Upload README.md

README.md

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+---
+license: cc-by-nc-4.0
+language:
+- am
+- crs
+- en
+- es
+- fr
+- ha
+- hi
+- id
+- mr
+- sw
+- ta
+- ur
+task_categories:
+- automatic-speech-recognition
+size_categories:
+- 1M<n<10M
+pretty_name: LisTAya transcripts
+tags:
+- slam-asr
+- tiny-aya
+- transcripts
+configs:
+- config_name: listaya
+  data_files:
+  - split: test
+    path: listaya/*.parquet
+- config_name: baselines
+  data_files:
+  - split: test
+    path: baselines/*.parquet
+---
+
+# LisTAya transcripts: the test-set evaluations of the LisTAya study
+
+This dataset holds the reference and the model output for every utterance of every test-set evaluation in the paper *Does Regional Decoder Specialization Help Low-Resource ASR Based on the SLAM-ASR Framework?* (ROCLING 2026). The trained models are described in the model card [ERISLab/LisTAya](https://huggingface.co/ERISLab/LisTAya) and listed in the collection [https://huggingface.co/collections/ERISLab/listaya-slam-asr-projectors-on-tiny-aya-rocling-2026-6ab031414095f3df8805ed7b](https://huggingface.co/collections/ERISLab/listaya-slam-asr-projectors-on-tiny-aya-rocling-2026-6ab031414095f3df8805ed7b).
+
+## Configs
+
+Each config has one split, `test`, with one row per utterance per run. A run is one model evaluated on one test set.
+
+| Config | Models | Runs | Utterances |
+|---|---|---|---|
+| `listaya` | The trained LisTAya checkpoints: the four Tiny Aya regional decoder variants (Global, Earth, Fire, Water) for each of the twelve languages, each checkpoint evaluated on the test sets of its own language. | 172 | 933,788 |
+| `baselines` | Three off-the-shelf models: `Qwen/Qwen2-Audio-7B`, `mistralai/Voxtral-Mini-3B-2507`, `openai/whisper-medium`. | 127 | 662,017 |
+
+## Test sets
+
+There are 43 test sets, all test splits:
+
+- **out-of-domain**: the FLEURS test split (`google/fleurs`) of 11 languages; FLEURS does not cover Kreol Seselwa.
+- **in-domain**: the WorldSpeech test split of the variety each language trains on, one per language (12). Hausa's is `ha_td`, because its checkpoints were selected on the `ha_ng` test split. Kreol Seselwa's is the `test_clean` split of `ERISLab/WorldSpeech` config `crs_sc`; every other language's comes from `disco-eth/WorldSpeech`.
+- **trained variety**: the WorldSpeech test split of a second variety in the training data: `sw_tz`, `ur_in` (2).
+- **held-out variety**: the WorldSpeech test splits of country varieties absent from training (18): `en_au`, `en_jm`, `en_ke`, `en_nz`, `en_pk`, `en_sl`, `en_zm`, `es_ar`, `es_cl`, `es_co`, `es_es`, `es_pe`, `es_pr`, `es_py`, `es_uy`, `fr_cd`, `fr_ci`, `ta_lk`.
+
+Every baseline is evaluated on all 43 test sets, except that `mistralai/Voxtral-Mini-3B-2507` and `openai/whisper-medium` produced no output on Kreol Seselwa, so `baselines` holds 127 runs (3 x 43 - 2).
