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So i have the task to study the feasability of setting up a Speech-To-Text engine in a production environnement, and i've been researching on this topic, so I tried Google's Speech-To-Text API and there is a quota on how many transcribtions you can use daily => That's a no-no. Second alternative, CMU Sphinx ( Open Source pretrained model for that purpose ), just tested it, and the output is messy for the LiveSpeech feature ( Perhaps i need to adjust some parameters first ). Third option, using the available deep learning tools to create my own SpeechRecognition engine ( I have tons of recorded conversations of people speaking French ).

Question : What should i use for my case? building an engine from scratch using neural networks seems complicated for a beginner in ML like me, using CMU Sphinx seems easy, but i have a feeling it's not. Any advice on what approach should i follow?

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This would be a gift to the world. I certainly hope someone is working on it even as we speak (ha ha). Let's both keep looking.

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    $\begingroup$ Actually, that gift is being done by the guys at Mozilla. github.com/mozilla/DeepSpeech is an open source speech to text engine that is released in English, and other languages will be released as soon as they have enough data. $\endgroup$ – Blenz Aug 27 '19 at 8:29
  • $\begingroup$ you can either use their architecture to train your own model with your own vocals, or use their pre-trained models which have been benchmarked, and are working at levels near Google's and other giant tech companies. $\endgroup$ – Blenz Aug 27 '19 at 8:31

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