---

name: tacotron description: "Guides the legacy TensorFlow 1.x Tacotron repository for text normalization, speech-data preprocessing, Tacotron graph inspection, training, checkpoint synthesis, evaluation, and the Falcon demo server." disable-model-invocation: true metadata: disco-role: operating license: MIT

Tacotron

Use this skill for the original keithito/tacotron-style implementation of Tacotron speech synthesis in TensorFlow 1.x. It is a repository workflow skill, not a modern TensorFlow 2 or Tacotron 2 guide. Keep the original model's checkpoint, hyperparameters, text cleaners, and spectrogram conventions consistent across preprocessing, training, and synthesis.

First route

• Text cleaning, number expansion, symbols, ARPAbet, or CMUDict: read text-normalization.
• LJ Speech/Blizzard layout, preprocessing, or custom data conversion: read data-preparation.
• Graph shapes, CBHG/prenet/attention internals, or audio dimensions: read model-architecture.
• Training commands, hparams, checkpoints, TensorBoard, or loss recovery: read training.
• Checkpoint WAV synthesis, batch evaluation, or HTTP serving: read synthesis-serving.

For a request spanning routes, use data-preparation → training → synthesis-serving, and use text-normalization whenever the cleaner or symbol contract changes.

Runtime contract

The repository is not packaged with setup.py, setup.cfg, or pyproject.toml. Use an isolated legacy Python environment and run repository entry points from a Tacotron checkout. The documented requirements omit TensorFlow; install a TensorFlow 1.x build compatible with the platform. TensorFlow 1.15.5 with Python 3.6 was verified for inspection, together with the pinned audio/text dependencies, but newer Python/TensorFlow combinations are not automatically compatible with tf.contrib.

Use explicit roots in command examples; replace these two assignments only when the checkout is stored elsewhere:

SKILL_ROOT=/path/to/tacotron-skill
CHECKOUT_ROOT=/path/to/tacotron-checkout

Bundled command builders run from SKILL_ROOT and print a command prefixed by cd "$CHECKOUT_ROOT". Native preprocessing, training, evaluation, and serving must therefore run with the checkout as their cwd; a skill directory is not a source checkout. The skill's audio path uses the repository's librosa/SciPy helpers and built-in Griffin-Lim reconstruction. It does not bundle or require a neural vocoder, but real WAV input is required for preprocessing and a real checkpoint is required for synthesis.

cd "$CHECKOUT_ROOT"
python -m pip install -r requirements.txt
# Select a platform-compatible TensorFlow 1.x package; do not use TensorFlow 2.x.
python -c "import tensorflow as tf; print(tf.__version__); print(hasattr(tf, 'contrib'))"

The repository's requirements pin old versions. If modern package resolution breaks scipy, librosa, or numba, follow the compatibility notes in references/installation.md rather than blindly upgrading all dependencies.

Verification boundary

The bundled text, metadata, fixture, environment, shape, and command helpers are offline or dry-run checks. They do not prove real audio decoding, spectrogram quality, checkpoint compatibility, generated WAV quality, GPU performance, full preprocessing, training convergence, or server security. Full training and checkpoint synthesis need external data/checkpoints and can be expensive. Use the bundled builders and validators before starting them.

Shared invariants

• The default sample rate is 20 kHz; audio uses 80 mel channels, 1025 linear frequency bins, 50 ms windows, and 12.5 ms frame shifts.
• outputs_per_step=5 and max_iters=200 affect both training and synthesis; use the same relevant hparams at both times.
• Preprocessing writes time-major .npy spectrogram arrays and train.txt rows with two filenames, frame count, and text. Validate this before training.
• Text conversion appends the ~ EOS symbol. Cleaner names and ARPAbet syntax must be available in both training and inference environments.
• Full training and checkpoint synthesis need real datasets/checkpoints and can be expensive. Use the bundled builders and validators before starting them.

Read references/troubleshooting.md for cross-cutting TensorFlow-version, dependency, path, checkpoint, and audio failure handling. Read references/repo-provenance.md before deciding whether this skill is stale for a changed checkout.