diff --git a/.assets/inference_pipeline.gif b/.assets/inference_pipeline.gif new file mode 100644 index 00000000..12f493b0 Binary files /dev/null and b/.assets/inference_pipeline.gif differ diff --git a/images/logo.png b/.assets/logo.png similarity index 100% rename from images/logo.png rename to .assets/logo.png diff --git a/images/project_logo.png b/.assets/project_logo.png similarity index 100% rename from images/project_logo.png rename to .assets/project_logo.png diff --git a/images/sign_lang.gif b/.assets/sign_lang.gif similarity index 100% rename from images/sign_lang.gif rename to .assets/sign_lang.gif diff --git a/.gitignore b/.gitignore index 7450e363..f0f7a79f 100644 --- a/.gitignore +++ b/.gitignore @@ -117,9 +117,8 @@ yb2audio/data/*/* #yb2audio/development # Development Enviroment -dev.py -dev_env.txt **/development/ +dev.py # Keys set_environment_variables.sh diff --git a/CONTRIBUTE.MD b/CONTRIBUTE.MD new file mode 100644 index 00000000..ea793fba --- /dev/null +++ b/CONTRIBUTE.MD @@ -0,0 +1,54 @@ +# Welcome Contributors + +Contributions are what makes the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are **greatly appreciated**. + +We're excited you're interested in contributing to this project! This guide outlines the process for submitting code contributions and provides some helpful tips for getting started. + +## Getting Started + +Before diving in, here are a few things to keep in mind: + +* **Issue Tracking:** We use GitHub issues to track bugs, feature requests, and other project tasks. Before working on a contribution, it's recommended to check the existing issues list and see if there's something you'd like to tackle. This helps avoid duplicate efforts and ensures your contribution aligns with project priorities. + +* **Development Setup:** We recommend following the project's setup instructions to configure your development environment. This ensures you have the necessary tools and dependencies to work on the project. + +## Making a Contribution + +Here's a general workflow for contributing code: + +1. **Fork the Repository:** Click the "Fork" button on the project's main page on GitHub. This creates a copy of the repository in your own account, allowing you to make changes without affecting the original project. + +2. **Clone Your Fork:** Use Git to clone your forked repository to your local machine. This creates a local working directory where you can make changes. + +3. **Create a Branch:** Create a new branch for your specific contribution. This helps keep your changes isolated from the main codebase. Use a descriptive branch name that reflects the nature of your contribution (e.g., "fix-login-bug" or "add-new-feature"). + + EXAMPLE --> (`git checkout -b feature/AmazingFeature`) + +4. **Make Your Changes:** Edit the relevant files and make your code changes. + +5. **Test Changes**: Make your changes and ensure that the code passes all tests. + +6. **Commit Your Changes:** Use Git to commit your changes with clear and concise commit messages. Each commit message should describe what you changed and why. + + EXAMPLE --> (`git commit -m 'Add some AmazingFeature'`) + +7. **Push Your Changes:** Push your commits to your forked repository on GitHub. + +8. **Create a Pull Request:** On GitHub, navigate to your forked repository and create a pull request. This sends a notification to the project maintainers about your proposed changes. +9. **Address Feedback:** Project maintainers may review your pull request and provide feedback or request changes. Be responsive to feedback and make any necessary adjustments. + +## Contribution Guidelines + +* **Code Style:** Follow the project's existing coding style guidelines (e.g., indentation, naming conventions) to ensure consistency. + +* **Testing:** Whenever possible, include unit tests for your code changes. This helps ensure the functionality you added or modified works as expected and doesn't introduce regressions. + +* **Documentation:** If your contribution adds new features or modifies existing functionality, consider updating the project's documentation to reflect the changes. + +## Additional Information + +* **License:** Be sure to understand the project's license before contributing. This clarifies how your code can be used and distributed. + +* **Community:** Feel free to reach out to the project maintainers (if available) if you have any questions or need help with your contribution. + +We appreciate your contributions! By following these guidelines, you can help us make this project even better. diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md new file mode 100644 index 00000000..e69de29b diff --git a/README.md b/README.md index 8a9137c4..c407ef76 100644 --- a/README.md +++ b/README.md @@ -12,7 +12,7 @@
- Logo + Logo

NSL-2-AUDIO

@@ -31,33 +31,25 @@

+> [!IMPORTANT] +> This project is currently in a very early development/experimental stage. There are a lot of unimplemented/broken features at the moment. Contributions are welcome to help out with the progress! + -
- Table of Contents -
    -
  1. - About The Project - -
    2. -
  2. - Getting Started - -
    3. -
  3. Usage
    4. -
  4. Roadmap
    5. -
  5. Contributing
    6. -
  6. License
    7. -
  7. Contact
    8. -
  8. Acknowledgments
    9. -
-
+## Table of Contents + +- [About](#about) +- [Demos](#demos) +- [System Architecture](#system-architecture) +- [Quick Start](#quick-start) +- [Getting Started](#getting-started) +- [Roadmap](#bulb-roadmap) +- [Contributing](#contributing) +- [License](#license) +- [Contact](#contact) +- [Acknowledgments](#acknowledgments) -## About The Project +## About ***Overview:*** \ This project is dedicated to the development of an Automatic Sign Language Translation system, with a specific focus on translating Nigerian Sign Language (NSL) into one of the Low-Resource Languages (LRLs) spoken in Nigeria. The primary objective is to address the communication challenges faced by the NSL community and contribute to inclusivity and employment opportunities in education and the workforce. @@ -79,30 +71,42 @@ Effective communication is a cornerstone of societal cohesion, and this project You can read the project proposal here, [Project Proposal](https://github.com/AISaturdaysLagos/Cohort8-Ransome-Kuti-Ladipo/blob/main/project-proposal.pdf) -[![DEMO][product-screenshot]](https://example.com) -![sign_lang_gif](images/sign_lang.gif) - -

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- ### Built With - [![Python][Python]][Python-url] - [![Pytorch][Pytorch]][Pytorch-url] +- [![GCP][GCP]][GCP-URL] +- [![MEAT][META]][META-url] - [![HuggingFace][HuggingFace]][HuggingFace-url] -

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- -## Getting Started +> [!NOTE] +> Currently, our support is exclusively for the Yoruba language.\ +However, we aspire to expand our services to include other low-resource Nigerian languages such as Igbo and Hausa in the future. -The project is structured into three distinct parts, each housed in separate directories as outlined in the project proposal. The initial phase involves translating sign language into English text, followed by the second phase, which focuses on translating the English text into Yoruba text. The final segment entails taking the translated Yoruba text and converting it into generated Yoruba audio. +## Demos -The `signa2text` directory is dedicated to the process of translating sign language into English text. Meanwhile, the `linguify_yb` directory serves the purpose of transforming English text into Yoruba text. Finally, the `yb2audio` directory is designated for utilizing the translated audio to generate Yoruba audio. +> This is a Temporary demo + +[![Video Title](https://img.youtube.com/vi/YOUTUBE_VIDEO_ID_HERE/0.jpg)](https://www.youtube.com/watch?v=YOUTUBE_VIDEO_ID_HERE) + +## System Architecture + + +### Architecture of Inference Pipeline + +![inference_pipeline.gif](.assets/inference_pipeline.gif.gif) + +### System Design + +![system_design](.assets\system_design) + +

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-To access any of the three directories, adhere to the specified prerequisites below and navigate into the respective directory. +## Quick Start -### Prerequisites +The easiest way to run the project locally: ```bash # Clone this repository @@ -111,50 +115,34 @@ $ git clone https://github.com/rileydrizzy/NSL_2_AUDIO # Go into the repository $ cd NSL_2_AUDIO -# Install dependencies -$ make setup -``` +# Go to app directory +$ cd app -

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+``` - -## Usage + +## Getting Started -

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+The project is structured into three distinct parts, each housed in separate directories as outlined in the project proposal. The initial phase involves translating sign language into English text, followed by the second phase, which focuses on translating the English text into Yoruba text. The final segment entails taking the translated Yoruba text and converting it into generated Yoruba audio. - -## Roadmap +The `signa2text` directory is dedicated to the process of translating sign language into English text. Meanwhile, the `linguify_yb` directory serves the purpose of transforming English text into Yoruba text. Finally, the `yb2audio` directory is designated for utilizing the translated audio to generate Yoruba audio. -- [ ] Feature 1 +In the `app` directory, hold the logic for the application. This directory serves as the core repository for all the essential functionalities and trained models required for the smooth operation of the application. Here, you'll find the code responsible for handling user interactions, processing of video data, managing state, and performing inference. -See the [open issues](https://github.com/rileydrizzy/NSL_2_AUDIO/issues) for a full list of proposed features (and known issues). +**To access any of the directories, navigate into the respective directory and adhere to the specified prerequisites in the respective directory README.**

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- -## Contributing +## :bulb: Roadmap -Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are **greatly appreciated**. - -If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". -Don't forget to give the project a star! Thanks again! - -1. Fork the Project -2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`) -3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`) -4. Push to the Branch (`git push origin feature/AmazingFeature`) -5. Open a Pull Request +## Contributing -

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+We welcome contributions to enhance NSL-2-AUDIO capabilities and improve its performance. To contribute, please see the `CONTRIBUTING.md` file for steps. - ## License -Distributed under the MIT License. See `LICENSE` for more information. +NSL-2-AUDIO is released under the [MIT License](https://opensource.org/licenses/MIT). See the `LICENSE` file for more information. -

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- - ## Contact If you have questions or need assistance, feel free to reach out to: @@ -164,8 +152,6 @@ If you have questions or need assistance, feel free to reach out to: **GitHub:** [@rileydrizzy](https://github.com/rileydrizzy) **Linkdeln:** [Ipadeola Ladipo](https://www.linkedin.com/in/ladipo-ipadeola/) -

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- ## Acknowledgments @@ -191,12 +177,13 @@ I would like to acknowledge the outstanding contributions of : [issues-url]: https://github.com/rileydrizzy/NSL_2_AUDIO/issues [license-shield]: https://img.shields.io/github/license/rileydrizzy/NSL_2_AUDIO.svg?style=for-the-badge [license-url]: https://github.com/rileydrizzy/NSL_2_AUDIO/blob/master/LICENSE.txt -[product-screenshot]: images/screenshot.png [Python-url]: [Python]: [Pytorch-url]: [Pytorch]: +[GCP-url]: +[GCP]: [HuggingFace-url]: -[HuggingFace]: -[GCP-url]: -[GCP]: <> +[HuggingFace]: +[META-url]: +[META]: diff --git a/app/README.md b/app/README.md new file mode 100644 index 00000000..e69de29b diff --git a/app/streamlit_app/app.py b/app/streamlit_app/app.py index 3f0226c0..5f7dc844 100644 --- a/app/streamlit_app/app.py +++ b/app/streamlit_app/app.py @@ -101,7 +101,6 @@ st.divider() # Introduction section -st.subheader("intro") st.markdown("** INTRODUCTION **", unsafe_allow_html=True) st.markdown( "

" diff --git a/app/streamlit_app/assets/lotties/Animation_1708486960708.json b/app/streamlit_app/assets/lottie/Animation_1708486960708.json similarity index 100% rename from app/streamlit_app/assets/lotties/Animation_1708486960708.json rename to app/streamlit_app/assets/lottie/Animation_1708486960708.json diff --git a/app/streamlit_app/components/configs.py b/app/streamlit_app/components/configs.py index b2fe887c..0d4baaaa 100644 --- a/app/streamlit_app/components/configs.py +++ b/app/streamlit_app/components/configs.py @@ -32,8 +32,8 @@ RESULT_PAGE = PAGES_DIR + "result_page.py" PROJECT_LOGO = "assets/images/logos/logo.png" NSL_SIGN_GIF = "assets/sign_lang.gif" -LOADING_ANIMATION = "assets/lotties/Animation_1708486960708.json" -PROCESSING_ANIMATION = "assets/lotties/Animation_1708486960708.json" +LOADING_ANIMATION = "assets/lottie/Animation_1708486960708.json" +PROCESSING_ANIMATION = "assets/lottie/Animation_1708486960708.json" # Slideshow images SLIDES_IMAGES = [ @@ -246,7 +246,7 @@

Copyright (c) 2024 Ladipo Ezekiel Ipadeola.

-

This software is distributed under an M!T licence. Please consult the LICENSE file for more details.

