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TAT-MOE
TAT (Taiwanese Across Taiwan) MOE 台文語音語料庫 -- speakers reading aloud example sentences from the Ministry of Education's official Taiwanese dictionary (臺灣台語常用詞 辭典), recorded simultaneously on 6 different microphones per sentence.
⚠️ Known incompleteness -- this is NOT the full official TAT-MOE corpus. The official release covers 328 train / 58 eval / 54 test speakers (86,072 / 16,357 / 15,962 sentences). What is actually available in the source COS bucket -- and therefore in this upload -- is smaller and unevenly distributed across institutions:
| split | official speakers | speakers here | official sentences | sentences here |
|---|---|---|---|---|
| train | 328 | 291 | 86,072 | 80,936 |
| eval | 58 | 47 | 16,357 | 13,269 |
| test | 54 | 45 | 15,962 | 13,087 |
Confirmed (2026-09-03) this is a genuine gap in the source data, not a mistake in how this
repo was built: two institutions (宜蘭I / 宜蘭II) are entirely missing from train, and
eval/test only contain the KH and TA institutions (every other institution -- IU, KN,
SO, TH, TI, TS -- is completely absent from eval/test). Checked directly against the
original zip archives' own file listings (not just what got extracted) and against an
alternate 7z-packaged copy of the same data (moe/full/corpus-full.7z.*, byte-for-byte the
same total size) -- neither contains the missing institutions either, so there is no more-
complete source to recover this from within this project's COS bucket. If you need the full
official corpus, re-source the missing institutions directly from the TAT-MOE release.
Dataset Structure
id:<speaker>@<sentence>_<rec_device>(e.g.TI_TIF1012@A067-5.2_condenser), unique per row (the same speaker/sentence appears once perrec_device, distinguished by this suffix).audio: audio clip (16kHz mono PCM WAV).text: Taiwanese (Han-lo) transcript, same language as the audio (originallytwin the source dataset).mandarin: Mandarin transcript for the same sentence (originallyzh).rec_device: which of the 6 simultaneous microphones this row's audio came from.
Statistics
| split | lang_name | hours | n_utts | n_chars_in_utts | secs/utt | chars/sec | n_sents | n_chars_in_sents |
|---|---|---|---|---|---|---|---|---|
| train | Taigi | 879.2111 | 485616 | 6625144 | 6.52 | 2.09 | 0 | 0 |
| eval | Taigi | 145.761 | 79614 | 1259562 | 6.59 | 2.4 | 0 | 0 |
| test | Taigi | 131.4756 | 78522 | 1006302 | 6.03 | 2.13 | 0 | 0 |
| Total | - | 1156.4477 | 643752 | 8891008 | 6.47 | 2.14 | 0 | 0 |
train's stats combine an exact count for 272,775 rows (this run) with an estimate for the
212,841 rows uploaded before an earlier rate-limit crash (extrapolated from a 22-shard
spread sample of those 256 pre-crash shards -- secs/utt/chars/sec were stable across the
sample, so this should be accurate to within a percent or two). eval/test are exact
(every row scanned in one continuous run).
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