feat: Demo tab can switch to All-In-One, smaller ONNX graphs

README.md

@@ -7,7 +7,6 @@
 
   <a href="#license"><img src="https://img.shields.io/badge/license-MIT%20code%20%7C%20AGPL--3.0%20project-brightgreen.svg" alt="License: MIT code, AGPL-3.0 project"></a>
   <a href="https://www.python.org/downloads/release/python-31213/" target="_blank" rel="noopener noreferrer"><img src="https://img.shields.io/badge/python-3.12-blue.svg" alt="Python 3.12"></a>
-  <img src="https://img.shields.io/badge/species-282-brightgreen" alt="Species 282">
 
 </div>
 
@@ -29,7 +28,7 @@ Everything you need, including installation instructions, an interactive demo, m
 
 ## Demo
 
-Try the [Demo](https://birdnet-team.github.io/BirdBox/demo/) in your browser: it runs the Just-Bird model on your device, with no installation and no upload. Load a recording, tune the confidence and phrase gap, review and edit the boxes, and export them in the BirdBox output formats.
+Try the [Demo](https://birdnet-team.github.io/BirdBox/demo/) in your browser: it runs the Just-Bird model (or All-In-One, from the model menu) on your device, with no installation and no upload. Load a recording, tune the confidence and phrase gap, review and edit the boxes, and export them in the BirdBox output formats.
 
 <picture>
   <source media="(prefers-color-scheme: dark)" srcset="docs/img/demo_screenshot_dark.png">

docs/demo.md

@@ -9,16 +9,19 @@ hide:
 
 ## About this demo
 
-The demo runs the self-contained Just-Bird graph `docs/models/Just-Bird_fp16.onnx` with [ONNX Runtime Web](https://onnxruntime.ai/docs/tutorials/web/){ target="_blank" rel="noopener noreferrer" } in your browser. The graph computes the PCEN spectrogram, runs the YOLO detector on 3 s windows every 1.5 s and applies NMS, just like [`detect_birds.py`](cli/detect-birds.md) with the Just-Bird model. There are no species labels: every box is a bird vocalization (`bird`).
+The demo runs a self-contained BirdBox graph with [ONNX Runtime Web](https://onnxruntime.ai/docs/tutorials/web/){ target="_blank" rel="noopener noreferrer" } in your browser. The graph computes the PCEN spectrogram, runs the YOLO detector on 3 s windows every 1.5 s and applies NMS, just like [`detect_birds.py`](cli/detect-birds.md) with the same model. The model menu at the top picks the graph:
 
-**Instant sliders.** The model runs once per recording, at the lowest confidence of the slider (0.05). The demo asks the graph for its per-window boxes (`raw_all`) and repeats the graph's window fusion in JavaScript: window ownership and weighted box fusion, joining calls into phrases, removing nested boxes. NMS only lets higher-scoring boxes suppress lower ones, so the boxes for any confidence and phrase gap are the same as a graph run with those values.
+- **Just-Bird** (`docs/models/Just-Bird_fp16.onnx`, the default): every box is a bird vocalization (`bird`), without species labels. For species, combine the boxes with [BirdNET](https://github.com/birdnet-team/BirdNET-Analyzer){ target="_blank" rel="noopener noreferrer" }.
+- **All-In-One** (`docs/models/All-In-One_fp16.onnx`): the [All-in-One](models-and-metrics/all-in-one.md) model with its 282 classes. The table names the species of each box.
+
+**Instant sliders.** The model runs once per recording, at the lowest confidence of the slider (0.05). The demo asks the graph for its per-window boxes (`raw_all`) and repeats the graph's merge in JavaScript. For Just-Bird that is window fusion: window ownership and weighted box fusion, joining calls into phrases, removing nested boxes. For All-In-One it is song reconstruction: boxes of one species closer than the song gap become one box. NMS only lets higher-scoring boxes suppress lower ones, so the boxes for any confidence and gap are the same as a graph run with those values.
 
 **Spectrogram.** The viewer shows an STFT spectrogram by default. In its display settings, *PCEN* switches to the PCEN mel spectrogram BirdBox detects on.
 
