Building the AI Music Copilot: Architecture and Integration

import torch
from transformers import pipeline
import librosa
import soundfile as sf

class MusicCopilot:
    def __init__(self):
        # Initialize separation model
        self.separator = pipeline(
            "audio-source-separation",
            model="hugggof/demucs_extra",
            device=0 if torch.cuda.is_available() else -1
        )
        
        # Initialize transcription
        self.transcriber = pipeline(
            "automatic-speech-recognition",
            model="openai/whisper-large-v3"
        )
        
        # Initialize generation
        self.generator = pipeline(
            "text-to-audio",
            model="facebook/musicgen-medium"
        )
    
    def deconstruct(self, audio_path):
        """Separate and transcribe audio"""
        # Separate into stems
        stems = self.separator(audio_path)
        
        # Transcribe vocals
        lyrics = self.transcriber(stems['vocals'])
        
        return {
            'stems': stems,
            'lyrics': lyrics['text']
        }
    
    def generate(self, prompt, duration=10):
        """Generate new music from text"""
        audio = self.generator(
            prompt,
            max_new_tokens=duration * 50  # ~50 tokens/sec
        )
        return audio
    
    def remix(self, audio_path, style_prompt):
        """Remix existing audio with new style"""
        # Deconstruct original
        components = self.deconstruct(audio_path)
        
        # Generate new accompaniment
        new_backing = self.generate(
            f"{style_prompt}, instrumental only"
        )
        
        # Mix with original vocals
        return self.mix_stems(
            components['stems']['vocals'],
            new_backing
        )

# Usage
copilot = MusicCopilot()
result = copilot.deconstruct("song.wav")
new_song = copilot.generate("upbeat electronic dance music")