Ask HN: Is there any work being done in speech-to-code with deep learning? | Hacker News Reader

Ask HN: Is there any work being done in speech-to-code with deep learning?

172 pointsraidicy61 comments

Is there any work being done on speech to code in a deep learning area . I have severe RSI which prevents me from coding at all . I have tried to use speech recognition software such as vocola and windows speech engine . but it required me to speak in such a way that I always would hurt my throat . I have also injured my throat multiple times so I am searching for a solution that is more conversational then command driven . I have written over 10000 lines of command Fargo Cola and they're still too many edge cases which require me to continually speak in an Abrupt manner that causes strain on my throat .

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Windows Speech Recognition is far from the best, so perhaps your trouble could be partly caused by how you had to speak in order to be understood, rather than the command style? I used to use WSR to code by voice, and it was far more laborious than my current setup.

I develop kaldi-active-grammar [0]. The Kaldi engine is state of the art for command and control. Although I don't have the data and resources for training a model like Microsoft/Nuance/Google, being an open rather than closed system allows me to train models that are far more personalized than the large commercial/generic ones you are used to. For example, see the video of me using it [1], where I can speak in a relaxed manner without having to over enunciate and strain my voice.

Gathering the data for such training does take some time, but the results can be huge [2]. Performing the actual training is currently complicated; I am working on making it portable and more turnkey, but it's not ready yet. However, I am running test training for some people. Contact me if you want me to use you as a guinea pig.

[0] https://github.com/daanzu/kaldi-active-grammar

[1] https://youtu.be/Qk1mGbIJx3s

[2] https://github.com/daanzu/kaldi-active-grammar/blob/master/d...

> Performing the actual training is currently complicated; I am working on making it portable and more turnkey, but it's not ready yet

I'm eagerly awaiting this! If I wanted to try to get something working now, I'd need to invest a lot of time - being able to get started quickly would be amazing.

It looks like Kaldi can use different backends, which I imagine have very different performance characteristics. Can you rank them from best to worst, with relative distances?

Just to be clear, the Dragonfly speech recognition command and control framework has multiple "backends" (speech recognition engines), including my Kaldi one. Probably the most used one currently is the Dragon Naturally Speaking backend.

The Kaldi engine, being developed primarily for research in speech recognition, can support a huge variety of "models". I think the consensus general best for most use cases (particularly for real time, low latency, streaming use) currently would be considered to be the "nnet3 chain" models, which are what my kaldi-active-grammar uses/supports.

Thank you, I think I understand partially, but not fully, as I'm not very well versed in speech recognition software.

Basically, my question (and I assume many other users') is "I run <Linux/Windows/Mac OS>, what are my options and how good will my recognition be with each?". Your answer above helps, but it doesn't entirely satisfy me, as I'm not sure if a model is the recognition engine, or if the engine uses the model, or how I can use it, etc.

I came across Serenade (https://serenade.ai/) recently. It's still beta but I was very impressed. In the past I've used vocola, and a few other open-source options. Serenade felt much more natural and powerful. The founders are also super hands-on and genuinely seem to care about the problem.

thank you this is worth looking into.

Yes, one of the founders is a coder who developed RSI and couldn’t find a good tool. In a form of UX bootstrapping, I’m pretty sure the product is being used to build itself

While (likely) not using directly deep learning, I found the following talk [1] by Emily Shea on her code dictation setup (based on Talon Voice) both insightful and impressive.

EDIT: Actual demo with coding starts at 18.00: https://youtu.be/YKuRkGkf5HU?t=1076

[1] https://www.youtube.com/watch?v=YKuRkGkf5HU

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 pipeline), and includes a number of low-footprint, pretrained language models for real-time streaming inference. Research has shown how to deploy efficient deep speech models on CPUs [6], so we're hoping those gains will translate to faster performance on commodity laptops soon. You can follow this issue [7] for updates on our progress. Contributions are welcome!

[1]: https://github.com/OpenASR/idear/

[2]: https://cmusphinx.github.io/

[3]: https://github.com/mozilla/DeepSpeech

[4]: https://github.com/alphacep/vosk-api

[5]: https://github.com/kaldi-asr/kaldi

[6]: https://ai.facebook.com/blog/a-highly-efficient-real-time-te...

