Author name: Salad Technologies

Parakeet TDT 1.1B GPU benchmark The Automatic Speech Recognition (ASR) model, Parakeet TDT 1.1B, is the latest addition to NVIDIA’s Parakeet family. Parakeet TDT 1.1B boasts unparalleled accuracy and significantly faster performance compared to other models in the same family. Using our latest batch-processing framework, we conducted comprehensive tests with Parakeet TDT 1.1B against extensive datasets, including English CommonVoice and Spoken Wikipedia Corpus English(Part1, Part2).  In this detailed GPU benchmark, we will delve into the design and construction of a high-throughput, reliable and cost-effective batch-processing system within SaladCloud. Additionally, we will conduct a comparative analysis of the inference performance between Parakeet TDT 1.1B and other popular ASR models like Whisper Large V3 and Distil-Whisper Large V2.  Advanced system architecture for batch jobs Our latest batch processing framework consists of: HTTP handlers using AWS Lambda or Azure Functions can be implemented for both the Job Queue System and the Job Recording System. This provides convenient access, eliminating the necessity of installing a specific Cloud Provider’s SDK/CLIs within the application container image. We aimed to keep the framework components fully managed and serverless to closely simulate the experience of using managed transcription services. A decoupled architecture provides the flexibility to choose the best and most cost-effective solution for each component from the industry. Enhanced Node Implementation for High Performance and Throughout We have refined the node implementation to further enhance the system performance and throughput. Within each node in the GPU resource pool in SaladCloud, we follow best practices by utilizing two processes: 1) Inference Process The transcription for audio involves resource-intensive operations on both CPU and GPU, including format conversion, re-sampling, segmentation, transcription and merging. The more CPU operations involved, the lower the GPU utilization experienced. While having the capacity to fully leverage the CPU, multiprocessing or multithreading-based concurrent inference over a single GPU might limit optimal GPU cache utilization and impact performance. This is attributed to each inference running at its own layer or stage. The multiprocessing approach also consumes more VRAM as every process requires a CUDA context and loads its own model into GPU VRAM for inference.  Following best practices, we delegate more CPU-bound pre-processing and post-processing tasks to the benchmark worker process. This allows the inference process to concentrate on GPU operations and run on a single thread. The process begins by loading the model, warming up the GPU, and then listens on a TCP port by running a Python/FastAPI app on a Unicorn server. Upon receiving a request, it invokes the transcription inference and promptly returns the generated assets.  Batch inference can be employed to enhance performance by effectively leveraging GPU cache and parallel processing capabilities. However, it requires more VRAM and might delay the return of the result until every single sample in the input is processed. The choice of using batch inference and determining the optimal batch size depends on model, dataset, hardware characteristics and use case. This also requires experimentation and ongoing performance monitoring. 2) Benchmark Worker Process The benchmark worker process primarily handles various I/O- and CPU-bound tasks, such as downloading/uploading, pre-processing, and post-processing. The Global Interpreter Lock (GIL) in Python permits only one thread to execute Python code at a time within a process. While the GIL can impact the performance of multithreaded applications, certain operations remain unaffected, such as I/O operations and calling external programs. To maximize performance with better scalability, we adopt multiple threads to concurrently handle various tasks, with several queues created to facilitate information exchange among these threads. Thread Description Downloader In most cases, we require 2 to 3 threads to concurrently pull jobs and download audio files, and efficiently feed the inference pipeline while preventing the download of excessive audio files. The actual number depends on the characteristics of the application and dataset, as well as network performance. It also performs the following pre-processing steps:1) Removal of bad audio files.2) Format conversion and re-sampling.3) Chunking very long audio into 15-minute clips.4) Metadata extraction (URL, file/clid ID, length). The pre-processed audio files and their corresponding metadata JSON files are stored in a shared folder. Simultaneously, the filenames of the JSON files are added to the transcribing queue. Caller It reads a JSON filename from the transcribing queue, retrieves the metadata by reading the corresponding file in the shared folder, and subsequently sends a synchronous request, including the audio filename, to the inference server. Upon receiving the response, it forwards the generated texts along with statistics to the reporting queue, while simultaneously sending the transcribed audio and JSON filenames to the cleaning queue.  The simplicity of the caller is crucial as it directly influences the inference performance. Reporter The reporter, upon reading the reporting queue, manages post-processing tasks, including merging results and format conversion. Eventually, it uploads the generated assets and reports the job results. Multiple threads may be required if the post-processing is resource-intensive. Cleaner After reading the cleaning queue, the cleaner deletes the processed audio files and their corresponding JSON files from the shared folder. By running two processes to segregate GPU-bound tasks from I/O and CPU-bound tasks, and fetching and preparing the next audio clips concurrently and in advance while the current one is still being transcribed, eliminates any waiting period.  After one audio clip is completed, the next is immediately ready for transcription. This approach not only reduces the overall processing time for batch jobs but also leads to even more significant cost savings. Single-Node Test using JupyterLab on SaladCloud Before deploying the application container image on a large scale in SaladCloud, we can build a specialized application image with JupyterLab and conduct the single-node test across various types of Salad nodes. With JupyterLab’s terminal, we can log into a container instance running on SaladCloud, gaining OS-level access. This enables us to conduct various tests and optimize the configurations and parameters of the model and application. These include: Analysis of single-node test using JupyterLab Based on our tests using JupyterLab, we found that the inference of Parakeet TDT 1.1B for audio files lasting