corpusgen: Language-Agnostic Speech Corpus Generation with Maximal Phoneme Coverage

Added CLI: corpusgen generate Full command-line interface for corpus generation with three backends: --backend repository (sentence pool with --file or --dataset) --backend llm_api (LLM via litellm with --model and --api-key) --backend local (HuggingFace transformers with --device, --quantization) Target inventory configuration: PHOIBLE-based with --phonemes for additive IPA symbols Coverage unit selection: --unit phoneme|diphone|triphone Priority weights: --weights (inline p:2.0,b:1.5 or JSON file) Stopping criteria: --target-coverage, --max-sentences, --max-iterations, --timeout Output: --format text|json, --output file, --candidates per iteration Built-in Scorers NgramPhonotacticScorer (corpusgen.generate.scorers.phonotactic): Inventory-derived mode (PHOIBLE baseline with Laplace smoothing) Corpus-trained mode via from_corpus() with G2P batch conversion Configurable n-gram order (bigram/trigram) save()/load() for JSON serialization and reproducibility PerplexityFluencyScorer (corpusgen.generate.scorers.fluency): Lazy-loading causal LM (default: gpt2) from_model() constructor for sharing model with LocalBackend (VRAM efficiency) Normalized [0, 1] scores via 1 / (1 + log(perplexity)) CLI flags for multi-objective scoring: --coverage-weight, --phonotactic-weight, --phonotactic-scorer ngram --phonotactic-corpus, --phonotactic-n --fluency-weight, --fluency-scorer perplexity, --fluency-model, --fluency-device Guidance Strategies (CLI) --guidance datg with flat flags: --datg-boost, --datg-penalty, --datg-anti-mode, --datg-freq-threshold, --datg-batch-size --guidance rl with --rl-adapter-path --guidance-config JSON file (overrides flat flags) HuggingFace Dataset Support --dataset for repository backend (e.g., --dataset wikitext) Options: --text-column, --split, --max-samples Mutually exclusive with --file Custom Prompt Templates --prompt-template (inline string or file path) for llm_api and local backends Required {target_units} placeholder with validation Model Sharing When fluency scorer model matches local backend model, model is shared via from_model() to avoid loading twice (saves VRAM) Backend built before scorer to enable early loading via _ensure_loaded() Testing 11 slow integration tests for CLI (generate, inventory, evaluate) using real espeak-ng + PHOIBLE 27 fast unit tests for phonotactic scorer (including 5 persistence tests) 14 fast unit tests for fluency scorer (12 fast + 2 slow) 20 fast CLI tests for scorer, guidance, dataset, template, and model-sharing flags