[7]: https://github.com/OpenASR/idear/issues/52

I agree with everything you said, but I would add that a critical component of voice command and control is strict grammars. There is so much structure and context in what we speak, and being able to limit what can be recognized to only what can be reasonably spoken (based on the current context) can allow massive increases in accuracy. (EDIT: ah, you edited to add a mention of this as well.)

And one shameless plug deserves another! Vosk is a great project, but my kaldi-active-grammar [0] (mentioned in another comment here) also uses the same Kaldi engine, but extends it and is designed specifically for this use case. It supports defining many grammars, in any combination, and activating/deactivating them at will instantly per-utterance. I think it's probably a better fit as a backend for your project than vosk. My work focuses on the backend technology, so it would be great to have more front ends using it to put it within users' reach (so to speak).

[0] https://github.com/daanzu/kaldi-active-grammar

I use dictation a bit for prose, but my voice wouldn't be able to handle more than a couple of hours a day of that.

Can you use a touch screen or mouse? I went ~13 years without using a keyboard, and typed with mice (some customised), trackballs, and touch screens, mostly using predictive typing software I wrote. In that time I did a lot of programming, including a whole applied maths PhD.

One of the best mouse setups I came up with a variety of versions of was moving the cursor with one hand, and clicking with the other. Holding the mouse still to click the button accurately is a surprisingly problematic movement. I made a button-less mouse with just a flat top to rest the side of my hand on, with a bit sticking up to grip. Standalone USB numeric keypads can be remapped to mouse clicks and common keys.

Touch screens can also be very good, if set up right, as all the movement can come from the big muscles and joints of your upper arm and shoulder, and your fingers and wrist don't need to do much. The screen needs to be positioned well, not out in front of you, but down close and angled in a comfortable position to hold your arm for long periods.

For me at least, dictation is actually the more straining mode of speech recognition, as compared to using my command grammars. With dictation, you might say anything, so the computer is given wide leeway in what to recognize, and so you must speak as clearly as possible. With commands (especially a nice simple command grammar), however, what you can say is greatly restricted, which allows you the freedom to speak indistinctly and still be understood by the computer. This can even be magnified by personalized training of the speech recognition model.

When using commands at my computer, I frequently find myself muttering and grunting things that even I think to myself (that is utterly un-understandable), yet the computer understands just fine. Dictating for prolonged periods can be tiring for me, but I can happily code by voice commands all night.

More info in my other comments here: https://news.ycombinator.com/item?id=23507363 https://news.ycombinator.com/item?id=23507829

Not sure exactly how bad "severe" is, but I had a lot of luck with my RSI switching to two-fingered typing for a (long) while. It's crucial to keep everything below your elbows utterly relaxed, like a pianist, sort of.

Also, I bought a keyboard tray that supported a deep negative angle, which helped me keep a very anatomical (relaxed and natural) position.

Also, figure out that mouse, somehow. Something like the above, plus switch sides frequently.

I've no idea if that could help you, but after a few years, I'm largely in remission.

I know this isn't really what you were asking, but I'm somewhat hopeful you can find relief. Good luck.

Is the hackernews process broken? I currently see three comments being downvoted without any apparent reason: https://news.ycombinator.com/item?id=23507041 https://news.ycombinator.com/item?id=23506992 https://news.ycombinator.com/item?id=23507486

I've been working on a similar use case at work (going from discoursive speech to cli-like commands, using a semi-rigid language), and I didn't find any off-the-shelf purely ML-based solution that would work for us.

In my experience, I've found any services claiming to do deep learning produced far worse results than what we could get with simple approaches. That is, when faced with non-grammatical sentences (or rather, sentences with a different grammar than English's). Of course that's because models are not typically trained with this use-case in mind! But the fact that you need a huge load of data to even slightly alter the expected inputs of the system, to me, was a deal breaker.

For the specific case of programming with voice, Silvius comes to mind. It's built and used by a developer with this same problem. It's a bit wonky having to spell words sometimes with alpha-beta-gamma speech, and it won't work without some customization, but on the other hand it's completely free and open source: https://github.com/dwks/us

You've see seen openai's new english -> bash demo right?

That said, Silvius is more of demo than a product, the IMO best voice programming options right now are (in alphabetical order):

- Caster/dragonfly (fully open-source if you use daanzu's Kaldi engine, which is way better than Silvius afaik, I think even the creator of silvius uses dragonfly with dragon instead of using silvius)

- Serenade (fully commercial, I haven't looked at it much recently but biggest caveats afaik are accuracy, the fact speech recognition is web based, and it's restricted to specific languages and IDEs while caster/talon are for full system control and not just programming)

- Talon (my project, semi-commercial as I work on it full time and draw income from it but aim to give all necessary features away for free, some benefits include a fully offline and open-source speech recognition engine, and I have other bonuses like eye tracking and noise recognition)

> You've see seen openai's new english -> bash demo right?

