feat: add expressive offline Russian book TTS

tools/tts/README.md

@@ -33,3 +33,53 @@ before producing WAV files.
 `train_piper_windows.ps1` provides the equivalent native Windows path and a `-SmokeTest` mode that executes one
 training and validation batch on CUDA before a long run. See `GPU_TRAINING.md` for host prerequisites and the
 acceptance gate.
+
+## Vosk BookTTS compression experiment
+
+`vosk_booktts_experiment.py` reproduces the Android Vosk frontend, prepares real acoustic-model feeds from
+`booktts_calibration_ru.txt`, performs calibrated QDQ int8 quantization of convolution layers, and renders
+deterministic baseline/candidate WAV pairs. Keep generated `.npz`, candidate ONNX, and WAV files outside tracked
+source directories, for example under `.codex-temp`. Do not replace the application asset until the candidate
+loads with ONNX Runtime, runs on the target phone, and passes listening comparison.
+Use `prepare --bert-model` plus `compare-feeds` to isolate a BERT replacement while keeping the acoustic model
+identical in both WAV branches. `compare-feeds --resume` preserves existing WAVs and atomically updates its
+report after every pair; `run_booktts_ab_windows.ps1` is the persistent Windows entry point.
+
+`booktts_student.py` builds token-level teacher targets from the current int8 BERT and trains a four-layer,
+256-dimensional Transformer student with a 768-dimensional drop-in output. Its small calibration corpus is only
+for validating the training/export pipeline; a candidate for the application requires a much larger licensed
+literary corpus, held-out evaluation, ONNX quantization, and listening tests. The optional
+`--max-training-batches` and `--max-validation-batches` limits are for CUDA memory smoke tests only.
+Each completed epoch atomically replaces `checkpoint.latest.pt`; pass `--resume` with the same output directory
+to continue an interrupted run.
+
+`run_booktts_student_windows.ps1` is the native Windows CUDA entry point for a full or resumed student run.
+The exported student includes the dataset's original-to-compact token map so its `input_ids` remain compatible
+with the existing Vosk vocabulary. `patch_booktts_student_token_map.py` adds the same lookup to an older export
+without retraining.
+
+`compare_booktts_bert.py` measures teacher/student and FP32/INT8 embedding error, inference time, and unseen-token
+coverage on a held-out text file.
+
+`build_booktts_prosody_corpus.py` creates a deterministic balance of questions, exclamations, quotations,
+ellipsis, and neutral prose. Build its teacher dataset with `--reuse-token-map`, then fine-tune from the prior
+`student.pt` with `--initial-model`; the held-out listening phrases must be passed through `--exclude`.
+`run_booktts_prosody_dataset_windows.ps1` builds this reused-vocabulary teacher dataset persistently on Windows.
+After auditing that dataset, `run_booktts_prosody_finetune_windows.ps1` starts the low-learning-rate CUDA run.
+
+`extract_wikisource_corpus.py` streams an official Russian Wikisource XML/BZip2 dump and writes a deduplicated,
+filtered sentence corpus plus provenance metadata. The source URL and dump size must be retained. Wikisource is
+not a blanket rights clearance for every included work, so review the resulting corpus and applicable source
+licenses before distributing a trained model.
+
+`run_booktts_corpus_windows.ps1` completes the resumable BITS download on the CUDA host, verifies the official
+dump size and SHA-1, extracts the configured number of sentences, and builds the streamed float16 teacher
+dataset. It intentionally stops before training so vocabulary coverage and a held-out evaluation plan can be
+reviewed first.
+
+`audit_booktts_text_corpus.py` records the corpus SHA-256, sentence-length and word-count distributions,
+residual wiki-markup counters, and deterministic review samples. Run it before training; its report is a
+technical quality check and does not replace a rights review of the source works.
+
+`audit_booktts_dataset.py` opens every generated teacher shard and verifies sample counts, tensor shapes,
+token ranges, binary masks, and finite teacher values. It also prints a reproducible SHA-256 over shard bytes.