A playground to test and experiment with spoken language identification.
This code base has been developed by Hertz-Lab as part of the project [»The Intelligent Museum«](#the-intelligent-museum).
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## Trained Models
Our trained models can be downloaded from [this location](https://cloud.zkm.de/index.php/s/83LwnXT9xDsyxGf). The `AttRnn` model expects 5 seconds of normalized audio sampled at 16kHz and outputs probabilities for Noise, English, French, German and Spanish in this order.
We hope to soon open up a demo page or a test script for you to run locally or at Google Colaboratory.
## Installation
To train a neural network we generally recommend to use a GPU.
## Demonstration
If you are only interested in running the trained model then please check out our notebook in the `demo/` folder. The notebook was developed to be [run in Google Colab](https://colab.research.google.com/github/zkmkarlsruhe/language-identification/blob/main/demo/LID_demo.ipynb). It will guide you through the necessary steps to run the model in your application.
You can also check out our [OpenFrameworks demonstration](https://git.zkm.de/Hertz-Lab/Research/intelligent-museum/LanguageIdentifier) which utilizes our [ofxTensorFlow2](https://github.com/zkmkarlsruhe/ofxTensorFlow2) addon.
## Training Setup
We highly recommend to use a (recent) GPU to train a neural network.
### Docker
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If you haven't installed docker with GPU support yet, please follow [these instructions](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html).
##### Building the image
Build the docker image using the [Dockerfile](Dockerfile). Make sure not to include large amounts of data in the build process (all files in the build directory are taken into account).
Build the docker image using the [Dockerfile](Dockerfile). Make sure not to include large amounts of data into the build process (all files in the build directory are taken into account).
