No licence is declared for this dataset. The recordings are the publicly available uploads of the Turkish audiobook YouTube channels credited in this card and their rights remain with those channels; nothing here grants or implies any right over them, and it is for you to judge whether your intended use is lawful. What is expected of you is attribution: credit the source channels listed in this card in addition to the dataset itself. If a rights holder objects, that channel's recordings are removed from the dataset. Access opens immediately once you accept.

KIRAAT — A Turkish Read-Speech Corpus

A sentence-aligned read-speech corpus built from publicly available recordings on Turkish audiobook YouTube channels. The channel credits are in the table at the end of this card; every clip carries the channel it came from in the channel column.


clips	1,840,404
duration	3,105.7 hours
recommended subset	1,547,494 clips / 2,575.2 hours
channels	27
speakers (clustered)	90
source recordings	2,680
words (ASR)	21,695,774
audio	24 kHz, mono, FLAC, embedded in the parquet shards
download size	~250 GB (clip files measured at 246 GB)
splits	`train` / `dev` / `test` / `rest` — the whole corpus is published
language	Turkish

Loading

from datasets import load_dataset

# the default training subset
train = load_dataset("serdarcaglar/kiraat", split="train")

# 250 GB is a lot to download for a look; stream instead
train = load_dataset("serdarcaglar/kiraat", split="train", streaming=True)
clip = next(iter(train))
clip["audio"]["array"], clip["text_spoken"], clip["speaker_id"]

# everything the policy left out — published, not deleted
rest = load_dataset("serdarcaglar/kiraat", split="rest", streaming=True)

Audio is embedded in the parquet shards, so there is no separate audio download; datasets decodes the audio column for you. This needs datasets>=4 — the shards were written with the List feature type, and on 3.x loading fails with Feature type 'List' not found before any row is read. Decoding the audio column also needs torchcodec installed; without it you can still read the raw FLAC bytes with .cast_column("audio", Audio(decode=False)).

Two things that set this corpus apart

Cuts land on sentence boundaries. Clips are not cut at silences; they are cut at sentence boundaries over word timestamps produced by ASR and forced alignment. This has a measured consequence. On the same recordings (206 recordings from 27 channels, 240.5 hours), silence-aligned segmentation starts 14.31% of its clips mid-sentence, and 12.81% with nothing in the data to indicate it. Sentence-aligned segmentation starts 1.39% mid-sentence and 0.00% without a marker: every broken start carries a forced_split or boilerplate flag, so it is visible in the data and it falls outside the recommended subset.

No threshold ever drops a clip. Quality measurements are published as columns. Which clips belong to the default training subset is decided by a versioned policy, and the reason for every exclusion is written into the exclusion_reasons column. The policy itself has been through blind listening review: two thresholds that are standard in this field (clip_ratio, word_confidence) did not survive that review and were removed. You can cut your own thresholds from the columns — clips with recommended=false have not been deleted.

Splits

split	clips	hours	recordings	channels	speakers
train	1,512,448	2,517.08	2,579	27	89
dev	5,750	9.54	40	27	26
test	5,473	9.30	45	27	26
rest	316,733	569.78	—	27	—
total	1,840,404	3,105.70	2,680	27	90

train, dev and test are drawn from the recommended subset. Every remaining clip is published in a fourth split, rest — nothing is deleted, and a clip's measurement columns are the same there as anywhere else:

what is in `rest`	clips	hours
`recommended=false`	292,910	530.5
recommended but unused: beyond a channel's dev/test duration target, or dropped by the text-leakage cleanup (these are never moved into `train`)	23,823	39.3

The recordings column above counts recordings whose clips form a split; rest draws clips from recordings across all three, and 16 of the 2,680 recordings have all of their clips in rest.

Splits are recording-level: all clips from one recording land in one split, so evaluation happens on a recording the model has never heard. dev and test are channel-balanced (equal duration target per channel). Text leakage was checked and cleaned: dev/test clips carrying a sentence identical to one in train were dropped, and the remaining overlap is zero.

