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README.md

@@ -51,9 +51,9 @@ Each config has one split, `test`, with one row per utterance per run. A run is
 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`.
+- **in-domain**: the WorldSpeech test split of the dialect 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 dialect**: the WorldSpeech test split of a second dialect in the training data: `sw_tz`, `ur_in` (2).
+- **held-out dialect**: the WorldSpeech test splits of dialects 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).
 
@@ -66,9 +66,9 @@ Every baseline is evaluated on all 43 test sets, except that `mistralai/Voxtral-
 | `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_config` | string | Config of the test set: the FLEURS language or the WorldSpeech dialect, 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. |
+| `domain` | string | `in-domain`, `out-of-domain`, `trained dialect` or `held-out dialect`, 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. |
@@ -80,7 +80,7 @@ The references are the `transcription` column of FLEURS and the `human_transcrip
 
 ## 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 all 299 runs. 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.
+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 all 299 runs. 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 dialects in the table of dialects.
 
 ## Load the transcripts and score a run