+

This software is distributed under an M!T license. Please consult the LICENSE file for more details.

github
""" diff --git a/app/streamlit_app/components/utils.py b/app/streamlit_app/components/utils.py index 2795d0a5..403addd9 100644 --- a/app/streamlit_app/components/utils.py +++ b/app/streamlit_app/components/utils.py @@ -4,7 +4,9 @@ Functions: - `load_lottiefile(filepath: str) -> dict`: Loads a Lottie animation file (.json) \ and returns its parsed contents as a dictionary. -- extract_landmarks_features() +- `extract_landmarks_features(video_file, startframe)`: Extracts landmarks features \ + from the uploaded NSL video file. + Dependencies: - cv2 @@ -17,11 +19,10 @@ """ -#import cv2 import json -#import numpy as np -#import pandas as pd -#import mediapipe as mp +import cv2 +import pandas as pd +import mediapipe as mp def load_lottiefile(filepath: str) -> dict: @@ -53,7 +54,7 @@ def load_lottiefile(filepath: str) -> dict: # TODO Debug -""""def extract_landmarks_features(cap, start_frame=0): +def extract_landmarks_features(cap, start_frame=0): if not isinstance(cap, cv2.VideoCapture): raise ValueError @@ -180,4 +181,3 @@ def load_lottiefile(filepath: str) -> dict: return pd.DataFrame( {"frame": frame, "type": type_, "landmark_index": index, "x": x, "y": y, "z": z} ) -""" \ No newline at end of file diff --git a/app/streamlit_app/pages/upload_page.py b/app/streamlit_app/pages/upload_page.py index 05858262..d31c616e 100644 --- a/app/streamlit_app/pages/upload_page.py +++ b/app/streamlit_app/pages/upload_page.py @@ -1,18 +1,26 @@ -"""upload page +""" +Upload Page + +This module defines the Streamlit upload page for the NSL-2-AUDIO project. Users can upload +videos containing Nigerian Sign Language (NSL) signs for translation into spoken audio. + +The upload process involves selecting a supported video file format (MP4, AVI, MKV) with a +front-facing view of a single individual signing. The uploaded video is then processed for +translation into spoken audio. + """ import time import streamlit as st -from streamlit_lottie import st_lottie, st_lottie_spinner -from components.configs import PROJECT_LOGO, PROCESSING_ANIMATION, RESULT_PAGE -# from components.utils import load_lottiefile, #extract_landmarks_features +from streamlit_lottie import st_lottie +from components.configs import PROJECT_LOGO, PROCESSING_ANIMATION +from components.utils import load_lottiefile, extract_landmarks_features # Set page configuration st.set_page_config(page_title="Upload", page_icon="🧏", initial_sidebar_state="auto") # Display project logo and title st.image(PROJECT_LOGO, width=300) - st.title("_Welcome to NSL-2-AUDIO_ 🧏") # Display project logo in the sidebar @@ -29,31 +37,55 @@ st.divider() +st.subheader( + "Hear your sign language come alive! Ready to translate your ASL video into spoken audio?" +) st.markdown( - "

" - "Please " - "