Speaker overlap was left in deliberately: 25 of the 26 speakers in test also appear in train, so this is a seen-speaker evaluation. If you want an unseen-speaker experiment, build your own split from the speaker_id column.

How it was built

Decode to 24 kHz mono with ffmpeg → long-form ASR with faster-whisper large-v3 (word timestamps) → forced alignment with torchaudio MMS_FA → channel-level boilerplate/announcement mining → sentence-boundary segmentation with audio-based boundary refinement → clip measurements (VAD speech ratio, silences, level, BS.1770 loudness; music via HDemucs + AudioSet; DNSMOS P.835; speaker embeddings via ECAPA-TDNN) → recommended subset via the versioned policy.

Every model revision and stage version is pinned in the run record (run.json), and the pipeline code is open: https://github.com/serdarildercaglar/kiraat. Every count, hour and share printed in this card is regenerated from the run database by scripts/verify_card.py — including the duplicate marking, the policy decision and the four splits, which are recomputed rather than read back.

Quality distributions (recommended subset)

column	p05	p50	p95
duration (s)	2.76	5.82	10.56
`loudness_lufs`	−27.99	−20.92	−12.54
`dnsmos_ovrl`	2.862	3.296	3.491
`dnsmos_sig`	3.271	3.563	3.710
`dnsmos_bak`	3.631	4.109	4.210
`speech_ratio`	0.851	0.977	1.000
`align_score_mean`	0.858	0.961	0.994
`word_confidence`	0.550	0.917	0.999
`n_words`	5	11	21

Across the whole corpus, 83.6% of clips have dnsmos_ovrl ≥ 3.0 — a threshold commonly used in the field (at ≥ 3.5 the share is 3.9%). Audio is not normalized; level is given as a column and the decision is left to you.

Recommended-subset policy (v6)

For recommended=true: speech_ratio ≥ 0.60, internal_silence_sec ≤ 1.0, and none of the flags oversize, forced_split, gap_split, short, duplicate, boilerplate, background_music.

This leaves out 292,910 clips (530.5 hours). What each rule contributes to that total — a clip can fail more than one rule:

rule	clips excluded
flag present	281,995
`internal_silence_sec > 1.0`	12,757
`speech_ratio < 0.60`	2,412

Flag frequencies over the whole corpus: background_music 207,196 (11.26%), forced_split 52,501 (2.85%), short 20,904 (1.14%), oversize 6,731 (0.37%), gap_split 5,141 (0.28%), boilerplate 1,053 (0.06%), duplicate 576 (0.03%).

Duplicate means the text and the audio are identical (duration, LUFS, RMS and peak all match exactly). A separate reading of the same text is not a duplicate; it is kept as prosodic variety, which is why the duplicate count is small.

