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name: model-architecture description: "Guides inspection and adaptation of the legacy Tacotron TensorFlow 1.x graph, including input shapes, CBHG blocks, attention, decoder outputs, and audio reconstruction settings." disable-model-invocation: true metadata: disco-role: operating license: MIT

Model architecture

Use this route when a task concerns graph construction, tensor shapes, model components, hparams that alter dimensions, or why a checkpoint cannot be used with a new configuration. This is the original Tacotron implementation using TensorFlow 1.x graph APIs and tf.contrib; it is not a TensorFlow 2 migration recipe.

Workflow

1. Start with the hparams that define input/output dimensions: num_mels, num_freq, outputs_per_step, embed_depth, encoder/attention/decoder depths, and max_iters.
2. Build the model through models.create_model('tacotron', hparams) and call Tacotron.initialize(inputs, input_lengths, mel_targets, linear_targets). Omit targets for inference; provide both for training.
3. Treat mel_outputs, linear_outputs, and alignments as the public graph outputs. Add loss and optimizer only in training graphs.
4. Use the bundled shape inspector for graph/API checks, and read the detailed architecture reference before changing dimensions or restoring a checkpoint.

Command roots and verification boundary

The inspector is a skill asset, while source imports must use the checkout root:

SKILL_ROOT=/path/to/tacotron-skill
CHECKOUT_ROOT=/path/to/tacotron-checkout
cd "$SKILL_ROOT" && python sub-skills/model-architecture/scripts/inspect_model_shapes.py --repo-root "$CHECKOUT_ROOT"
cd "$SKILL_ROOT" && python sub-skills/model-architecture/scripts/inspect_model_shapes.py --repo-root "$CHECKOUT_ROOT" --build-graph

The graph smoke requires a compatible TensorFlow 1.x runtime and checks graph construction only. It loads no checkpoint and does not validate real audio, Griffin-Lim quality, vocoder output, training convergence, or GPU behavior.

Read references/architecture.md for the graph flow, references/api-reference.md for shape contracts, and references/troubleshooting.md for TensorFlow-version and checkpoint mismatch guidance. The bundled scripts/inspect_model_shapes.py is safe by default and only builds a tiny graph when --build-graph is explicitly used.