", - unsafe_allow_html=True, + "We bridge the communication gap, making your sign language understandable for everyone. " + "Here's how:" +) +st.write("""**1. Click "Choose File".**""") + +st.write( + "**2. Select your ASL video. Ensure it's in a supported format (MP4, AVI, MKV) and features:**" +) +st.write( + "- **A single individual signing:** For optimal accuracy, only one person should be signing in the video." ) +st.write("- **Front-facing view:** The signer should be facing the camera directly.") +st.markdown( + """**3. Click "Upload". Lean back and relax! Our system will analyze your video and translate the signs to spoken audio.**""" +) + +st.write("**Please note:**") +st.write( + "- This technology is still under development, and translation accuracy may vary." +) +st.write("- Currently, we only support Yoruba language for now.") +st.write( + "- Videos with multiple signers or those not facing the camera might experience decreased accuracy." +) + +# Allow users to upload sign language video sign_lang_video = st.file_uploader( - label=":red[Upload Video]", type=["MP4", "PNG"], on_change=None + label="Choose File", type=["MP4", "AVI", "MKV", "PNG"], on_change=None ) -# processing_animation_ = load_lottiefile(PROCESSING_ANIMATION) +processing_animation_ = load_lottiefile(PROCESSING_ANIMATION) if sign_lang_video is not None: st.video(sign_lang_video) st.success("Your file has been uploaded successfully!") st.write("Click the button to perform translation.") - if st.button(label=""): - # st_lottie(processing_animation_) + if st.button(label="Start Translation"): + st_lottie(processing_animation_) # TODO Extract feature landmarks # data = extract_landmarks_features(sign_lang_video) # TODO Send data to model inference - 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"0.2.8" -description = "Measures the displayed width of unicode strings in a terminal" -optional = false -python-versions = "*" -files = [ - {file = "wcwidth-0.2.8-py2.py3-none-any.whl", hash = "sha256:77f719e01648ed600dfa5402c347481c0992263b81a027344f3e1ba25493a704"}, - {file = "wcwidth-0.2.8.tar.gz", hash = "sha256:8705c569999ffbb4f6a87c6d1b80f324bd6db952f5eb0b95bc07517f4c1813d4"}, -] - -[[package]] -name = "webencodings" -version = "0.5.1" -description = "Character encoding aliases for legacy web content" -optional = false -python-versions = "*" -files = [ - {file = "webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78"}, - {file = "webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"}, -] - -[[package]] -name = "win32-setctime" -version = "1.1.0" -description = "A small Python utility to set file creation time on Windows" -optional = false -python-versions = ">=3.5" -files = [ - {file = "win32_setctime-1.1.0-py3-none-any.whl", hash = "sha256:231db239e959c2fe7eb1d7dc129f11172354f98361c4fa2d6d2d7e278baa8aad"}, - {file = "win32_setctime-1.1.0.tar.gz", hash = "sha256:15cf5750465118d6929ae4de4eb46e8edae9a5634350c01ba582df868e932cb2"}, -] - -[package.extras] -dev = ["black (>=19.3b0)", "pytest (>=4.6.2)"] - -[metadata] -lock-version = "2.0" -python-versions = ">=3.10,<3.12" -content-hash = "bece9fd91b8e7eb714410dca5fd61f8bb9d884c7d18857d13fab0b2c6dff0f1b" diff --git a/pyproject.toml b/pyproject.toml deleted file mode 100644 index a43455ac..00000000 --- a/pyproject.toml +++ /dev/null @@ -1,33 +0,0 @@ -[tool.poetry] -name = "NSL-2-AUDIO" -version = "0.1.0" -description = "A Machine Learning System for Sign Language Video Translation and Speech Generation into Low-Resource Languages (LRLs) in Nigeria" -authors = ["Ipadeola Ladipo Ezekiel <105759894+rileydrizzy@users.noreply.github.com>"] -readme = "README.md" - -[tool.poetry.dependencies] -python = ">=3.10,<3.12" -pre-commit = "^3.5.0" -opendatasets = "^0.1.22" -loguru = "^0.7.2" -wandb = "^0.15.12" -transformers = "^4.34.0" -pandas = "^2.1.1" -hydra-core = "^1.3.2" -torch = "^2.1.0" -torchaudio = "^2.1.0" -torchvision = "^0.16.0" -ipykernel = "^6.26.0" -opencv-python = "^4.8.1.78" -torchprofile = "^0.0.4" -pydantic = "^2.5.2" -pytest = "^7.4.3" -pyarrow = "^14.0.1" - - -[tool.poetry.group.dev.dependencies] -ipykernel = "^6.25.2" - -[build-system] -requires = ["poetry-core"] -build-backend = "poetry.core.masonry.api" diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index cf460204..00000000 --- a/requirements.txt +++ /dev/null @@ -1,103 +0,0 @@ -annotated-types==0.6.0 ; python_version >= "3.10" and python_version < "3.12" -antlr4-python3-runtime==4.9.3 ; python_version >= "3.10" and python_version < "3.12" -appdirs==1.4.4 ; python_version >= "3.10" and python_version < "3.12" -appnope==0.1.3 ; python_version >= "3.10" and python_version < "3.12" and (platform_system == "Darwin" or sys_platform == "darwin") -asttokens==2.4.0 ; python_version >= "3.10" and python_version < "3.12" -backcall==0.2.0 ; python_version >= "3.10" and python_version < "3.12" -bleach==6.1.0 ; python_version >= "3.10" and python_version < "3.12" -certifi==2023.7.22 ; python_version >= "3.10" and python_version < "3.12" -cffi==1.16.0 ; python_version >= "3.10" and python_version < "3.12" and implementation_name == "pypy" -cfgv==3.4.0 ; python_version >= "3.10" and python_version < "3.12" -charset-normalizer==3.3.0 ; python_version >= "3.10" and python_version < "3.12" -click==8.1.7 ; python_version >= "3.10" and python_version < "3.12" -colorama==0.4.6 ; python_version >= "3.10" and python_version < "3.12" and (sys_platform == "win32" or platform_system == "Windows") -comm==0.1.4 ; python_version >= "3.10" and python_version < "3.12" -debugpy==1.8.0 ; python_version >= "3.10" and python_version < "3.12" -decorator==5.1.1 ; python_version >= "3.10" and python_version < "3.12" -distlib==0.3.7 ; python_version >= "3.10" and python_version < "3.12" -docker-pycreds==0.4.0 ; python_version >= "3.10" and python_version < "3.12" -exceptiongroup==1.1.3 ; python_version >= "3.10" and python_version < "3.11" -executing==2.0.0 ; python_version >= "3.10" and python_version < "3.12" -filelock==3.12.4 ; python_version >= "3.10" and python_version < "3.12" -fsspec==2023.9.2 ; python_version >= "3.10" and python_version < "3.12" -gitdb==4.0.10 ; python_version >= "3.10" and python_version < "3.12" -gitpython==3.1.38 ; python_version >= "3.10" and python_version < "3.12" -huggingface-hub==0.17.3 ; python_version >= "3.10" and python_version < "3.12" -hydra-core==1.3.2 ; python_version >= "3.10" and python_version < "3.12" -identify==2.5.30 ; python_version >= "3.10" and python_version < "3.12" -idna==3.4 ; python_version >= "3.10" and python_version < "3.12" -iniconfig==2.0.0 ; python_version >= "3.10" and python_version < "3.12" -ipykernel==6.26.0 ; python_version >= "3.10" and python_version < "3.12" -ipython==8.16.1 ; python_version >= "3.10" and python_version < "3.12" -jedi==0.19.1 ; python_version >= "3.10" and python_version < "3.12" -jinja2==3.1.2 ; python_version >= "3.10" and python_version < "3.12" -jupyter-client==8.4.0 ; python_version >= "3.10" and python_version < "3.12" -jupyter-core==5.4.0 ; python_version >= "3.10" and python_version < "3.12" -kaggle==1.5.16 ; python_version >= "3.10" and python_version < "3.12" -loguru==0.7.2 ; python_version >= "3.10" and python_version < "3.12" -markupsafe==2.1.3 ; python_version >= "3.10" and python_version < "3.12" -matplotlib-inline==0.1.6 ; python_version >= "3.10" and python_version < "3.12" -mpmath==1.3.0 ; python_version >= "3.10" and python_version < "3.12" -nest-asyncio==1.5.8 ; python_version >= "3.10" and python_version < "3.12" -networkx==3.2 ; python_version >= "3.10" and python_version < "3.12" -nodeenv==1.8.0 ; python_version >= "3.10" and python_version < "3.12" -numpy==1.26.1 ; python_version >= "3.10" and python_version < "3.12" -omegaconf==2.3.0 ; python_version >= "3.10" and python_version < "3.12" -opencv-python==4.8.1.78 ; python_version >= "3.10" and python_version < "3.12" -opendatasets==0.1.22 ; python_version >= "3.10" and python_version < "3.12" -packaging==23.2 ; python_version >= "3.10" and python_version < "3.12" -pandas==2.1.1 ; python_version >= "3.10" and python_version < "3.12" -parso==0.8.3 ; python_version >= "3.10" and python_version < "3.12" -pathtools==0.1.2 ; python_version >= "3.10" and python_version < "3.12" -pexpect==4.8.0 ; python_version >= "3.10" and python_version < "3.12" and sys_platform != "win32" -pickleshare==0.7.5 ; python_version >= "3.10" and python_version < "3.12" -pillow==10.1.0 ; python_version >= "3.10" and python_version < "3.12" -platformdirs==3.11.0 ; python_version >= "3.10" and python_version < "3.12" -pluggy==1.4.0 ; python_version >= "3.10" and python_version < "3.12" -pre-commit==3.5.0 ; python_version >= "3.10" and python_version < "3.12" -prompt-toolkit==3.0.39 ; python_version >= "3.10" and python_version < "3.12" -protobuf==4.24.4 ; python_version >= "3.10" and python_version < "3.12" -psutil==5.9.6 ; python_version >= "3.10" and python_version < "3.12" -ptyprocess==0.7.0 ; python_version >= "3.10" and python_version < "3.12" and sys_platform != "win32" -pure-eval==0.2.2 ; python_version >= "3.10" and python_version < "3.12" -pyarrow==14.0.2 ; python_version >= "3.10" and python_version < "3.12" -pycparser==2.21 ; python_version >= "3.10" and python_version < "3.12" and implementation_name == "pypy" -pydantic-core==2.16.2 ; python_version >= "3.10" and python_version < "3.12" -pydantic==2.6.1 ; python_version >= "3.10" and python_version < "3.12" -pygments==2.16.1 ; python_version >= "3.10" and python_version < "3.12" -pytest==7.4.4 ; python_version >= "3.10" and python_version < "3.12" -python-dateutil==2.8.2 ; python_version >= "3.10" and python_version < "3.12" -python-slugify==8.0.1 ; python_version >= "3.10" and python_version < "3.12" -pytz==2023.3.post1 ; python_version >= "3.10" and python_version < "3.12" -pywin32==306 ; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version >= "3.10" and python_version < "3.12" -pyyaml==6.0.1 ; python_version >= "3.10" and python_version < "3.12" -pyzmq==25.1.1 ; python_version >= "3.10" and python_version < "3.12" -regex==2023.10.3 ; python_version >= "3.10" and python_version < "3.12" -requests==2.31.0 ; python_version >= "3.10" and python_version < "3.12" -safetensors==0.4.0 ; python_version >= "3.10" and python_version < "3.12" -sentry-sdk==1.32.0 ; python_version >= "3.10" and python_version < "3.12" -setproctitle==1.3.3 ; python_version >= "3.10" and python_version < "3.12" -setuptools==68.2.2 ; python_version >= "3.10" and python_version < "3.12" -six==1.16.0 ; python_version >= "3.10" and python_version < "3.12" -smmap==5.0.1 ; python_version >= "3.10" and python_version < "3.12" -stack-data==0.6.3 ; python_version >= "3.10" and python_version < "3.12" -sympy==1.12 ; python_version >= "3.10" and python_version < "3.12" -text-unidecode==1.3 ; python_version >= "3.10" and python_version < "3.12" -tokenizers==0.14.1 ; python_version >= "3.10" and python_version < "3.12" -tomli==2.0.1 ; python_version >= "3.10" and python_version < "3.11" -torch==2.1.0 ; python_version >= "3.10" and python_version < "3.12" -torchaudio==2.1.0 ; python_version >= "3.10" and python_version < "3.12" -torchprofile==0.0.4 ; python_version >= "3.10" and python_version < "3.12" -torchvision==0.16.0 ; python_version >= "3.10" and python_version < "3.12" -tornado==6.3.3 ; python_version >= "3.10" and python_version < "3.12" -tqdm==4.66.1 ; python_version >= "3.10" and python_version < "3.12" -traitlets==5.11.2 ; python_version >= "3.10" and python_version < "3.12" -transformers==4.34.1 ; python_version >= "3.10" and python_version < "3.12" -typing-extensions==4.8.0 ; python_version >= "3.10" and python_version < "3.12" -tzdata==2023.3 ; python_version >= "3.10" and python_version < "3.12" -urllib3==2.0.7 ; python_version >= "3.10" and python_version < "3.12" -virtualenv==20.21.0 ; python_version >= "3.10" and python_version < "3.12" -wandb==0.15.12 ; python_version >= "3.10" and python_version < "3.12" -wcwidth==0.2.8 ; python_version >= "3.10" and python_version < "3.12" -webencodings==0.5.1 ; python_version >= "3.10" and python_version < "3.12" -win32-setctime==1.1.0 ; python_version >= "3.10" and python_version < "3.12" and sys_platform == "win32" diff --git a/run_setup.sh b/run_setup.sh deleted file mode 100755 index d72d0e3c..00000000 --- a/run_setup.sh +++ /dev/null @@ -1,5 +0,0 @@ -#!