• Training Tutorial
  • 1. Install packages
  • 2. Prepare transcripts files
  • 3. Prepare config file
  • 4. [Optional][Required if using TPUs] Create tfrecords
  • 5. Generate vocabulary and metadata
  • 6. Run training

Training Tutorial

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

1. Installation

./setup.sh [tpu|gpu|cpu] install

2. Prepare transcripts files

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

python examples/datasets/librispeech/prepare_transcript.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, please make your own script to prepare the transcript files, take a look at the prepare_transcript.py file for more reference

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

For example:

{% import "examples/datasets/librispeech/sentencepiece/sp.yml.j2" as decoder_config with context %}
{{decoder_config}}

{% import "examples/models/transducer/conformer/small.yml.j2" as config with context %}
{{config}}

4. [Optional] Create tfrecords

If you want to train with tfrecords

tensorflow_asr utils create_tfrecords \
    --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.

tensorflow_asr utils create_datasets_metadata \
    --config-path=/path/to/config.yml.j2 \
    --datadir=/path/to/datadir \
    --dataset-type="slice"

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

6. Run training

tensorflow_asr train \
    --config-path=/path/to/config.yml.j2 \
    --modeldir=/path/to/modeldir \
    --datadir=/path/to/datadir \
    --dataset-type=tfrecord \ # or "generator" or "slice" \
    --dataset-cache \
    --mxp=strict \
    --bs=4 \
    --ga-steps=8 \
    --verbose=1 \
    --jit-compile \
    --device-type=tpu \
    --tpu-address=local
## See others params
tensorflow_asr train --help