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Ask HN: Non-cloud voice recognition for home use?

• skamoen 7y ago
  I've read good things about Mycroft [1], though I haven't tried it myself. Ticks all the boxes though [1] https://mycroft.ai/ https://mycroft.ai/
  • ghaff 7y ago
    Mycroft can only kinda sorta be run offline according to the project/company: https://mycroft-ai.gitbook.io/docs/about-mycroft-ai/faq https://mycroft-ai.gitbook.io/docs/about-mycroft-ai/faq
• DataDrivenMD 7y ago
  Have you considered the Almond integration for Home Assistant? (https://www.home-assistant.io/integrations/almond/ https://www.home-assistant.io/integrations/almond/) Alternatively, you could just fork the Almond project directly and take it from there: https://github.com/stanford-oval/almond-cloud https://github.com/stanford-oval/almond-cloud
  • rs23296008n1 7y ago
    No I hadn't. This was why I asked: I know there's alternatives out there but there's so much noise. Thanks.
    • Shawnecy 7y ago
      Almond has been posted on HN previously[1]. User voltagex_ commented[2] that self-hosting, while possible is not recommended in the official installation instructions[3] as it is considered significantly more challenging to manage. This may or may not effect your decision to go forward with Almond. [1] https://news.ycombinator.com/item?id=17532003 https://news.ycombinator.com/item?id=17532003 [2] https://news.ycombinator.com/item?id=17534793 https://news.ycombinator.com/item?id=17534793 [3] https://github.com/stanford-oval/almond-cloud/blob/master/doc/installing-almond-cloud.md https://github.com/stanford-oval/almond-cloud/blob/master/do...
      • saghm 7y ago
        Is there any product where self-hosting _isn't_ more difficult? That seems like a generic warning that could apply to pretty much any product in this space. It seems more like a warning to non-technical users who might not have the experience or know-how to successfully set up a server.
        • Shawnecy 7y ago
          If it was simply an issue of just "more difficult", then it wouldn't be worth pointing out. However, the words "significantly more challenging to manage" are straight from their documentation that I linked which I think makes it worth pointing out. Whether or not it is too challenging is for each individual to decide for themselves.
          • amenod 7y ago
            It also gives an indication what the preferred method of deployment is (from authors' point of view). In this case I read it as a warning that it might stop being supported in the future.
• winkelwagen 7y ago
  I've had some good experience with https://snips.ai https://snips.ai . Works like advertised. easy to implement. Hardest thing was getting the microphone and the pie to get along.
  • arendtio 7y ago
    Did you visit the website lately? Doesn't seem to be an option anymore :-/
    • dillonmckay 7y ago
      That sucks. Like Pebble.
    • rs23296008n1 7y ago
      They only seem to do audio equipment. Did they once do more things more general?
      • arendtio 7y ago
        They were acquired by Sonos. A few months ago they were offering tools to build your own speech recognition agents.
      • stragies 7y ago
        Before they were bought, you could build (with the help of a WebApp hosted by them) an offline usable speech recognition module, that could comfortably run on a Pi, and that would output parsed sentences in JSON format onto MQTT. Easy to integrate with everything in IOT. I loved it. Now i'm also looking for an alternative for the Speech-to-text(-to-json) part, like you.
    • vineet 7y ago
      There code is open source: https://github.com/snipsco https://github.com/snipsco Though there are about 100 repositories there. I am not sure if it is easy to put it all together.
• carbon85 7y ago
  I was not able to find the same article online, but the Volume 72 of Make Magazine has a great overview of different non-cloud voice recognition platforms. Here is a preview: https://www.mydigitalpublication.com/publication/?m=38377&i=649256&p=0 https://www.mydigitalpublication.com/publication/?m=38377&i=...
  • jvyduna 7y ago
    Today they published an online version which covers many of the platforms listed in these comments: https://makezine.com/2020/03/17/private-by-design-free-and-private-voice-assistants/ https://makezine.com/2020/03/17/private-by-design-free-and-p...
• villgax 7y ago
  Google has papers on device speech recognition, these are used in the keyboard & for live caption on Pixel devices.
