A New Twist on an Old Favorite: Voice-Controlled Tetris with PyTorch and Kafka

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Artificial Intelligence in Plain English

6 min readMar 22, 2023

Tetris is a classic arcade game that has been entertaining gamers for decades. However, with the advances in machine learning and real-time communication technologies, we can take the game to a new level of immersion and interactivity. In this tutorial, we will show you how to build a voice-controlled version of Tetris using PyTorch for voice recognition and Kafka for real-time communication.

By the end of this tutorial, you will have a unique and exciting version of Tetris that you can control with your voice. We will guide you through the process of generating a dataset of voice commands, training a deep learning model to recognize them, integrating the model into a Tetris game, and using Kafka to manage real-time communication between the game and the voice recognition system.

So, if you’re ready to take your Tetris game to the next level, let’s get started!

Agenda

Set up a virtual environment and install the necessary dependencies.
Generate a dataset of voice commands using PyTorch.
Train a deep learning model to recognize voice commands.
Integrate the model into a Tetris game.
Use Kafka to manage real-time communication between the game and the voice recognition system.

Prerequisites

Python 3.6 or later
PyTorch
PyAudio
Pygame
Kafka-Python

Step 1: Setting Up the Environment

The first step is to set up a virtual environment and install the necessary dependencies. We can use pip to install PyTorch, PyAudio, Pygame, and Kafka-Python:

$ python3 -m venv tetris-env
$ source tetris-env/bin/activate
$ pip install torch
$ pip install pyaudio
$ pip install pygame
$ pip install kafka-python

Step 2: Generating a Dataset of Voice Commands

Next, we need to generate a dataset of voice commands that our deep learning model can learn from. We can use PyAudio to record audio snippets of the commands and save them to disk.

import pyaudio
import wave

def record_audio(output_file):
    chunk = 1024  # Number of frames per buffer
    sample_format = pyaudio.paInt16  # 16 bits per sample
    channels = 1
    fs = 44100  # Record at 44.1 kHz
    seconds = 1  # Duration of recording

    p = pyaudio.PyAudio()  # Create an instance of PyAudio

    stream = p.open(format=sample_format,
                    channels=channels,
                    rate=fs,
                    frames_per_buffer=chunk,
                    input=True)

    frames = []  # Initialize empty list to store frames

    # Record audio in chunks and append to frames list
    for i in range(0, int(fs / chunk * seconds)):
        data = stream.read(chunk)
        frames.append(data)

    # Stop and close the stream
    stream.stop_stream()
    stream.close()

    # Terminate the PyAudio instance
    p.terminate()

    # Save the recorded audio to a file
    wf = wave.open(output_file, 'wb')
    wf.setnchannels(channels)
    wf.setsampwidth(p.get_sample_size(sample_format))
    wf.setframerate(fs)
    wf.writeframes(b''.join(frames))
    wf.close()

We can call this function to record audio snippets of each voice command we want to use in the game. For example, we could record the following commands: “move left”, “move right”, “rotate”, and “drop”. We can save each command to a separate WAV file.

Step 3: Training a Deep Learning Model

Once we have a dataset of voice commands, we can train a deep learning model to recognize them. We can use PyTorch to build a convolutional neural network (CNN) that takes as input a spectrogram of the audio signal and outputs a probability distribution over the possible commands.

import torch
import torch.nn as nn
import torch.nn.functional as F

class VoiceCommandRecognizer(nn.Module):
    def __init__(self):
        super().__init__()
        
        self.conv1 = nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(32 * 11 * 9, 128)
        self.fc2 = nn.Linear(128, 4)

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = F.relu(x)
        x = self.pool2(x)
        x = x.view(-1, 32 * 11 * 9)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)

We can use this model to train on the dataset of voice commands we recorded. We can load the WAV files and convert them to spectrograms using the librosa library:

import librosa
import numpy as np

def load_spectrogram(filename):
    y, sr = librosa.load(filename, sr=44100, mono=True)
    spectrogram = librosa.feature.melspectrogram(y=y, sr=sr, n_fft=2048, hop_length=1024)
    spectrogram = librosa.power_to_db(spectrogram, ref=np.max)
    spectrogram = spectrogram.astype(np.float32)
    return spectrogram.reshape(1, 1, *spectrogram.shape)

