Let me elaborate on what I am after. I want to build a voice command use case on Android app using deepspeech. The default model for android works fine for the use case but is slow. It takes 4 seconds for 2 seconds voice command. This is not going to help me. I want to reduce the latency. A similar discussion happened in this thread where you suggested to reduce complexity of the model (by reducing n_hidden=2048 to lower value, I plan to use 256) and retrain it. I believe the latency should reduce with this new model. Now I need data to train it. I think I can not use the large dataset that the deepspeech main model is trained with (Correct me if wrong). Hence I thought to generate the voice command dataset from voice corpus.
On the voice command use case, I would be using this in various applications and hence I can not have fix command set. For each application, I might have a new command set. Hence I am looking forward for a way to generate my training voice command dataset.