--- { "pretty_name": "CHIME — BirdSet (prepared candidate)", "license": "other", "tags": [ "audio", "webdataset", "chime", "prepared-candidate" ], "configs": [ { "config_name": "default", "data_files": [ { "split": "test", "path": "data/test/*.tar" }, { "split": "train", "path": "data/train/*.tar" } ] } ], "license_name": "original-source-terms", "license_link": "https://github.com/DBD-research-group/BirdSet" } --- # CHIME · BirdSet This is a source-preserving **prepared candidate**, not a scientifically admitted CHIME benchmark release. It repackages the selected upstream release into split-specific WebDataset TAR shards with native audio and per-example JSON. A Parquet index permits metadata filtering without downloading audio shards. ## Source and selected scope - Upstream: [BirdSet](https://github.com/DBD-research-group/BirdSet). - Pinned source release: `HSN Zenodo7525805 + POW Zenodo4656848`. - Selection: Bounded HSN and POW soundscape components from original WAV releases; native originals replace the author HF OGG re-encodings. - Original license/access conditions: HSN CC-BY-4.0; POW CC0-1.0. Preserve each original source attribution and BirdSet task mapping. Public direct HTTPS; preserve all attribution and license notices. No access account or signed agreement. - Source notes: Acquire only pinned objects. Preserve native audio and raw source labels. Subset and quality rules are in the matching decision; scientific admission is pending. Preparation2026-10-29: preserve177 original FLAC/WAV assets; measured decoded PCM5,873,903,266bytes,6GB cap. Original authors, licensors and recording contributors retain their rights. This preparation was assembled for public noncommercial research access; this repository does not grant broader rights than the original sources. Preserve per-recording attribution and notices. `provenance/` contains original annotations/docs/licenses, source provenance and the redistribution audit. FSD50K/Clotho audio has per-recording terms; Clotho captions retain the complete custom Tampere experimental/noncommercial license. The CHIME code license does not apply to these data. ## Representation and preservation There are **17,460 audio assets**, 6,196,094,616 stored-audio bytes and 13,380,506,032 decoded-PCM-equivalent bytes. Native sample rate, duration and channel count are retained. No loudness normalization, silence trimming, resampling or downmixing is applied. Each sample retains its native audio codec (WAV/FLAC/MP3/OGG or another supported native container). Each payload is accompanied by `.json` in an ordinary TAR. The JSON contains `split`, stored audio metadata, provenance, source groups and source targets. `manifest.jsonl` also records shard/member offsets and checksums. `metadata.parquet` provides audio and shard columns, common source/group/license columns, and exact arbitrary annotations in `metadata_json` (decode with `json.loads`). It does not duplicate audio payloads. Keeping heterogeneous annotations as JSON preserves empty objects and differing target schemas without Arrow type coercion. `release.json` hashes the audio shards and record manifest, and `publication.json` inventories all published files including attribution assets. CHiME-3 background recordings interleave six synchronized native mono PCM16 channels into one six-channel PCM16 WAV per session, with exact decoded-sample checks and an explicit channel map. The excluded CHiME speech corpus is not included. Other audio payloads are copied byte for byte. ## Source splits - `test`: 12,000 original audio assets. - `train`: 5,460 original audio assets. These are original source partitions or explicitly `unspecified`/`background` partitions. ESC-50 retains five official folds; no fixed benchmark train/test assignment is invented. VocalSet's split is the supplied technique-classification singer protocol, not a universal task protocol. Background noise is not converted into artificial word or silence examples. Linked views/channels share provenance groups. ## Scientific limitations Scientific admission remains **pending**. Native file integrity and packaging checks do not establish label correctness, perceptual deduplication, contamination freedom or benchmark validity. Existing upstream annotation errata and ambiguity are retained/documented. The source audit found declared Freesound ID overlaps within some official folds/partitions and across ESC-50, FSD50K and Clotho; FSD50K train/validation also share uploaders. Use the supplied source IDs, group metadata and `provenance/source-audit.json` when defining an evaluation protocol. Do not claim these candidate splits are universally leakage-free. Scores from this repository alone are not official CHIME scores. ## Loading Pin an immutable Hub commit revision in experiments. A generic Hugging Face WebDataset loader can read the shard configurations: ```python from datasets import load_dataset samples = load_dataset("ltuncay/chime-birdset", revision="", streaming=True) ``` For local source-native decoding and checksum verification, use CHIME's `iter_release(path, split=..., verify_hashes=True)`. Its audio arrays have shape `[frames, channels]`; the evaluated model owns its preprocessing. Download only selected shards/index files when appropriate; benchmark execution must not silently fetch data. ## Provenance and citation Cite the upstream dataset and recording authors as specified in the original documentation under `provenance/`. This preparation does not claim authorship of the source recordings or annotations. Exact upstream objects, checksums, release selection, preparation metadata and source URLs are recorded in `release.json`. Source Zenodo metadata/citations are retained when available. See the CHIME project implementation brief and scientific paper for the evolving evaluation protocol. ## CHIME v1 benchmark usage > **Do not train on CHIME evaluation data:** 12000 records in the release split(s) `test`, listed one key per line in `provenance/benchmark-v1/eval-keys.txt`. Models submitted to CHIME must not see these recordings (or their source recordings) during pretraining or fine-tuning. | Task | Family | Metric | Folds | Train | Validation | Test (eval) | |---|---|---|---|---|---|---| | `birdset-hsn-species` | multilabel | macro_ap | 1 | train | validation | test | Exact per-record partition assignments (including records excluded from training because they share a source with evaluation data, and digitally silent assets) are in `provenance/benchmark-v1/.jsonl`; the frozen task definitions are in `provenance/benchmark-v1/usage.json`. Splits never depend on the evaluation seed.