+
+## Columns
+
+| Column | Type | Content |
+|---|---|---|
+| `model` | string | Hub id of the evaluated model. In `listaya` it is the checkpoint repository, whose name gives the decoder, the training language and the checkpoint step. |
+| `decoder` | string | `listaya` only: the Tiny Aya decoder variant, Global, Earth, Fire or Water. |
+| `language` | string | Language of the test set: Amharic, English, French, Hausa, Hindi, Indonesian, Kreol Seselwa, Marathi, Spanish, Swahili, Tamil, Urdu. |
+| `language_code` | string | ISO 639 code of `language`. |
+| `eval_dataset` | string | Hub path of the test set. |
+| `eval_config` | string | Config of the test set: the FLEURS language or the WorldSpeech country variety, such as `en_au`. |
+| `eval_split` | string | Split of the test set. |
+| `domain` | string | `in-domain`, `out-of-domain`, `trained variety` or `held-out variety`, as defined above. |
+| `sample_index` | int64 | Position of the utterance among the evaluated utterances of the split, in split order. |
+| `duration` | float64 | Length of the audio in seconds. |
+| `reference` | string | The normalised reference transcript. |
+| `hypothesis` | string | The normalised model output. |
+
+## Text normalisation
+
+The references are the `transcription` column of FLEURS and the `human_transcript` column of WorldSpeech. References and model outputs pass through the same normaliser before scoring, for every language: lowercasing; removal of text inside square brackets, angle brackets and parentheses; Unicode NFKD decomposition, with nonspacing combining marks (category Mn) deleted and all other marks, symbols and punctuation replaced by a space; removal of any remaining character that is neither a word character nor whitespace; and collapsing of whitespace. In scripts that write vowels as combining signs, such as Devanagari and Tamil, the normalised text therefore keeps only part of each syllable. Utterances whose normalised reference is empty are left out of the evaluation, so `sample_index` equals the row index of the source split only where none were left out.
+
+## Relation to the paper
+
+The CER of a run is the corpus-level character error rate of its rows in percent, computed with the `cer` metric of the `evaluate` library and rounded to two decimals. Recomputed from these rows, it equals the CER the paper uses for 298 of the 299 runs. The rows of `ERISLab/q2a_openai_whisper-medium_CohereLabs_tiny-aya-earth_ws-ha_ng-600` on `google/fleurs` `ha_ng` come from a second evaluation of that checkpoint with the same settings; they score 46.29 CER, and the paper uses the 46.64 of the evaluation it logged. The paper's per-test-set tables print, for each test set, the lowest of these CERs among the three baselines and among the language's four trained checkpoints: the in-domain and out-of-domain test sets in the table that compares training with the baselines, and the trained and held-out varieties in the table of country varieties.
+
+## Load the transcripts and score a run
+
+```python
+import evaluate
+from datasets import load_dataset
+
+rows = load_dataset("ERISLab/LisTAya-transcripts", "listaya", split="test")
+run = rows.filter(lambda r: r["model"] == "ERISLab/q2a_openai_whisper-medium_CohereLabs_tiny-aya-global_ws-en_us-500" and r["eval_dataset"] == "google/fleurs")
+cer = evaluate.load("cer").compute(references=run["reference"], predictions=run["hypothesis"])
+print(f"{100 * cer:.2f}")  # 6.22
+```
+
+## Licence and attribution
+
+The dataset is released under CC BY-NC 4.0. The references are normalised transcripts from FLEURS ([google/fleurs](https://huggingface.co/datasets/google/fleurs), CC BY 4.0) and WorldSpeech ([disco-eth/WorldSpeech](https://huggingface.co/datasets/disco-eth/WorldSpeech), CC BY-NC 4.0), whose non-commercial term this release follows. The Kreol Seselwa references come from [ERISLab/WorldSpeech](https://huggingface.co/datasets/ERISLab/WorldSpeech), config `crs_sc`, recorded sessions of the National Assembly of Seychelles.
+
+## Citation
+
+```bibtex
+@inproceedings{rios-etal-2026-regional,
+    title = "Does Regional Decoder Specialization Help Low-Resource {ASR} Based on the {SLAM}-{ASR} Framework?",
+    author = "Rios, Edwin Arkel  and
+      Zaruma, Jocelyn  and
+      Ewoorkar, Girish  and
+      Sourabh, Sneh  and
+      Mack, Julian  and
+      Juan, Hung-Hui  and
+      Huang, Stephen  and
+      Lai, Bo-Cheng",
+    booktitle = "Proceedings of the 38th Conference on Computational Linguistics and Speech Processing (ROCLING 2026)",
+    year = "2026",
+    publisher = "Association for Computational Linguistics"
+}
+```