  • cjbassi 7y ago
    This article from the Google AI blog about the Gboard speech recognition is really interesting: https://ai.googleblog.com/2019/03/an-all-neural-on-device-speech.html?m=1 https://ai.googleblog.com/2019/03/an-all-neural-on-device-sp...
  • teapourer 7y ago
    They are trained on a ton of non-public data though, and I’m not sure if pre-trained models are around.
    • villgax 7y ago
      Nope, they aren't available. CC YouTube videos with captions or radio broadcasts + transcripts could prove helpful for multiple languages as well as being able to create a multilingual ASR.
• ftyers 7y ago
  Mozilla DeepSpeech trained on the Common Voice dataset for English. You can get pretrained models too. They have a nice matrix channel where you can get help, and pretty good documentation. It is also actively developed by several engineers. http://voice.mozilla.org/en/datasets http://voice.mozilla.org/en/datasets and http://github.com/mozilla/DeepSpeech/ http://github.com/mozilla/DeepSpeech/
  • dmos62 7y ago
    The models (`deepspeech-0.6.1-models.tar.gz`) weigh 1.14 gb, if anyone's interested.
  • kingo55 7y ago
    Interesting. Are there any projects actively using this today?
    • nmstoker 7y ago
      I haven't tried it directly myself but there is this project, Dragonfire, which looks quite reasonable using DeepSpeech: https://github.com/DragonComputer/Dragonfire https://github.com/DragonComputer/Dragonfire There's a minimal demo app I put together here too: https://github.com/nmstoker/SimpleSpeechLoop https://github.com/nmstoker/SimpleSpeechLoop
    • dsteinman 7y ago
      I've been using DeepSpeech to learn to build voice controls for all sorts of things in JavaScript. And I've got a way to connect it to the web so you'll be able write speech recognition enabled web pages using client side JavaScript. https://github.com/jaxcore/deepspeech-plugin https://github.com/jaxcore/deepspeech-plugin
  • magicalhippo 7y ago
    I had limited luck using the provided language model, but very good results when providing my own. So if you feel the results are poor, try building your own language model. AFAIK DeepSpeech works by using the neural net to detect characters from speech, and then the language model is used to try to make a sentence out of the character stream, by doing a kind of graph search. Thus if the language model doesn't contain the words you want it to recognize, it'll have a hard time giving good output. Anyway, I used the following tutorial[1] as a base to build the language model. For the kenlm tools I used Ubuntu WSL, and the generate_trie executable was part of the DeepSpeech native tools package for Windows. [1]: https://discourse.mozilla.org/t/tutorial-how-i-trained-a-specific-french-model-to-control-my-robot/22830/2 https://discourse.mozilla.org/t/tutorial-how-i-trained-a-spe...
• albertzeyer 7y ago
  Are you searching for a complete solution including NLP and an engine to perform actions? Some of these are already posted, like Home Assistant, and Mycroft. Sphinx is just for the automatic speech recognition (ASR) part. But there are better solutions for that: Kaldi (https://kaldi-asr.org/ https://kaldi-asr.org/) is probably the most comprehensive ASR solution, which yields very competitive state-of-the-art results. RASR (https://www-i6.informatik.rwth-aachen.de/rwth-asr/ https://www-i6.informatik.rwth-aachen.de/rwth-asr/) is for non-commercial use only but otherwise similar as Kaldi. If you want to use a simpler ASR system, nowadays end-to-end models perform quite well. There are quite a huge number of projects which support these: RETURNN (https://github.com/rwth-i6/returnn https://github.com/rwth-i6/returnn) is non-commercial TF-based. (Disclaimer: I'm one of the main authors.) Lingvo (https://github.com/tensorflow/lingvo https://github.com/tensorflow/lingvo), from Google, TF-based. ESPnet (https://github.com/espnet/espnet https://github.com/espnet/espnet), PyTorch/Chainer. ...
  • rs23296008n1 7y ago
    Already got the action engine: all the lights, hvac, tv, calculator, computers, etc are all controllable. None require internet now. Or any kind of location services for that matter. I really just want the speech-to-text. Ideally I'd also like it to recognise who's talking. But that's a bonus.
