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.gitattributes

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+qa/train.jsonl filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,265 @@
----
-license: cc-by-nc-sa-4.0
----
+# SO-Dataset: Spatial FOA Audio with FSD63 Event Labels
+
+SO-Dataset is a large-scale spatial audio dataset in first-order ambisonics (FOA) format. Each example contains one FOA waveform and spatial event annotations in DCASE-style CSV files. The dataset combines simulated spatial scenes and real FOA recordings, and all sound event labels are mapped into a unified 63-class FSD taxonomy.
+
+The public release stores audio and annotations as tar shards. The tar files preserve the same relative paths used by the metadata, so extracting the archives recreates the `audio/` and `annotations/` directories expected by the JSONL files.
+
+## Dataset Contents
+
+- **Audio format**: FOA waveform files (`.wav`)
+- **Spatial annotations**: DCASE-style CSV files
+- **Labels**: unified FSD63 label set
+- **Splits**: `train`, `valid`, `test`
+- **Metadata**: one JSON object per audio scene
+- **Packaging**: path-preserving tar shards for easier download and upload
+
+## File Structure
+
+```text
+SO-Dataset/
+  label_mapping.json
+  tar_shard_summary.json
+
+  metadata/
+    train.jsonl
+    valid.jsonl
+    test.jsonl
+
+  manifests/
+    audio-train.jsonl
+    audio-valid.jsonl
+    audio-test.jsonl
+    annotations-train.jsonl
+    annotations-valid.jsonl
+    annotations-test.jsonl
+
+  archives/
+    audio/
+      train/
+        audio-train-000000.tar
+        audio-train-000001.tar
+        ...
+      valid/
+      test/
+    annotations/
+      train/
+        annotations-train-000000.tar
+      valid/
+        annotations-valid-000000.tar
+      test/
+        annotations-test-000000.tar
+```
+
+After extraction, the archives create:
+
+```text
+audio/{train,valid,test}/*.wav
+annotations/{train,valid,test}/*.csv
+```
+
+These paths match the paths stored in `metadata/*.jsonl`.
+
+## Dataset Statistics
+
+| Split | Scenes / FOA files | Audio shards | Annotation files | Annotation shards |
+|---|---:|---:|---:|---:|
+| train | 329,610 | 188 | 1,032,417 | 1 |
+| valid | 35,093 | 23 | 127,028 | 1 |
+| test | 35,237 | 22 | 106,460 | 1 |
+| total | 399,940 | 233 | 1,265,905 | 3 |
+
+Total tar files: **236**.
+
+Approximate payload sizes:
+
+| Group | Payload bytes |
+|---|---:|
+| audio/train | 939,040,161,296 |
+| audio/valid | 110,868,182,152 |
+| audio/test | 108,399,164,604 |
+| annotations/train | 2,350,160,868 |
+| annotations/valid | 313,645,376 |
+| annotations/test | 263,433,222 |
+
+## Metadata Format
+
+Each line in `metadata/{split}.jsonl` is a JSON object describing one FOA scene.
+
+Example:
+
+```json
+{
+  "schema_version": "spatial_foa_scene_v1",
+  "split": "train",
+  "dataset": "sim_static",
+  "data_source": "sim_static",
+  "scene_id": "train/ov2_000000",
+  "audio": {
+    "duration_seconds": 20.0,
+    "foa_path": "audio/train/foa_fed1992f629ae5f3db28.wav"
+  },
+  "scene_annotation_csv_path": "annotations/train/foa_fed1992f629ae5f3db28.csv",
+  "sources": [
+    {
+      "source_id": "1",
+      "track_id": 0,
+      "original_label": "telephone_alarm",
+      "label": "telephone_alarm",
+      "label_id": 50,
+      "active_duration_seconds": 19.999937,
+      "active_times": [[0.000063, 20.0]],
+      "source_trajectory_csv_path": "annotations/train/foa_fed1992f629ae5f3db28_src00.csv",
+      "motion": {
+        "is_moving": false,
+        "pattern": null,
+        "description": null,
+        "scene_description": null
+      }
+    }
+  ]
+}
+```
+
+Important fields:
+
+- `audio.foa_path`: relative path to the FOA waveform after extraction.
+- `audio.duration_seconds`: audio duration in seconds.
+- `scene_annotation_csv_path`: combined scene-level DCASE-style CSV annotation.
