i am trying to figure out how to create language model with support of hot words. Hot words (or speech context or speech boost) are great feature for dynamically tune ASR with given context in voice assistant. For instance you want to authorize person through their surname and you know with some probability (based on image, or phone number) that his surname is for instance “Lysek” so you give it to speech boost (Google ASR) and it will transcribe correctly with great probability (oposed to without boost it will transcribe Lesek). Custom LM is one solution but it is not suitable for every use case.
I have two questions:
- Is this hotword feature (with BPE and kenlm model) on your roadmap? (If yes and in far future, will it be based on some opensource like nemo where we can contribute?)
- Is it possible to get from Riva pipeline transcription logits from model to use it with this repository? GitHub - kensho-technologies/pyctcdecode: A fast and lightweight python-based CTC beam search decoder for speech recognition.
Thanks for great work!