| --- |
| { |
| "pretty_name": "CHIME — LibriSpeech (prepared candidate)", |
| "license": "cc-by-4.0", |
| "tags": [ |
| "audio", |
| "webdataset", |
| "chime", |
| "prepared-candidate" |
| ], |
| "configs": [ |
| { |
| "config_name": "default", |
| "data_files": [ |
| { |
| "split": "test", |
| "path": "data/test/*.tar" |
| }, |
| { |
| "split": "train", |
| "path": "data/train/*.tar" |
| }, |
| { |
| "split": "validation", |
| "path": "data/validation/*.tar" |
| } |
| ] |
| } |
| ] |
| } |
| --- |
| |
| # CHIME · LibriSpeech |
|
|
| 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: [LibriSpeech](https://www.openslr.org/12). |
| - Pinned source release: `OpenSLR-12-clean-100`. |
| - Selection: train-clean-100, dev-clean and test-clean only; exclude train-clean-360, train-other-500 and original MP3 parents. |
| - Original license/access conditions: CC-BY-4.0 Public download; retain attribution. |
| - Source notes: An explicit clean-speech subset, not full LibriSpeech or coverage of difficult recording conditions. |
|
|
| 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 **39,665 audio assets**, 14,038,276,576 stored-audio bytes and 14,036,531,316 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 `<key>.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`: 5,559 original audio assets. |
| - `train`: 28,539 original audio assets. |
| - `validation`: 5,567 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-librispeech", revision="<commit-sha>", 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:** 5559 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) | |
| |---|---|---|---|---|---|---| |
| | `librispeech-asr` | asr | wer | 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/<task>.jsonl`; the frozen task definitions are in `provenance/benchmark-v1/usage.json`. Splits never depend on the evaluation seed. |
|
|
|
|