 **Review.** Boxes can be verified, moved, resized, removed and drawn. The exports use the BirdBox [output formats](data/outputs.md): the JSON of `--output-format json-with-algorithm-metadata`, the simplified CSV and the Raven selection table, plus a CSV with every column of the table.
 
 !!! warning "Browser limits"
-    - The model is downloaded once (about 35 MB) and then comes from the browser cache.
+    - The model is downloaded once (about 20 MB for Just-Bird, 51 MB for All-In-One) and then comes from the browser cache.
     - Audio is decoded locally, resampled to 32 kHz mono and truncated to **10 minutes**.
     - The browser's resampler is not librosa's, so files that are not recorded at 32 kHz can give slightly different boxes than the CLI.
     - ONNX Runtime runs single-threaded here: about 10 s for 2 minutes of audio on a laptop. Safari can be slower than Chromium or Firefox.
@@ -28,7 +31,7 @@ The sample soundscape is the example recording of [BirdNET-Analyzer](https://git
 
 ## Adding models
 
-The demo runs the first model in `docs/models/manifest.json` with `"merge_method": "fusion"`. Only window-fusion exports work: the instant sliders need the graph's per-window boxes (`raw_all`) and the JavaScript port of its window fusion (`docs/javascripts/demo/fusion.js`). The export uses window fusion for Just-Bird, Hawaii and Southern-Sierra-Nevada (`--merge auto`); `--merge fusion` forces it for the other models.
+The model menu lists the models in `docs/models/manifest.json`; the first one is the default. The instant sliders need the graph's per-window boxes (`raw_all`) and a JavaScript port of its merge: `docs/javascripts/demo/fusion.js` for window fusion (`fusion`, the export's default for Just-Bird, Hawaii and Southern-Sierra-Nevada) and `docs/javascripts/demo/songs.js` for song reconstruction (`reconstruct`, the default for the other models). The Just-Bird frequency gate (`--merge reconstruct` with Just-Bird) has no port; such entries are skipped.
 
 1. Export the model.
 
@@ -53,11 +56,13 @@ The demo runs the first model in `docs/models/manifest.json` with `"merge_method
             --precision fp16
         ```
 
-    `--precision fp16` stores the network weights in fp16, which makes the file about a third smaller (35 MB instead of 53 MB for Just-Bird). Feature extraction and merging stay in fp32. On 13 test recordings, 99.4% of the fp32 graph's boxes were reproduced (mean IoU 0.996). The export prints the merge it baked in (`Merge (baked in)`) and the suggested defaults (`Default conf`, `Default song_gap`), which it also stores in the ONNX metadata.
+    For another model, add `--pt-model`, for example `--pt-model models/All-In-One.pt --output-path docs/models/All-In-One_fp16.onnx`.
+
+    `--precision fp16` stores the network weights in fp16, which halves the file (20 MB instead of 38 MB for Just-Bird, 51 MB instead of 99 MB for All-In-One). Feature extraction and merging stay in fp32. On 13 test recordings (`tests/onnx_graph_parity.py`), the fp16 graphs reproduced the boxes of `detect_birds.py` with the `.pt` model: 99.8% for Just-Bird (mean IoU 0.995) and 99.7% for All-In-One, with the same species (mean IoU 0.997). The export prints the merge it baked in (`Merge (baked in)`) and the suggested defaults (`Default conf`, `Default song_gap`), which it also stores in the ONNX metadata.
 
 2. Place the `.onnx` file under `docs/models/` if the export path was elsewhere.
 
-3. Add an entry to `docs/models/manifest.json`, first in the list if the demo should run it.
+3. Add an entry to `docs/models/manifest.json`, first in the list if the demo should start with it.
 