    • thomas536 7y ago
      I'll second the recommendation for Kaldi. It's more complicated to get running vs pocketsphinx, but in my experience Kaldi has better accuracy/lower latency in general cases vs pocketsphinx (assuming caveats below). https://github.com/gooofy/zamia-speech/ https://github.com/gooofy/zamia-speech/ has been training good [acoustic] models which are worth looking at (including training with robustness against noise). They've also got lots of code and docker images and documentation. pocketsphinx isn't actually that bad to use with their latest acoustic models and small vocabularies (so its utility depends on your exact use case). But it's not generally good with far field mics/dsp processed audio, not really good with noise, and in my experiments quite not as fast as Kaldi. Better/larger language models in my experience make a world of difference (esp in the general vocab case) for improving accuracy for either of kaldi or pocketsphinx. Nobody really seems to talk about this(?), since everyone always uses the news corpus from like the 80s as the default language model. I haven't really ever gotten the various ~deepspeech systems working, so I can't speak to them.
• cosmic_ape 7y ago
  As an aside, it seems you're interested in speech recognition, or speech to text, not voice recognition. Voice recognition is a different problem, where the particular speaker needs to be recognized from voice.
  • rs23296008n1 7y ago
    I'd primarily like speech-to-text and the ability to know who is speaking. I have low expectations of the identification of speaker however.
    • peglasaurus 7y ago
      If you're wanting a lot of people to use your solution as you described, recognition of who is speaking could add a lot of extra possibilities.
  • catblast 7y ago
    I used to work in the speech reco space for many years. Fighting this battle was lost long long long ago. Virtually no one outside of the space really cares, and people use speech and voice recognition interchangeably. It’s really more the case that voice recognition is just an ambiguous term. It’s pretty hard to blame lay people when speech reco products like Dragon are widely marketed as “voice recognition”. If you want to be clear and not just pedantic just call it speaker recognition.
• perturbation 7y ago
  If you don't mind getting your hands dirty a bit, I think Nvidia's model [Jasper](https://arxiv.org/pdf/1904.03288.pdf https://arxiv.org/pdf/1904.03288.pdf) is near SOTA, and they have [pretrained models](https://ngc.nvidia.com/catalog/models/nvidia:jaspernet10x5dr https://ngc.nvidia.com/catalog/models/nvidia:jaspernet10x5dr) and [tutorials / scripts](https://nvidia.github.io/NeMo/asr/tutorial.html https://nvidia.github.io/NeMo/asr/tutorial.html) freely available. The first is in their library "nemo", but they also have it available in [vanilla Pytorch](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechRecognition/Jasper https://github.com/NVIDIA/DeepLearningExamples/tree/master/P...) as well.
  • homarp 7y ago
    and you have a version for the jetson nano: https://github.com/NVIDIA/OpenSeq2Seq/blob/master/example_configs/speech2text/jasper-Mini-for-Jetson.py https://github.com/NVIDIA/OpenSeq2Seq/blob/master/example_co... with install scripts: https://github.com/NVIDIA/OpenSeq2Seq/blob/master/scripts/jetson_install_dependencies.sh https://github.com/NVIDIA/OpenSeq2Seq/blob/master/scripts/je...
  • rs23296008n1 7y ago
    Do you have any experience/opinions on those?
• gok 7y ago
  Don't bother. The cloud based solutions are so vastly superior to the current non-cloud solutions that unless you're something of an expert in ASR you're just going to get frustrated. If you're worried about privacy, Google lets you pay a little extra to immediately delete the audio after you send it to their servers.
  • supermatt 7y ago
    Poster specifically asks non cloud.
  • mister_hn 7y ago
    But none is sure that Google does keep a copy of the data. Cloud = giving out your data
    • hutzlibu 7y ago
      And some of us still have those odd privacy concerns .. But seriously, it feels sometimes, we are very small minority.
  • rs23296008n1 7y ago
    I think I will bother, the use cases are valid and worthwhile to pursue for multiple reasons, many of which aren't even on my radar. Bonus: I can have my cake AND eat it too.
• wtvanhest 7y ago
  This feels like an area where a Dropbox of self hosted solution will emerge.
• awinter-py 7y ago
  important question I think there's a group of highly technical people who feel increasingly left behind by 'convenience tech' because of what they have to give up in order to use it
  • rs23296008n1 7y ago
    Well I've got all the gadgets controllable now without internet as a requirement. Only the voice part requires it now for us at least. Google home/Amazon echos and phone apps can communicate with the house and surrounds without issue. Loss of internet access is not an excuse for ignoring basic voice commands in my opinion. Privacy is also an important factor but not the primary driver for us.
