New version link : V2
The goal of this project is to build a application to indentify $ Indian languages.
The solution proposed for the above problem is that we have used Deep learning to solve the above problem to identify spoken language from audio. We have used the Pytorch framework to solve the above problem also we created our custom Language Identification network with the help of PyTorch. Then we created an API that takes in the audio.mp3 and predicts the language. Then we dockerized the application and deployed the model on the GCP cloud.
This is a dataset of audio samples of 4 different Indian languages. Each audio sample is of 5 seconds duration. This dataset was created using regional videos available on YouTube.
This is constrained to Indian Languages only but could be extended.
Languages present in the dataset - Hindi, Kannada, Tamil, Telugu.
git clone "https://github.com/Deep-Learning-01/language-identification-using-cnn-pytorch.git" repository
conda create -p env python=3.10 -y
conda activate env/
pip install -r requirements.txt
export AWS_ACCESS_KEY_ID=<AWS_ACCESS_KEY_ID>
export AWS_SECRET_ACCESS_KEY=<AWS_SECRET_ACCESS_KEY>
export AWS_DEFAULT_REGION=<AWS_DEFAULT_REGION>
Before running server application make sure your s3
bucket is available and empty
python app.py
http://localhost:8080/train
http://localhost:8080
-
Check if the Dockerfile is available in the project directory
-
Build the Docker image
docker build -t langapp .
- Run the Docker image
docker run -d -p 8080:8080 <IMAGEID>
👨💻 Tech Stack Used
- Python
- Flask
- Pytorch
- Docker
- CNN
🌐 Infrastructure Required.
- AWS S3
- GAR (Google Artifact repository)
- GCE (Google Compute Engine)
- GitHub Actions
Artifact : Stores all artifacts created from running the application
Components : Contains all components of Machine Learning Project
- DataIngestion
- DataTransformation
- ModelTrainer
- ModelEvaluation
- ModelPusher
Custom Logger and Exceptions are used in the project for better debugging purposes.
Can be used for language Identification in videos and other audio files in any organization.
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