/bin/bash - -curl -sSL https://install.python-poetry.org | python3 - -poetry install --no-root -echo "Environment setup complete" diff --git a/signa2text/Makefile b/signa2text/Makefile new file mode 100644 index 00000000..f817edf9 --- /dev/null +++ b/signa2text/Makefile @@ -0,0 +1,30 @@ +.DEFAULT_GOAL := help + +help: + @echo " setup Set up the environment with the required dependencies and environment variables" + @echo " freeze_reqs Save the dependencies onto the requirements text file" + @echo " precommit Runs precommit on all files" + @echo " download_data Download " + @echo " run_train Training" + +setup: + @echo "Installing and setting up dependencies..." + . ./run_setup.sh + + @echo "Setting Enviroment Variables" + . ./set_environment_variables.sh + +precommit: + @echo "Running precommit on all files" + python pre-commit run --all-files + +freeze_reqs: + @echo "Exporting dependencies to requirements file" + pip freeze > requirements.txt + +download_data: + @echo "Downloading Development Data" + python src/download_dev_data.py + +run_train: + . ./train_model.sh \ No newline at end of file diff --git a/signa2text/development/code_dev.ipynb b/signa2text/development/code_dev.ipynb deleted file mode 100644 index 251ebeaf..00000000 --- a/signa2text/development/code_dev.ipynb +++ /dev/null @@ -1,203 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def resize_pad(x):\n", - " if x.shape[0] < FRAME_LEN:\n", - " x = F.pad(x, (0, 0, 0, FRAME_LEN - x.shape[0], 0, 0))\n", - " else:\n", - " x = x.unsqueeze(0) # Add batch and channel dimensions\n", - " x = torch.nn.functional.interpolate(\n", - " x, size=(FRAME_LEN, x.shape[1]), mode=\"bilinear\", align_corners=False\n", - " ).squeeze(0)\n", - "\n", - " return x\n", - "\n", - "\n", - "def frames_preprocess(x):\n", - " x = torch.tensor(x)\n", - " rhand = x[:, RHAND_IDX]\n", - " lhand = x[:, LHAND_IDX]\n", - " rpose = x[:, RPOSE_IDX]\n", - " lpose = x[:, LPOSE_IDX]\n", - "\n", - " rnan_idx = torch.any(torch.isnan(rhand), dim=1)\n", - " lnan_idx = torch.any(torch.isnan(lhand), dim=1)\n", - "\n", - " rnans = torch.sum(rnan_idx)\n", - " lnans = torch.sum(lnan_idx)\n", - "\n", - " if rnans > lnans:\n", - " hand = lhand\n", - " pose = lpose\n", - "\n", - " hand_x = hand[:, 0 * (len(LHAND_IDX) // 3) : 1 * (len(LHAND_IDX) // 3)]\n", - " hand_y = hand[:, 1 * (len(LHAND_IDX) // 3) : 2 * (len(LHAND_IDX) // 3)]\n", - " hand_z = hand[:, 2 * (len(LHAND_IDX) // 3) : 3 * (len(LHAND_IDX) // 3)]\n", - " hand = torch.cat([1 - hand_x, hand_y, hand_z], dim=1)\n", - "\n", - " pose_x = pose[:, 0 * (len(LPOSE_IDX) // 3) : 1 * (len(LPOSE_IDX) // 3)]\n", - " pose_y = pose[:, 1 * (len(LPOSE_IDX) // 3) : 2 * (len(LPOSE_IDX) // 3)]\n", - " pose_z = pose[:, 2 * (len(LPOSE_IDX) // 3) : 3 * (len(LPOSE_IDX) // 3)]\n", - " pose = torch.cat([1 - pose_x, pose_y, pose_z], dim=1)\n", - " else:\n", - " hand = rhand\n", - " pose = rpose\n", - "\n", - " hand_x = hand[:, 0 * (len(LHAND_IDX) // 3) : 1 * (len(LHAND_IDX) // 3)]\n", - " hand_y = hand[:, 1 * (len(LHAND_IDX) // 3) : 2 * (len(LHAND_IDX) // 3)]\n", - " hand_z = hand[:, 2 * (len(LHAND_IDX) // 3) : 3 * (len(LHAND_IDX) // 3)]\n", - " hand = torch.cat(\n", - " [hand_x.unsqueeze(-1), hand_y.unsqueeze(-1), hand_z.unsqueeze(-1)], dim=-1\n", - " )\n", - "\n", - " mean = torch.mean(hand, dim=1).unsqueeze(1)\n", - " std = torch.std(hand, dim=1).unsqueeze(1)\n", - " hand = (hand - mean) / std\n", - "\n", - " pose_x = pose[:, 0 * (len(LPOSE_IDX) // 3) : 1 * (len(LPOSE_IDX) // 3)]\n", - " pose_y = pose[:, 1 * (len(LPOSE_IDX) // 3) : 2 * (len(LPOSE_IDX) // 3)]\n", - " pose_z = pose[:, 2 * (len(LPOSE_IDX) // 3) : 3 * (len(LPOSE_IDX) // 3)]\n", - " pose = torch.cat(\n", - " [pose_x.unsqueeze(-1), pose_y.unsqueeze(-1), pose_z.unsqueeze(-1)], dim=-1\n", - " )\n", - "\n", - " x = torch.cat([hand, pose], dim=1)\n", - " # print(f\"befor re{x.shape}\")\n", - " x = resize_pad(x)\n", - " # print(f\"after re{x.shape}\")\n", - " x = torch.where(torch.isnan(x), torch.zeros_like(x), x)\n", - " # print(x.shape)\n", - "\n", - " #! CRITICAL Debug\n", - " # x = x.view(FRAME_LEN, len(LHAND_IDX) + len(LPOSE_IDX))\n", - " return x" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"doc\n", - "\"\"\"\n", - "\n", - "FRAME_LEN = 128\n", - "\n", - "LPOSE = [13, 15, 17, 19, 21]\n", - "RPOSE = [14, 16, 18, 20, 22]\n", - "POSE = LPOSE + RPOSE\n", - "\n", - "X = (\n", - " [f\"x_right_hand_{i}\" for i in range(21)]\n", - " + [f\"x_left_hand_{i}\" for i in range(21)]\n", - " + [f\"x_pose_{i}\" for i in POSE]\n", - ")\n", - "Y = (\n", - " [f\"y_right_hand_{i}\" for i in range(21)]\n", - " + [f\"y_left_hand_{i}\" for i in range(21)]\n", - " + [f\"y_pose_{i}\" for i in POSE]\n", - ")\n", - "Z = (\n", - " [f\"z_right_hand_{i}\" for i in range(21)]\n", - " + [f\"z_left_hand_{i}\" for i in range(21)]\n", - " + [f\"z_pose_{i}\" for i in POSE]\n", - ")\n", - "\n", - "FEATURE_COLUMNS = X + Y + Z\n", - "\n", - "X_IDX = [i for i, col in enumerate(FEATURE_COLUMNS) if \"x_\" in col]\n", - "Y_IDX = [i for i, col in enumerate(FEATURE_COLUMNS) if \"y_\" in col]\n", - "Z_IDX = [i for i, col in enumerate(FEATURE_COLUMNS) if \"z_\" in col]\n", - "\n", - "RHAND_IDX = [i for i, col in enumerate(FEATURE_COLUMNS) if \"right\" in col]\n", - "LHAND_IDX = [i for i, col in enumerate(FEATURE_COLUMNS) if \"left\" in col]\n", - "RPOSE_IDX = [\n", - " i\n", - " for i, col in enumerate(FEATURE_COLUMNS)\n", - " if \"pose\" in col and int(col[-2:]) in RPOSE\n", - "]\n", - "LPOSE_IDX = [\n", - " i\n", - " for i, col in enumerate(FEATURE_COLUMNS)\n", - " if \"pose\" in col and int(col[-2:]) in LPOSE\n", - "]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(63, 63)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(RHAND_IDX), len(LHAND_IDX)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def read_file(file, file_id, landmarks_metadata_path):\n", - " phrase_list = []\n", - " frames_list = []\n", - " metadata_train_dataframe = pd.read_csv(landmarks_metadata_path)\n", - " file_id_df = metadata_train_dataframe.loc[\n", - " metadata_train_dataframe[\"file_id\"] == file_id\n", - " ]\n", - " saved_parueat_df = pq.read_table(\n", - " file, columns=[\"sequence_id\"] + FEATURE_COLUMNS\n", - " ).to_pandas()\n", - " for seq_id, phrase in zip(file_id_df.sequence_id, file_id_df.phrase):\n", - " frames = saved_parueat_df[saved_parueat_df.index == seq_id].to_numpy()\n", - " # NaN\n", - " right_num_nan = np.sum(np.sum(np.isnan(frames[:, RHAND_IDX]), axis=1) == 0)\n", - " left_num_nan = np.sum(np.sum(np.isnan(frames[:, LHAND_IDX]), axis=1) == 0)\n", - " \n", - " total_num_nan = max(right_num_nan, left_num_nan)\n", - " if 2 * len(phrase) < total_num_nan:\n", - " frames_list.append(frames)\n", - " phrase_list.append(phrase)\n", - " return (frames_list, phrase_list)\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/signa2text/development/data_dev.ipynb b/signa2text/development/data_dev.ipynb deleted file mode 100644 index cade00e3..00000000 --- a/signa2text/development/data_dev.ipynb +++ /dev/null @@ -1,503 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from torch.utils.data import Dataset,DataLoader\n", - "import pandas as pd\n", - "import json\n", - "import os\n", - "import pyarrow.parquet as pq" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "#%pip install fastparquet\n", - "#%pip install pyarrow" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "file_path = \"/workspace/Cohort8-Ransom-Kuti-Ladipo/linguify_yb/data/raw/train_landmarks/105143404.parquet\"\n", - "parquet_file = pd.read_parquet(\"/workspace/Cohort8-Ransom-Kuti-Ladipo/linguify_yb/data/raw/train_landmarks/105143404.parquet\")\n", - "meta_data = pd.read_csv(\"/workspace/Cohort8-Ransom-Kuti-Ladipo/linguify_yb/data/raw/train.csv\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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1train_landmarks/5414471.parquet54144711816825349107scales/kuhaylah
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" - ], - "text/plain": [ - " path file_id sequence_id participant_id \\\n", - "0 train_landmarks/5414471.parquet 5414471 1816796431 217 \n", - "1 train_landmarks/5414471.parquet 5414471 1816825349 107 \n", - "\n", - " phrase \n", - "0 3 creekhouse \n", - "1 scales/kuhaylah " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "meta_data.head(2)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "FRAME_LEN = 128\n", - "\n", - "LPOSE = [13,15,17,19,21]\n", - "RPOSE = [14,16,18,20,22]\n", - "POSE = LPOSE+RPOSE\n", - "\n", - "X = [f'x_right_hand_{i}' for i in range(21)] + [f'x_left_hand_{i}' for i in range(21)] + [f'x_pose{i}' for i in POSE]\n", - "Y = [f'y_right_hand_{i}' for i in range(21)] + [f'y_left_hand_{i}' for i in range(21)] + [f'y_pose{i}' for i in POSE]\n", - "Z = [f'z_right_hand_{i}' for i in range(21)] + [f'z_left_hand_{i}' for i in range(21)] + [f'z_pose{i}' for i in POSE]\n", - "\n", - "FEATURE_COLIMNS = X+Y+Z\n", - "\n", - "X_IDX = [i for i, col in enumerate(FEATURE_COLIMNS) if \"x_\" in col]\n", - "Y_IDX = [i for i, col in enumerate(FEATURE_COLIMNS) if \"y_\" in col]\n", - "Z_IDX = [i for i, col in enumerate(FEATURE_COLIMNS) if \"z_\" in col]\n", - "\n", - "RHAND_IDX = [i for i, col in enumerate(FEATURE_COLIMNS) if \"right\" in col]\n", - "LHAND_IDX = [i for i, col in enumerate(FEATURE_COLIMNS) if \"leftt\" in col]\n", - "RPOSE_IDX = [i for i, col in enumerate(FEATURE_COLIMNS) if \"pose\" in col and int(col[-2:]) in RPOSE]\n", - "LPOSE_IDX = [i for i, col in enumerate(FEATURE_COLIMNS) if \"pose\" in col and int(col[-2:]) in LPOSE]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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pathfile_idsequence_idparticipant_idphrase
999train_landmarks/105143404.parquet1051434041784552841188https://www.xn--pearol-xwa.org
1000train_landmarks/105143404.parquet105143404178457416989+44-645-14-4280-31
1001train_landmarks/105143404.parquet1051434041784587545219dan navarro
1002train_landmarks/105143404.parquet105143404178463091581mephimle.com/shine-papeleria/
1003train_landmarks/105143404.parquet105143404178467255521facartes.uniandes.edu.co
..................
1994train_landmarks/105143404.parquet1051434041816603189706114 mueschke road
1995train_landmarks/105143404.parquet10514340418166545562462762 valmora dr
1996train_landmarks/105143404.parquet10514340418166642951071481 carson williams
1997train_landmarks/105143404.parquet10514340418166893811536401 west pleasant lake drive
1998train_landmarks/105143404.parquet1051434041816790518196230 west 84th lane
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1000 rows × 5 columns

