training.md

Training 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/train-clean-100 \
    --output=/path/to/dataset/train-clean-100/transcripts.tsv

Do the same thing with train-clean-360, train-other-500, dev-clean, dev-other, 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

4. [Optional][Required if using TPUs] Create tfrecords

python scripts/create_tfrecords.py \
    --config-path=/path/to/config.yml.j2 \
    --mode=\["train","eval","test"\] \
    --datadir=/path/to/datadir

You can reduce the flag --modes to --modes=\["train","eval"\] to only create train and eval datasets

5. Generate vocabulary and metadata

This step requires defining path to vocabulary file and other options for generating vocabulary in config file.

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

6. Run training

python examples/train.py \
    --mxp=auto \
    --jit-compile \
    --config-path=/path/to/config.yml.j2 \
    --dataset-type=tfrecord \
    --modeldir=/path/to/modeldir \
    --datadir=/path/to/datadir
## See others params
python examples/train.py --help