/AIML_NLP

AIML Natural Language Processing - Speech, Audio

Primary LanguageJavaMIT LicenseMIT

AIML NLP

AIML Natural Language Processing

NLP Speech to Text , Text Translation and Sentiment Analysis! 🗣️🎙️🔤 😊

This weekend project marks my initial foray into the dynamic realm of AI and Machine Learning, where I've utilised preexisting components to swiftly create something for educational purposes.

🎧 Example Use Case: By leveraging a 20-second audio clip in Hindi, I transcribed, translated it into English, and conducted sentiment analysis on the resulting text. This demonstration showcases the remarkable versatility, intuitive design, user-friendly nature, practicality and real-world utility of pre-trained models (openai/whisper-large-v3) and libraries (Transformers).

🔍 Key Features:

  1. Speech to Text Conversion: Transcribe spoken words into text.
  2. Text Translation: Translate text from one language to another.
  3. Sentiment Analysis: Gain insights into the emotional tone of the text, enhancing understanding and interpretation.
  4. API Invocation: The NLP service can be invoked via API calls, enabling integration into various applications and systems with the accompanying Java project.

🛠️ Technologies & Libraries Utilised:

  1. Python
  2. Transformers Library by Hugging Face
  3. OpenAI's Whisper-Large-v3 model (a pre-trained model for automatic speech recognition (ASR))

🔌 No External Dependencies: Notably, this project operates entirely offline, ensuring data privacy and accessibility even without internet connectivity.

💡 Insights: Leveraging pre-trained models like Whisper has accelerated development, highlighting the immense value of open-source resources in driving innovation.

🌱 Simplicity & Collaboration: With just a few lines of code, this project underscores the power of simplicity in achieving complex tasks. It's a testament to the collaborative spirit of the open-source ecosystem, without which this endeavour wouldn't have been possible.

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Details

Reporting Issues/Suggest Improvements

This Project uses GitHub's integrated issue tracking system to record bugs and feature requests. If you want to raise an issue, please follow the recommendations below:

  • Before you log a bug, please search the issue tracker to see if someone has already reported the problem.
  • If the issue doesn't already exist, create a new issue
  • Please provide as much information as possible with the issue report.
  • If you need to paste code, or include a stack trace use Markdown +++```+++ escapes before and after your text.

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

Kindly refer to CONTRIBUTING.md for important Pull Request Process details

  1. In the top-right corner of this page, click Fork.

  2. Clone a copy of your fork on your local, replacing YOUR-USERNAME with your Github username.

    git clone https://github.com/YOUR-USERNAME/AIML_NLP.git

  3. Create a branch:

    git checkout -b <my-new-feature-or-fix>

  4. Make necessary changes and commit those changes:

    git add .

    git commit -m "new feature or fix"

  5. Push changes, replacing <add-your-branch-name> with the name of the branch you created earlier at step #3. :

    git push origin <add-your-branch-name>

  6. Submit your changes for review. Go to your repository on GitHub, you'll see a Compare & pull request button. Click on that button. Now submit the pull request.

That's it! Soon I'll be merging your changes into the master branch of this project. You will get a notification email once the changes have been merged. Thank you for your contribution.

Kindly follow Conventional Commits to create an explicit commit history. Kindly prefix the commit message with one of the following type's.

build : Changes that affect the build system or external dependencies (example scopes: gulp, broccoli, npm)
ci : Changes to our CI configuration files and scripts (example scopes: Travis, Circle, BrowserStack, SauceLabs)
docs : Documentation only changes
feat : A new feature
fix : A bug fix
perf : A code change that improves performance
refactor: A code change that neither fixes a bug nor adds a feature
style : Changes that do not affect the meaning of the code (white-space, formatting, missing semi-colons, etc)
test : Adding missing tests or correcting existing tests

License

Distributed under the MIT License. See LICENSE.md for more information.

The End

In the end, I hope you enjoyed the application and find it useful, as I did when I was developing it to learn about AI/ML.

If you would like to enhance, please:

  • Open PRs,

  • Give feedback,

  • Add new suggestions, and

  • Finally, give it a 🌟.

  • Happy Coding ...* 🙂

Contact

Anantha Raju C - @anantharajuc - arcswdev@gmail.com

Project Link: https://github.com/AnanthaRajuC/AIML_NLP