![]() I am choosing Python for this article because most speech cloud services and ASR software packages have Python libraries. So there is no constraint of using Python. ![]() In the worst case, you can create bindings yourself. Same is true for speech packages, these come with bindings in various programming languages. In the worst case, you can always use HTTP endpoints. Most speech services provide libraries in popular programming languages. So if you have a streaming application, that eliminates some of the choices. It may send interim results, but the final result is available at the end.Īll services and software packages have batch APIs, but some lack streaming APIs at the moment. In the case of streaming API, it is repeatedly invoked with available chunks of the audio buffer. in voice-controlled applications, video subtitles), you will need streaming API. ![]() Streaming: If you need to process speech in realtime (e.g. In batch API, an audio file is passed as a parameter, and speech-to-text transcribing is done in one shot. You need to determine whether your application requires batch ASR or streaming ASR.īatch: If you have audio recordings that need to transcribe it offline, then batch processing will suffice as well more economical. You can design your code to limit the blast-radius of such reversal, as well as in case if you migrate to another SaaS or software package. For example, you can start with a cloud service, and if needed, move to your own deployment of a software package and vice versa. Trade-offs of using speech cloud service vs. But it requires expertise and upfront efforts to train and deploy the models. Software packages offer you full control as you are hosting it, and also the possibility of creating smaller models tailored for your application, and deploying it on-device/edge without needing network connectivity. However, for reasonable large usage, it typically cost more money. You have to sigh-up for a SaaS, and get key/credentials, and you are all set to use it in your code, either through HTTP endpoints or libraries in the programming languages of your choice. There are two possibilities: make calls to Speech-to-Text SaaS on the cloud or host one of the ASR software package in your application. This exploration of existing ASR solutions is the result of that curiosity. But, naturally, we are curious about the state of art in ASR, NLU and TTS even though we do not expose these parts of our tech stack as separate SaaS offerings. Our Android and Web SDKs provide simple APIs suitable from the perspective of app programmers, while Slang platform handles the burden of the complexity of stitching together ASR, NLU and Text-to-Speech (TTS). We are interested in ASR and NLU in general, and their efficacy in the voice-to-action loop in apps in particular. transcribing dictation, or producing real-time subtitles for videos. There are also stand-alone applications of ASR, e.g. Then this text is fed to a Natural Language Processing/Understanding (NLP/NLU) to understand and extract key information (such as intentions, sentiments), and then appropriate action is taken. In ASR, an audio file or speech spoken to a microphone is processed and converted to text, therefore it is also known as Speech-to-Text (STT). We are very interested in Conversational AI for Indic languages.Īutomatic Speech Recognition (ASR) is the necessary first step in processing voice. At Slang Labs, we are building a platform for programmers to easily augment existing apps with voice experiences. With the growing popularity of voice assistants like Alexa, Siri and Google Assistant, several apps (e.g., YouTube, Gana, Paytm Travel, My Jio) are beginning to have functionalities controlled by voice. Speech recognition technologies have been evolving rapidly for the last couple of years, and are transitioning from the realm of science to engineering.
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