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Speech to text translator project build12/6/2023 With this option, you can build the engine based on your own data which guarantees you good performance. Now that you know the pros and cons of open source and cloud engines, please consider that there is a third option: build your own speech-to-text engine. The speech-to-text provider also has servers that can support millions of requests per second without losing performance or rapidity. In exchange, the provider is processing millions of data to provide a very performant engine. In this case, you have to accept that your data will transit to the provider cloud. On the other hand, cloud speech-to-text engines are paying but the AI provider will handle the server for you, maintain and improve the model. Moreover, you will have no trouble with data privacy because you will have to host the engine with your own server, which also means that you will need to set up this server, maintain it and insure you that you will have enough computing power to handle all the requests. It allows you to potentially modify the source code, hyperparameterize the model. It means that this is free to use and you can use the code in the way you want. Of course, the main advantage of open source speech-to-text engines is that they are open source. When you are looking for a speech-to-text engine, the first question you need to ask you is: which kind of engine am I going to choose? How to choose between open source and cloud engines ? They can sell requests with a license model (you pay a monthly subscription corresponding to a certain amount of requests) or a pay-per-use model (you pay only for requests you send). On the contrary, speech-to-text cloud engines are provided by AI providers, they are selling you requests that you can process via their APIs. You just need to download the library and use these engines directly from your machine. Open source engines are available for free, you can often find those solutions on github. We will see on this article that there are many ways to do it, including open source and cloud APIs engines. This article briefly treats how to use Speech-to-Text with Python. Many solutions are based on several functionalities combined. This list does not represent an exhaustive list of all speech recognition functionalities. Speech Translation: allows to translate an audio speech from a specific language into an audio speech from another language.Speech Diarization: Allows you to identify and differentiate the different speakers speaking in the same audio (by accents, specificities, etc.).Speech Analysis: allows to analyze an audio speech in order to extract information such as: gender, age, emotions of the speaker.Text-to-Speech: allows you to transcribe a text into audio.Speech-to-Text: allows you to transcribe audio into text.Speech recognition includes various functionalities : This popularity is due to the huge diversity of applications and needs : call center, broadcasting, traduction, health care, banking, voice assistant, etc. In recent years, within the world of Artificial Intelligence, one of the most popular applications is Speech recognition.
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