Not yet, but will do, thanks!

However, I'd still be hesitant to build a product on top of that: Does voice to bash help us if we now want to do, say, voice to python? At least we'd need to re-train the system with completely new data, and even if we use transfer learning to our advantage, it's not an easy task. There's also no guarantees that the chosen neural network architecture that works for bash, will work the same for any programming language (think of a radically different syntax, like Lisp for example).

The training must also be re-done for any variation in the input format to some extent. i.e., accent, expected background noise levels, and of course (human speaker) language.

ML has its use case, but I typically see these nice demos as that, demos. When you have to build a real product and solve user problems, you can't rely on a black box doing what you want.

I think some of your comment does not apply to GPT3 in the conventional sense, they did not do any specialized training for text2bash afaik. They've been tooting about "one shot learning". If their demo is to be believed, text2bash is just their _massive_ generic model + a few lines of examples.

Also they do have a related Python demo: https://news.ycombinator.com/item?id=23507145

Speech is a completely different stack to this, but honestly (english) speech is much more of a solved problem here than general knowledge.

Related: there was a famous thread here a few months ago that would be very helpful.

Ask HN: I'm a software engineer going blind, how should I prepare? (https://news.ycombinator.com/item?id=22918980)

On a mac there is a tool called 'voice control' which can trigger custom 'commands' or keyboard shortucts. You can use it to trigger shortcuts in any IDE. So if your IDE supports custom shortcuts for templating you're away.

Indeed. And I really don't get why this has been downvoted. Yes, the OP wrote he didn't like a command driven approach. But thats on windows, which comes with its own problems, see e.g., https://news.ycombinator.com/item?id=23507363 .

Voice Control is much worse than WSR at this task, not for accuracy reasons, but for API and extensibility reasons. (I went above and beyond to try to make it work, the underlying APIs crash and/or hang if you try to load in large grammars, too much custom vocab, or repeatable commands). In Voice Control you can basically only define simple commands that require pauses, and there's no way to build a system for something like spelling words or inserting specific special characters without requiring a large. pause. between. every. single. thing. you. say.

I'm also curious about this.

The best project I've seen for voice coding is Talon Voice, but I doubt anything novel is being done with it and deep learning. I'd suggest trying it out if you haven't. They also have a pretty active slack channel, you might have some luck asking them if they know about anything on the horizon.

https://talonvoice.com/

Talon uses deep learning for the speech part, but not so much (yet) the code part. However, the continuous command recognition it uses can cause less strain than individual abrupt commands. You can string a dozen command words together more like a sentence instead of repeatedly saying a command then abruptly stopping and waiting to say the next command (which is how many older systems worked).

I'm definitely open to incorporating deep learning directly. I've already signed up for the GPT3 API waiting list and I have some ideas on how to use it, and I generally have some ideas on how I might otherwise approach more natural feeling voice programming down the line.

Check out https://serenade.ai - another startup working on this!

I really hate to see programmers/typists suffering from RSI when it is entirely preventable with the right ergonomics. Having worked on production NLP systems, I have to say I think typing will remain a more effective way of coding for many years to come ( for many reasons, but primarily because syntaxes change often and training for syntax and context is hard ). I also had RSI for many years, and finally it started to affect me playing sports and esports.

So first, I switched my mouse to my non dominant hand ( left hand for me ), as that hand already has many things to deal with. I'm also using a workstation that allows me to mount my displays at eye-level while sitting or standing. Not hunching over is ergonomics 101. Second, I switched from a standard keyboard to a split keyboard. I tried many -- Goldtouch, Kinesis Advantage2, Kinesis Freestyle -- and ultimately settled on the Ultimate Hacking Keyboard.

I could write many more paragraphs on how I customized it and why it won out, but the most important thing is that is is split and it "felt" best, once I mastered the key placements ( arrows are in different places ).

Third, I started learning VIM. Vim is really awesome but up until recently didn't have great IDE or other editor support. Now it does so there's no reason to not use it. I mostly use it for quickly jumping around files and going to line numbers.