Columns

column	type	unit	stage	description
`audio`	audio	24 kHz	export	The clip audio itself, mono FLAC embedded in the parquet shards. `datasets` decodes it to `{array, sampling_rate}`; there is no separate audio download. No gain is applied — see `loudness_lufs`.
`id`	string		segment	Clip id, `srcNNNNN-KKKKK`: the source recording's number and the clip's index within that recording.
`source_id`	string		prepare	The source recording the clip was cut from (`srcNNNNN`). Every clip of one recording carries the same value; this is the key to group by if you build your own recording-level splits.
`channel`	string		prepare	The YouTube channel the source recording came from (folder name). This is not a speaker identity; a channel may have several readers.
`source_sample_rate`	int	Hz	prepare	The source recording's actual sample rate in its container. Clips are resampled to 24 kHz, so sources below 24 kHz have been upsampled and this column is what tells them apart.
`source_flags`	list[string]		prepare	Recording-level flags. `truncated_source`: the decoded audio is shorter than 95% of the duration the container reports (a corrupt or partial download).
`start`	float	s	segment	Clip start time within the source recording, taken from the word timestamp at the sentence boundary, with boundary refinement and lead padding applied.
`end`	float	s	segment	Clip end time within the source recording, same as `start`.
`duration`	float	s	segment	Clip duration, `end - start`.
`text_raw`	string		asr	Untouched ASR output (Whisper large-v3), joined over the clip's word span.
`text`	string		segment	Lightly cleaned text: whitespace, quotes and repeated punctuation fixed; numbers and abbreviations left as they are. The readable/archival version.
`text_spoken`	string		segment	Text converted to how it is spoken: numbers, ordinals, times/dates/currencies and common abbreviations written out in Turkish. This is the field intended for TTS training.
`n_words`	int		segment	Word count in the clip (ASR words, with clitics — de/da, mi, ki — attached to the preceding word).
`word_confidence`	float		segment	The lowest word probability in the clip, either a Whisper word probability or an aligner score depending on `confidence_source`. A single rare word (proper noun, interjection) drags it down; it is a ranking signal, not a gate.
`word_confidence_mean`	float		segment	The clip mean of word probabilities from the same source.
`confidence_source`	string		segment	Where the `word_confidence` columns come from: `asr` (Whisper word probability) or `align` (forced-alignment score).
`align_score_min` (optional)	float		align	The lowest per-word mean-square probability from the forced aligner (MMS_FA, CTC) in the clip. Measures audio–text agreement independently of Whisper; 0 = could not be aligned.
`align_score_mean` (optional)	float		align	The clip mean of the aligner's word scores.
`lead_gap_sec` (optional)	float	s	segment	Gap between the clip's first word and the previous word in the recording. Empty if it is the recording's first word. A small value means the previous sentence risks bleeding into the clip.
`trail_gap_sec` (optional)	float	s	segment	Gap between the clip's last word and the next word in the recording; empty if it is the recording's last word.
`leading_silence_sec`	float	s	clip_qc	Time from the clip start to the first speech found by VAD (Silero), from a separate no-padding, short-silence-threshold VAD pass.
`trailing_silence_sec`	float	s	clip_qc	Time from VAD's last speech to the clip end, from the same no-padding pass.
`internal_silence_sec`	float	s	clip_qc	The longest speechless stretch inside the clip (largest gap between VAD regions).
`speech_ratio`	float		clip_qc	The fraction of clip duration VAD counts as speech (0–1).
`peak_dbfs`	float	dBFS	clip_qc	Absolute peak sample level.
`rms_dbfs`	float	dBFS	clip_qc	RMS level of the clip (no gain applied — the source's own level).
`clip_ratio`	float		clip_qc	Fraction of samples at full scale (\|x\| ≥ 0.99); a measure of digital clipping.
`loudness_lufs` (optional)	float	LUFS	clip_qc	BS.1770 integrated loudness (pyloudnorm). No gain is applied to the audio; level normalization is left to you, and this column is its input. Empty for clips shorter than the 0.4 s measurement block or entirely silent.
`music_score_audioset` (optional)	float		music	Maximum score of an AudioSet AST classifier over its music labels (Music, Background music, Soundtrack…), taken as the max over clip windows. A cheap screening signal; it can coexist with speech. Empty for unreadable clips (`unreadable_audio`).
