feat(serde): add serde support for ChunkingOptions

CHANGELOG.md

@@ -4,6 +4,16 @@ All notable changes to this workspace will be documented in this file.
 
 ## Unreleased
 
+## 0.3.0 - 2026-04-21
+
+### `soundevents`
+
+- Add serde support for `Options` and `ChunkingOptions`.
+- Breaking changes:
+  - `Options::new` no longer takes a model path; construct options separately from model loading.
+  - Chunked classification APIs now take `&ChunkingOptions` instead of `ChunkingOptions`.
+  - `ChunkingOptions` is no longer `Copy`; pass it by reference or clone it explicitly where needed.
+
 ## 0.2.0 - 2026-04-08
 
 ### `soundevents`

README.md

@@ -31,66 +31,7 @@ Production-oriented Rust inference for [CED](https://arxiv.org/abs/2308.11957) A
 
 ```toml
 [dependencies]
-soundevents = "0.2"
-```
-
-```rust,no_run
-use soundevents::{Classifier, Options};
-
-fn load_mono_16k_audio(_: &str) -> Result<Vec<f32>, Box<dyn std::error::Error>> {
-    Ok(vec![0.0; 16_000])
-}
-
-fn main() -> Result<(), Box<dyn std::error::Error>> {
-    let mut classifier = Classifier::from_file("soundevents/models/tiny.onnx")?;
-
-    // Bring your own decoder/resampler — soundevents expects mono f32
-    // samples at 16 kHz, in [-1.0, 1.0].
-    let samples: Vec<f32> = load_mono_16k_audio("clip.wav")?;
-
-    // Top-5 predictions for a clip up to ~10 s long.
-    for prediction in classifier.classify(&samples, 5)? {
-        println!(
-            "{:>5.1}%  {:>3}  {}  ({})",
-            prediction.confidence() * 100.0,
-            prediction.index(),
-            prediction.name(),
-            prediction.id(),
-        );
-    }
-    Ok(())
-}
-```
-
-### Long clips: chunked inference
-
-`Classifier::classify_chunked` slides a window over the input and aggregates each chunk's per-class confidences. The defaults (10 s window, 10 s hop, mean aggregation) match CED's training setup; tune them for overlap or peak-pooling.
-
-```rust,no_run
-use soundevents::{ChunkAggregation, ChunkingOptions, Classifier};
-
-fn load_long_clip() -> Result<Vec<f32>, Box<dyn std::error::Error>> {
-    Ok(vec![0.0; 320_000])
-}
-
-fn main() -> Result<(), Box<dyn std::error::Error>> {
-    let mut classifier = Classifier::from_file("soundevents/models/tiny.onnx")?;
-    let samples: Vec<f32> = load_long_clip()?;
-
-    let opts = ChunkingOptions::default()
-        // 5 s overlap (50%) between adjacent windows
-        .with_hop_samples(80_000)
-        // Batch up to 4 equal-length windows per session.run()
-        .with_batch_size(4)
-        // Keep the loudest detection in any window instead of averaging
-        .with_aggregation(ChunkAggregation::Max);
-
-    let top3 = classifier.classify_chunked(&samples, 3, opts)?;
-    for prediction in top3 {
-        println!("{}: {:.2}", prediction.name(), prediction.confidence());
-    }
-    Ok(())
-}
+soundevents = "0.3"
 ```
 
 ## Models
@@ -130,21 +71,7 @@ sources and attribution details.
 Enable the `bundled-tiny` feature to embed `models/tiny.onnx` into your binary — useful for CLI tools and self-contained services where you don't want to ship a separate model file.
 
 ```toml
-soundevents = { version = "0.2", features = ["bundled-tiny"] }
-```
-
-```rust
-# #[cfg(feature = "bundled-tiny")]
-use soundevents::{Classifier, Options};
-
-# fn main() -> Result<(), Box<dyn std::error::Error>> {
-# #[cfg(feature = "bundled-tiny")]
-# {
-let mut classifier = Classifier::tiny(Options::default())?;
-# let _ = &mut classifier;
-# }
-# Ok(())
-# }
+soundevents = { version = "0.3", features = ["bundled-tiny"] }
 ```
 
 ## Features

soundevents/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "soundevents"
-version = "0.2.1"
+version = "0.3.0"
 edition = "2024"
 description = "Production-oriented Rust inference wrapper for CED AudioSet classifiers."
 license.workspace = true