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" - ], - "text/plain": [ - " path file_id sequence_id \\\n", - "999 train_landmarks/105143404.parquet 105143404 1784552841 \n", - "1000 train_landmarks/105143404.parquet 105143404 1784574169 \n", - "1001 train_landmarks/105143404.parquet 105143404 1784587545 \n", - "1002 train_landmarks/105143404.parquet 105143404 1784630915 \n", - "1003 train_landmarks/105143404.parquet 105143404 1784672555 \n", - "... ... ... ... \n", - "1994 train_landmarks/105143404.parquet 105143404 1816603189 \n", - "1995 train_landmarks/105143404.parquet 105143404 1816654556 \n", - "1996 train_landmarks/105143404.parquet 105143404 1816664295 \n", - "1997 train_landmarks/105143404.parquet 105143404 1816689381 \n", - "1998 train_landmarks/105143404.parquet 105143404 1816790518 \n", - "\n", - " participant_id phrase \n", - "999 188 https://www.xn--pearol-xwa.org \n", - "1000 89 +44-645-14-4280-31 \n", - "1001 219 dan navarro \n", - "1002 81 mephimle.com/shine-papeleria/ \n", - "1003 21 facartes.uniandes.edu.co \n", - "... ... ... \n", - "1994 70 6114 mueschke road \n", - "1995 246 2762 valmora dr \n", - "1996 107 1481 carson williams \n", - "1997 153 6401 west pleasant lake drive \n", - "1998 196 230 west 84th lane \n", - "\n", - "[1000 rows x 5 columns]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sample_id = 105143404\n", - "sample = meta_data.loc[meta_data[\"file_id\"]==sample_id]\n", - "sample" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "saved_df = pq.read_table(file_path, columns= ['sequence_id'] + FEATURE_COLUMNS).to_pandas()\n", - "saved_df" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/signa2text/development/dev.ipynb b/signa2text/development/dev.ipynb deleted file mode 100644 index 27c00cb4..00000000 --- a/signa2text/development/dev.ipynb +++ /dev/null @@ -1,123 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Random seed set as 42\n" - ] - } - ], - "source": [ - "import random\n", - "import os\n", - "import torch\n", - "import torch.nn as nn\n", - "import numpy as np\n", - "from torchprofile import profile_macs\n", - "\n", - "def set_seed(seed: int = 42) -> None:\n", - " np.random.seed(seed)\n", - " random.seed(seed)\n", - " torch.manual_seed(seed)\n", - " torch.cuda.manual_seed(seed)\n", - " # When running on the CuDNN backend, two further options must be set\n", - " torch.backends.cudnn.deterministic = True\n", - " torch.backends.cudnn.benchmark = False\n", - " # Set a fixed value for the hash seed\n", - " os.environ[\"PYTHONHASHSEED\"] = str(seed)\n", - " print(f\"Random seed set as {seed}\")\n", - "\n", - "set_seed()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "sample_sentence_token = [60,51,39,40,50,0,40,50,0,32,0,51,36,50,51,0,49,52,45,61]\n", - "# Padding the token\n", - "sample_sentence_token = sample_sentence_token + ([59] * (64 - len(sample_sentence_token)))\n", - "sample_sentence_token = torch.tensor(sample_sentence_token)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "64" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(sample_sentence_token)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([60, 51, 39, 40, 50, 0, 40, 50, 0, 32, 0, 51, 36, 50, 51, 0, 49, 52,\n", - " 45, 61, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59,\n", - " 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59,\n", - " 59, 59, 59, 59, 59, 59, 59, 59, 59, 59])" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sample_sentence_token" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "app_title-D-s_o9_K-py3.10", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/signa2text/development/trans_dev.ipynb b/signa2text/development/trans_dev.ipynb deleted file mode 100644 index e2c673c6..00000000 --- a/signa2text/development/trans_dev.ipynb +++ /dev/null @@ -1,697 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Random seed set as 42\n" - ] - } - ], - "source": [ - "import random\n", - "import os\n", - "import torch\n", - "import torch.nn as nn\n", - "import numpy as np\n", - "from loguru import logger\n", - "from torchprofile import profile_macs\n", - "\n", - "def set_seed(seed: int = 42) -> None:\n", - " np.random.seed(seed)\n", - " random.seed(seed)\n", - " torch.manual_seed(seed)\n", - " torch.cuda.manual_seed(seed)\n", - " # When running on the CuDNN backend, two further options must be set\n", - " torch.backends.cudnn.deterministic = True\n", - " torch.backends.cudnn.benchmark = False\n", - " # Set a fixed value for the hash seed\n", - " os.environ[\"PYTHONHASHSEED\"] = str(seed)\n", - " print(f\"Random seed set as {seed}\")\n", - "\n", - "set_seed()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "Baseline Transformer Module\n", - "\n", - "This module contains the implementation of a Transformer model for sign language tasks.\n", - "\n", - "Classes:\n", - "- TokenEmbedding: Create embedding for the target seqeunce\n", - "- LandmarkEmbedding: Create embedding for the source(frames)seqeunce\n", - "- Encoder: Implements the transformer encoder stack.\n", - "- Decoder: Implements the transformer decoder stack.\n", - "- Transformer: The main transformer model class with methods for training and inference.\n", - "\n", - "Methods:\n", - "- Transformer.generate: Perform inference on a new sequence\n", - "\"\"\"\n", - "import torch\n", - "from torch import nn\n", - "\n", - "\n", - "class TokenEmbedding(nn.Module):\n", - " \"\"\"Embed the tokens with postion encoding\"\"\"\n", - "\n", - " def __init__(self, num_vocab, maxlen, embedding_dim):\n", - " \"\"\"_summary_\n", - "\n", - " Parameters\n", - " ----------\n", - " num_vocab : int\n", - " number of vocabulary\n", - " maxlen : int\n", - " maximuin length of sequence\n", - " embedding_dim : int\n", - " embedding output dimension\n", - " \"\"\"\n", - " super().__init__()\n", - " self.token_embed_layer = nn.Embedding(num_vocab, embedding_dim)\n", - " self.postion_embed_layer = nn.Embedding(maxlen, embedding_dim)\n", - "\n", - " def forward(self, x):\n", - " \"\"\"_summary_\n", - "\n", - " Parameters\n", - " ----------\n", - " x : tensors\n", - " _description_\n", - "\n", - " Returns\n", - " -------\n", - " tensors\n", - " _description_\n", - " \"\"\"\n", - " maxlen = x.size(-1)\n", - " x = self.token_embed_layer(x)\n", - " positions = torch.arange(0, maxlen).to(x.device)\n", - " positions = self.postion_embed_layer(positions)\n", - " return x + positions\n", - "\n", - "\n", - "class LandmarkEmbedding(nn.Module):\n", - " \"\"\"_summary_\"\"\"\n", - "\n", - " def __init__(self, embedding_dim):\n", - " super().__init__()\n", - " # Calculate the padding for \"same\" padding\n", - " padding = (11 - 1) // 2\n", - "\n", - " # Define three 1D convolutional layers with ReLU activation and stride 2\n", - " self.conv1 = nn.Conv1d(\n", - " in_channels=1, out_channels=64, kernel_size=11, stride=2, padding=padding\n", - " )\n", - " self.conv2 = nn.Conv1d(\n", - " in_channels=64, out_channels=128, kernel_size=11, stride=2, padding=padding\n", - " )\n", - " self.conv3 = nn.Conv1d(\n", - " in_channels=128, out_channels=256, kernel_size=11, stride=2, padding=padding\n", - " )\n", - "\n", - " # Output embedding layer\n", - " self.embedding_layer = nn.Linear(256, embedding_dim)\n", - "\n", - " def forward(self, x):\n", - " # Input x should have shape (batch_size, input_size, input_dim)\n", - " x = x.unsqueeze(1) # Add a channel dimension for 1D convolution\n", - "\n", - " # Apply convolutional layers with ReLU activation and stride 2\n", - " x = torch.relu(self.conv1(x))\n", - " x = torch.relu(self.conv2(x))\n", - " x = torch.relu(self.conv3(x))\n", - "\n", - " # Global average pooling to reduce spatial dimensions\n", - " x = torch.mean(x, dim=2)\n", - "\n", - " # Apply the linear embedding layer\n", - " x = self.embedding_layer(x)\n", - "\n", - " return x\n", - "\n", - "\n", - "class TransformerEncoder(nn.Module):\n", - " \"\"\"_summary_\"\"\"\n", - "\n", - " def __init__(\n", - " self,\n", - " embedding_dim,\n", - " num_heads,\n", - " feed_forward_dim,\n", - " rate=0.1,\n", - " ):\n", - " \"\"\"_summary_\n", - "\n", - " Parameters\n", - " ----------\n", - " embedding_dim : _type_\n", - " _description_\n", - " num_heads : _type_\n", - " _description_\n", - " feed_forward_dim : _type_\n", - " _description_\n", - " rate : float, optional\n", - " _description_, by default 0.1\n", - " \"\"\"\n", - " super().__init__()\n", - " self.multi_attention = nn.MultiheadAttention(embedding_dim, num_heads)\n", - " self.ffn = nn.Sequential(\n", - " nn.Linear(embedding_dim, feed_forward_dim),\n", - " nn.ReLU(),\n", - " nn.Linear(feed_forward_dim, embedding_dim),\n", - " )\n", - "\n", - " self.layernorm1 = nn.LayerNorm(embedding_dim, eps=1e-6)\n", - " self.layernorm2 = nn.LayerNorm(embedding_dim, eps=1e-6)\n", - " self.dropout1 = nn.Dropout(rate)\n", - " self.dropout2 = nn.Dropout(rate)\n", - "\n", - " def forward(self, inputs_x):\n", - " multi_attention_out, _ = self.multi_attention(inputs_x, inputs_x, inputs_x)\n", - " multi_attention_out = self.dropout1(multi_attention_out)\n", - " out1 = self.layernorm1(inputs_x + multi_attention_out)\n", - "\n", - " ffn_out = self.ffn(out1)\n", - " ffn_out = self.dropout2(ffn_out)\n", - " x = self.layernorm2(out1 + ffn_out)\n", - " return x\n", - "\n", - "\n", - "class TransformerDecoder(nn.Module):\n", - " \"\"\"_summary_\"\"\"\n", - "\n", - " def __init__(self, embedding_dim, num_heads, feed_forward_dim, dropout_rate=0.1):\n", - " super().__init__()\n", - " self.num_heads_ = num_heads\n", - " self.layernorm1 = nn.LayerNorm(embedding_dim, eps=1e-6)\n", - " self.layernorm2 = nn.LayerNorm(embedding_dim, eps=1e-6)\n", - " self.layernorm3 = nn.LayerNorm(embedding_dim, eps=1e-6)\n", - " self.decoder_multi_attention = nn.MultiheadAttention(embedding_dim, num_heads)\n", - " self.encoder_multi_attention = nn.MultiheadAttention(embedding_dim, num_heads)\n", - " self.decoder_dropout = nn.Dropout(0.5)\n", - " self.encoder_dropout = nn.Dropout(dropout_rate)\n", - " self.ffn_dropout = nn.Dropout(dropout_rate)\n", - " self.ffn = nn.Sequential(\n", - " nn.Linear(embedding_dim, feed_forward_dim),\n", - " nn.ReLU(),\n", - " nn.Linear(feed_forward_dim, embedding_dim),\n", - " )\n", - "\n", - " def _causal_attention_mask(self, sequence_length, batch_size=1, device=None):\n", - " mask = torch.triu(torch.ones(sequence_length, sequence_length), diagonal=1).to(\n", - " device\n", - " )\n", - " mask = mask.unsqueeze(0).expand(\n", - " batch_size * self.num_heads_, sequence_length, sequence_length\n", - " )\n", - " return mask\n", - "\n", - " def forward(\n", - " self,\n", - " encoder_out,\n", - " src_target_,\n", - " ):\n", - " input_shape = src_target_.size()\n", - " batch_size = 1 # input_shape[0]\n", - " seq_len = input_shape[0]\n", - " x_device = src_target_.device\n", - "\n", - " # Mask\n", - " causal_mask = self._causal_attention_mask(\n", - " sequence_length=seq_len, batch_size=batch_size, device=x_device\n", - " )\n", - "\n", - " target_att, _ = self.decoder_multi_attention(\n", - " src_target_, src_target_, src_target_, attn_mask=causal_mask\n", - " )\n", - " target_norm_out = self.layernorm1(\n", - " src_target_ + self.decoder_dropout(target_att)\n", - " )\n", - "\n", - " encoder_out, _ = self.encoder_multi_attention(\n", - " target_norm_out, encoder_out, encoder_out\n", - " )\n", - " enc_out_norm = self.layernorm2(encoder_out + self.encoder_dropout(encoder_out))\n", - "\n", - " ffn_out = self.ffn(enc_out_norm)\n", - " ffn_out_norm = self.layernorm3(enc_out_norm + self.ffn_dropout(ffn_out))\n", - " return ffn_out_norm\n", - "\n", - "\n", - "class ASLTransformer(nn.Module):\n", - " def __init__(\n", - " self,\n", - " num_hidden_dim=64,\n", - " multi_num_head=8,\n", - " num_feed_forward=128,\n", - " target_maxlen=64,\n", - " num_layers_enc=4,\n", - " num_layers_dec=4,\n", - " ):\n", - " \"\"\"_summary_\n", - "\n", - " Parameters\n", - " ----------\n", - " num_hidden_dim : int, optional\n", - " _description_, by default 64\n", - " multi_num_head : int, optional\n", - " _description_, by default 8\n", - " num_feed_forward : int, optional\n", - " _description_, by default 128\n", - " target_maxlen : int, optional\n", - " _description_, by default 64\n", - " num_layers_enc : int, optional\n", - " _description_, by default 4\n", - " num_layers_dec : int, optional\n", - " _description_, by default 4\n", - " \"\"\"\n", - " super().