• romwell 7y ago
  TL; DR: Win 10 IoT for RasPi does it. ----------------- Windows 10 IoT for Raspberry Pi comes with offline speech recognition API. It was not hard to slap some code together that turns on a light when someone says "banana" at a hackathon. Sounds like exactly what you need. >If it helps I'm happy to reduce vocabulary to a dictionary of words You will do it with an XML grammar file for offline recognition[4]. [1]https://docs.microsoft.com/en-us/windows/iot-core/tutorials/rpi https://docs.microsoft.com/en-us/windows/iot-core/tutorials/... [2]https://docs.microsoft.com/en-us/windows/iot-core/extend-your-app/speech https://docs.microsoft.com/en-us/windows/iot-core/extend-you... Someone's demo project: [3]https://www.hackster.io/krvarma/rpivoice-051857 https://www.hackster.io/krvarma/rpivoice-051857 [4]https://docs.microsoft.com/en-us/windows/uwp/design/input/speech-recognition https://docs.microsoft.com/en-us/windows/uwp/design/input/sp...
  • coredog64 7y ago
    The Microsoft offline speech recognizer is pretty good. I did some work with it many years ago [0]. The only problem we had was with accents: My French co-worker had to use his most obnoxiously over-the-top American accent for reasonable accuracy. ISTR that we could switch to Australian English for the Aussies and Kiwis. [0] https://github.com/spc-ofp/ObserverLengthSampler https://github.com/spc-ofp/ObserverLengthSampler
  • lucb1e 7y ago
    This is really interesting, but I have a few questions: - The setup guide shows a Windows system making a Windows iot version. Can't I just download an iso and flash it to an sdcard with dd? Does it need a license? - The demo projects show C# and while I can develop in monodevelop, I don't have a Windows to compile it with. Is a C# compiler included in Windows iot's .NET distribution or are there also cross-platform (interpreted) languages that run on Windows iot (e.g. Python3)?
    • thejosh 7y ago
      You might be able to use .NET Core which is opensource and can run on Linux/Mac.
    • 7y ago
• notemaker 7y ago
  https://rhasspy.readthedocs.io https://rhasspy.readthedocs.io Haven't used it, but seems very nice. https://youtu.be/ijKTR_GqWwA https://youtu.be/ijKTR_GqWwA
  • rs23296008n1 7y ago
    The description from a superficial read looks good. Thanks!
  • stragies 7y ago
    It can use https://github.com/cmusphinx/pocketsphinx https://github.com/cmusphinx/pocketsphinx (offline) or Kaldi(offline), or Google(online) for the "speech-to-text"-part.
  • synesthesiam 7y ago
    Rhasspy author here in case you have any questions :) If you're looking for something for the command-line, check out https://voice2json.org https://voice2json.org
    • rs23296008n1 7y ago
      I think I'll dig in first. Cheers.
• belbob 7y ago
  maybe https://jasperproject.github.io/ https://jasperproject.github.io/
  • stragies 7y ago
    Jasper is another project that uses CMUSPINX from https://cmusphinx.github.io/wiki/faq/ https://cmusphinx.github.io/wiki/faq/ under the hood.
• ParanoidShroom 7y ago
  Android has a local speech recognizer, maybe give that a go ? You'll have to make an Android app tho.
• Havoc 7y ago
  >I'm happy if they talk back via wifi to a server in my office for whatever real processing. The server might have 16 cores and 128Gb ram. Pretty sure Mycroft is capable of that - in theory - you'll need to config it manually. The standard raspberry pi route isn't powerful enough for local. Check out reespeaker for a raspberry microphone. You'll want one of the more expensive ones for range. Though at like 40 bucks they're not that wildly expensive. Make sure it's a rasp 4 since wake word is processed locally. And you probably don't need 128gb RAM. No idea what they use but doubt that much.
  • rs23296008n1 7y ago
    As discussed elsewhere in this thread by others, mycroft can't do offline processing, according to their faq at least. 128GB is the minimum I use for general purpose servers so this machine would be a repurposed machine rather than something specially ordered or built.
