whapa VS gpt-2-simple

I searched a lot on this subreddit and found two github solutions: B16f00t/whapa: WhatsApp Parser Toolset v1.59 (github.com) and ElDavoo/wa-crypt-tools: Decrypts WhatsApp .crypt12, .crypt14 and .crypt15 files. (github.com)

To batch it (I think, trying it now): https://github.com/B16f00t/whapa

I've used "WHAGODRI" from https://github.com/B16f00t/whapa to download my backup from Google Drive. However, the message database (msgstore.db.crypt15) requires a key file to decrypt. My phone is not rooted, so I am unable to access the decryption key. I have tried https://github.com/YuvrajRaghuvanshiS/WhatsApp-Key-Database-Extractor but I did not succeed in retrieving the key.

If you are technically inclined, you can also check some free tools. I used one some times ago: https://github.com/B16f00t/whapa

You can try whapa to download your database backup from Google Drive, then you clear WhatsApp data, copy the backup to the corresponding folder in /storage/emulated/0/Android/media/com.whatsapp/WhatsApp/Databases and then see if the WhatsApp app can recover the local backup. Note that you lost your local backups because uninstalling the app will trigger Android to clear its local data (all backups and media files)

The result is a sqlite database that can be dumped or browsed with the regular (database) tools; there are 2 projects I know of that are actually reading the data and presenting it nicely but both are broken just now as Whatsapp changed the name of some tables (the changes are only cosmetic, the data is still there): https://github.com/B16f00t/whapa and https://github.com/andreas-mausch/whatsapp-viewer .

Whatsapp Parser Toolset

I've used this: https://github.com/B16f00t/whapa awesome repository and works like a charm. Had a bit of a problem with finding my key until I rooted though.

I download from my Google Drive backup with https://github.com/B16f00t/whapa.

https://github.com/B16f00t/whapa (among other tools) appears to download it from Google without requiring any key from facebook or a rooted device.

Tap the contact's name in WhatsApp (I think it only works on a phone) and at the bottom of that screen there's Export Chat.

For finetuning GPT-2 I think I used this thing on Google Colab. (My friend ran it on his GPU, it should be doable on most modern-ish GPUs.)

https://github.com/minimaxir/gpt-2-simple

I tried doing something with this a few months ago though and it was a bit of a hassle to get running (needed to use a specific python version for some dependencies...), I forget the details sorry!

I mentioned in a different reply that I used https://github.com/minimaxir/gpt-2-simple

You will need a few hundred bucks, python experience, and a simple implementation such as this repo https://github.com/minimaxir/gpt-2-simple

I just used the gpt-2-simple python package and ran it overnight in an jupyter notebook, but you could copy the code to any python compiler and it should also work.

I'll note that if you're just doing text generation it is a simple project as far as ML goes, there are some nice libraries you can use that require minimal ML knowlege -eg https://github.com/minimaxir/gpt-2-simple

Then, I use the GPT-2 AI libraries, wrapped in a python library GPT-2 Simple to generate the content. My actual code is basically just their code sample, so basically 6 lines of python. With GPT-2, you train the existing AI to your specific dataset, which in my case is this text file of tweets.

gpt_2_simple is your best bet! Its super easy to use, you just need to downgrade TensorFlow and some other packages in your environment.

I used the GPT-2 Simple program by minimaxir to train the algorithm on every card in Magic's history that was released in a main expansion. Then I generated about 2,000 (it was actually more, but the algorithm really liked giving me cards that already exist) new names which I searched through to find the best ones.

Might I suggest starting Starting here, to learn on Simple GPT2. They have a Google Colab Notebook if your CPU GPU is shit, and what helped me learn best is dissect the code, and basically make my own Colab notebook piece by piece, learning what each function does.

The last couple of months, I've been running a Twitter bot that posts GPT-2-generated content, trained off of Tweets from existing accounts using gpt-2-simple. In my more recent training sessions, it seems like the quality of the output has been decreasing; it often gives outputs that are just barely modified from the original training data, if not verbatim.