---

name: training description: "Guides Tacotron training command construction, hyperparameter overrides, data-feeder contracts, logs, checkpoints, TensorBoard, restore runs, and loss troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: MIT

Training

Use this route for training a Tacotron model, preparing a restore run, changing hparams, interpreting logs/checkpoints, or planning resource requirements. Full training is long-running and data-dependent; build and validate the command before launching it.

Workflow

1. Complete the data-preparation route and validate training/train.txt.
2. Choose a stable --base_dir, --input, and --name. The default log directory is <base_dir>/logs-<name-or-model>.
3. Pass hparam overrides as comma-separated name=value pairs. Preserve the same audio/model hparams for evaluation and serving.
4. Use --restore_step N only when <log_dir>/model.ckpt-N exists. Use --git only in a clean source checkout; it intentionally fails on dirty state.
5. Monitor summaries, generated audio, and alignments. If loss explodes or attention is lost, stop safely and consider restoring before the spike.

Command roots and training boundary

Build commands from the skill root; execute the printed command only from the Tacotron checkout root. The input and output paths remain relative to the selected --base-dir, not to the skill directory.

SKILL_ROOT=/path/to/tacotron-skill
CHECKOUT_ROOT=/path/to/tacotron-checkout
cd "$SKILL_ROOT" && python sub-skills/training/scripts/build_train_command.py --checkout-root "$CHECKOUT_ROOT" --base-dir /data/tacotron --input training/train.txt --hparams batch_size=16,max_iters=300

The builder is dry-run only. Actual training requires validated metadata, decoded/preprocessed audio arrays, the compatible TensorFlow 1.x stack, disk, memory, and potentially GPU resources; it may write checkpoints, WAV samples, alignments, and logs. No training convergence or checkpoint restore is implied by command construction or metadata validation.

Read references/cli-reference.md for flags, references/hparams.md for defaults and duration calculations, references/workflows.md for a safe sequence, and references/troubleshooting.md for data, loss, and restore failures. Use scripts/build_train_command.py to construct commands without starting a training loop.