- Introduction and Setup
- Course Introduction
- Environment Setup
- Advanced Neural Network Architectures
- Advanced Convolutional Neural Networks (CNNs)
- Implementing ResNet for Image Classification Tasks from Scratch
- Recurrent Neural Networks (RNNs) and LSTMs
- Understanding RNNs
- Code Sample for RNN
- Understanding LSTMs
- Code Sample for LSTM
- Understanding GRUs
- Code Sample for GRU
- Generative Adversarial Networks (GANs)
- Understanding the Architecture of GANs
- The Role of the Generator and Discriminator
- Implementing a Simple GAN for Image Generation
- Advanced GAN Techniques
- Implementing a Conditional GAN for MNIST Digit Generation
- Wasserstein GANs (WGANs)
- Implementing a WGAN for MNIST Digit Generation
- CycleGANs for image translation
- Implementing a CycleGAN for Image Translation
- Variational Autoencoders (VAEs)
- Understanding the Architecture and Functioning of VAEs
- The Concept of Latent Space and Reconstruction
- Practical Coding: Implementing VAEs
- Enhancing the VAE
- Transformer Models
- Architecture of the Transformer model
- The Attention Mechanism and Its Importance
- Practical Coding: Implementing Transformers
- Application: Implementing a Transformer for text generation
- Advanced Applications of Generative Models
- Fine-tuning GPT-3 for Custom Text Generation Tasks
- Enhancing Fine-Tuning with More Training Data
- Implementing neural style transfer
- Using GANs for High-Resolution Image Synthesis
- Music and Audio Generation
- Optimization and Fine-Tuning
- Techniques for Optimizing Model Performance
- Using Grid Search and Random Search for Hyperparameter Tuning
- Transfer Learning with BERT Model
- Real-World Projects
- Generating Realistic Faces with GANs
- Building a Chatbot with Transformer Models
- Best Practices and Advanced Tips
- Model Evaluation and Validation
- Measurements of Quantitative Importance for Text Generation
- Deploying Generative Models
- Future Trends and Research Directions
- Emerging Trends in Generative AI
- Ethical Considerations
- Conclusion
Music and Audio Generation
Music and Audio Generation
We will use LSTM (Long Short-Term Memory) networks in PyTorch to build a simple model for making music in this guide. We'll take a set of MIDI files, clean them up, teach the model, and then make new music.
Step 1: Setup and Import Necessary Libraries
First, we need to get the packages we need and install them.
| # Install necessary libraries !pip install torch music21 numpy # Import libraries import torch import torch.nn as nn import torch.optim as optim import numpy as np from music21 import converter, instrument, note, chord, stream import glob import os |
We use pip to set up PyTorch, music21, and numpy.
We bring in PyTorch and its tools for neural networks and optimizers.
We use numpy to do tasks with numbers.
We bring in music21 to work with MIDI files and do jobs related to music theory.
Step 2: Preprocess the Data
We need to load the MIDI files, take out the sounds and notes, and get the data ready for training.
| # Function to get notes and chords from MIDI files def get_notes(midi_files): notes = [] for file in midi_files: midi = converter.parse(file) notes_to_parse = None parts = instrument.partitionByInstrument(midi) if parts: # File has instrument parts notes_to_parse = parts.parts[0].recurse() else: # File has flat notes notes_to_parse = midi.flat.notes for element in notes_to_parse: if isinstance(element, note.Note): notes.append(str(element.pitch)) elif isinstance(element, chord.Chord): notes.append('.'.join(str(n) for n in element.normalOrder)) return notes # Load the dataset midi_files = glob.glob('path_to_midi_files/*.mid') notes = get_notes(midi_files) # Save unique notes unique_notes = sorted(set(notes)) # Create a mapping from notes to integers note_to_int = {note: num for num, note in enumerate(unique_notes)} # Prepare the sequences used by the Neural Network sequence_length = 100 network_input = [] network_output = [] for i in range(len(notes) - sequence_length): seq_in = notes[i:i + sequence_length] seq_out = notes[i + sequence_length] network_input.append([note_to_int[char] for char in seq_in]) network_output.append(note_to_int[seq_out]) n_patterns = len(network_input) # Reshape and normalize the input network_input = np.reshape(network_input, (n_patterns, sequence_length, 1)) network_input = network_input / float(len(unique_notes)) network_output = np.array(network_output) |
To get notes and chords from MIDI files, we define the get_notes method.
