Thank you! Just tested with Turkish and yes the quality doesn't seem to be the best, however in the future I plan to add translation models with higher quality, so I hope that those will do better with this language.
The position sent to Google is an error that I didn't see (I will correct it in 5 minutes), the rest, as I say in the readme, is due to the fact that the privacy policy used for RTranslator 2.0 for now is the same as RTranslator 1.0 (that used Google's Api), I will make a new privacy policy contract for RTranslator 2.0 with a lawyer soon.
With a Snapdragon 8 Plus Gen 1, I measured about 2 seconds for speech recognition of 30s of audio (although for shorter durations it takes not much less than 2s), while for translation about 2 seconds for a text of 300 characters (in this case, however, the performance scales linearly based on the length of the text).
Thank you for the feedback! In a little while I plan to redo the entire graphic interface, and yes a method to make it clearer when the microphone is listening is already in the plans. As for the cutting of sentences you can probably solve it by increasing the microphone's sensitivity from the app settings (from there you can also change other settings regarding the activation of the microphone, but most likely it is a sensitivity problem).
That would be the end goal (very far :D). But yeah, joking aside, I aim to make the translation better and better as technology advances, the current level is a usable start but maybe one day it will be truly seamless.
I think it is too long for the preface, but I will better specify that my code is open source in the libraries and models section, thank you for the suggestion and the appreciation!
Yes I agree with you, moreover for now I am much better as a Java or C developer than as a web developer, so for me the native approach is also simpler. However, yes, I practically never use the emulator even for Android precisely because I have to test Bluetooth, which at least in my experience on Android, is the most difficult part.
Ok, I think I found the perfect solution, In the readme I added near open-source "(almost)" with a link to the libraries and models section when I explained clearer what components are open-source, closed-source, and cc-by-nc.
it's the closest, but it's a very broad and not so well known term, maybe defining my app open source is not technically a perfect term, but it's the clearest in describing it (considering that in the libraries and models section I specified which are open-source and which are not, Ml-Kit, used to recognize the language, for example is closed source), if a developer doesn't see the open source writing anywhere he will only get confused in understanding what the license of my app is. However, based on your feedback I added the specification (in the "libraries and models" section) that NLLB has a non-commercial license.
Thank you! The app can be used on any device that has more than 6GB of RAM, with a phone with the right amount of RAM but a slower CPU the only effect is that the app takes longer to perform the translation and voice recognition (at least from my tests it shouldn't lag, so it's always usable), but yes, maybe in the future when I have the opportunity to test it with enough devices I will make a list with the performances for various Socs.
Thank you! I've done the launch on ProductHunt years ago, when the app still used Google APIs and required the developer key, I'll make another one soon. Regarding the release of the iOS version for now it's not in my plans, but if the app is successful and I make enough money with donations in the future I might consider it (since I would have to buy a Mac and at least 2 iPhones to test the Conversation mode), and always if the App store's policies will not be too restrictive.
Thank you, for now it's not in my plans, but if the app is successful and I make enough money with donations in the future I might consider it (since I would have to buy a Mac and at least 2 iPhones to test the Conversation mode), and always if the App store's policies will not be too restrictive.
Meta defines NLLB as open source, but assuming it isn't (I know Meta It's not a company that has a problem with lying), my code is open source, so how should I define my app? I personally make a distinction between open source and completely open source (or 100% open source), because otherwise there is no intermediate definition, according to the OSI definition my app is not open source, but it is not closed source either, OSI does it have an intermediate definition?
For now I have tested the app with Samsung's and Google's TTS, and with these 2 it should work, I am investigating the problem and if possible I will add support for other TTS engines.
I was considering adding it in the future but I don't know if it meets all the necessary conditions given that uses Ml-kit (closed source) for language recognition in the WalkieTalkie mode.
I tested the app with the Google and Samsung TTS, in these days I will also test with other TTS including the 2 mentioned here and I will try to solve it, I will update you in the issue you opened.
I know that, but since I make money only from donations without giving anything in return of the money I am considered non commercial. For now I decided to use NLLB instead of Opus because it has good quality and is a single model for all languages (so I didn't have to implement the download management logic for the various languages and I was able to make the app faster), but it is a temporary solution, I will certainly replace NLLB in the future, either with OpusMt Big or with other models with a less restrictive license.
Yeah, for short inputs the models are not that precise, but for long and complicated ones they work quite good, probably NLLB has been trained with very few short texts.