This repository provides all the necessary tools to perform language identification from speech recordings with SpeechBrain. The system uses a model pretrained on the CommonLanguage dataset (45 languages). You can download the dataset here The provided system can recognize the following 45 languages from short speech recordings:

For a better experience, we encourage you to learn more about SpeechBrain. The given model performance on the test set is:

This system is composed of an ECAPA model coupled with statistical pooling. A classifier, trained with Categorical Cross-Entropy Loss, is applied on top of that.

The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling classify_file if needed. Make sure your input tensor is compliant with the expected sampling rate if you use encode_batch and classify_batch.

First of all, please install SpeechBrain with the following command:

Please notice that we encourage you to read our tutorials and learn more about SpeechBrain.

To perform inference on the GPU, add run_opts={"device":"cuda"} when calling the from_hparams method.

The model was trained with SpeechBrain (a02f860e). To train it from scratch follow these steps:

cd speechbrain
pip install -r requirements.txt
pip install -e .

cd recipes/CommonLanguage/lang_id
python train.py hparams/train_ecapa_tdnn.yaml --data_folder=your_data_folder

You can find our training results (models, logs, etc) here.

The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.

Please, cite SpeechBrain if you use it for your research or business.