     ```json
     {
@@ -70,6 +75,16 @@ The demo runs the first model in `docs/models/manifest.json` with `"merge_method
           "phrase_gap": 0.05,
           "species_mapping": "Just-Bird",
           "names": { "0": "bird" }
+        },
+        {
+          "file": "All-In-One_fp16.onnx",
+          "label": "All-In-One_fp16.onnx",
+          "merge_method": "reconstruct",
+          "conf": 0.2,
+          "song_gap": 0.1,
+          "species_mapping": "All-In-One",
+          "names": { "0": "????", "1": "amabaw1", "…": "…" },
+          "common_names": { "0": "Unknown", "1": "Amazonian Barred-Woodcreeper", "…": "…" }
         }
       ]
     }
@@ -78,19 +93,23 @@ The demo runs the first model in `docs/models/manifest.json` with `"merge_method
     | Field | Meaning |
     | :--- | :--- |
     | `file` | The ONNX file, relative to `docs/models/` |
-    | `label` | Name shown at the top of the demo |
-    | `merge_method` | `fusion`, as printed by the export. Other entries are skipped. |
+    | `label` | Name in the model menu |
+    | `merge_method` | `fusion` or `reconstruct`, as printed by the export. Other entries are skipped. |
     | `conf` | Where the confidence slider starts: the printed `Default conf` (0.15 for Just-Bird, 0.2 for the other models) |
-    | `phrase_gap` | Where the phrase gap slider starts: the printed `Default song_gap` (0.05 for Just-Bird, 0 for Hawaii and Southern-Sierra-Nevada) |
+    | `phrase_gap` | `fusion` only: where the phrase gap slider starts, the printed `Default song_gap` (0.05 for Just-Bird, 0 for Hawaii and Southern-Sierra-Nevada) |
+    | `song_gap` | `reconstruct` only: where the song gap slider starts, the printed `Default song_gap` (0.1) |
     | `species_mapping` | Written to `model_config` of the exported JSON |
-    | `names` | Class labels for the exports, by class index (ONNX Runtime Web does not expose ONNX metadata) |
+    | `names` | eBird codes by class index, for the table and the exports (ONNX Runtime Web does not expose ONNX metadata) |
+    | `common_names` | Optional: common names by class index, shown in the table instead of the codes |
+
+    For a species model, `names` and `common_names` come from `id_to_ebird` and `ebird_to_name` of its mapping in `src/config.py`. With more than one class, the table gets a species column.
 
-4. Check that the demo's fusion still matches the graph:
+4. Check that the demo's merge still matches the graph:
 
     ```bash
     cd tests/js && npm install
-    node demo_fusion_parity.mjs ../../docs/models/Just-Bird_fp16.onnx
+    node demo_merge_parity.mjs ../../docs/models/Just-Bird_fp16.onnx
     ```
 
-    The script compares `fusion.js` with the graph's own detections for 16 slider settings on `tests/test.wav` and fails on any difference. Run it after exporting a new model or changing the fusion in `src/inference/onnx_export.py`.
+    The script compares `fusion.js` or `songs.js`, whichever the manifest entry's `merge_method` names, with the graph's own detections for 16 slider settings on `tests/test.wav` and fails on any difference. Run it after exporting a new model or changing a merge in `src/inference/onnx_export.py`.
 

docs/index.md

@@ -6,8 +6,7 @@
   <p><strong>Deep Learning Bird Call Detection & Evaluation System</strong></p>
 
   <a href="#license"><img src="https://img.shields.io/badge/license-MIT%20code%20%7C%20AGPL--3.0%20project-brightgreen.svg" alt="License: MIT code, AGPL-3.0 project"></a>
-  <a href="https://www.python.org/downloads/release/python-3120/" target="_blank" rel="noopener noreferrer"><img src="https://img.shields.io/badge/python-3.12-blue.svg" alt="Python 3.12"></a>
-  <img src="https://img.shields.io/badge/species-282-brightgreen" alt="Species 282">
+  <a href="https://www.python.org/downloads/release/python-31213/" target="_blank" rel="noopener noreferrer"><img src="https://img.shields.io/badge/python-3.12-blue.svg" alt="Python 3.12"></a>
 
 </div>
 
@@ -58,7 +57,7 @@ The inference software found 4 different species vocalizations and was able to l
 ## Quick Links
 
 - [Installation](installation.md) - set up the environment
-- [Demo](demo.md) - Just-Bird in your browser
+- [Demo](demo.md) - BirdBox in your browser
 - [How it works](how-it-works/overview.md) - pipeline description
 - [Data In/Out](data/fileflows.md) - datasets and output-format descriptions
 - [Models and Metrics](models-and-metrics/overview.md) - list of models with corresponding metrics