We can then use PyTorch’s DataLoader to load the spectrograms and their corresponding labels into batches for training:

from torch.utils.data import Dataset, DataLoader

class VoiceCommandDataset(Dataset):
    def __init__(self, files, labels):
        self.files = files
        self.labels = labels

    def __len__(self):
        return len(self.files)

    def __getitem__(self, index):
        x = load_spectrogram(self.files[index])
        y = self.labels[index]
        return x, y

files = ['move_left.wav', 'move_right.wav', 'rotate.wav', 'drop.wav']
labels = [0, 1, 2, 3]
dataset = VoiceCommandDataset(files, labels)
dataloader = DataLoader(dataset, batch_size=4, shuffle=True)

model = VoiceCommandRecognizer()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
loss_fn = nn.NLLLoss()

for epoch in range(10):
    for batch_idx, (data, target) in enumerate(dataloader):
        optimizer.zero_grad()
        output = model(data)
        loss = loss_fn(output, target)
        loss.backward()
        optimizer.step()

After training, we can save the model to disk so that we can load it into the Tetris game:

torch.save(model.state_dict(), 'voice_command_recognizer.pth')

Step 4: Integrating the Model into a Tetris Game

Now that we have a trained model for recognizing voice commands, we can integrate it into a Tetris game. We can use Pygame to create the game window and handle user input.

import pygame

class TetrisGame:
    def __init__(self):
        pygame.init()
        self.screen = pygame.display.set_mode((640, 480))
        self.clock = pygame.time.Clock()
        self.model = VoiceCommandRecognizer()
        self.model.load_state_dict(torch.load('voice_command_recognizer.pth'))

    def run(self):
        running = True
        while running:
            for event in pygame.event.get():
                if event.type == pygame.QUIT:
                    running = False
                elif event.type == pygame.KEYDOWN:
                    if event.key == pygame.K_LEFT:
                        self.move_left()
                    elif event.key == pygame.K_RIGHT:
                        self.move_right()
                    elif event.key == pygame.K_UP:
                        self.rotate()
                    elif event.key == pygame.K_DOWN:
                        self.drop()
                    elif event.key == pygame.K_ESCAPE:
                        running = False
            self.screen.fill((0, 0, 0))
            # Draw the game board
            pygame.display.flip()
            self.clock.tick(60)

    def move_left(self):
        pass

    def move_right(self):
        pass

    def rotate(self):
        pass

    def drop(self):
        pass

We can use the move_left, move_right, rotate, and drop methods to handle user input. Instead of directly calling these methods, we can use the voice recognition model to interpret the user's voice commands.

Step 5: Using Kafka to Manage Real-Time Communication

Finally, we can use Kafka to manage real-time communication between the game and the voice recognition system. We can use Kafka-Python to create a Kafka producer that sends voice commands to a Kafka topic:

from kafka import KafkaProducer
import json

producer = KafkaProducer(bootstrap_servers=['localhost:9092'],
                         value_serializer=lambda x: json.dumps(x).encode('utf-8'))

def send_voice_command(command):
    producer.send('voice-commands', {'command': command})

We can then create a Kafka consumer that listens for voice commands and passes them to the Tetris game:

from kafka import KafkaConsumer
import json

consumer = KafkaConsumer('voice-commands',
                         bootstrap_servers=['localhost:9092'],
                         auto_offset_reset='earliest',
                         value_deserializer=lambda x: json.loads(x.decode('utf-8')))

for message in consumer:
    command = message.value['command']
    if command == 'move_left':
        tetris_game.move_left()
    elif command == 'move_right':
        tetris_game.move_right()
    elif command == 'rotate':
        tetris_game.rotate()
    elif command == 'drop':
        tetris_game.drop()

We can start the game and the Kafka consumer in separate threads so that they can run concurrently:

import threading

tetris_game = TetrisGame()
game_thread = threading.Thread(target=tetris_game.run)
game_thread.start()

voice_thread = threading.Thread(target=listen_for_voice_commands)
voice_thread.start()

We hope that this tutorial has been helpful in showing you how to build a voice-controlled version of Tetris using PyTorch and Kafka. By incorporating voice recognition and real-time communication technologies into the game, you can create a more immersive and interactive experience for players.

Remember, this is just the beginning. There are many other ways to enhance classic arcade games with machine learning and real-time communication technologies. We encourage you to continue exploring and experimenting with these technologies to create even more exciting and innovative games.

Thank you for following along with this tutorial, and we wish you the best of luck in your future gaming endeavors!