Fourth, I'm always looking to optimize non-vim shortcuts in my editor. For example, expand-region ( now standard in VSCode ) is one of my favorite plugins.

Fifth, I'm very conscious of using my laptop for long stretches of time. Mousing on the mousepad is much more RSI inducing than using a nice gaming mouse and the UHK keyboard.

All of this to say that RSI doesn't have to be career ending. If you're doing software work and you have functioning hands and wrists you should definitely look to optimize typing before looking to speech to code. Good luck!

Probably not good for your case, but end of the summer we are going in beta launch for our product which is a visual + speech controlled programming language. It's very niche as it's a new language and IDE, from scratch, but so far it's been fun working on it.

i'd be interested in seeing it. can i follow you somewhere

Not coding - but openai's API in beta has a speech to bash function https://openai.com/blog/openai-api/

*english to bash

I haven't seen any demos actually using speech recognition as the input, but I'm excited to try that as soon as I'm off the waitlist. One of the caveats I have in mind is that speech recognition language models are going to be worse than GPT3, so the best option might be getting GPT3 to do the language decoding in-domain _and_ do the task. (The other caveat I have is it seems pretty slow for interactive speech use, but I have some ideas on how the knowledge could be cached in a sense over time.)

You have to figure they will eventually try to go further than just bash.

thank you I ended up signing up for the waitlist I saw the demo for the English to bash one shot learning if I could somehow use that and combination with a speech recognition software I might be able to achieve something along the lines said I'm looking for

Not to be too forward - but maybe ask then on twitter - there are a few conversations around accessibility and perhaps you could give them feedback from your needs.

thank you for the suggestion I've never really used much of Twitter but it is worth a shot . if i just reply to their thread will they see it?

OpenAI does not have any speech recognition APIs yet

Not sure if off topic but it wasn’t the RSI that put the nail in the coffin for my programming. It was spine injury and I had to have a surgery. There’s lots of jobs around programming that don’t require as much typing and even when you do, it’s easier to dictate email than code. Basically you got RSI from coding and generally spending too much time with the keyboard. Maybe at least consider alternatives where you are not spending lots of screen time again.

I was hoping this was a question about applying NLP to coding tasks, but based on the answers it is about voice to text for the special use case of coders.

I am not a coder, I am a writer. I wonder why all these AI people are trying to create things that will displace my means of earning a living instead of something that will create applications?

Why can't I tell my Mac: "Computer: take this collection of files and extract all the addresses of people in Indiana."

> I wonder why all these AI people are trying to create things that will displace my means of earning a living instead of something that will create applications?

AI writing is currently terrible. If your writing income can be replaced by AI, you're likely summarizing simple topics (e.g. unemotional, analysis-free facts of a recent event, like a change in a stock price). In that case, your job isn't safe anyway, because there are $2/hr humans in other countries that can also take your job.

Note that all software is capable of eliminating jobs, whether it's automating writing or something else.

> Why can't I tell my Mac: "Computer: take this collection of files and extract all the addresses of people in Indiana."

NLP is nowhere near being able to generally understand requests like this. Try talking to your Google Assistant and doing anything other than a simple lookup. It fails spectacularly.

Think about all the cognition in this task: first it has to understand what you're asking it. Then it has to figure out which collection of files you mean. Then it has to understand what an "address" is and what it looks like. Finally, it has to have a concept of "Indiana" and an understanding that an address can be in Indiana.

In terms of effort, it's far more efficient for you to learn the simple programming skills to do this yourself (or to use a purpose-built tool) than to create AI that does this generally, which will cost at least billions of dollars more in research time.

I dunno... English to sql query is probably easier than English to French. You don't actually need to understand that Indiana is a place, just recognize that it's one of those tokens that's getting stuffed into the where clause...

The tough part is really probably the implicit knowledge of what the database columns are called. Is Indiana a "state" field or a substring in an address field? Depends on the database.

This mixing of "soft" ml queries with "hard" requirements is a really interesting space, which I think will produce massive results if we can get it under control. It's similar to the problems in the AI dungeon master space... And probably tutoring, as well...

> I dunno... English to sql query is probably easier than English to French.

You can have substantial success in transforming English to French without understanding what is said. Some big grammar rulebooks, some dictionaries, and a few terabytes of sentence-aligned parallel texts and data on statistical colocations in the languages will get you most of the way. It's far from 100% accurate but the human brain is surprisingly good at filling in the gaps with the mangled grammar and odd turns of phrase.

