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Multimedia Automation software for multimedia processing and multilingual transcription using Python based libraries - Tkinter SpeechRecognition API and DeepSpeech(open-source STT/TTS engine) ,NLP technique ”Named Entity extraction” for multimedia information extraction using SpaCy RoBERTa based on Transformers(BERT) .

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NER-recognition

To Run GUI Locally

1) Clone the repository by running the following command in Command Prompt or Anaconda prompt :-

git clone https://github.com/GundlaTrinath/NER-recognition

2) installing dependencies :-

pip install requirements.txt

3) Navigate to :-

  NER-recognition\GUI\GUIAPP2.py

and execute "GUIAPP2.py"

3) To execute in command Prompt:-

  python GUIAPP2.py

GUI Usage :-

--> For English

1) Click on "Upload(English)" button.
2) Wait until it shows "Extract completed".
3) Click on "Download Audio file(English)" and wait for sometime.
4) Audio will be saved as "extracted.wav" in the "GUI" Folder locally.
5) Now click on "Download Text file(English)"
6) Text will be saved as "Output(English).txt" in the "GUI" Folder locally.
7) Click on "Download MetaData" button
8) Metadata will be saved as "Entities.csv"

--> For Hindi

1) Click on "Upload(Hindi)" button.
2) Wait until it shows "Extract completed".
3) Click on "Download Audio file(Hindi)" and wait for sometime.
4) Audio will be saved as "extracted.wav" in the "GUI" Folder locally.
5) Now click on "Download Text file(Hindi)"
6) Text will be saved as "Output(Hindi).txt" in the "GUI" Folder locally.

--> "A&T Processing.IPYNB" jupyter notebook has code implemented for Video to audio , Audio to text and text to audio.

--> "Model2.IPYNB" jupyter notebook has code implemented for NER into cvs/xlsx file.

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Multimedia Automation software for multimedia processing and multilingual transcription using Python based libraries - Tkinter SpeechRecognition API and DeepSpeech(open-source STT/TTS engine) ,NLP technique ”Named Entity extraction” for multimedia information extraction using SpaCy RoBERTa based on Transformers(BERT) .

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