    • Havoc 7y ago
      >mycroft can't do offline processing, according to their faq at least. Not sure what FAQ you're reading there. There is a whole section of STT engines you can plug into it. https://mycroft-ai.gitbook.io/docs/using-mycroft-ai/customizations/stt-engine https://mycroft-ai.gitbook.io/docs/using-mycroft-ai/customiz... Including a local one. It's gonna be a pain in the ass but as I said it's possible
• lunixbochs 7y ago
  Hi, I'm the dev behind https://talonvoice.com https://talonvoice.com I've been working with Facebook's wav2letter project and the results (speed on CPU, command accuracy) are extremely good in my experience. They also hold the "state of the art" for librispeech (a common benchmark) on wer_are_we [1]. Granted, that's with a 2GB model that doesn't run very well on CPU, but I think most of the fully "state of the art" models are computationally expensive and expected to run on GPU. Wav2letter has other models that are very fast on CPU and still extremely accurate. You can run their "Streaming ConvNets" model on CPU to transcribe multiple live audio streams in parallel, see their wav2letter@anywhere post for more info [2] I am getting very good accuracy on the in-progress model I am training for command recognition (3.7% word error rate on librispeech clean, about 8% WER on librispeech other, 20% WER on common voice, 3% WER on "speech commands"). I plan to release it alongside my other models here [5] once I'm done working on it. There's a simple WER comparison between some of the command engines here [3] Between this and wer_are_we [1] it should give you a general idea of what to expect when talking about Word Error Rate (WER). (Note the wav2letter-talonweb entry in [3] is a rather old model I trained, known to have worse accuracy, it's not even the same NN architecture). ---- As far as constraining the vocabulary, you can try train a kenlm language model for kaldi, deepspeech, and wav2letter by grabbing KenLM and piping normalized (probably lowercase it and remove everything but ascii and quotes) text into lmplz: cat corpus.txt | kenlm/build/bin/lmplz -o 4 > model.arpa And you can turn it into a compressed binary model for wav2letter like this: kenlm/build/bin/build_binary -a 22 -q 8 -b 8 trie model.arpa model.bin There are other options, like using a "strict command grammar", but I don't have enough context as to how you want to program this to guide you there. I also have tooling I wrote around wav2letter, such as wav2train [4] which builds wav2letter training and runtime data files for you. I'm generally happy to talk more and answer any questions. ---- [1] https://github.com/syhw/wer_are_we https://github.com/syhw/wer_are_we [2] https://ai.facebook.com/blog/online-speech-recognition-with-wav2letteranywhere/ https://ai.facebook.com/blog/online-speech-recognition-with-... [3] https://github.com/daanzu/kaldi-active-grammar/blob/master/docs/models.md https://github.com/daanzu/kaldi-active-grammar/blob/master/d... [4] https://github.com/talonvoice/wav2train https://github.com/talonvoice/wav2train [5] https://talonvoice.com/research/ https://talonvoice.com/research/
  • rs23296008n1 7y ago
• reaperducer 7y ago
  I wish you luck with this, and more importantly, hope that it inspires many people to start building similar projects. I know virtually nothing about voice recognition, but my spidey sense tells me that it should be possible with the hardware you specify. A Commodore 64 with a Covox VoiceMaster could recognize voice commands and trigger X-10 switches around a house. (Usually. My setup had about a 70% success rate, but pretty good for the time!) Surely a 16 core, 128GB RAM machine should be able to do far more.
  • rs23296008n1 7y ago
    Its beginning to take shape. Already got a bunch of good candidates for experimentation next week based on the answers so far.
• thesuperbigfrog 7y ago
  Modern web browsers will support the Web Speech API (https://developer.mozilla.org/en-US/docs/Web/API/Web_Speech_API/Using_the_Web_Speech_API https://developer.mozilla.org/en-US/docs/Web/API/Web_Speech_...) which may or may not involve a cloud service. Here is the Google Chrome Web Speech API demo page: https://www.google.com/intl/en/chrome/demos/speech.html https://www.google.com/intl/en/chrome/demos/speech.html
  • detaro 7y ago
    In which browser doesn't it involve a cloud service?
