testing.md

Testing Tutorial

These commands are example for librispeech dataset, but we can apply similar to other datasets

1. Install packages

If you use google colab, it's recommended to use the tensorflow version pre-installed on the colab itself

pip uninstall -y TensorFlowASR # uninstall for clean install if needed
pip install ".[tf2.x]"

2. Prepare transcripts files

This is the example for preparing transcript files for librispeech data corpus

python scripts/create_librispeech_trans.py \
    --directory=/path/to/dataset/test-clean \
    --output=/path/to/dataset/test-clean/transcripts.tsv

Do the same thing with test-clean, test-other

For other datasets, you must prepare your own python script like the scripts/create_librispeech_trans.py

3. Prepare config file

The config file is under format config.yml.j2 which is jinja2 format with yaml content

Please take a look in some examples for config files in examples/*/*.yml.j2

The config file is the same as the config used for training

4. [Optional][Required if not exists] Generate vocabulary and metadata

Use the same vocabulary file used in training

python scripts/prepare_vocab_and_metadata.py \
    --config-path=/path/to/config.yml.j2 \
    --datadir=/path/to/datadir

The inputs, outputs and other options of vocabulary are defined in the config file

5. Run testing

python examples/test.py \
--config-path /path/to/config.yml.j2 \
--dataset_type slice \
--datadir /path/to/datadir \
--outputdir /path/to/modeldir/tests \
--h5 /path/to/modeldir/weights.h5
## See others params
python examples/test.py --help