`music_to_speech_db` (optional)	float	dB	music	A physical measurement from source separation (HDemucs): the ratio of accompaniment energy (drums+bass+other) to vocal energy. If the separator did not run, the floor value −80. The `background_music` flag is derived from this column using the threshold in the config. Empty for unreadable clips.
`music_db_separated` (optional)	bool		music	Whether the separator actually ran on this clip. If not, `music_to_speech_db` is the floor value rather than a measurement (the AudioSet score was below the screening threshold). Empty for unreadable clips.
`music_stem_db` (optional)	string (JSON)	dB	music	Per-stem energy levels for the separator's four stems (drums, bass, other, vocals); written only for clips the separator ran on. `music_to_speech_db` is derived from these. Published as a JSON object encoded in a string (the key set is not fixed), so parse it with `json.loads`.
`dnsmos_sig` (optional)	float		dnsmos	DNSMOS P.835 speech quality estimate (SIG, 1–5): distortion of the speech itself. Matches the reference implementation exactly: 9.01 s windows, polynomial mapping, window averaging. Empty for unreadable clips (`unreadable_audio`).
`dnsmos_bak` (optional)	float		dnsmos	DNSMOS P.835 background estimate (BAK, 1–5): how intrusive the background noise is; higher means cleaner. Empty for unreadable clips.
`dnsmos_ovrl` (optional)	float		dnsmos	DNSMOS P.835 overall quality estimate (OVRL, 1–5). Not a gate: a policy rule may only be added after blind listening review (see the policy v4 record). Empty for unreadable clips.
`flags`	list[string]		export	Clip flags; none of them removes a clip. `forced_split`: a sentence that exceeded the duration ceiling on its own was split at internal punctuation; `gap_split`: the same sentence was split at a long silence; `oversize`: no internal punctuation to split on, so the sentence was kept whole and exceeds the ceiling; `short`: duration below `segment.min_sec`; `background_music`: `music_to_speech_db` above threshold; `duplicate`: a repeat of the same text within the same recording/channel (see `duplicate_of`); `boilerplate`: an intro/announcement pattern that recurs across the channel's recordings; `unreadable_audio`: the clip file could not be read and has no measurements.
`duplicate_of` (optional)	string		export	If the clip is a duplicate, the id of the copy that was kept; otherwise empty. A duplicate means the text AND the audio are identical: if the audio identity `dedupe.identity_fields` (duration, LUFS, RMS, peak) matches exactly, the clip is a copy of the same recording. A separate reading of the same text is NOT a duplicate — it is kept as prosodic variety; channel is not part of the key.
`speaker_id` (optional)	string		export	The recording's speaker cluster (`spkNNNN`). Clustering runs over the whole corpus: the merge threshold is taken from the equal-error point of two measured distributions (within-recording clip similarity, and cross-channel recording similarity), and no cluster count is imposed. The identity is recording-level; all clips of a recording carry the same value. Not a gate — the policy has no rule on it.
`speaker_consistency` (optional)	float		speaker	Mean pairwise cosine similarity among the recording's embedded clips (0–1). A low value indicates more than one voice in the recording (interview, multi-voice reading) and says that recording cannot be treated as a single speaker.
`speaker_margin` (optional)	float		export	The recording's similarity to its own cluster centroid minus its similarity to the nearest other cluster. A small value points to a recording near a cluster boundary; a negative value says it may be in the wrong cluster.
`recommended`	bool		export	Whether the versioned policy (`recommended_subset`) recommends this clip for the default training subset. It removes no data; you can set your own rule from the measurement columns.
`exclusion_reasons`	list[string]		export	If `recommended=false`, the list of rules that were not met: `metric<threshold`, `metric>threshold`, `flag:name[,name]`, `missing_measurement:metric`. Empty for recommended clips.
`policy_version`	string		export	The policy version that produced `recommended` and `exclusion_reasons`; the rules are versioned in the config, and when they change only these three columns are recomputed.
`split`	string		export	The published split the row belongs to: `train`, `dev`, `test` or `rest`.

