Add audio module: meeting minutes with Spring AI speech-to-text, structured output and text-to-speech

OpenAiAudioTranscriptionModel -> ChatClient.entity(Minutes.class) -> OpenAiAudioSpeechModel,
WAV chunking at a silence under the 25 MB upload limit, SDK retry behaviour, the 4096-character
speech limit and a cost model. Audio endpoints are a local mock and the chat model a stub; no real
model was called. Spring Boot 4.1.2, Spring AI 2.0.2, Java 25.

Co-Authored-By: Claude Sonnet 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01CifR2BE7PBvRy5hT8TS9oz

README.md

@@ -28,5 +28,6 @@ Runnable companion code for the Spring AI articles on [ankurm.com](https://ankur
 | [`rag-ingestion/`](rag-ingestion) | PDF, Word and HTML versions of one 12-section runbook through `PagePdfDocumentReader`, `TikaDocumentReader` and `JsoupDocumentReader`, then `TokenTextSplitter` at six sizes, a paragraph chunker, a sentence splitter and a section chunker; retrieval hit@1/3/5 on 37 fixed questions, metadata in the embedding, content-hash idempotent ingestion into pgvector and the re-embedding cost of one edit. Local all-MiniLM-L6-v2; a synthetic corpus, so differences of two or three questions are noise. Spring Boot 4.1.2, Spring AI 2.0.2, Java 25. | [Spring AI RAG Ingestion Pipeline: PDF, Word and HTML Readers with Chunking Strategies Compared](https://ankurm.com/spring-ai-rag-ingestion-pipeline-pdf-word-html-chunking/) |
 | [`hybrid-search/`](hybrid-search) | Vector, `tsvector` and a Java Okapi BM25 over one PostgreSQL table (pgvector HNSW + GIN), fused with Reciprocal Rank Fusion in Java and in a single SQL statement, behind a `DocumentRetriever` for `RetrievalAugmentationAdvisor`. 60 questions in four kinds over 94 passages: hybrid helps exact error codes and hurts paraphrases; `k` tuned on the evaluation set did not hold up on held-out folds; plans and latency at 94 and 50,094 rows. Local all-MiniLM-L6-v2. Spring Boot 4.1.2, Spring AI 2.0.2, Java 25. | [Hybrid Search in Java: BM25 + Vector with PostgreSQL and Reciprocal Rank Fusion](https://ankurm.com/hybrid-search-java-pgvector-tsvector-reciprocal-rank-fusion/) |
 | [`rerank/`](rerank) | A bi-encoder (all-MiniLM-L6-v2) retrieves candidates, a cross-encoder (ms-marco-MiniLM-L-6-v2) rescores them, and a `DocumentPostProcessor` keeps the top 5 for `RetrievalAugmentationAdvisor`. Both models run in-process through ONNX Runtime 1.31.0 with DJL tokenizers 0.39.0 (no database, no API key). 60 questions over 94 passages: hit@1 41 to 49 and MRR 0.78 to 0.87, almost all of it on exact error codes, with paraphrases slightly worse; pool size, batch size, threads and int8 against latency on 2 vCPUs; and five traps reproduced (missing `token_type_ids`, swapped pair order, logits are not probabilities, truncation, a pool that is too small). Spring Boot 4.1.2, Spring AI 2.0.2, Java 25. | [Reranking RAG Results in Java: Cross-Encoder Reranker with ONNX Runtime Before the LLM](https://ankurm.com/reranking-rag-results-java-cross-encoder-onnx-runtime-spring-ai/) |
+| [`audio/`](audio) | A meeting-minutes generator: `OpenAiAudioTranscriptionModel` -> `ChatClient.entity(Minutes.class)` -> `OpenAiAudioSpeechModel`. Splitting a 30 MB recording at a silence under the 25 MB upload limit (and what a blind split does), SDK retries that re-upload the file, the 4096-character speech limit, three replies that fail to parse, and a cost model with formulas. The audio endpoints are a local mock and the chat model a stub; no real model was called. Spring Boot 4.1.2, Spring AI 2.0.2, Java 25. | [Speech-to-Text and Text-to-Speech with Spring AI: Build a Meeting-Minutes Generator](https://ankurm.com/spring-ai-speech-to-text-text-to-speech-meeting-minutes/) |
 
 Upgrading from Spring AI 1.x: [migration guide](https://ankurm.com/spring-ai-1-to-2-migration-guide/).