__init__()\n", - " self.num_layers_enc = num_layers_enc\n", - " self.num_layers_dec = num_layers_dec\n", - " self.target_maxlen = target_maxlen\n", - " self.num_classes = 62\n", - "\n", - " self.encoder_input = LandmarkEmbedding(embedding_dim=num_hidden_dim)\n", - " self.decoder_input = TokenEmbedding(\n", - " num_vocab=self.num_classes,\n", - " embedding_dim=num_hidden_dim,\n", - " maxlen=target_maxlen,\n", - " )\n", - "\n", - " self.encoder = nn.Sequential(\n", - " self.encoder_input,\n", - " *[\n", - " TransformerEncoder(\n", - " embedding_dim=num_hidden_dim,\n", - " num_heads=multi_num_head,\n", - " feed_forward_dim=num_feed_forward,\n", - " )\n", - " for _ in range(num_layers_enc)\n", - " ],\n", - " )\n", - "\n", - " for i in range(num_layers_dec):\n", - " self.add_module(\n", - " f\"decoder_layer_{i}\",\n", - " TransformerDecoder(\n", - " embedding_dim=num_hidden_dim,\n", - " num_heads=multi_num_head,\n", - " feed_forward_dim=num_feed_forward,\n", - " ),\n", - " )\n", - "\n", - " self.classifier = nn.Linear(\n", - " in_features=num_hidden_dim, out_features=self.num_classes\n", - " )\n", - "\n", - " def _decoder_run(self, enc_out, target):\n", - " decoder_out = self.decoder_input(target)\n", - " for i in range(self.num_layers_dec):\n", - " decoder_out = getattr(self, f\"decoder_layer_{i}\")(enc_out, decoder_out)\n", - " return decoder_out\n", - "\n", - " def forward(self, source, target):\n", - " if len(source.shape) == 2: # Check if single input\n", - " source = source.unsqueeze(0) # Add batch dimension\n", - " if len(target.shape) == 1: # Check if single input\n", - " target = target.unsqueeze(0) # Add batch dimension\n", - "\n", - " encoder_out = self.encoder(source)\n", - " transformer_output = self._decoder_run(encoder_out, target)\n", - " return self.classifier(transformer_output)\n", - "\n", - " def generate(self, source, target_start_token_idx=60):\n", - " if len(source.shape) == 2: # Check if single input\n", - " source = source.unsqueeze(0) # Add batch dimension\n", - "\n", - " encoder_out = self.encoder(source)\n", - " decoder_input = (\n", - " torch.ones((source.shape[0], 1), dtype=torch.long)\n", - " .to(source.device)\n", - " * target_start_token_idx\n", - " )\n", - " dec_logits = []\n", - "\n", - " for _ in range(self.target_maxlen - 1):\n", - " decoder_out = self._decoder_run(encoder_out, decoder_input)\n", - " logits = self.classifier(decoder_out)\n", - "\n", - " logits = torch.argmax(logits, dim=-1, keepdim=True)\n", - " last_logit = logits[:, -1]\n", - " dec_logits.append(last_logit)\n", - " decoder_input = torch.cat([decoder_input, last_logit], dim=-1)\n", - "\n", - " return decoder_input.squeeze(0) if len(source.shape) == 2 else decoder_input" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m2023-12-11 03:21:41.460\u001b[0m | \u001b[31m\u001b[1mERROR \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m\u001b[0m:\u001b[36m24\u001b[0m - \u001b[31m\u001b[1m ERROR Message ==> Expected 2D (unbatched) or 3D (batched) input to conv1d, but got input of size: [1, 1, 128, 345]\u001b[0m\n", - "\u001b[33m\u001b[1mTraceback (most recent call last):\u001b[0m\n", - "\n", - " File \"\", line 198, in _run_module_as_main\n", - " File \"\", line 88, in _run_code\n", - " File \"c:\\Users\\Yinka\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\ipykernel_launcher.py\", line 17, in \n", - " app.launch_new_instance()\n", - " │ └ >\n", - " └ \n", - " └ \n", - " File \"c:\\Users\\Yinka\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 724, in start\n", - " self.io_loop.start()\n", - " │ │ └ \n", - " │ └ \n", - " └ \n", - " File \"c:\\Users\\Yinka\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 215, in start\n", - " self.asyncio_loop.run_forever()\n", - " │ │ └ \n", - " │ └ <_WindowsSelectorEventLoop running=True closed=False debug=False>\n", - " └ \n", - " File \"c:\\Users\\Yinka\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\asyncio\\base_events.py\", line 607, in run_forever\n", - " self._run_once()\n", - " │ └ \n", - " └ <_WindowsSelectorEventLoop running=True closed=False debug=False>\n", - " File \"c:\\Users\\Yinka\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\asyncio\\base_events.py\", line 1922, in _run_once\n", - " handle._run()\n", - " │ └ \n", - " └ , ...],))>)>\n", - " File \"c:\\Users\\Yinka\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\asyncio\\events.py\", line 80, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " │ │ │ │ │ └ \n", - " │ │ │ │ └ , ...],))>)>\n", - " │ │ │ └ \n", - " │ │ └ , ...],))>)>\n", - " │ └ \n", - " └ , ...],))>)>\n", - " File \"c:\\Users\\Yinka\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 512, in dispatch_queue\n", - " await self.process_one()\n", - " │ └ \n", - " └ \n", - " File \"c:\\Users\\Yinka\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 501, in process_one\n", - " await dispatch(*args)\n", - " │ └ ([, , >\n", - " File \"c:\\Users\\Yinka\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 408, in dispatch_shell\n", - " await result\n", - " └ \n", - " File \"c:\\Users\\Yinka\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 731, in execute_request\n", - " reply_content = await reply_content\n", - " └ \n", - " File \"c:\\Users\\Yinka\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 417, in do_execute\n", - " res = shell.run_cell(\n", - " │ └ \n", - " └ \n", - " File \"c:\\Users\\Yinka\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 540, in run_cell\n", - " return super().run_cell(*args, **kwargs)\n", - " │ └ {'store_history': True, 'silent': False, 'cell_id': 'vscode-notebook-cell:/c%3A/Main%20Workspace/Cohort8-Ransom-Kuti-Ladipo/l...\n", - " └ ('# Create a sample input\\nbatch_source_sequence = torch.randn(2, 128, 345) # Sample source sequence (batch_size, maxlen, nu...\n", - " File \"c:\\Users\\Yinka\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2945, in run_cell\n", - " result = self._run_cell(\n", - " │ └ \n", - " └ \n", - " File \"c:\\Users\\Yinka\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3000, in _run_cell\n", - " return runner(coro)\n", - " │ └ \n", - " └ \n", - " File \"c:\\Users\\Yinka\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 129, in _pseudo_sync_runner\n", - " coro.send(None)\n", - " │ └ \n", - " └ \n", - " File \"c:\\Users\\Yinka\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3203, in run_cell_async\n", - " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", - " │ │ │ │ └ 'C:\\\\Users\\\\Yinka\\\\AppData\\\\Local\\\\Temp\\\\ipykernel_3812\\\\4012752373.py'\n", - " │ │ │ └ [, , \n", - " │ └ \n", - " └ \n", - " File \"c:\\Users\\Yinka\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3382, in run_ast_nodes\n", - " if await self.run_code(code, result, async_=asy):\n", - " │ │ │ │ └ False\n", - " │ │ │ └ at 0x0000017533D041B0, file \"C:\\Users\\Yinka\\AppData\\Local\\Temp\\ipykernel_3812\\4012752373.py\", line 1>\n", - " │ └ \n", - " └ \n", - " File \"c:\\Users\\Yinka\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3442, in run_code\n", - " exec(code_obj, self.user_global_ns, self.user_ns)\n", - " │ │ │ │ └ {'__name__': '__main__', '__doc__': '\\nBaseline Transformer Module\\n\\nThis module contains the implementation of a Transforme...\n", - " │ │ │ └ \n", - " │ │ └ \n", - " │ └ \n", - " └ at 0x0000017533D041B0, file \"C:\\Users\\Yinka\\AppData\\Local\\Temp\\ipykernel_3812\\4012752373.py\", line 1>\n", - "\n", - "> File \"\u001b[32mC:\\Users\\Yinka\\AppData\\Local\\Temp\\ipykernel_3812\\\u001b[0m\u001b[32m\u001b[1m4012752373.py\u001b[0m\", line \u001b[33m18\u001b[0m, in \u001b[35m\u001b[0m\n", - " \u001b[1mpredictions\u001b[0m \u001b[35m\u001b[1m=\u001b[0m \u001b[1mtransformer_model\u001b[0m\u001b[1m(\u001b[0m\u001b[1msingle_src_seq\u001b[0m\u001b[1m,\u001b[0m \u001b[1msingle_trg_seq\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m │ │ └ \u001b[0m\u001b[36m\u001b[1mtensor([53, 3, 20, 29, 50, 25, 29, 13, 15, 32, 10, 15, 32, 36, 23, 50, 53, 5,\u001b[0m\n", - " \u001b[36m │ │ \u001b[0m\u001b[36m\u001b[1m 43, 37, 41, 58, 30, 12, 6, 40, 31, 4...\u001b[0m\n", - " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1mtensor([[0.4061, 0.6164, 0.6337, ..., 0.5066, 0.4483, 0.4846],\u001b[0m\n", - " \u001b[36m │ \u001b[0m\u001b[36m\u001b[1m [0.0074, 0.1099, 0.6021, ..., 0.5536, 0.7102, 0.4944...\u001b[0m\n", - " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1mASLTransformer(\u001b[0m\n", - " \u001b[36m \u001b[0m\u001b[36m\u001b[1m (encoder_input): LandmarkEmbedding(\u001b[0m\n", - " \u001b[36m \u001b[0m\u001b[36m\u001b[1m (conv1): Conv1d(1, 64, kernel_size=(11,), stride=(2,), padding=(5,)...\u001b[0m\n", - "\n", - " File \"c:\\Users\\Yinka\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1501, in _call_impl\n", - " return forward_call(*args, **kwargs)\n", - " │ │ └ {}\n", - " │ └ (tensor([[0.4061, 0.6164, 0.6337, ..., 0.5066, 0.4483, 0.4846],\n", - " │ [0.0074, 0.1099, 0.6021, ..., 0.5536, 0.7102, 0.494...\n", - " └ \u001b[0m\n", - " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"c:\\Users\\Yinka\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1501, in _call_impl\n", - " return forward_call(*args, **kwargs)\n", - " │ │ └ {}\n", - " │ └ (tensor([[[[0.4061, 0.6164, 0.6337, ..., 0.5066, 0.4483, 0.4846],\n", - " │ [0.0074, 0.1099, 0.6021, ..., 0.5536, 0.7102, 0...\n", - " └ \n", - " File \"c:\\Users\\Yinka\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\torch\\nn\\modules\\conv.py\", line 313, in forward\n", - " return self._conv_forward(input, self.weight, self.bias)\n", - " │ │ │ │ └ Conv1d(1, 64, kernel_size=(11,), stride=(2,), padding=(5,))\n", - " │ │ │ └ Conv1d(1, 64, kernel_size=(11,), stride=(2,), padding=(5,))\n", - " │ │ └ tensor([[[[0.4061, 0.6164, 0.6337, ..., 0.5066, 0.4483, 0.4846],\n", - " │ │ [0.0074, 0.1099, 0.6021, ..., 0.5536, 0.7102, 0....\n", - " │ └ \n", - " └ Conv1d(1, 64, kernel_size=(11,), stride=(2,), padding=(5,))\n", - " File \"c:\\Users\\Yinka\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\torch\\nn\\modules\\conv.py\", line 309, in _conv_forward\n", - " return F.conv1d(input, weight, bias, self.stride,\n", - " │ │ │ │ │ │ └ (2,)\n", - " │ │ │ │ │ └ Conv1d(1, 64, kernel_size=(11,), stride=(2,), padding=(5,))\n", - " │ │ │ │ └ Parameter containing:\n", - " │ │ │ │ tensor([-0.1697, -0.2340, -0.0869, 0.0877, -0.2328, 0.2527, -0.2036, 0.0177,\n", - " │ │ │ │ -0.2673, -0.069...\n", - " │ │ │ └ Parameter containing:\n", - " │ │ │ tensor([[[-0.1422, -0.0277, 0.0092, -0.0871, 0.2008, -0.2052, -0.2548,\n", - " │ │ │ 0.0125, 0.0531, 0...\n", - " │ │ └ tensor([[[[0.4061, 0.6164, 0.6337, ..., 0.5066, 0.4483, 0.4846],\n", - " │ │ [0.0074, 0.1099, 0.6021, ..., 0.5536, 0.7102, 0....\n", - " │ └ \n", - " └ {error}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "torch.Size([128, 346])\n" - ] - } - ], - "source": [ - "import torch\n", - "\n", - "# Your original sequence tensor\n", - "original_tensor = torch.randn(118, 346)\n", - "\n", - "# Define the desired output shape\n", - "desired_shape = (128, 346)\n", - "\n", - "# Calculate the padding on the first dimension from the bottom\n", - "padding_bottom = max(0, desired_shape[0] - original_tensor.size(0))\n", - "\n", - "# Pad the tensor along the first dimension from the bottom\n", - "padded_tensor = torch.nn.functional.pad(original_tensor, (0, 0, 0, padding_bottom))\n", - "\n", - "# Now, padded_tensor has the shape (128, 346)\n", - "print(padded_tensor.shape)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[ 0.0490, 1.5782, -0.0793, -0.8889, -0.6999],\n", - " [ 0.3881, 1.1002, -0.7594, -1.0423, 1.1450],\n", - " [ 2.1911, 0.6852, 0.7096, -1.1343, -0.3205]])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "original_tensor[115:,:5]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[ 0.0490, 1.5782, -0.0793, -0.8889, -0.6999],\n", - " [ 0.3881, 1.1002, -0.7594, -1.0423, 1.1450],\n", - " [ 2.1911, 0.6852, 0.7096, -1.1343, -0.3205],\n", - " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n", - " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n", - " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n", - " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n", - " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n", - " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n", - " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n", - " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n", - " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n", - " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]])" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "padded_tensor[115:,:5]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/signa2text/requirements.txt b/signa2text/requirements.txt new file mode 100644 index 00000000..35565b39 --- /dev/null +++ b/signa2text/requirements.txt @@ -0,0 +1,94 @@ +aiohttp==3.9.3 +aiosignal==1.3.1 +antlr4-python3-runtime==4.9.3 +appdirs==1.4.4 +asttokens==2.4.1 +attrs==23.2.0 +bleach==6.1.0 +certifi==2024.2.2 +charset-normalizer==3.3.2 +click==8.1.7 +comm==0.2.2 +debugpy==1.8.1 +decorator==5.1.1 +docker-pycreds==0.4.0 +executing==2.0.1 +filelock==3.13.1 +frozenlist==1.4.1 +fsspec==2024.3.0 +gitdb==4.0.11 +GitPython==3.1.42 +hydra-core==1.3.2 +idna==3.6 +iniconfig==2.0.0 +ipykernel==6.29.3 +ipython==8.22.2 +jedi==0.19.1 +Jinja2==3.1.3 +jupyter_client==8.6.1 +jupyter_core==5.7.2 +kaggle==1.6.6 +lightning==2.2.1 +lightning-utilities==0.10.1 +loguru==0.7.2 +MarkupSafe==2.1.5 +matplotlib-inline==0.1.6 +mpmath==1.3.0 +multidict==6.0.5 +nest-asyncio==1.6.0 +networkx==3.2.1 +numpy==1.26.4 +nvidia-cublas-cu12==12.1.3.1 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cudnn-cu12==8.9.2.26 +nvidia-cufft-cu12==11.0.2.54 +nvidia-curand-cu12==10.3.2.106 +nvidia-cusolver-cu12==11.4.5.107 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-nccl-cu12==2.19.3 +nvidia-nvjitlink-cu12==12.4.99 +nvidia-nvtx-cu12==12.1.105 +omegaconf==2.3.0 +packaging==24.0 +pandas==2.2.1 +parso==0.8.3 +pexpect==4.9.0 +platformdirs==4.2.0 +pluggy==1.4.0 +prompt-toolkit==3.0.43 +protobuf==4.25.3 +psutil==5.9.8 +ptyprocess==0.7.0 +pure-eval==0.2.2 +pyarrow==15.0.1 +Pygments==2.17.2 +pytest==8.1.1 +python-dateutil==2.9.0.post0 +python-slugify==8.0.4 +pytorch-lightning==2.2.1 +pytz==2024.1 +PyYAML==6.0.1 +pyzmq==25.1.2 +requests==2.31.0 +sentry-sdk==1.42.0 +setproctitle==1.3.3 +six==1.16.0 +smmap==5.0.1 +stack-data==0.6.3 +sympy==1.12 +text-unidecode==1.3 +torch==2.2.1 +torchmetrics==1.3.1 +tornado==6.4 +tqdm==4.66.2 +traitlets==5.14.2 +triton==2.2.0 +typing_extensions==4.10.0 +tzdata==2024.1 +urllib3==2.2.1 +wandb==0.16.4 +wcwidth==0.2.13 +webencodings==0.5.1 +yarl==1.9.4 diff --git a/set_environment_variables_template.sh b/signa2text/set_environment_variables_template.sh similarity index 93% rename from set_environment_variables_template.sh rename to signa2text/set_environment_variables_template.sh index 0061c475..1f07a6ef 100644 --- a/set_environment_variables_template.sh +++ b/signa2text/set_environment_variables_template.sh @@ -1,4 +1,4 @@ -#!