Datasets:
license: other
language:
- uz
task_categories:
- automatic-speech-recognition
- text-to-speech
pretty_name: Uzbek Audiobooks (raw audio + machine transcripts)
Uzbek Audiobooks
Uzbek audiobook recordings with machine-generated, structurally filtered transcripts.
| source audio | 321 audiobook files, 9.49 GB |
| transcribed | 313 books |
| gold segments | 35,730 (160.20 h) |
| held in review | 417 (2.85 h) |
| rejected | 7 |
| script | 32,332 Latin / 19 Cyrillic segments gold |
Layout
audio/— the raw full-length recordings, unmodified.manifest/gold.jsonl— segments that pass every structural check. Training set.manifest/review.jsonl— segments held back with the reason recorded. Kept, not discarded.manifest/reject.jsonl— segments failing a hard check.manifest/SUMMARY.json— counts, hours, and the gate thresholds actually used.
One manifest record:
{"id": "uzab_s2_1f3c9a2b41_00007", "audio_hf": "audio/<file>.mp3",
"start": 41.22, "end": 52.86, "duration": 11.64,
"text": "...", "text_norm": "...", "script": "latn",
"avg_logprob": -0.114, "no_speech_prob": 0.01, "tier": "gold"}
audio_hf is a path inside this repo, so a segment can be cut directly from audio/.
text is the transcript as decoded; text_norm is case-folded and punctuation-stripped with the
okina (U+02BB) preserved as a letter and apostrophe variants folded onto it, which is the form to
score against.
How the transcripts were made
Transcribed with an in-house Uzbek/Central-Asian fine-tune of Whisper (whisper-ca e2, 10.88 WER
on FLEURS uz), faster-whisper + Silero VAD, beam 5, chunk_length=20.
Segments then pass a structural gate: duration 1–30 s, 4–24 characters/second, avg_logprob ≥ −1.0,
no_speech_prob ≤ 0.6, compression ratio ≤ 2.6, and ≥60% of letters in one Uzbek script (Latin or
Cyrillic both accepted — text that is predominantly neither, usually Russian or English, is held).
Label trust is lower than for a dual-model corpus, and we state that rather than paper over it. Our Tajik pipeline gated on agreement between two different architectures. No comparable second Uzbek model exists in-house — the next best is 53.54 WER against Whisper's 10.88, and a peer that much weaker measures its own errors, not Whisper's. So for Uzbek the structural gate is the primary filter, not a tiebreaker. These are machine labels: good enough to train on, not human-verified.
Known gaps
Three source downloads are corrupt (two truncated at power-of-two boundaries, one empty) and are excluded; every other source file is present and complete. Some gold-adjacent segments exceeded the 30 s window and sit in review; re-segmenting them would need a narrower window than the one measured to help, so they were left in review rather than forced through.
Licence
The recordings are third-party audiobook material collected for research. No ownership is claimed over the audio. Access is manually gated; request access stating your intended use.