+- `sources`: list of individual sound sources in the scene.
+- `sources[*].source_trajectory_csv_path`: per-source trajectory CSV.
+- `sources[*].original_label`: label before mapping.
+- `sources[*].label` and `sources[*].label_id`: final FSD63 label and class id.
+- `sources[*].active_times`: one or more `[start_seconds, end_seconds]` intervals.
+- `sources[*].track_id`: source/event track id used in the CSV annotation.
+
+## Label Mapping
+
+`label_mapping.json` defines the FSD63 label space:
+
+- `class_set`: `fsd63`
+- `class_count`: `63`
+- `class_names`: ordered class names
+- `class_name_to_id`: class name to integer id
+- `class_id_to_name`: integer id to class name
+- `raw_label_aliases`: aliases used before final mapping, such as mapping singing variants to `singing`
+
+Use `label_mapping.json` as the canonical class-id definition.
+
+## Download
+
+Install the Hugging Face CLI:
+
+```bash
+pip install -U "huggingface_hub[cli]"
+```
+
+Download the full dataset:
+
+```bash
+hf download dieKarotte/SO-Dataset \
+  --repo-type dataset \
+  --local-dir SO-Dataset
+```
+
+Download only metadata, manifests, and the label mapping:
+
+```bash
+hf download dieKarotte/SO-Dataset \
+  --repo-type dataset \
+  --local-dir SO-Dataset \
+  --include "metadata/*" \
+  --include "manifests/*" \
+  --include "label_mapping.json" \
+  --include "tar_shard_summary.json"
+```
+
+Download only the training audio shards:
+
+```bash
+hf download dieKarotte/SO-Dataset \
+  --repo-type dataset \
+  --local-dir SO-Dataset \
+  --include "archives/audio/train/*" \
+  --include "metadata/train.jsonl" \
+  --include "label_mapping.json"
+```
+
+## Extraction
+
+Extract all audio and annotation shards from the dataset root:
+
+```bash
+cd SO-Dataset
+
+find archives/audio -name "*.tar" -print0 | xargs -0 -n 1 -P 4 tar -xf
+find archives/annotations -name "*.tar" -print0 | xargs -0 -n 1 -P 4 tar -xf
+```
+
+Extract only the training split:
+
+```bash
+cd SO-Dataset
+
+find archives/audio/train -name "*.tar" -print0 | xargs -0 -n 1 -P 4 tar -xf
+find archives/annotations/train -name "*.tar" -print0 | xargs -0 -n 1 -P 4 tar -xf
+```
+
+After extraction, metadata paths are directly usable:
+
+```text
+audio/train/foa_....wav
+annotations/train/foa_....csv
+annotations/train/foa_...._src00.csv
+```
+
+## Reading Metadata
+
+```python
+import json
+from pathlib import Path
+
+root = Path("SO-Dataset")
+
+with (root / "metadata" / "train.jsonl").open("r", encoding="utf-8") as f:
+    item = json.loads(next(f))
+
+audio_path = root / item["audio"]["foa_path"]
+scene_csv_path = root / item["scene_annotation_csv_path"]
+source_csv_paths = [
+    root / src["source_trajectory_csv_path"]
+    for src in item["sources"]
+]
+
+print(audio_path)
+print(scene_csv_path)
+print(source_csv_paths[:3])
+```
+
+## Manifests
+
+The files in `manifests/` list the tar shards for each group and split.
+
+Example row:
+
+```json
+{
+  "kind": "audio",
+  "split": "train",
+  "shard": "archives/audio/train/audio-train-000000.tar",
+  "files": 1741,
+  "payload_bytes": 4999665664,
+  "estimated_tar_bytes": 5000501760
+}
+```
+
+## Notes
+
+- The release is anonymized: public audio and CSV filenames use hashed names.
+- Metadata paths are relative to the dataset root.
+- The tar archives are not compressed. This keeps extraction fast and avoids heavy CPU cost for already-large waveform data.
+- Scene-level CSV files contain the combined annotation for all active sources in a FOA scene.
+- Per-source CSV files are also provided and are referenced from `sources[*].source_trajectory_csv_path`.
+
+## Citation and License
+
+Please cite this dataset as appropriate for your use. If you redistribute or use the dataset in downstream work, make sure your usage is compatible with the licenses of the underlying audio and spatial data sources.

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