    • thesuperbigfrog 7y ago
      Use Firefox. Google Chrome does use a cloud service. Edit: Firefox does not support the Web Speech API at this time. There are not currently any offline versions of this API as far as I can tell.
      • detaro 7y ago
        According to MDN, Firefox doesn't support it? https://developer.mozilla.org/en-US/docs/Web/API/Web_Speech_API#Browser_compatibility https://developer.mozilla.org/en-US/docs/Web/API/Web_Speech_... EDIT: According to other documentation, it is behind a config flag. If you enable it, it will send the data to Google's API through a Mozilla-operated proxy: https://wiki.mozilla.org/index.php?title=Web_Speech_API_-_Speech_Recognition&oldid=1220468#Where_does_the_audio_go.3F https://wiki.mozilla.org/index.php?title=Web_Speech_API_-_Sp...
        • thesuperbigfrog 7y ago
          You're right. It looks like there is no offline version :(
• mirimir 7y ago
  If cloud services are such an issue (as they would be for me) then it's worth considering the security of local logs and whatever. Maybe limit lifetime, or even use FDE.
• otodic 7y ago
  My company develops SDKs for on-device speech recognition on Android/iOS: https://keenresearch.com/keenasr-docs https://keenresearch.com/keenasr-docs (Raspberry Pi is an option too, we'll have a GA release in Q2) We license this on commercial bases but would be open to indy-developer friendly licensing. We offer a trial SDK that makes testing/evaluation super easy (it works for 15min at the time). Ogi ogi@keeenresearch.com
  • lunixbochs 7y ago
    > Currently, the SDK supports English and Spanish out of the box. Additional ASR Bundles for most major spoken languages can be provided upon request within 6-8 weeks. Does this mean you have a standing offer to train a new language on demand?
    • otodic 7y ago
      Yes, for a number of European and some Asian languages we can do this; it's mainly the question of business opportunity.
      • lunixbochs 7y ago
        That's very cool. Did you somehow solve autogenerating a training corpus for a new language as long as it's popular? Based on my impressions working with other people in the space, coming up with the training data seems like a big bottleneck - as the available engines are pretty good at learning new languages already.
        • otodic 7y ago
          We have relevant training data for a number of languages.
• Animats 7y ago
  Is there a voice dialer for Android that doesn't use Google? There used to be, but it disappeared in the "upgrade" which has the mothership listening all the time.
• stevewilhelm 7y ago
  Question, what aspect of your product restricts the software architecture to not use "off-site cloud?"
  • rs23296008n1 7y ago
    If you've got an amazon echo or equivalent, try this experiment: disconnect your internet. Now issue a voice command to it. Why is your light not switching on? Why is alexa not answering your questions? Now you know why I want "off-site cloud" for particular things. Not everything. Just what matters. A few things here and there. There's nothing wrong with the cloud and nothing stops me from using its benefits. Except one minor detail. My use-cases are likely different from others. I wish to keep cloud access as needed but also just a little bit more.
• guptaneil 7y ago
  Disclaimer: I am the founder of Hiome, a smart home startup focused on private by design local-only products. What actions are you looking to handle with the assistant? Reason I ask is because a voice assistant is a command line interface with no auto-complete or visual feedback. It doesn’t scale well as you add more devices or commands to your home, because it becomes impossible to remember all the phrases you programmed. We’ve found the person who sets up the voice assistant will use it for simple tasks like “turn off all lights” but nobody else benefits and it gets little use beyond timers and music. They are certainly nice to have, but they don’t significantly improve the smart home experience. If you’re looking to control individual devices, I suggest taking a look at actual occupancy sensors like Hiome (https://hiome.com https://hiome.com), which can let you automate your home with zero interaction so it just works for everyone without learning anything (like in a sci-fi movie). Even if you’re the only user, it’s much nicer to never think about your devices again. Happy to answer any questions about Hiome or what we’ve learned helping people with smart homes in general! -> neil@hiome.com
  • byteshock 7y ago
    You really trying to promote your product? Corporate promotions really ruin the atmosphere... Take your corporate shilling elsewhere. I'm sure OP doesn't appreciate it.
    • etrabroline 7y ago
      I don't think mentioning your smart home startup in response to someone asking for recommendations for their smart home is unappreciated "corporate shilling".
