Consider a statement and say it aloud thrice to yourself. If someone were to record this speech and then compare it point-by-point with the others, they would discover that not even one phrase was precisely the same as the others. Human speech differs from one another in the same way that various angles, resolutions, and lighting conditions change in pictures. Pitch, timing, amplitude, and even the way foundation units of speech – morphemes and phonemes – bind together to form words are all varied in human speech. Machine comprehension of human speech has, as a result, fascinated and challenged scholars and innovators throughout history.
Automatic speech recognition (ASR) is the initial step of the conversational AI pipeline and a branch of speech processing that is concerned with the conversion of voice to text. Assistive listening technology (ASR) enables us to create hands-free text messages to friends and helps those who are hard of hearing to engage with spoken-word communications. It also serves as a foundation for machine learning and comprehension. Thus, human language is accessible and actionable, allowing developers to infer sophisticated analytics such as speaker identification and sentiment analysis from it.
What exactly Is Kaldi?
Developed at Johns Hopkins University, the “Kaldi”, a project for recognition of Speech, and its Toolkit started in 2009 with the goal of creating strategies that would minimise the money price and also the amount of time necessary to construct a system that recognises speech. The Kaldi project, which began with a concentration on ASR assistance for emerging languages and genres, has gradually grown both in size and capacity, enabling academicians and researchers to contribute to the field's improvement. Kaldi, which is now the industry's de-facto toolkit that recognises speech, contributes to the availability of speech services that are used by several users day in and day out.
How to run Kaldi?
NVIDIA GPUs must be supported by your Docker environment before you can use the NGC deep learning framework container on that environment. Using the correct command and provide the repository, registry, and tags to begin running the container. Please see the NGC Container User Guide for further information on how to make use of NGC containers.
The method that you use in your system is determined by the version of DGX OS installed (for DGX systems), the specific NGC Cloud Image given by a Cloud Service Provider, or the software installed for running NGC containers on Quadro PCs, TITAN PCs, or vGPUs.
- To run a container image release, go to the Tags tab first and choose the container image release you wish to run.
- To copy the docker pull command from the Pull Tag column, you need to click the icon in the column.
- Open a command prompt and write down the pull command or paste the command. The process of retrieving the container image gets underway. Before moving on to the following stage, double-check that the pull was completed successfully.
- Start the container image by pressing F5.
Among the most important characteristics for this role are the following:
- Support for concurrent model execution: Many models, or different instances of the same model, may run on the same graphics processing unit at the same time.
- Instead of using an existing framework like PyTorch or TensorFlow, individual models may be constructed using proprietary backends. Using a custom backend, the model may implement whatever logic it desires while also benefiting from concurrent execution, GPU support, dynamic batching, and other capabilities given by the server. For more information, see Custom Backends.
- Support for multiple GPUs: Triton Server is capable of distributing inferencing over all of the GPUs on a server.
- An example of a dynamic batcher is the server's ability to combine inference requests such that they are merged into a single batch that is formed dynamically, which results in the same enhanced performance observed for batched inference requests.
- The sequence batcher combines all non-batched inference requests into a single batch that is formed dynamically by the sequence batcher. It should be used for stateful models in which a succession of inference requests must be sent to the same model instance, as opposed to dynamic batchers.
- The sequence batcher is used by the Kaldi Triton Server integration to improve performance. More information may be found in the Triton Inference Server User Guide, which is available online.
What Comes Next
Following your experience with the GPU-accelerated Kaldi speech recognition toolkit, you'll undoubtedly be interested in learning more about how NVIDIA significantly boosted its throughput by using CUDA. To learn more about the thinking process and factors that went into the rapid implementation, watch this GTC presentation.
But, what is it that the NVIDIA team is working on right now? A particular task is optimising even more stages of Kaldi's processing flow, now that the feature extractor has been successfully completed. Another goal is to put Kaldi through its paces on smaller GPUs, such as the Jetson Xavier or Nano development boards. Keep an eye out for further information!