isabella232 / alexa-end-to-end-slu

This setup allows to train end-to-end neural models for spoken language understanding (SLU).

Alexa End-to-End SLU

This setup allows to train end-to-end neural models for spoken language understanding (SLU). It uses either the Snips SLU or the Fluent Speech dataset (FSC). This framework is built using pytorch with torchaudio and the transformer package from HuggingFace. We tested using pytorch 1.5.0 and torchaudio 0.5.0.

Installation and data preparation

To install the required python packages, please run pip install -r requirements.txt. This setup uses the bert-base-cased model. Typically, the model will be downloaded (and cached) automatically when running the training for the first time. In case you want to download the model explicitly, you can run the download_bert.py script from the dataprep/ directory, e.g. python download_bert.py bert-base-cased ./models/bert-base-cased

This setup expects the FluentSpeechCommands dataset to reside under fluent/ and the Snips SLU dataset under snips_slu/. Please download and extract the datasets to these locations (or create a symlink). To preprocess the Snips dataset, please run prepare_snips.py (located in the dataprep/ directory) from within the snips_slu/ folder dataset. This will generate additional files within the snips_slu/ folder required by the dataloader.

Running experiments

Core to running experiments is the train.py script. When called without any parameters, it will train a model using triplet loss on the FSC dataset. The default location for saving intermediate results is the runs/ directory. In case it does not yet exist, it will be created.

To customize the experiments, several command line options are available (for a full list, please refer to parser.py):

• --dataset (The dataset to use, e.g. fsc)
• --experiment (The experiment class to run, e.g. experiments.experiment_triplet.ExperimentRunnerTriplet)
• --scheduler (Learning rate scheduler)
• --output-prefix (The prefix under which the training artifacts are being stored.)
• --bert-model-name (Name or path of pretrained BERT model to use)
• --infer-only (Only run inference on the saved model)

Example runs

To check if everything is installed correctly, training a model with either Snips SLU or Fluent Speech Commands should produce the following results:

Fluent Speech Commands

python train.py --dataset fsc -lr-bert 3e-5 --scheduler plateau --num-enc-layers 5 --val-every 250 --num-epochs 10 --early-stop

Final test acc = 0.9707, test loss = 0.4130

Snips SLU

python train.py --dataset snips --scheduler cycle -lr 6e-3 --num-enc-layers 3 --early-stop

Final test acc = 0.7410, test loss = 3.6841

Security

See CONTRIBUTING for more information.

License

This project is licensed under the Apache-2.0 License.