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?