      • detaro 7y ago
        It feels very borderline to me, given that it doesn't really address the question, but instead goes more like "actually, you don't want what you ask. instead, what I'm selling is better". At least that's how it comes across to me.
    • mthoms 7y ago
• nshm 7y ago
  You are welcome to try Vosk https://github.com/alphacep/vosk-api 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
  • rs23296008n1 7y ago
    Sound great, thanks
• vinniejames 7y ago
  I've been waiting for Mycroft to release something new, https://mycroft.ai/ https://mycroft.ai/ Caviat, they keep having delays and may never release v2 imo
• microtherion 7y ago
  Apple platforms offer an API (SFSpeechRecognizer) which for some languages supports on-device recognition. Trivial to set up, super easy to use, and pretty reasonable accuracy. Disclaimer: Working for Apple, not directly on this API but in related subjects.
  • deca6cda37d0 7y ago
    I guess not always. “The speech recognition process involves capturing audio of the user’s voice and sending that data to Apple’s servers for processing. The audio you capture constitutes sensitive user data, and you must make every effort to protect it. You must also obtain the user’s permission before sending that data across the network to Apple’s servers. You request authorization using the APIs of the Speech framework.“ https://developer.apple.com/documentation/speech/asking_permission_to_use_speech_recognition https://developer.apple.com/documentation/speech/asking_perm... Which languages are processed on device and not send to Apple’s servers?
    • microtherion 7y ago
      > I guess not always. Yeah, I suppose I should have formulated that more clearly. The API offers cloud speech recognition for a set of languages, and on-device speech recognition for a subset of these. > Which languages are processed on device and not send to Apple’s servers? It's not a static set, because (1) availability tends to expand over time and (2) when you start using a new language, the on-device model needs to be downloaded first. So what you need to do is create a SFSpeechRecognizer and then test the supportsOnDeviceRecognition property. If that is set, you can set requiresOnDeviceRecognition on the SFSpeechRecognitionRequest.
• abrichr 7y ago
  Precise [1], Snowboy [2], and Porcupine [3] are all designed to work offline. [1] https://github.com/MycroftAI/mycroft-precise https://github.com/MycroftAI/mycroft-precise [2] https://github.com/kitt-ai/snowboy https://github.com/kitt-ai/snowboy [3] https://github.com/Picovoice/porcupine https://github.com/Picovoice/porcupine
  • lukifer 7y ago
    All good projects (I’m using Snowboy), but these are all just wake words, which are very different beasts from full text recognition.
• beerandt 7y ago
  Homeseer automation software has this built in, with client listening apps for different platforms. I haven't used the voice recognition beyond testing, but I've been very happy with the software overall. It's relatively expensive, but goes on sale for about half price once or twice a year. There's a free 30 day trial. I think there's two id phrases per sub-device by default, but using virtual devices vastly expands the software's capability. Especially for mapping virtual switches to multiple devices. They also have zwave devices that for the most part are much better than most. https://www.homeseer.com https://www.homeseer.com
• lukifer 7y ago
  I’m currently assembling an offline home assistant setup using Node-RED and voice2json, all running on Raspberry Pi’s: http://voice2json.org/ http://voice2json.org/ https://nodered.org/ https://nodered.org/ Requires a little customization and/or coding, but it’s quite elegant, and all voice recognition happens on-device. Part of what makes the recognition much more accurate (subjectively, 99%ish) is the constrained vocabulary; the grammars are compiled from a simple user-defined markup language, and then parsed into JSON intents, containing both the full text string and appropriate keywords/variables split out into slots. Just finished a similar rig in my car, acting as a voice-controlled MP3 player, with thousands of artists and albums compiled into intents from iTunes XML database. Works great, and feels awesome to have a little 3-watt baby computer doing a job normally delegated to massive corporate server farms. ;)
  • stragies 7y ago
    Hi Lukifer, thanks for chiming in! I had a setup using snips, that i'm looking to replace. Please do document your setup, and your little helper scripts in a blogblost or such, and ping me/us :)
• LargeWu 7y ago
  The most recent edition of Make magazine had a pretty good overview of some different options. Doesn't go too much into depth but provides a good starting point.