/bin/bash +#!/usr/bin/env bash # replace placeholders and rename this file to `set_environment_variables.sh` #replace with Kaggle username and key diff --git a/signa2text/src/config.py b/signa2text/src/config.py index 68763213..aec73b98 100644 --- a/signa2text/src/config.py +++ b/signa2text/src/config.py @@ -1,18 +1,6 @@ """doc """ -from pydantic import BaseModel -import hydra +# from pydantic import BaseModel - -class Arg_type(BaseModel): - save_every: int - - -@hydra.main(config_name="train", config_path="config", version_base="1.2") -def main(cfg): - A = cfg.wandb_params.model_run_id - print(type(A)) - - -main() +PROJECT_NAME = "NSL_2_AUDIO" diff --git a/signa2text/src/dev.ipynb b/signa2text/src/dev.ipynb deleted file mode 100644 index f6cf6d6d..00000000 --- a/signa2text/src/dev.ipynb +++ /dev/null @@ -1,1288 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/workspace/.pyenv_mirror/user/current/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n", - "/workspace/.pyenv_mirror/user/current/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/accelerator_connector.py:552: You passed `Trainer(accelerator='cpu', precision='16-mixed')` but AMP with fp16 is not supported on CPU. Using `precision='bf16-mixed'` instead.\n", - "Using bfloat16 Automatic Mixed Precision (AMP)\n", - "GPU available: False, used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n" - ] - } - ], - "source": [ - "import os\n", - "import torch\n", - "from torch import nn\n", - "import torch.nn.functional as F\n", - "from torch.utils.data import DataLoader\n", - "import lightning as L\n", - "from trainer import LitModule, wandb_logger, profiler" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "os.getenv(\"WANDB_API_KEY\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def check_model_w(model):\n", - " # Access the model's state dictionary\n", - " state_dict = model.state_dict()\n", - "\n", - " # Print the keys and shapes of the model's parameters\n", - " for key, value in state_dict.items():\n", - " print(f\"Parameter: {key}, Size: {value.size()}\")\n", - "\n", - " # Alternatively, you can directly print the state_dict\n", - " print(state_dict)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "class Encoder(nn.Module):\n", - " def __init__(self):\n", - " super().__init__()\n", - " self.l1 = nn.Sequential(nn.Linear(28 * 28, 64), nn.ReLU(), nn.Linear(64, 3))\n", - "\n", - " def forward(self, x):\n", - " return self.l1(x)\n", - "\n", - "\n", - "class Decoder(nn.Module):\n", - " def __init__(self):\n", - " super().__init__()\n", - " self.l1 = nn.Sequential(nn.Linear(3, 64), nn.ReLU(), nn.Linear(64, 28 * 28))\n", - "\n", - " def forward(self, x):\n", - " return self.l1(x)\n", - "\n", - "\n", - "class TESTAUTOENCODER(nn.Module):\n", - " def __init__(self):\n", - " super().__init__()\n", - " self.encoder = Encoder()\n", - " self.decoder = Decoder()\n", - "\n", - " def forward(self, x, y):\n", - " z = self.encoder(x)\n", - " x_hat = self.decoder(z)\n", - " return x_hat" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch 1:\n", - "Input shape: torch.Size([64, 784])\n", - "Target shape: torch.Size([64, 784])\n" - ] - } - ], - "source": [ - "import torch\n", - "from torch.utils.data import Dataset, DataLoader\n", - "import numpy as np\n", - "import torch.nn as nn\n", - "\n", - "class CustomDataset(Dataset):\n", - " def __init__(self, data):\n", - " self.data = data\n", - " \n", - " def __len__(self):\n", - " return len(self.data)\n", - " \n", - " def __getitem__(self, idx):\n", - " sample = self.data[idx]\n", - " return sample.view(-1), sample.view(-1) # Reshape to match the input and output size\n", - "\n", - "# Generate sample data\n", - "num_samples = 1000\n", - "input_dim = 28 * 28 # Adjusted to match the input size of the Encoder\n", - "\n", - "# Create synthetic data (random numbers)\n", - "data = torch.randn(num_samples, input_dim)\n", - "\n", - "# Create an instance of the CustomDataset\n", - "dataset = CustomDataset(data)\n", - "\n", - "# Define batch size for DataLoader\n", - "batch_size = 64\n", - "\n", - "# Create a DataLoader\n", - "train_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)\n", - "\n", - "# Test the DataLoader by iterating through a few batches\n", - "for batch_idx, (inputs, targets) in enumerate(train_loader):\n", - " print(f\"Batch {batch_idx + 1}:\")\n", - " print(\"Input shape:\", inputs.shape)\n", - " print(\"Target shape:\", targets.shape)\n", - " break # Stop after the first batch for demonstration\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Parameter: encoder.l1.0.weight, Size: torch.Size([64, 784])\n", - "Parameter: encoder.l1.0.bias, Size: torch.Size([64])\n", - "Parameter: encoder.l1.2.weight, Size: torch.Size([3, 64])\n", - "Parameter: encoder.l1.2.bias, Size: torch.Size([3])\n", - "Parameter: decoder.l1.0.weight, Size: torch.Size([64, 3])\n", - "Parameter: decoder.l1.0.bias, Size: torch.Size([64])\n", - "Parameter: decoder.l1.2.weight, Size: torch.Size([784, 64])\n", - "Parameter: decoder.l1.2.bias, Size: torch.Size([784])\n", - "OrderedDict([('encoder.l1.0.weight', tensor([[-0.0161, 0.0290, -0.0210, ..., 0.0026, 0.0268, 0.0169],\n", - " [-0.0016, -0.0153, -0.0268, ..., 0.0312, 0.0130, -0.0154],\n", - " [ 0.0150, -0.0332, 0.0200, ..., 0.0074, 0.0344, -0.0218],\n", - " ...,\n", - " [-0.0328, 0.0232, 0.0154, ..., -0.0357, 0.0335, -0.0088],\n", - " [-0.0296, -0.0015, -0.0194, ..., 0.0030, 0.0158, 0.0228],\n", - " [ 0.0085, 0.0144, -0.0004, ..., -0.0089, 0.0054, 0.0033]])), ('encoder.l1.0.bias', tensor([ 0.0029, -0.0243, 0.0221, -0.0050, -0.0082, -0.0077, 0.0009, 0.0130,\n", - " -0.0181, -0.0223, 0.0344, 0.0262, 0.0338, -0.0156, -0.0088, -0.0090,\n", - " 0.0106, -0.0137, 0.0143, 0.0227, 0.0113, -0.0217, -0.0056, 0.0225,\n", - " 0.0180, -0.0205, 0.0010, -0.0065, -0.0351, 0.0092, -0.0330, 0.0156,\n", - 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] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mrileydrizzy\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n" - ] - }, - { - "data": { - "text/html": [ - "wandb version 0.16.3 is available! To upgrade, please run:\n", - " $ pip install wandb --upgrade" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Tracking run with wandb version 0.15.12" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Run data is saved locally in ./wandb/run-20240218_152929-787okxtp" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Resuming run festive-envelope-13 to Weights & Biases (docs)
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - " View project at https://wandb.ai/rileydrizzy/NSL_2_AUDIO" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - " View run at https://wandb.ai/rileydrizzy/NSL_2_AUDIO/runs/787okxtp" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[34m\u001b[1mwandb\u001b[0m: logging graph, to disable use `wandb.watch(log_graph=False)`\n", - "/workspace/.pyenv_mirror/user/current/lib/python3.11/site-packages/lightning/pytorch/utilities/parsing.py:199: Attribute 'model' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['model'])`.\n" - ] - } - ], - "source": [ - "# model\n", - "MODEL_ = LitModule(model=test_model, loss_criterion=F.mse_loss, metric=None)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Parameter: model.encoder.l1.0.weight, Size: torch.Size([64, 784])\n", - "Parameter: model.encoder.l1.0.bias, Size: torch.Size([64])\n", - "Parameter: model.encoder.l1.2.weight, Size: torch.Size([3, 64])\n", - "Parameter: model.encoder.l1.2.bias, Size: torch.Size([3])\n", - "Parameter: model.decoder.l1.0.weight, Size: torch.Size([64, 3])\n", - "Parameter: model.decoder.l1.0.bias, Size: torch.Size([64])\n", - "Parameter: model.decoder.l1.2.weight, Size: torch.Size([784, 64])\n", - "Parameter: model.decoder.l1.2.bias, Size: torch.Size([784])\n", - "OrderedDict([('model.encoder.l1.0.weight', tensor([[-0.0161, 0.0290, -0.0210, ..., 0.0026, 0.0268, 0.0169],\n", - 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" 1.1706e-01, 8.1988e-02, 1.1969e-02, 3.6497e-02, 4.4288e-04,\n", - " 8.5908e-02, -1.9977e-03, -7.9481e-02, 1.0641e-01, -9.2023e-02,\n", - " 3.3674e-02, 5.0291e-04, -4.4374e-03, -4.6683e-02, 4.2323e-02,\n", - " -9.7228e-02, 1.0310e-01, 1.1294e-01, -2.3064e-02, -9.4661e-02,\n", - " -1.1938e-01, -2.7283e-02, -7.8253e-02, -5.0774e-02, -1.1842e-01,\n", - " -2.5792e-02, -1.2862e-03, -7.5456e-02, -4.9305e-02, 9.3443e-02,\n", - " 2.2346e-02, -2.3583e-02, -4.7395e-02, 5.6247e-02, -8.4862e-03,\n", - " 2.5871e-02, -1.1900e-01, -6.6600e-02, 9.8455e-02, 9.6956e-02,\n", - " 1.2230e-01, 6.0214e-02, -7.1027e-02, 5.8924e-02, 7.8913e-03,\n", - " 5.6636e-02, 3.5537e-02, 3.7864e-02, 6.6084e-02, 6.8382e-03,\n", - " -7.2687e-02, -7.6546e-02, -4.4388e-02, -3.7135e-02, -1.1852e-01,\n", - " -3.8720e-02, 2.5432e-02, 1.1149e-04, -1.1155e-01, -7.1943e-02,\n", - " 4.9687e-02, 1.1744e-01, -2.0265e-02, 1.1799e-02, 1.2332e-01,\n", - " 4.4423e-02, -1.0187e-01, -7.4852e-02, -7.9031e-03, -6.8050e-03,\n", - " 7.3423e-02, 9.5772e-02, 1.1727e-01, 5.9597e-02, -1.1710e-01,\n", - " 7.1536e-02, 1.1756e-01, 3.8345e-02, 4.8359e-02, 4.3071e-02,\n", - " 1.0686e-01, -6.6669e-02, -7.4686e-02, -4.7133e-02, -2.2593e-02,\n", - " 2.9394e-02, -3.4635e-02, -2.2354e-02, 7.7806e-02, 1.2047e-01,\n", - " -5.6874e-02, -7.3498e-02, -5.3431e-02, 9.3585e-02, -5.1219e-02,\n", - " 1.0976e-01, -9.3627e-02, -5.3812e-02, 1.1300e-01, 4.7074e-02,\n", - " -9.7930e-02, -5.7770e-02, 1.1041e-02, 8.9856e-02, 1.1482e-01,\n", - " -2.9930e-02, 8.2576e-02, 1.2270e-01, 7.9538e-03, 1.8046e-02,\n", - " 1.0287e-01, -1.0555e-01, -6.9573e-02, 3.9952e-02, 1.8373e-02,\n", - " 9.3662e-02, 8.5799e-02, 1.2417e-01, -7.8419e-02, -3.4493e-02,\n", - " -6.6691e-02, 7.6119e-03, 7.7165e-02, 6.9167e-02, -9.1499e-02,\n", - " 4.5646e-02, -7.2709e-03, -2.4126e-02, 3.1146e-02, -5.3018e-03,\n", - " 2.3287e-02, 6.4497e-02, -7.6322e-02, -7.5927e-02, -7.7227e-02,\n", - " -1.8314e-02, 6.7873e-03, -9.6870e-02, 1.1539e-01, -9.4570e-02,\n", - " -8.7918e-02, 3.1682e-02, -2.4789e-02, 9.9458e-02, -6.4607e-02,\n", - " -8.7402e-02, 1.1708e-01, -6.2988e-02, -6.2958e-03, -4.1805e-02,\n", - " 2.3832e-02, -1.0176e-01, -5.2356e-02, -5.2288e-02, 8.5669e-02,\n", - " 6.8505e-02, -6.3248e-02, -1.6879e-02, -2.7382e-02, -2.1840e-02,\n", - " 2.3431e-02, -7.9552e-02, -1.0208e-01, 2.8098e-02, -1.1157e-01,\n", - " -1.7213e-02, -8.4040e-02, -1.3586e-03, -1.1422e-01, -2.0420e-02,\n", - " -3.9404e-03, -2.7651e-02, 1.1988e-01, -6.9561e-03, -2.9447e-02,\n", - " -9.9871e-02, -1.2078e-01, 3.3731e-02, 2.8366e-02, 6.9814e-02,\n", - " -6.6926e-02, -4.5807e-02, -4.9858e-02, 8.2351e-02, 3.9172e-02,\n", - " -3.9391e-02, 1.1587e-01, 1.1197e-01, 6.9504e-02, -3.5825e-02,\n", - " -7.0754e-02, 7.0469e-02, -1.1818e-01, 7.9868e-02, 6.6377e-03,\n", - " -7.3947e-03, -7.9162e-03, 1.7170e-02, -4.2426e-02]))])\n" - ] - } - ], - "source": [ - "check_model_w(MODEL_)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/workspace/.pyenv_mirror/user/current/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/accelerator_connector.py:552: You passed `Trainer(accelerator='cpu', precision='16-mixed')` but AMP with fp16 is not supported on CPU. Using `precision='bf16-mixed'` instead.\n", - "Using bfloat16 Automatic Mixed Precision (AMP)\n", - "GPU available: False, used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "/workspace/.pyenv_mirror/user/current/lib/python3.11/site-packages/lightning/pytorch/utilities/parsing.py:44: attribute 'model' removed from hparams because it cannot be pickled\n", - "The following callbacks returned in `LightningModule.configure_callbacks` will override existing callbacks passed to Trainer: ModelCheckpoint\n", - "/workspace/.pyenv_mirror/user/current/lib/python3.11/site-packages/lightning/pytorch/callbacks/model_checkpoint.py:652: Checkpoint directory /workspace/NSL_2_AUDIO/signa2text/src/artifacts/test exists and is not empty.