Creating an AI-Powered Beat Saber Map Generator Using Transformers
Harnessing Advanced Machine Learning for Rhythm-Synced Gameplay
Key Takeaways
- Comprehensive Data Preprocessing: Aligning musical features with map elements ensures rhythm synchronization.
- Advanced Transformer Architecture: Utilizing sequence-to-sequence models captures temporal dependencies effectively.
- Iterative Refinement and Evaluation: Continual testing and model adjustments enhance map quality and playability.
Introduction
Beat Saber has revolutionized rhythm-based gaming with its immersive gameplay that combines music and physical movement. An AI-powered map generator leverages machine learning, specifically transformer models, to create synchronized and engaging Beat Saber maps automatically. This comprehensive guide details the steps to build such a generator, from data collection to model training and map generation.
Data Collection and Preparation
Gathering and Aligning Data
The foundation of an effective AI map generator lies in the quality and quantity of data. Collect a substantial dataset comprising existing Beat Saber maps paired with their corresponding audio files. This dataset will serve as the training ground for the transformer model, enabling it to learn the intricate relationship between musical beats and in-game map elements.
Steps Involved:
- Dataset Compilation: Aggregate a diverse range of Beat Saber maps and associated audio tracks to cover various genres and difficulty levels.
- Data Alignment: Preprocess the data to ensure that the audio beats align precisely with the map notes. This involves:
- Onset Detection: Identifying the exact moments when beats occur in the audio.
- Feature Extraction: Extracting features such as pitch, rhythm, and tempo to represent the musical content accurately.
- Data Formatting: Convert the aligned data into a format suitable for training, typically sequences that the transformer model can process effectively.
Feature Extraction
Preparing Audio Data for the Transformer
Transforming raw audio into meaningful features is crucial for the model to understand and generate synchronized maps. Commonly used audio features include Mel-spectrograms, MFCCs (Mel-Frequency Cepstral Coefficients), and onset strength envelopes.
Key Features:
- Mel-Spectrogram: Represents the power spectrum of the audio signal, emphasizing perceptually relevant frequencies.
- MFCCs: Capture the timbral aspects of the audio, useful for differentiating between different sounds.
- Onset Strength: Highlights the initiation of musical notes, aiding in precise beat detection.
Transformer Model Architecture
Designing the Sequence-to-Sequence Model
Transformers excel in handling sequence data, making them ideal for tasks that require understanding temporal dependencies, such as music and game map synchronization. A sequence-to-sequence transformer model can effectively map audio feature sequences to corresponding Beat Saber map sequences.
Model Components:
- Embedding Layer: Transforms input features into a higher-dimensional space suitable for the transformer.
- Positional Encoding: Adds information about the position of each element in the sequence, enabling the model to understand the order of beats.
- Transformer Layers: Consist of multiple attention heads and feed-forward networks that capture complex dependencies within the data.
- Output Layer: Generates the sequence of map elements, including note positions, types, and timings.
Training the Model
Optimizing for Accurate Map Generation
Training involves feeding the preprocessed data into the transformer model and adjusting its parameters to minimize the difference between generated maps and ground truth maps. Key considerations during training include the choice of loss function, optimization algorithm, and training epochs.
Training Steps:
- Loss Function: Utilize Cross-Entropy Loss to measure discrepancies between predicted and actual map sequences.
- Optimization: Employ optimizers like Adam to update model weights efficiently.
- Epochs: Train the model over multiple epochs, monitoring performance metrics to prevent overfitting.
Generating Beat Saber Maps
Inference and Map Construction
Once trained, the model can generate new Beat Saber maps by inputting unseen audio tracks. The generation process involves predicting map elements sequentially, ensuring synchronization with the music's rhythm and structure.
Generation Process:
- Input Processing: Convert the new audio track into the same feature representation used during training.
- Map Prediction: Use the transformer model to predict the sequence of map elements based on the input features.
- Map Formatting: Structure the predicted elements into Beat Saber-compatible formats (e.g., JSON files).
Refinement and Exporting Maps
Ensuring Playability and Quality
Generated maps may require further refinement to enhance their playability and ensure they are engaging. This step could involve manual adjustments, automated optimization techniques, or additional model training with feedback loops.
Refinement Techniques:
- Manual Editing: Fine-tune map elements to improve flow and challenge levels.
- Automated Optimization: Implement algorithms that adjust map parameters based on predefined criteria for quality.
- Feedback Integration: Use playtester feedback to iteratively improve the model's output.
Exporting Maps:
Convert the final map data into Beat Saber-compatible formats, ensuring compatibility with the game’s custom map requirements. This typically involves structuring the map data into JSON files that the game can interpret and load.
Evaluation and Iteration
Assessing and Enhancing Model Performance
Continuous evaluation is essential to ensure that the generated maps meet quality standards. This involves comparing generated maps with human-created ones and conducting extensive playtesting to identify areas for improvement.
Evaluation Metrics:
- Rhythm Synchronization: Measure how well the map aligns with the song's beats and rhythms.
- Gameplay Quality: Assess the fun and challenge level of the generated maps through playtester feedback.
- Consistency: Ensure that the model consistently generates high-quality maps across different songs and genres.
Iteration Strategy:
-
Model Tuning: Adjust model parameters, architecture, or training data based on evaluation results.
-
Data Augmentation: Enhance the training dataset with more diverse and complex maps to improve model generalization.
-
Feedback Loops: Incorporate user and playtester feedback into the training process to align the model with player expectations.
