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name: acoustic-models description: "Route ASRT acoustic model work for Keras/CTC training, evaluation, prediction, weights, GPU expectations, and PyTorch caveats." metadata: disco-role: operating disable-model-invocation: true license: GPL 3.0

acoustic-models

Use this sub-skill when the task is about ASRT's acoustic model layer: TensorFlow/Keras model classes, the ModelSpeech CTC wrapper, training/evaluation/prediction calls, saved acoustic weights, or the experimental PyTorch path.

Read first

• Keras model classes and weight naming: references/keras-acoustic-models.md.
• Default Keras train/evaluate/resume recipes: references/training-and-evaluation.md.
• Single WAV acoustic prediction workflow: references/prediction-workflow.md.
• Experimental PyTorch route and caveats: references/pytorch-backend.md.
• Common failures and fixes: references/troubleshooting.md.

Bundled scripts

• scripts/inspect_keras_model.py constructs a selected Keras acoustic model without data or weights and reports shapes, model names, TensorFlow version, and visible devices.
• scripts/predict_file_template.py is a parameterized single-file acoustic prediction template that emits ASRT pinyin tokens from a WAV file and acoustic weights.

Run both scripts with --help before use. They are templates for a target ASRT codebase or installed ASRT modules and do not contain hard-coded source paths.

Route elsewhere

• Dataset lists, asrt_config.json, pinyin dictionary format, WAV schema, and feature extractor details belong to the sibling data-and-features sub-skill.
• Pinyin-to-Chinese language-model internals belong to language-model.
• HTTP/gRPC servers and clients belong to serving-clients.
• Do not make benchmark or accuracy claims unless the active task provides the exact weights, datasets, and evaluation procedure used for that claim.