I'm not so sure the same is true about English to SQL query. The exact meaning of the English must be understood, in context, in order to construct the query correctly. No room for error or fuzziness. The database engine doesn't have human cognition to fall back on should the query be slightly malformed or ambiguous.

> I wonder why all these AI people are trying to create things that will displace my means of earning a living instead of something that will create applications?

I think this has to do with overestimating the power of current ML techniques combined with severely underestimating the complexity of jobs like writing. This tends to result in solutions that "overreach" and end up disappointing. If we scale back a bit, we could build some really fantastic tools that work with people.

One great example of this is the movie Spiderman: Into the Spiderverse. In order to draw the comic-book lines on faces, they used a tool that would learn over time from the artists themselves. It would guess, the artist would adjust, it would learn. This is the perfect fit for ML: try to help the human, not replace them.

Here is an article summarizing some nice stuff : https://www.nature.com/articles/d41586-018-05588-x

I always wanted to learn vimspeak: https://news.ycombinator.com/item?id=5660633

I remember watching a video of this guy from maybe 10-12 years ago who used IBM Dragon natural speak into some kind of vim commands and shorthand language which enabled him to write code faster than he could using the keyboard. It was a fascinating demo, but the efficiency seemed to come from his short language that he developed for his workflow like "bop chop hop" etc.

Probably this clip of Tavis - https://youtu.be/8SkdfdXWYaI?t=530?

I have similar issues. I use a combination of kinesis advantage, penguin mouse, and dragonfly/DNS. Having a good microphone does make a difference, as does retraining/tweaking command vocab. The biggest thing overall is the ergonomics of desk work - I take a break every 15 minutes (or try to) by setting timers.

I can’t help you with actually converting speech to code but it occurs to me this would be a benefit to everyone. Speaking the words that are represented by the code we write would require a much deeper understanding of what we are doing and why.

Food for thought for sure. Good luck.

There's been some pretty cool work in this area recently: https://www.youtube.com/watch?v=fZSFNUT6iY8

That's seriously cool. I think that a tool that could take a high level description of a small piece of code and write it for me (it doesn't have to be correct- I can double check it and fix it quicker than I write it) would make my coding much quicker and less stressful. There are so many times in which I think: "I know exactly how to do this, step by step: I'm just annoyed at having to type it down for the umpteenth time".

Wow. This is exactly what I imagined but it already exists. This is a great illustration of how we as humans could use our abilities to disambiguate to collaborate with a computer and write code. Very impressive stuff.

To me it seems like learning how to talk to Alexa or Cortana or "Google" is a limitation or regression for humans. This shows that it could actually be beneficial.

Thanks for this philosophical rabbit hole just in time for a weekend.

As someone who works in voice tech, I think talking to Alexa is setting back our expectations of voice tech by at least a decade. The actual tech and capabilities we have available right now are so much better than static capabilities over a high latency internet connection.

sorry i am using dictation software.

I have found this research paper[0] that describes using a machine translation with a CNN and a r n n. it seems one of the main problems that come up with is a lack of training data so to progress this further it seems like we would need a big corpus of parallel text between natural English describing what the code does in the actual source code. I only have some novice experience with training machine learning models through transfer learning with fast AI but I have found a repository of tutorials[1] that teach you how to do machine translation between two languages and it seems maybe this approach might be applicable. There is a small corpus of text[2] with python code with annotations but and the paper itself at States. It probably needs more to get a higher than a 74% accuracy.

the only thing I can really think is to scrape GitHub make a website where you can get people to crowdsource annotations for code samples. I'm not sure on how to deal with how much and how little code would be for a sampling. I eat how much of the code would you need for a good training sample versus just using isolated functions. Perhaps that something like this might be used in correspondence with GPT.

[0]https://www.groundai.com/project/machine-translation-from-na... [1]https://github.com/bentrevett/pytorch-seq2seq [2]https://ahclab.naist.jp/pseudogen/

One of my friends was just diagnosed with ALS. Such softwares listed in this topic make their life useful and enjoyable for their remaining years. Guys, keep up the good work! I'll definitely check where I could contribute too.

Have you tried VoiceCode? https://voicecode.io/

That in combination with switching to a Lisp (Clojure) almost made it feasible for me to code with RSI.

I just became a manager instead because I couldn’t work from home and talking like that in the office was a no-go for me.