• coryrc 7y ago
  I tried to use Julius for this. I may have misconfigured it, but it would always match something to what it was hearing. I encoded some sounds in my grammar to error terms that it would detect in quiet noise (like 'aa' and 'hh'), but it would still occasionally match words when nothing was going on. Later I worked on the Microsoft Kinect with its 4-microphone array. With only a single microphone, it's so much harder to filter out background noise. If you don't find a system based on multiple microphones, I don't believe you can be successful if there's any ongoing noise (dishwasher, loud fans, etc), but a system that works in only quiet conditions is possible.
• daanzu 7y ago
  I develop Kaldi Active Grammar [1], which is mainly intended for use with strict command grammars. Compared to normal language models, these can provide much better accuracy, assuming you can describe (and speak) your command structure exactly. (This is probably more acceptable for a voice assistant for an audience that is more technical.) The grammar can be specified by a FST, or you can use KaldiAG through Dragonfly, which allows you to specify them (and their resultant actions) in Python. However, KaldiAG can also do simple plain dictation if you want. KaldiAG has an English model available, but other models could be trained. Although you can't just drop in and use a standard Kaldi model with KaldiAG, the modifications required are fairly minimal and don't require any training or modification of its acoustic model. All recognition is performed locally and off line by default, but you can also selectively choose to do some recognition in the cloud, too. Kaldi generally performs at the state of art. As a hybrid engine, although training can be more complicated, it generally requires far less training data to achieve high accuracy, compared to "end to end" engines. [1] https://github.com/daanzu/kaldi-active-grammar https://github.com/daanzu/kaldi-active-grammar
  • daanzu 7y ago
    Too late to edit, but I should probably have noted that KaldiAG also would make it easy to define "contexts" when (groups of) commands are active for recognition. For example, if the TV is on, you could have commands for adjusting the volume/etc. But if it is off, those commands are disabled, so they can't be recognized, and further, the engine knows this and can therefore better recognize the other commands that remain active.
    • barrystaes 7y ago
      Could Home Assistant use such commands by running a docker? Also the video demo is rather impressive, in how accurate (and predictable) it recognises.
      • daanzu 7y ago
        I don't know much about Home Assistant, but that certainly should be possible to set up. The KaldiAG API is pretty low level, but basically: you define a set of rules, and send in audio data, along with a bit mask of which rules are active at the beginning of each utterance, and receive back the recognized rule and text. The easy solution is probably to go through Dragonfly, which makes it easy to define the rules, contexts, and actions. It might be a little hacky to do, but you should be able to wire it up with Home Assistant somehow. Although I mainly use it for computer control as demonstrated in the video, I do have many commands akin to home automation, like adjusting the lights, HVAC, etc.
• gibs0ns 7y ago
  I was in the process of planning my multi-room voice-AI setup based on SnipsAI (to be integrated with Home Assistant) when it was announced they were bought by Sonos, which killed their open source project. Since then I have been left trying various projects that meet my needs. Among those, I tried MyCroft, which still requires a cloud account to config various things on it, and it doesn't support a multi-room setup at this time. I've since switched to Rhasspy, which offers a larger array of config options and engines, and also multi-room (I'm yet to config multi-room tho) In the long-term I plan to "train" the voice-AI for various additions, including a custom wake word - No, I'm not calling it `Jarvis` ;) I'm running each of these voice-AI's on a Raspberry Pi 4 (4GB model), though I'm considering switching them to Pi 3's. I'm using the `ReSpeaker 2mic Pi-Hat` on each pi for the mic input. I'm planning to configure all the satellite nodes (voice-AI in each room) to PXE boot, that way they don't require an sd-card and I can easily update their images/configs from a central location.
  • OlympusMonds 7y ago
    Is it possible to make your config available, e.g., GitHub? I'm just starting to get going with Rhasspy, integrating with Home Assistant, and the docs miss just enough that I hit walls everytime I try. Thanks for the info you've already provided though, sounds like I want exactly what you do.
• JanisL 7y ago
  I was one of the maintainers of the Persephone project which is an automated phonetic transcription tool. This came about from a research project that required a non-cloud solution. This project is open source and can be found on GitHub: https://github.com/persephone-tools https://github.com/persephone-tools This may be a little too low level for what as there's no language model but maybe it's helpful as part of your system