We tell it where to find the MIDI files in order to load the dataset.
We take notes and chords out of the MIDI files and save them as separate notes.
We make a connection between notes and numbers.
The input and output patterns that the neural network will use are set up.
The raw data is reshaped and normalized.
Step 3: Define the LSTM Model
We are going to set up an LSTM model for making music.
| # Define the LSTM Model class MusicLSTM(nn.Module): def __init__(self, input_size, hidden_size, output_size): super(MusicLSTM, self).__init__() self.hidden_size = hidden_size self.lstm = nn.LSTM(input_size, hidden_size, num_layers=2, batch_first=True) self.fc = nn.Linear(hidden_size, output_size) def forward(self, x): h_0 = torch.zeros(2, x.size(0), self.hidden_size).to(device) c_0 = torch.zeros(2, x.size(0), self.hidden_size).to(device) out, _ = self.lstm(x, (h_0, c_0)) out = self.fc(out[:, -1, :]) return out # Initialize the model input_size = 1 hidden_size = 256 output_size = len(unique_notes) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = MusicLSTM(input_size, hidden_size, output_size).to(device) |
We give the MusicLSTM class sizes for input, secret, and output.
The LSTM is made up of two layers and then a fully linked layer.
We set the model's input size, secret size, and output size at the beginning.
It is moved to the device (GPU if possible).
Step 4: Train the Model
We are going to set up the planner and loss function and teach the model.
| # Define loss function and optimizer criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=0.001) # Prepare data for training train_x = torch.tensor(network_input, dtype=torch.float32).to(device) train_y = torch.tensor(network_output, dtype=torch.long).to(device) # Train the model num_epochs = 200 for epoch in range(num_epochs): model.train() optimizer.zero_grad() output = model(train_x) loss = criterion(output, train_y) loss.backward() optimizer.step() if (epoch + 1) % 10 == 0: print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}') |
Cross-entropy loss is how we describe the loss function.
Adam with a learning rate of 0.001 is our choice for the planner.
To get the training data ready, we change it into PyTorch tensors and move it to the device.
For a certain number of epochs, we train the model and print the loss every 10 epochs.
Step 5: Generate New Music
We will use the trained model to make new songs.
| # Function to generate new music def generate_music(model, start_sequence, int_to_note, num_notes=500): model.eval() generated = [] sequence = start_sequence for _ in range(num_notes): sequence_input = torch.tensor(sequence, dtype=torch.float32).unsqueeze(0).to(device) prediction = model(sequence_input) _, top_note = torch.max(prediction, 1) generated_note = top_note.item() sequence = np.append(sequence[1:], generated_note) generated.append(int_to_note[generated_note]) return generated # Map integers back to notes int_to_note = {num: note for num, note in enumerate(unique_notes)} # Generate new music start_sequence = network_input[0] generated_notes = generate_music(model, start_sequence, int_to_note) # Convert generated notes to MIDI output_notes = [] for pattern in generated_notes: if ('.' in pattern) or pattern.isdigit(): chord_notes = pattern.split('.') notes = [note.Note(int(n)) for n in chord_notes] new_chord = chord.Chord(notes) output_notes.append(new_chord) else: new_note = note.Note(pattern) output_notes.append(new_note) output_midi = stream.Stream(output_notes) output_midi.write('midi', fp='generated_music.mid') |
We set up the generate_music method to use the learned model to make new music.
We connect numbers to notes.
We start making new songs with a given order.
The notes that were made are saved as a MIDI file.
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