This is a guest post by Carter Huffman, CTO and Co-founder at Modulate.
Modulate is a Boston-based startup on a mission to build richer, safer, more inclusive online gaming experiences for everyone. We’re a team of world-class audio experts, gamers, allies, and futurists who are eager to build a better online world and make voice chat safer for all players. We’re doing just that with ToxMod, our proactive, voice-native moderation platform. Game publishers and developers use ToxMod to proactively moderate voice chat in their games according to their own content policies, codes of conduct, and community guidelines.
We chose AWS for the scalability and elasticity that our application needed as well as the great customer service it offers. Using Amazon Elastic Compute Cloud (Amazon EC2) G5g instances featuring NVIDIA T4G Tensor Core GPUs as the infrastructure for ToxMod has helped us lower our costs by a factor of 5 (compared to G4dn instances) while achieving our goals on throughput and latency. As a nimble startup, we can reinvest these cost savings into further innovation to help serve our mission. In this post, we cover our use case, challenges, and alternative paths, and a brief overview of our solution using AWS.
The changing metaverse and need for ToxMod
Modern online games and metaverse platforms have become far more social than their predecessors. Historically, games have focused on providing a specific curated experience to players. Today, they have evolved to be more of a communal space, where players and their friends can congregate and choose a variety of experiences to partake in. With this evolution, toxicity and verbal abuse can often ruin otherwise great online experiences.
In fact, according to a recent study from the Anti-Defamation League, toxicity in games is worse than ever: exposure to white supremacist ideologies in games more than doubled in 2022. Over three-quarters of adult gamers reported experiencing severe harassment in online games. More than 17 million young gamers were exposed to harm and harassment in the past year. The problem is only getting worse, and with upcoming regulations that will require studios to take a more active role in managing and reporting on toxicity, the need for proactive voice moderation is more urgent than ever.
ToxMod helps game publishers and platforms proactively moderate their voice chat according to their own policies and guidelines, keeping their communities safe and positive. ToxMod runs a series of machine learning (ML) models that analyze the emotional, textual, and conversational aspects of voice conversations to determine if there are any violations of the publisher’s or platform’s content policies. Violations are flagged to human moderators who can take action against bad actors. Our ML models include emotion detection, transcription, and NLP-powered conversational analysis that categorizes violations and provides a rank score to determine how confident it is that a violation has occurred. These detections occur in real time and enable game publishers to proactively moderate their communities as toxicity is occurring, preventing harm to players and dangerous conversations from escalating.
Economic and technical considerations
We have two types of constraints: economic and technical. On the economic side, our problem is variable demand and the uncertain scale of the required compute infrastructure. In the games industry, developers and publishers launch games with minimal margins and only scale up as the game becomes more successful. That success can mean that our largest customers are processing millions of hours of voice chat per month. ToxMod’s costs scale with the number of hours of audio processed, which is very dynamic based on players’ behavior and external factors affecting a game’s popularity. Operating our own servers to power ToxMod is prohibitively expensive in terms of both cost and team bandwidth. On-premise servers lack this scalability and would often go underutilized, meaning the right choice for ToxMod is the cloud. With AWS, we can dynamically scale to match our customers’ demand while keeping costs at a minimum.
On the technical side, as with building any voice process application, we need to strike a balance between latency and throughput. Some of our users want the ability to address situations that may arise in their communities within a minute or two of them happening. To meet our latency budgets, we go as low level as possible. We happen to have a lot of experience with ARM devices because a lot of the ToxMod code base runs on client-side devices that often run on an ARM processor. The EC2 G5g instances powered by NVIDIA T4G Tensor Core GPUs and featuring AWS Graviton2 processors were a natural fit for some of the custom neural network inference code that had developed for client-side usage.
EC2 G5g instances for cost-efficiency and AWS reliability
With these considerations, we decided to use G5g instances as the infrastructure for ToxMod because they are cost-effective and provide familiar environments to test and deploy our models. This choice ultimately helped us lower our costs by a factor of 5 (compared to G4dn instances). To be able to iterate quickly, we needed a compute environment that was familiar to our data scientists and ML engineers. We were able to get our machine image with all the relevant drivers, libraries, and environment variables running on G5g instances within a day. We started off on G4dn instances, and our initial tests on G5g enabled us to lower our costs by 40%. Many of our most expensive models to run are GPU-bound, so we were able to further optimize our costs by right-sizing to an instance size that enabled us to maximize the CPU utilization while still having access to a single GPU.
Beyond G5g instances working particularly well for our configuration, we knew we could count on AWS’s technical support and account management to help us resolve issues quickly and maintain extremely high uptime while experiencing highly variable load. When we started, we were spending less than double digits per month, and yet a real person reached out to learn about our use case and a team of people worked with us to make our application not only work, but work in the most cost-efficient manner.
Overview of our solution
ToxMod’s solution begins with audio ingestion, which is accomplished through integration of our SDK into a game’s or platform’s voice chat infrastructure. The use of an SDK (over an API or other interface) is critical because when you process audio, you have to be extremely resource-efficient. For any single audio stream, we need to process it and hand it back to the rest of the system quickly or customers will encounter glitches in the audio, which is something we want to avoid at all costs. A lot of things can cause glitches—including memory allocation, garbage collection, and system calls—so we’ve developed the ToxMod SDK to ensure the smoothest audio processing possible.
From the SDK, voice chats are encoded in short buffers and sent over the internet. On the ingestion side, we buffer a couple of seconds of audio, and we try to find natural break points in voice conversations before sending the package to the AWS Cloud, where we save the incoming data via AWS Lambda functions. From there, analysis of the audio conversation is done via processing on G5g instances running our variety of ML audio models. We minimize overhead by batching all the packets we receive and sending those off to the GPUs in the G5g instances. The G5g instances are fed through queues of audio clips to process, which we have hooked up to auto scaling groups that efficiently scale up or down as traffic varies throughout the day.
ToxMod is built for studios of all sizes, from small indie dev teams to AAA, multi-team developers and publishers. Today, we’re better positioned than ever to provide the level of support, product development, and robust features that enterprise teams at the largest studios expect from their software partners. With multilingual support for 18 languages, 24/7 enterprise-grade support, available single-tenant licenses for studios with multiple games, and the support of the scalable ML infrastructure that AWS provides, we’re here to help AAA studios make voice chat safe for their players.
If you would like to learn more about how EC2 G5g instances can help you cost-effectively deploy your ML workloads, refer to Amazon EC2 G5g instances.
About the Authors
Carter Huffman is the CTO and co-founder of Modulate, a voice technology startup that aims to fight online toxicity and enhance voice communication in games. He has a background in physics, machine learning, and data analysis, and previously worked at NASA’s Jet Propulsion Laboratory. He is passionate about understanding and manipulating human speech using deep neural networks. He graduated from MIT with a Bachelor of Science in Physics.
Shruti Koparkar is a Senior Product Marketing Manager at AWS. She helps customers explore, evaluate, and adopt EC2 accelerated computing infrastructure for their machine learning needs.