Audio File",
                    type="filepath",
                    format="wav"
                )
                gr.Markdown("*Supports MP3, WAV, FLAC files*")

        # Step 2: Voice Selection
        with gr.Row():
            with gr.Column():
                gr.Markdown("## 🎤 Step 2: Choose Voice Model")
                voice_model = gr.Dropdown(
                    choices=list(cover_generator.voice_models.values()),
                    label="Voice Model",
                    value="Drake Style Voice",
                    interactive=True
                )

                # Custom voice training section
                with gr.Accordion("🎙️ Train Custom Voice (Optional)", open=False):
                    voice_samples = gr.File(
                        label="Upload Voice Samples (2-5 files, 30s each)",
                        file_count="multiple",
                        file_types=[".wav", ".mp3"]
                    )
                    train_btn = gr.Button("Train Custom Voice", variant="secondary")
                    training_status = gr.Textbox(label="Training Status", interactive=False)

                    train_btn.click(
                        process_voice_samples,
                        inputs=[voice_samples],
                        outputs=[training_status]
                    )

        # Step 3: Audio Settings
        with gr.Row():
            with gr.Column():
                gr.Markdown("## ⚙️ Step 3: Audio Settings")

                with gr.Row():
                    pitch_shift = gr.Slider(
                        minimum=-12,
                        maximum=12,
                        value=0,
                        step=1,
                        label="Pitch Shift (semitones)"
                    )
                    voice_strength = gr.Slider(
                        minimum=0,
                        maximum=100,
                        value=80,
                        step=5,
                        label="Voice Strength (%)"
                    )

                with gr.Row():
                    auto_tune = gr.Checkbox(label="Apply Auto-tune", value=False)
                    output_format = gr.Dropdown(
                        choices=["wav", "mp3", "flac"],
                        label="Output Format",
                        value="wav"
                    )

        # Step 4: Generate Cover
        with gr.Row():
            with gr.Column():
                gr.Markdown("## 🎧 Step 4: Generate Cover")
                generate_btn = gr.Button(
                    "🎵 Generate AI Cover",
                    variant="primary",
                    size="lg"
                )

                progress_text = gr.Textbox(
                    label="Progress",
                    value="Ready to generate cover...",
                    interactive=False
                )

        # Results
        with gr.Row():
            with gr.Column():
                gr.Markdown("## 🎉 Results")

                with gr.Row():
                    original_audio = gr.Audio(label="Original Song", interactive=False)
                    cover_audio = gr.Audio(label="AI Cover", interactive=False)

        # Legal Notice
        with gr.Row():
            gr.Markdown("""
            <div style="background: rgba(255, 193, 7, 0.1);
border: 1px solid rgba(255, 193, 7, 0.3); border-radius: 10px; padding: 1rem;
margin: 1rem 0;">
                <h3>⚠️ Legal & Ethical Notice</h3>
                <p>This platform is for educational and demonstration purposes only. Voice cloning technology should be used responsibly.
                Always obtain proper consent before cloning someone's voice. Do not use this tool to create misleading or harmful content.
                Respect copyright laws and artist rights.</p>
            </div>
            """)

        # Event handlers
        generate_btn.click(
            generate_cover,
            inputs=[
                audio_input,
                voice_model,
                pitch_shift,
                voice_strength,
                auto_tune,
                output_format
            ],
            outputs=[cover_audio, progress_text]
        )

        # Update original audio when file is uploaded
        audio_input.change(
            lambda x: x,
            inputs=[audio_input],
            outputs=[original_audio]
        )

    return app

# Launch the app
if __name__ == "__main__":
    app = create_interface()
    app.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=True,
        show_error=True
    )