Introduction

At ShitOps, we faced a major challenge when it came to speech-to-text transcription for our television projects. Our team was using outdated technology, and the quality of transcriptions just wasn’t always meeting our standards. So, we put on our thinking caps and went looking for an innovative solution.

After trying out a variety of options, including off-the-shelf software and third-party tools, we finally produced a new proprietary solution. Leveraging cutting-edge technologies, our revamped system is optimized to provide top-tier speech-to-text transcription at a level that simply isn’t achievable with other technology.

The Solution

Our revolutionary speech-to-text transcription solution is built on three key technological pillars: DockerHub, Rust, and Kubernetes. Using these technologies in combination has enabled us to produce the most accurate and reliable transcription service currently available.

We’ll outline each pillar of this ground-breaking approach below:

DockerHub

DockerHub has been our go-to platform for this project’s containerization needs. We’ve found DockerHub to be the optimal choice for creating and maintaining containers because of its extensive library of pre-built containers, allowing our team to build, test and deploy code quickly and painlessly.

Rust

For those unfamiliar with Rust, it’s a low-level programming language designed to replace C++ as the workhorse language of complex systems. Rust is renowned for its speed, safety, and concurrency support. At ShitOps, we’ve opted to use this modern and leading-edge language for our speech-to-text engine for its outstanding performance with audio signal processing and streaming. A huge bonus is Rust’s ability to guarantee memory safety at compile time.

Kubernetes

Kubernetes has been pivotal in our deployment of our speech-to-text engine. We’ve employed a complex Kubernetes setup that allows us to distribute intensive transcription workloads across multiple nodes, massively accelerating the transcription process. This way, we can efficiently deploy containerized components of our system written in Rust within minutes.

The Implementation Process

Our implementation process started by building an optimized model for our machine learning solution. We collected over 10,000 hours of audio samples to enable fine-tuning of acoustic models. After that, we created an efficient data pipeline that processes the raw audio files, extracts features, and finally creates the final training dataset - this part of the process was managed through Kubernetes, leveraging custom GPU instances from AWS EC2 Spot fleet.

In order to optimize the performance of the Rust service during transcription generation, we used a high-throughput message broker like Apache Kafka to interconnect the individual components responsible for streming pre-processing, feature extraction, speaker diarization, and the transcription itself.

The DockerHub platform played a significant role in simplifying the deployment of each component, ensuring that they could be quickly scaled and moved wherever needed. Furthermore, Kubernetes allowed us to easily manage and orchestrate each Dockerized component, making sure all nodes had optimal resources dedicated to them.

Lastly, for post-processing automation, we created an integration pipeline connecting containers writing the final transcription to S3 buckets, enabling access to the newly generated ‘.txt’ documents from third-party systems if required.

flowchart LR A(Dockerize Solution) --> B{Orchestration} B --> C(GPU Infrastructure) B --> D(Peer-to-Peer Services) C --> E(Kubernetes) D --> F(Apache Kafka Integration) F --> G(Load Balancing) B --> H(Full Automation Pipeline)

Conclusion

At ShitOps, our ultimate goal is to provide high-quality solutions for our clients. Through our innovative and cutting-edge solution, we have been able to revolutionize the speech-to-text industry by leveraging the latest in technology.

While our approach might seem complex, those who work with us know that each piece of technology plays a part in driving success. Our implementation of Rust has made our speech-to-text engine lightning-fast while also ensuring maximum stability using Docker containers on Kubernetes clusters.

We’re excited about what this means for our future projects & cannot wait to share with you more milestones as they come!