If that’s your cup of tea you’d be surprised at how happy upper management is to have someone who’s actually good at technology be willing to engage with them.

voicecode has been defunct for years now

I'd love to help! If anyone's working on this in this thread, lemme know :)

The options are Caster and Talon. Talon is closed source.

Though unfortunately I cannot provide you with the conversational solution you are looking for, I believe there are some steps you can take/solutions that are currently available and that could help make your voice programming experience less exhausting, so it might be worth it if you gave them a try

1) try to minimize the amount you have to speak by leveraging auto completion as much as possible. For me TabNine [1] has been great help in that regard

2) try to use snippets as much as possible to reduce boilerplate code and because you can simply tab through the various fields. For me it has been great help that with sublime it is possible [2] without installing anything to have all of my snippets inside dragonfly grammars or even generate them dynamically [10] providing for much-needed structural control over what you write. I know this is more primitive (at least for the time being, there are ideas to improve it) than what you are asking for but for me it has been enough to make C++ enjoyable again! unfortunately my pull request to integrate this into Caster [3] has fallen behind but all of the basic functionality along with various additional utilities is there if you want to give it a try. Just be aware of these little bugger [4] that applies here as well!

3) not directly related to code generation but if you find yourself spending a lot of time and vocal effort for navigation consider either adding eye tracking to the mix or utilizing one of the at least three project that provide syntactical navigation capabilities. As author and more importantly as a user of PythonVoiceCodingPlugin [5], I have seen quite a bit of difference since I got it up to speed, because a) even though it is command driven ,command sound natural and smooth b) though they can get longer ,in practice utterances are usually 3 to 5(maybe 6) words , which makes them long enough so that you do not to speak abruptly but short enough that you do not have to hurry to speak them before you run out of breath c) and yeah I personally need less commands compared to using only keyboard shortcuts so less load for your voice! The other two project in this area I am aware of are Serenade [6] and VoiceCodeIdea [7] so see if something fits your use case!

4) use noise input where you can to reduce voice strain. Talon [8][9] is by far the way to go in this field but you might be able to get inferior but decent results with other engines as well. For instance, DNS 15 Home can recognize some 30+ letter like "sounds" like "fffp,pppf, tttf,shhh,ssss/'s,shhp,pppt,xxxx,tttp,kkkp" , you just have to make sure that you use 4 or more letters in your grammar (so for instance ffp will not work). recognition accuracy is going to degrade if you overloaded too much but it is still good enough to simplify a lot of common tasks.

5) give it a try with a different engine, I was not really that much satisfiedwith WSR either

6) see if any of the advise from [11] helps and seek out professional help!

I realize that my post diverges from what you originally asked for but I feel the points raised here might help you lessen the impact of voice strain for the time being until more robust solutions like the gpt3 mentioned in one of the comments above are up and running. My apologies if this is completely off topic!

[1] https://www.tabnine.com/ [2] https://github.com/mpourmpoulis/CasterSublimeSnippetInterfac... [3] https://github.com/dictation-toolbox/Caster [4] https://github.com/mpourmpoulis/PythonVoiceCodingPlugin/issu... [5] https://packagecontrol.io/packages/PythonVoiceCodingPlugin [6] https://serenade.ai/ [7] https://plugins.jetbrains.com/plugin/10504-voice-code-idea [8] https://talonvoice.com/ [9] https://noise.talonvoice.com/ [10] https://github.com/mpourmpoulis/CasterSublimeSnippetInterfac... [11] https://dictation-toolbox.github.io/dictation-toolbox.org/vo...

I don't know about your specific RSI case, but moving from Java or C to a scripting language like Perl or Python can be helpful, since there's up to 10x less LOC.

Also, talk to an ergonomics person about it, and it sounds like notebooks are out at this point unless you have an external keyboard, mouse and monitor.

Are you sure you have RSI and not TMS (tension myositis syndrome)? It's a condition that causes real physical symptoms (of which wrist pain is a common one) that are not due to pathological or structural abnormalities. Rather, the symptoms are caused by stress and repressed emotions.

Check out this success forum of people who have healed from all kinds of chronic pain symptoms by dealing with stress and changing their mindset:

https://www.tmswiki.org/forum/forums/success-stories-subforu...

I have definitely found my RSI like symptoms are dependant on my mindset/stress levels.

Rarely does the RSI flare up when I'm doing something I enjoy. Stress is definitely a component.

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