Just as we can use AI-powered Speech-to-Text models to automatically transcribe audio, so too can we use AI-powered Translation models (like the Google Translate API) to automatically translate that text into over 100 supported languages. Translating SpeechĬaptioning is useful in and of itself, but text transcripts are also extremely helpful in that they allow us to apply additional machine learning models on top of that text, for more sophisticated use cases. The endpoint returns intermediary transcriptions and ultimately resolves a “final” transcription when the speaker pauses. In this setup, audio is continuously streamed to the Speech endpoint. ![]() To see how to use this API to automatically generate subtitles, check out this awesome video:ĭevelopers can also build out this real-time functionality with Google Cloud’s Speech API. These APIs give you not only a transcription of audio, but also timestamps so that you can link these transcriptions back to the original content. You can easily create this functionality in your own app, either with the Google Cloud Speech API for pure audio, or the Video Intelligence APIfor transcribing videos. ![]() With the click of a button, content creators can use Google’s internal STT models to generate captions for their videos, so they can be enjoyed with or without audio. Generating captions is one of the simplest and most useful applications of speech-to-text (STT) technology-so useful, in fact, that this feature is built directly into YouTube. In this post, we’ll explore some of the most interesting and practical applications of speech technology, as well as the Google Cloud tools that make building these sorts of apps possible. Thanks to AI, speaking out loud has become a primary way of not only communicating with machines (like when you tell your smartphone to send a text or ask your smart speaker about the weather), but also enriching human interactions (such as generating captions in near real-time during a video meeting).
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