Detailed Code Implementation
Building the Transformer-Based Map Generator
Below is a comprehensive Python implementation using PyTorch and the Hugging Face Transformers library. This example outlines the steps from data preprocessing to map generation.
1. Data Preprocessing: Extracting Audio Features
import torch
import torchaudio
import librosa
import numpy as np
def preprocess_song(audio_path, sample_rate=16000, n_mels=128):
waveform, sr = torchaudio.load(audio_path)
resampler = torchaudio.transforms.Resample(orig_freq=sr, new_freq=sample_rate)
waveform = resampler(waveform)
mel_spectrogram = torchaudio.transforms.MelSpectrogram(
sample_rate=sample_rate,
n_mels=n_mels
)(waveform)
mfcc = librosa.feature.mfcc(y=waveform.numpy()[0], sr=sample_rate, n_mfcc=20)
onset_env = librosa.onset.onset_strength(y=waveform.numpy()[0], sr=sample_rate)
tempo, _ = librosa.beat.beat_track(y=waveform.numpy()[0], sr=sample_rate)
features = np.concatenate([
mel_spectrogram.numpy(),
mfcc,
onset_env.reshape(1, -1),
np.full((1, mel_spectrogram.shape[2]), tempo)
], axis=0)
return torch.FloatTensor(features)
2. Defining the Transformer Model
from transformers import TransformerModel
class BeatSaberTransformer(nn.Module):
def __init__(self, input_dim, embed_dim, num_heads, num_layers, output_dim):
super(BeatSaberTransformer, self).__init__()
self.embedding = nn.Linear(input_dim, embed_dim)
self.positional_encoding = nn.Parameter(torch.zeros(1, 500, embed_dim))
encoder_layer = nn.TransformerEncoderLayer(d_model=embed_dim, nhead=num_heads)
self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
self.output_layer = nn.Linear(embed_dim, output_dim)
def forward(self, x):
x = self.embedding(x) + self.positional_encoding[:, :x.size(1), :]
x = self.transformer_encoder(x)
x = self.output_layer(x)
return x
3. Training the Model
def train_model(train_loader, model, criterion, optimizer, epochs=10):
model.train()
for epoch in range(epochs):
total_loss = 0
for mel_spectrogram, map_data in train_loader:
optimizer.zero_grad()
outputs = model(mel_spectrogram)
loss = criterion(outputs, map_data)
loss.backward()
optimizer.step()
total_loss += loss.item()
print(f"Epoch {epoch + 1}, Loss: {total_loss / len(train_loader)}")
4. Generating Map Sequences
def generate_map(model, mel_spectrogram, sequence_length=500):
model.eval()
with torch.no_grad():
outputs = model(mel_spectrogram.unsqueeze(0))
return outputs.squeeze(0).argmax(dim=1).tolist()
5. Saving the Map in Beat Saber Format
import json
class BeatMap:
def __init__(self):
self.notes = []
self.obstacles = []
self.events = []
def add_note(self, time_step, note_type, position=(0,0), color=0):
note = {
"_time": time_step,
"_lineIndex": position[0],
"_lineLayer": position[1],
"_type": note_type,
"_cutDirection": color
}
self.notes.append(note)
def export_map(self, filename="output_map.json"):
map_data = {
"_version": "2.0.0",
"_notes": self.notes,
"_obstacles": self.obstacles,
"_events": self.events
}
with open(filename, 'w') as f:
json.dump(map_data, f, indent=2)
6. Full Example Usage
if __name__ == "__main__":
# Preprocess audio
mel = preprocess_song("example_song.wav")
# Define model parameters
input_dim = mel.size(0)
embed_dim = 128
num_heads = 4
num_layers = 4
output_dim = 5 # e.g., lineIndex, lineLayer, type, color
# Initialize model, criterion, optimizer
model = BeatSaberTransformer(input_dim, embed_dim, num_heads, num_layers, output_dim)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
# Assume train_loader is predefined with training data
# train_model(train_loader, model, criterion, optimizer)
# Generate map for new song
generated_sequence = generate_map(model, mel)
# Convert sequence to BeatMap
beat_map = BeatMap()
for time_step, note in enumerate(generated_sequence):
if note > 0:
position = (int(note % 4), int(note / 4)) # Example position logic
beat_map.add_note(time_step, note, position, color=0)
# Export the generated map
beat_map.export_map("generated_map.json")
Enhancements and Best Practices
Improving Model Performance and Map Quality
To ensure the generated maps are both accurate and enjoyable, consider the following enhancements:
Advanced Embedding Techniques
Incorporate additional metadata such as BPM (Beats Per Minute), song genre, and difficulty levels into the embedding layers to provide more context to the model.
Data Augmentation
Expand the training dataset with augmented versions of existing maps and audio tracks to improve the model's ability to generalize across different songs and styles.
Hyperparameter Tuning
Experiment with different hyperparameters, such as learning rates, batch sizes, and the number of transformer layers, to optimize model performance.
Regular Evaluation Metrics
Implement robust evaluation metrics that assess both the technical accuracy of the map synchronization and the subjective quality of the gameplay experience.
Conclusion
Developing an AI-powered Beat Saber map generator using transformers is a multifaceted endeavor that combines data science, machine learning, and game design principles. By meticulously collecting and preparing data, leveraging advanced transformer architectures, and iteratively refining the model based on comprehensive evaluations, it is possible to create maps that enhance the Beat Saber gaming experience. Continuous advancements in AI and machine learning will further improve the capabilities and quality of such generators, paving the way for more dynamic and personalized rhythm-based gameplay.
References
For further reading and resources, please refer to the following links:
Last updated January 28, 2025