This program collects a message dataset from open telegram chats. It then uses it to trin a neural network, which learns to simulate human speech. In dataset_full.txt, messages collected from Mi A1 and Mi 9T community chats are stored.
The idea behind this program was to try to simulate human speech using a smalll net.
Please note that this program is under testing & development. The main pending problems include:
- The
collect_dataset.pyscript should be modified to accept special non-unicode symbols. - The program module structure should be rebuilt.
First, clone the repository into you current folder:
$ git clone https://github.com/michael-2956/SmartphoneFlashNet.git
Get into the root folder of the repository:
$ cd SmartphoneFlashNet
For the scripts to run, you need tensorflow, pytg and numpy preinstalled. To install all the needed libraries in your activated local or global environment, run:
$ pip install -r requirements.txt
When all the needed libraries are installed, you are ready to execute the train & run scripts.
To start collecting the dataset, you need telegram-cli to be running on some port in json mode. For example:
$ screen ./path/to/telegram-cli -W -P 4480 --json -p main
Where 4480 is the port number and main is the user session. To detach the screen session, press Ctrl + a followed by d.
Now you are ready to run the collector script. It will save all the incoming messages to dataset.txt. To do this, run the following:
$ python3 collect_dataset.py 4480
Where 4480 is the port number. The script now should start appending the messages to dataset.txt. To append the resulting dataset to the current one, run the following:
$ cat dataset.txt >> dataset_full.txt
To train the model, run the following:
$ python3 dataset_train.py
It uses the dataset_full.txt file by default. To train on custom file, type the following:
$ python3 dataset_train.py dataset.txt
When the network is trained, the model.ckpt files will appear in the current folder. You can now run the network by the following:
$ python3 runNN.py
Mykhailo Bondarenko