vosk-api

Jasper is very old and not very accurate. For offline recognition on RPi try something like Vosk https://github.com/alphacep/vosk-api

Not a big problem given so many alternatives around like E.g. some very active projects are: * Kaldi (https://github.com/kaldi-asr/kaldi/) obviously, probably the most famous one, and most mature one. For standard hybrid NN-HMM models and also all their more recent lattice-free MMI (LF-MMI) models / training procedure. This is also heavily used in industry (not just research). * ESPnet (https://github.com/espnet/espnet), for all kind of end-to-end models, like CTC, attention-based encoder-decoder (including Transformer), and transducer models. * Espresso (https://github.com/freewym/espresso). * Google Lingvo (https://github.com/tensorflow/lingvo). This is the open source release of Googles internal ASR system, and used by Google in production (their internal version of it, which is not too much different). * NVIDIA OpenSeq2Seq (https://gi

Try https://github.com/alphacep/vosk-api. It supports 10 languages, works on Android and RPi and also has big and more accurate server models. Other good ones are https://github.com/daanzu/kaldi-active-grammar and https://talonvoice.com/ There are toolkits for research like https://github.com/kaldi-asr/kaldi, https://github.com/espnet/espnet, wav2letter, Espresso, Nvidia/Nemo, https://github.com/didi/athena. You can try them too if you want to go deep. Some of them have interesting capabilities.

I develop kaldi-active-grammar [0]. The Kaldi engine itself is state of the art and open source, but is focused on research rather than usability. My project has a simple interface and comes with a pretty good open source speech model. However, kaldi-active-grammar specializes in real time command and control, with advanced features that don't really apply to your use case. Vosk [1] is probably a simpler, better fit for you. It likewise uses Kaldi and can use my models, and offers some others of its own as well. Neither are particularly focused on transcription per se, but they are open. [0] https://github.com/daanzu/kaldi-active-grammar [1] https://github.com/alphacep/vosk-api

Shameless plug, but I have been working on an open source IDE plugin [1] for the IntelliJ Platform which attempts to do this. Previously, we used an older HMM-based speech toolkit called CMUSphinx [2], but are currently transitioning to a deep speech recognition system. We also tried a number of cloud APIs including Amazon Lex and Google Cloud Speech, but they were too slow -- offline STT is really important for low latency UX applications. For navigation and voice typing, we need something customizable and fairly responsive. Custom grammars would be nice for various contexts and programming languages. There are a few good OSS offline deep speech libraries including Mozilla DeepSpeech [3], but their resource footprint is too high. We settled on the currently less mature vosk [4], which is based on Kaldi [5] (a more popular deep speech pip

Would be nice to test something opensource alongside. Like https://github.com/alphacep/vosk-api which runs on Android and iPhone offline.

You are welcome to try Vosk https://github.com/alphacep/vosk-api Advantages are: 1) Supports 7 languages - English, German, French, Spanish, Portuguese, Chinese, Russian 2) Works offline even on lightweight devices - Raspberry Pi, Android, iOS 3) Install it with simple `pip install vosk` 4) Model size per language is just 50Mb 5) Provides streaming API for the best user experience (unlike popular speech-recognition python package) 6) There are APIs for different languages too - java/csharp etc. 7) Allows quick reconfiguration of vocabulary for best accuracy. 8) Supports speaker identification beside simple speech recognition

Ok, if you want to start with Kaldi it is probably easier to check kaldi-active-grammar mention above or https://github.com/alphacep/vosk-api