\n", - "\n", - " | Name | Type | Params\n", - "------------------------------------------\n", - "0 | model | TESTAUTOENCODER | 101 K \n", - "------------------------------------------\n", - "101 K Trainable params\n", - "0 Non-trainable params\n", - "101 K Total params\n", - "0.407 Total estimated model params size (MB)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " " - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/workspace/.pyenv_mirror/user/current/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:492: Your `val_dataloader`'s sampler has shuffling enabled, it is strongly recommended that you turn shuffling off for val/test dataloaders.\n", - "/workspace/.pyenv_mirror/user/current/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=15` in the `DataLoader` to improve performance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/workspace/.pyenv_mirror/user/current/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=15` in the `DataLoader` to improve performance.\n", - "/workspace/.pyenv_mirror/user/current/lib/python3.11/site-packages/lightning/pytorch/loops/fit_loop.py:298: The number of training batches (16) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 14: 100%|██████████| 16/16 [00:01<00:00, 10.84it/s, v_num=kxtp]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "`Trainer.fit` stopped: `max_epochs=15` reached.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 14: 100%|██████████| 16/16 [00:01<00:00, 10.82it/s, v_num=kxtp]\n" - ] - } - ], - "source": [ - "# train model\n", - "trainer = L.Trainer(\n", - " accelerator=\"auto\",\n", - " precision=\"16-mixed\",\n", - " logger=wandb_logger,\n", - " profiler=profiler,\n", - " strategy=\"auto\",\n", - " check_val_every_n_epoch=5,\n", - " max_epochs=15\n", - ")\n", - "\n", - "trainer.fit(\n", - " model=MODEL_,\n", - " train_dataloaders=train_loader,\n", - " val_dataloaders=train_loader, ckpt_path = None)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# BREAK" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Parameter: model.encoder.l1.0.weight, Size: torch.Size([64, 784])\n", - "Parameter: model.encoder.l1.0.bias, Size: torch.Size([64])\n", - "Parameter: model.encoder.l1.2.weight, Size: torch.Size([3, 64])\n", - "Parameter: model.encoder.l1.2.bias, Size: torch.Size([3])\n", - "Parameter: model.decoder.l1.0.weight, Size: torch.Size([64, 3])\n", - "Parameter: model.decoder.l1.0.bias, Size: torch.Size([64])\n", - "Parameter: model.decoder.l1.2.weight, Size: torch.Size([784, 64])\n", - "Parameter: model.decoder.l1.2.bias, Size: torch.Size([784])\n", - "OrderedDict([('model.encoder.l1.0.weight', tensor([[-0.0210, 0.0298, -0.0125, ..., 0.0038, 0.0356, 0.0230],\n", - " [-0.0032, -0.0128, -0.0298, ..., 0.0432, 0.0109, -0.0089],\n", - " [ 0.0122, -0.0500, 0.0212, ..., 0.0224, 0.0129, -0.0353],\n", - " ...,\n", - " [-0.0350, 0.0299, 0.0092, ..., -0.0325, 0.0414, 0.0038],\n", - " [-0.0198, 0.0006, -0.0258, ..., -0.0216, 0.0161, 0.0072],\n", - " [ 0.0133, 0.0207, -0.0245, ..., -0.0178, 0.0005, 0.0099]])), ('model.encoder.l1.0.bias', tensor([ 0.0062, -0.0248, 0.0121, 0.0022, -0.0090, -0.0069, 0.0119, -0.0033,\n", - " -0.0237, -0.0008, 0.0296, 0.0252, 0.0446, -0.0282, 0.0140, -0.0091,\n", - " 0.0086, -0.0026, 0.0194, 0.0202, 0.0054, -0.0184, -0.0076, 0.0229,\n", - " 0.0193, -0.0267, 0.0009, -0.0097, -0.0214, 0.0220, -0.0289, 0.0100,\n", - " -0.0248, 0.0201, 0.0088, 0.0207, 0.0020, -0.0105, -0.0128, 0.0160,\n", - " -0.0040, -0.0057, 0.0219, -0.0320, -0.0224, 0.0332, -0.0059, -0.0122,\n", - " 0.0178, -0.0121, -0.0092, 0.0275, 0.0465, 0.0148, 0.0062, 0.0261,\n", - " -0.0024, -0.0160, -0.0249, 0.0302, 0.0149, 0.0489, 0.0158, 0.0236])), ('model.encoder.l1.2.weight', tensor([[ 6.9274e-02, -7.5529e-02, 6.0693e-03, 7.2484e-02, 1.2199e-02,\n", - " 1.0271e-01, -4.1468e-02, -4.3240e-02, -4.6281e-02, 1.2571e-01,\n", - " -8.5449e-02, 4.6357e-02, 1.2251e-01, 2.0674e-02, 2.1539e-02,\n", - " -5.5167e-02, -7.4029e-02, -6.4833e-02, -1.2041e-01, -2.8743e-02,\n", - " 1.4320e-04, -4.0567e-02, 5.1052e-02, 1.0420e-01, -6.0000e-02,\n", - " 3.4384e-02, 1.0103e-02, 7.4126e-02, 3.0616e-02, 1.0212e-01,\n", - " 4.6894e-02, 3.0109e-03, -3.7498e-02, -7.4668e-02, -1.0156e-01,\n", - " -3.9994e-02, 7.8412e-02, 2.2870e-02, 8.6772e-02, -6.5163e-02,\n", - " -3.6942e-02, -1.0492e-02, 5.7533e-02, -9.4895e-02, 2.8295e-02,\n", - " 1.0956e-01, 7.9914e-02, 5.0903e-02, 1.6608e-02, -7.2153e-03,\n", - " 1.1555e-01, -6.8021e-02, -6.0011e-02, -1.2695e-01, 1.1792e-01,\n", - " -3.6230e-02, -6.9247e-02, 5.4702e-02, -9.2952e-02, -2.1146e-02,\n", - " 9.9330e-02, 8.9216e-02, -9.4121e-02, 7.8249e-02],\n", - " [-1.1575e-01, -5.1897e-02, 1.0679e-01, -6.7454e-02, 9.3848e-02,\n", - " -1.2586e-01, 7.7469e-02, -8.2629e-02, 1.4323e-02, 1.0670e-01,\n", - " -1.0372e-01, -6.4982e-02, 5.9872e-02, 8.2531e-02, -1.4330e-02,\n", - " 1.2318e-01, -3.4196e-02, -3.8210e-02, 3.3245e-02, 9.5731e-02,\n", - " 4.2358e-02, -2.4778e-02, -1.2134e-01, -3.1321e-02, -2.1474e-02,\n", - 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" -7.3938e-03, 4.5084e-02, 1.3532e-01, 3.0131e-02, -3.2576e-03,\n", - " 7.5903e-02, -9.3906e-02, -5.3140e-02, 4.0723e-02, 4.1423e-02,\n", - " 9.0686e-02, 6.8372e-02, 1.3464e-01, -9.1501e-02, -4.8266e-02,\n", - " -7.0657e-02, 2.3607e-02, 8.0476e-02, 5.8574e-02, -9.3793e-02,\n", - " 5.0338e-02, -9.6929e-03, -3.3096e-02, 2.2787e-02, 1.4759e-02,\n", - " 1.4796e-02, 6.5653e-02, -7.5869e-02, -8.0484e-02, -8.2850e-02,\n", - " 3.2352e-03, 1.7724e-02, -7.9537e-02, 1.0465e-01, -9.8474e-02,\n", - " -8.8483e-02, 2.9263e-02, -2.6299e-02, 1.2068e-01, -5.6644e-02,\n", - " -7.3026e-02, 1.3149e-01, -7.6489e-02, -2.5422e-02, -4.8026e-02,\n", - " 2.6889e-02, -9.8976e-02, -5.0784e-02, -4.7562e-02, 8.3888e-02,\n", - " 6.7930e-02, -6.5345e-02, -2.3397e-02, -2.1531e-02, -1.1532e-02,\n", - " 2.3375e-02, -7.0807e-02, -8.6473e-02, 1.7951e-02, -1.0602e-01,\n", - " -3.6343e-02, -8.2065e-02, 6.4148e-03, -9.3989e-02, -2.3969e-04,\n", - " 1.4089e-02, -1.1225e-02, 1.3749e-01, -3.4601e-02, -4.1253e-02,\n", - " -1.1495e-01, -1.3460e-01, 1.8964e-02, 4.1703e-02, 6.5068e-02,\n", - " -4.9527e-02, -4.6610e-02, -3.5785e-02, 6.7825e-02, 2.4006e-02,\n", - " -3.2438e-02, 1.0445e-01, 9.2612e-02, 7.2684e-02, -2.9991e-02,\n", - " -6.3186e-02, 6.7917e-02, -9.4241e-02, 1.0861e-01, 2.4773e-02,\n", - " -8.4209e-03, -5.2269e-03, 2.6935e-02, -2.1932e-02]))])\n" - ] - } - ], - "source": [ - "check_model_w(MODEL_)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/signa2text/src/dev2.ipynb b/signa2text/src/dev2.ipynb deleted file mode 100644 index 243d5ff1..00000000 --- a/signa2text/src/dev2.ipynb +++ /dev/null @@ -1,220 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Previous directory: /workspace/NSL_2_AUDIO/signa2text\n" - ] - } - ], - "source": [ - "import os\n", - "\n", - "# Get the current working directory\n", - "current_dir = os.getcwd()\n", - "\n", - "# Go back to the previous directory\n", - "os.chdir('..')\n", - "\n", - "# Now, the current working directory is the previous directory\n", - "previous_dir = os.getcwd()\n", - "\n", - "print(\"Previous directory:\", previous_dir)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'/workspace/NSL_2_AUDIO/signa2text'" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pwd" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from models.baseline_transformer import ASLTransformer, LandmarkEmbedding, TokenEmbedding\n", - "from dataset.dataset_loader import get_dataset, prepare_dataloader # get_test_dataset\n", - "from dataset.dataset_paths import get_dataset_paths" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "train_data_paths, valid_data_paths = get_dataset_paths(dev_mode=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "train_dataset = get_dataset(train_data_paths)\n", - "train_dataset = prepare_dataloader(train_dataset,10)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "for source, target in train_dataset:\n", - " break" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(torch.Size([10, 128, 345]), torch.Size([10, 64]))" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "source.shape , target.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "source_emb = LandmarkEmbedding(64)\n", - "target_emd = TokenEmbedding(num_vocab=62, embedding_dim= 64, maxlen=64)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(torch.Size([10, 64]), torch.Size([10, 128, 345]))" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "target.size(), source.size()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "source_emb_ans= source_emb(source)\n", - "target_emd_ans = target_emd(target)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([10, 64, 64])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "target_emd_ans.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([10, 64])" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "source_emb_ans.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/signa2text/src/download_dev_data.py b/signa2text/src/download_dev_data.py index 1232840b..caa0ec79 100644 --- a/signa2text/src/download_dev_data.py +++ b/signa2text/src/download_dev_data.py @@ -138,13 +138,13 @@ def main(): for file in DATA_FILES: logger.info(f"Downloading {file} in {DATA_DIR}") - # Swtiching "FILE_NAME" in cmd list with the actual file name in kaggle + # Switching "FILE_NAME" in cmd list with the actual file name in kaggle COMMAND[6] = file zip_file_path = DATA_DIR + file + ".zip" download_file(COMMAND, zip_file_path, DATA_DIR) logger.info(f"{file} downloaded successfully") - # Swtiching the directory to download the landmarks into + # Switching the directory to download the landmarks into COMMAND[8] = DATA_DIR + TRAIN_LANDMARKS_DIR # Downloading the LANDMARKS files @@ -152,7 +152,7 @@ def main(): logger.info(f"Current Available space {check_storage()}GB") file_path = TRAIN_LANDMARKS_DIR + parquet_file - # Swtiching "FILE_NAME" in cmd list with the actual file name in kaggle + # Switching "FILE_NAME" in cmd list with the actual file name in kaggle COMMAND[6] = file_path zip_file_path = DATA_DIR + file_path + ".zip" download_file(COMMAND, zip_file_path, DATA_DIR + TRAIN_LANDMARKS_DIR) diff --git a/signa2text/src/main.py b/signa2text/src/main.py index 32ca32e9..f3c79729 100644 --- a/signa2text/src/main.py +++ b/signa2text/src/main.py @@ -16,8 +16,8 @@ from dataset.dataset_loader import get_dataset, prepare_dataloader # get_test_dataset from dataset.dataset_paths import get_dataset_paths from lightning import Trainer -from trainer import LitModule, PROJECT_NAME, profiler - +from trainer import LitModule, profiler +from config import PROJECT_NAME from lightning.pytorch.loggers import WandbLogger diff --git a/signa2text/src/trainer.py b/signa2text/src/trainer.py index 498545b0..2e7a03d1 100644 --- a/signa2text/src/trainer.py +++ b/signa2text/src/trainer.py @@ -9,8 +9,6 @@ import torch import lightning as L - -# from utils.logging import logger from lightning.pytorch.callbacks import ( ModelCheckpoint, EarlyStopping, @@ -18,10 +16,9 @@ ) from lightning.pytorch.profilers import SimpleProfiler +# from utils.logging import logger # from metrics import NormalizedLevenshteinDistance -PROJECT_NAME = "NSL_2_AUDIO" - # Checkpoint Filename Template FILENAME_TEMPLATE = "NSL-2-AUDIO-{epoch}-{val_loss:.2f}" @@ -31,7 +28,9 @@ class LitModule(L.LightningModule): """_summary_""" - def __init__(self, model, loss_criterion, metric, save_ckpt_every=5, model_name="test"): + def __init__( + self, model, loss_criterion, metric, save_ckpt_every=5, model_name="test" + ): super().__init__() self.model = model self.loss_criterion = loss_criterion diff --git a/signa2text/run_train.sh b/signa2text/train_model.sh similarity index 87% rename from signa2text/run_train.sh rename to signa2text/train_model.sh index 0bcd4e52..6b1faa4d 100644 --- a/signa2text/run_train.sh +++ b/signa2text/train_model.sh @@ -1,4 +1,4 @@ -#!/bin/bash +#!/usr/bin/env bash # Display a header with script information echo "=== Running Train Script ===" diff --git a/yb2audio/notebooks/iroyin_data_eda.ipynb b/yb2audio/notebooks/iroyin_data_eda.ipynb index d468dcbb..547d47fd 100644 --- a/yb2audio/notebooks/iroyin_data_eda.ipynb +++ b/yb2audio/notebooks/iroyin_data_eda.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ diff --git a/yb2audio/set_environment_variables_template.sh b/yb2audio/set_environment_variables_template.sh new file mode 100644 index 00000000..1f07a6ef --- /dev/null +++ b/yb2audio/set_environment_variables_template.sh @@ -0,0 +1,11 @@ +#!/usr/bin/env bash +# replace placeholders and rename this file to `set_environment_variables.sh` + +#replace with Kaggle username and key +export KAGGLE_USERNAME=username +export KAGGLE_KEY=xxxxxxxxxxxxxx + +#replace with WANDB key +export WANDB_API_KEY=xxxxxxxxxxxxxx + +echo "Setting Enviroment Variables Completed" \ No newline at end of file