Known limitations

The text is ASR output, not corrected by hand; text_raw, text and text_spoken are all derived from the same ASR transcript.
Digits are not force-aligned. The aligner (MMS_FA) is character-based and its dictionary has no digit characters, so words containing digits ("1923", "15'inci", "%20") get their timestamps from ASR rather than the aligner, and those words do not contribute to align_score. Share of affected clips: 3.08% (2.91% within the recommended subset). Converting the text to its spoken form before alignment would have solved this, but it would change the published text itself, so it was not done; the text_spoken column provides the spoken form separately.
Garbage alignment on short sentence-initial words occurs (measured share 2.12%); align_score_min makes it visible.
Channel and speaker concentration: in the recommended subset the top two channels account for 38.2% of the hours, and the top five of the 90 speakers carry 54.4% of the hours.
A channel is not a speaker: 13 of the 27 channels have more than one reader. Speaker identity comes from clustering and is recording-level.
A single encoding profile: sources are ~129 kb/s AAC; the median source bandwidth cut is 15.7 kHz, and around 13 kHz on two channels.
The test set has not been verified by hand; the leakage check is automatic.
2,699 recordings were downloaded and 2,680 produced clips: one could not be decoded, and 18 more yielded no sentence-bounded clip.

Channel credits

Built from the publicly available recordings of the channels below. Work using this corpus is expected to carry these credits.

channel	recordings	clips	hours	recommended hours	speakers
seslikitaplarmavi	279	332,847	580.7	539.5	2
BirDinle	235	285,797	487.9	444.8	6
dinleyiniz	83	138,254	239.4	223.2	32
sess-Seslikitap	176	149,786	226.2	199.2	4
Peri_Mia	216	132,779	206.9	80.1	1
Pandoramedyaseslikitap	67	112,170	193.6	91.0	1
ZubeyirSener	99	110,294	185.6	148.5	1
ses-arşiv	234	92,190	154.6	142.6	7
cantadakitap	291	74,328	126.4	111.9	1
anahtarca	175	61,242	111.7	92.5	3
kitaplar	146	63,096	103.0	73.4	2
seslikutuphanemkanali	50	45,723	77.9	74.4	1
kitapdinle	33	41,540	72.0	68.6	1
idea_stüdyo	43	33,102	54.9	52.6	1
MuratKaraOfficial2021	71	24,059	39.3	34.9	1
seslimakalem	182	19,144	37.5	30.9	1
SesliKitaPodcast	41	18,232	31.1	22.7	1
seslikitapturkish	27	17,238	28.9	25.8	7
sesli-kitaplar	44	13,255	24.5	15.3	3
eba	39	15,104	24.4	23.4	9
Seslendiriyor	28	14,262	24.1	20.2	7
OkumaSaati	58	14,889	22.0	21.5	1
SESLİKİTAPEVİ	29	13,041	21.2	8.5	1
denizinötesindekisesler	16	7,786	14.8	13.4	1
seskitap	12	5,558	9.1	8.7	2
bizimkütüphane	3	4,024	6.9	6.7	1
KitaplarinKedisi	3	664	1.0	1.0	2
total	2,680	1,840,404	3,105.7	2,575.2	90

The speaker column counts the distinct clusters heard on each channel, so it adds up to more than 90: a cluster can appear on several channels — the same reader published by more than one of them. The hours columns are rounded per channel and add up to 3,105.6 / 2,575.3.

Use, attribution and takedown

No licence is declared for this dataset. The recordings are the publicly available uploads of the Turkish audiobook YouTube channels credited above and their rights remain with those channels. This card grants no rights over them and none should be inferred from its absence of a licence: the annotation layer produced here — the segmentation, the transcripts, every measurement column, the policy and the splits — is published alongside the audio without a licence declaration.

What this means in practice is that whether a given use is lawful is a question you have to answer for your own jurisdiction and purpose. Nothing on this page is a permission grant and nothing on this page is legal advice.

What is expected of you is attribution: if you use the corpus, credit both this dataset and the channel credits above.

If a rights holder objects, that channel's recordings are removed from the dataset; reaching us through the repository or the dataset's discussion page is enough.

Personal and sensitive information

The audio is human speech, so it carries identifiable voices: 90 speaker clusters read these recordings. speaker_id is a cluster derived from speaker embeddings, not a name — no identity, contact or demographic information is published, and no attempt was made to link a cluster to a person. The material is read literature from public uploads rather than private conversation, so no personal information beyond the voices themselves is expected in the text; the transcripts were not screened for it by hand. Requests to remove a voice are handled the same way as rights holder requests above.

Citation

Serdar İlder Çağlar — ORCID 0000-0002-5776-2431

@misc{kiraat2026,
  title  = {KIRAAT: A Sentence-Aligned Turkish Read-Speech Corpus},
  author = {Serdar {\.I}lder {\c{C}}a{\u{g}}lar},
  orcid  = {0000-0002-5776-2431},
  year   = {2026},
  url    = {https://github.com/serdarildercaglar/kiraat},
  note   = {ORCID: https://orcid.org/0000-0002-5776-2431}
}

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