music_generator.py

import os
import numpy as np
import torch
import librosa
import soundfile as sf
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import music21  # Added this import to fix the NameError

# Assuming you're using other modules for text-to-singing functionality
# Add any other imports you need here

class EmotionDetector:
    def __init__(self):
        self.tokenizer = AutoTokenizer.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
        self.model = AutoModelForSequenceClassification.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
    
    def detect_emotion(self, text):
        # Simple emotion detection based on sentiment
        inputs = self.tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
        outputs = self.model(**inputs)
        sentiment_score = outputs.logits[0].softmax(dim=0)
        sentiment_value = (sentiment_score * torch.tensor([1, 2, 3, 4, 5])).sum().item() / 5.0
        
        # Map sentiment score to emotion
        if sentiment_value > 0.7:
            return "Happy", sentiment_value * 2 - 1
        elif sentiment_value < 0.3:
            return "Sad", 1 - sentiment_value * 2
        else:
            return "Neutral", 0.0

# Function to synthesize speech from text
def synthesize_speech(text, output_path="temp_speech.wav"):
    # Placeholder for your text-to-speech implementation
    # This should create a speech file at output_path
    print(f"Speech synthesized and saved to {output_path}")
    # Your actual implementation here
    
    return output_path

# Function to convert speech to singing
def convert_to_singing(speech_path, emotion="Neutral", emotion_intensity=0.5, output_path="temp_singing.wav"):
    # Placeholder for speech-to-singing conversion
    # This should implement your DiffSinger model to convert speech to singing
    print(f"Singing audio saved to {output_path}")
    # Your actual implementation here
    
    return output_path

# Function to generate musical accompaniment
def generate_accompaniment(
    lyrics, 
    melody_path, 
    output_path="output_accompaniment.mid", 
    tempo_value=120,
    key="C",
    time_signature="4/4",
    style="pop"
):
    # Create a music21 score
    score = music21.stream.Score()
    
    # Create a part for the melody
    melody_part = music21.stream.Part()
    
    # Set the tempo - this was causing the error
    t = music21.tempo.MetronomeMark(number=tempo_value)
    melody_part.append(t)
    
    # Set the key signature
    ks = music21.key.Key(key)
    melody_part.append(ks)
    
    # Set the time signature
    ts = music21.meter.TimeSignature(time_signature)
    melody_part.append(ts)
    
    # Here you would add notes based on your melody_path
    # This is just a placeholder - you'll need to implement your actual note generation
    
    # For example, adding a simple C major scale
    notes = ['C4', 'D4', 'E4', 'F4', 'G4', 'A4', 'B4', 'C5']
    for note_name in notes:
        n = music21.note.Note(note_name)
        n.quarterLength = 1.0
        melody_part.append(n)
    
    # Add the melody part to the score
    score.append(melody_part)
    
    # Add accompaniment based on style
    # This is a placeholder - implement your actual accompaniment generation
    if style == "pop":
        # Add pop-style accompaniment
        pass
    elif style == "classical":
        # Add classical-style accompaniment
        pass
    # Add more styles as needed
    
    # Write the score to a MIDI file
    score.write('midi', fp=output_path)
    
    return output_path

# Function to combine singing and accompaniment
def combine_audio(singing_path, accompaniment_path, output_path="final_output.wav"):
    # Load the singing audio
    singing, sr = librosa.load(singing_path, sr=None)
    
    # If accompaniment is MIDI, convert it to audio first
    if accompaniment_path.endswith('.mid'):
        # Convert MIDI to audio - implement your conversion method
        # This is a placeholder
        accompaniment = np.zeros_like(singing)  # Just a placeholder
    else:
        # Load the accompaniment audio
        accompaniment, sr_acc = librosa.load(accompaniment_path, sr=sr)
        
        # Make sure they're the same length
        if len(accompaniment) > len(singing):
            accompaniment = accompaniment[:len(singing)]
        else:
            # Pad accompaniment with zeros if it's shorter
            accompaniment = np.pad(accompaniment, (0, max(0, len(singing) - len(accompaniment))))
    
    # Mix the singing and accompaniment
    # You might want to adjust the volumes
    singing_volume = 0.7
    accompaniment_volume = 0.3
    
    mixed = singing_volume * singing + accompaniment_volume * accompaniment
    
    # Normalize to prevent clipping
    if np.max(np.abs(mixed)) > 1.0:
        mixed = mixed / np.max(np.abs(mixed))
    
    # Save the mixed audio
    sf.write(output_path, mixed, sr)
    
    return output_path

# Main function to process text to singing
def text_to_singing(text, output_path="final_output.wav"):
    # Detect emotion in the text
    emotion_detector = EmotionDetector()
    emotion, emotion_intensity = emotion_detector.detect_emotion(text)
    print(f"Detected emotion: {emotion}")
    print(f"Sentiment score: {emotion_intensity}")
    
    # Synthesize speech from text
    speech_path = synthesize_speech(text)
    
    # Convert speech to singing with emotion
    singing_path = convert_to_singing(speech_path, emotion, emotion_intensity)
    
    # Generate musical accompaniment
    accompaniment_path = generate_accompaniment(text, singing_path)
    
    # Combine singing and accompaniment
    final_output = combine_audio(singing_path, accompaniment_path, output_path)
    
    return final_output, emotion, emotion_intensity