Lipi-Ghor: A Large-Scale, Diarized, Multi-Domain Bengali Speech Corpus and a Scalable Pipeline for Building It

Sanjid Hasan; A H M Fuad; Risalat Labib; Bayazid Hasan Simon

cs.CLOct 26, 2026

Lipi-Ghor: A Large-Scale, Diarized, Multi-Domain Bengali Speech Corpus and a Scalable Pipeline for Building It

Authors: Sanjid Hasan, A H M Fuad, Risalat Labib, Bayazid Hasan Simon

Organizations: Department of Electrical and Electronic Engineering, KUET, Khulna, Bangladesh · Department of Computer Science and Engineering, BUET, Dhaka, Bangladesh

Abstract

Bengali, spoken by over 230 million people, remains severely under-resourced in spoken language research. Existing open Bengali ASR corpora are confined to short, read or crowd-sourced speech and lack speaker diarization and long-form audio. We present Lipi-Ghor, a large-scale weakly-supervised Bengali speech corpus comprising 885 hours of audio sourced from 998 YouTube videos across 596 unique channels, spanning 17 diverse domains including audiobooks, drama, religious sermons, news, and regional dialects. Each recording is annotated in SSTT format (Speaker, Speech, Transcription, Timestamp), which combines automated diarization with Bengali YouTube caption tracks. To our knowledge, Lipi-Ghor is the first Bengali speech corpus to pair large-scale coverage with speaker-diarized, long-form audio annotations, both of which are absent from existing open Bengali resources. We also present the construction methodology as a replicable pipeline for any language with a sufficient captioned YouTube presence. Targeted channel curation across a handful of rich domains yields corpus scales that were previously out of reach for low-resource languages. We benchmark four widely-used ASR models under zero-shot and fine-tuned conditions. All four degrade consistently on emotive, dialectal, and long-form content. Because caption targets are noisy, we also release a human-annotated gold evaluation split, verified by 17 volunteer annotators over 30 hours of human-in-the-loop correction and stratified across all 17 domains. We release our original contributions (video-ID lists, diarization output, pipeline code, and the gold annotations) under CC-BY-4.0, and distribute caption-derived transcripts for research use only; no raw audio is redistributed.

Figures & tables

Panel of Figure 1 — Figure 1: The Lipi-Ghor construction pipeline. Four stages turn a curated set of Bengali YouTube channels into a speaker-diarized, SSTT-annotated corpus. The methodology is language-agnostic; only Stage 1 curation is language-specific.

Corpus	Hrs	Dom.	Diar.	Long
Common Voice bn	$\sim$ 40	1	✗	✗
BengaliAI Speech	$\sim$ 1200 ∗	few	✗	✗
OOD-Speech (test)	23	17	✗	✗
BengaliLoop (eval)	small	few	✓	✓
Lipi-Ghor	885	17	✓	✓

Table 1: Open Bengali speech resources by scale, domain count, speaker diarization, and long-form audio. ∗ Predominantly short, read/controlled speech.

Figure 2: Pareto view of audio duration by domain. Seven of the seventeen domains account for roughly 83% of the corpus, reflecting where Bengali caption coverage is densest on YouTube.

Property	Value
Total Videos	998
Unique Channels	596
Total Duration	885.34 hours
Avg. Duration/Video	$\sim$ 52 minutes
Domains	17
Total Word Tokens	5,832,680

Table 2: Lipi-Ghor corpus summary. ∗ CC-BY-4.0 applies to our original contributions; caption-derived transcripts are for research use only (§ 9 ).

Property	Value
Diarized recordings	1,019
Speaker-attributed segments	984,647
Speaker labels (all recordings)	9,056
Speech / non-speech	639 h / 243 h
Speech ratio (mean)	0.76
Speakers per rec. (median, IQR)	4 (1–12)

Table 3: Conversational structure of the diarized corpus. Speech ratio is the fraction of each recording covered by at least one speaker turn, averaged over recordings.

Domain	Spk	Solo	Turn	SR	Seg
Audio-book	1	62	3.4	0.74	1.83
Talk-show	5	2	20.1	0.86	2.38
Movie	24	14	20.0	0.58	0.95
Natok	18	2	21.6	0.56	1.14
Podcast	3	10	21.9	0.85	1.73
Waz	2	32	5.8	0.84	2.21

Table 4: Conversational structure by domain. Spk = median speakers per recording; Solo = % of single-speaker recordings; Turn = mean speaker turns per minute; SR = mean speech ratio; Seg = median segment duration (s). Ordered by total duration.

Figure 3: Cumulative distribution of diarized segment durations (log-scaled x-axis). Nearly two-thirds of the 984,647 segments are shorter than two seconds.

Domain	WER	CER
Cartoon	28.57	25.85
Vlog	14.17	4.07
Commentary	10.81	5.33
News	10.34	6.57
Movie	7.32	5.28
Waz	6.51	3.88

Table 5: Domain-wise disagreement between the weakly supervised YouTube transcripts and the human-corrected gold references. WER and CER are corpus-level percentages calculated separately for each domain.

Figure 4: Global WER for the four zero-shot systems (blue) and two fine-tuned models (orange). Fine-tuned Faster Whisper Medium reaches 20.70%, below the best zero-shot system (Hishab TITU, 30.51%).

Model	WER	CER
Hishab TITU Conformer-L	30.51	18.23
Meta MMS 1B	43.06	21.16
Seamless M4T-v2	66.71	45.54
Whisper large-v3	84.53	72.92

Table 6: Zero-shot ASR performance on Lipi-Ghor (%).

Domain	TITU	MMS	SM4T	Wh-v3
Audio-book	21.02	33.04	57.09	80.87
News	28.03	41.68	57.83	86.23
Talk-show	29.88	43.38	64.81	86.17
Waz	35.75	45.02	74.27	85.15
Natok	44.37	60.14	73.94	89.01
Comedy/Satire	44.56	56.18	85.37	88.64

Table 7: Domain-specific WER (%) for representative domains. SM4T = Seamless M4T-v2.

Figure 5: Zero-shot WER across all 17 Lipi-Ghor domains, sorted by Hishab TITU’s error rate. The three Bengali-capable systems degrade together as speech becomes more spontaneous and dialectal; Whisper large-v3 fails near-uniformly across domains. Table 7 lists a representative subset.

Figure 6: Median diarized segment duration vs. zero-shot WER (Hishab TITU) across the seven domains of Table 7 . Marker area proportional to domain hours.

Quartile	TITU	MMS	SM4T	Wh-v3
Short	30.54	41.98	66.11	85.01
Medium	29.14	41.79	64.49	84.25
Long	28.46	40.74	63.55	84.00
Very Long	32.60	45.53	70.13	84.93

Table 8: WER (%) by utterance duration quartile.

Model	WER	CER
Fast Conformer (A)	24.67	15.56
Faster Whisper Med. (B)	20.70	11.90
Moonshine-Base Bengali †	21.54	10.79

Table 9: Fine-tuned model performance on Lipi-Ghor (%). † Hasan and Rahman (2026) ; RTF 0.0053.

Appendix figures & tables3 assets

Supplementary material from the paper’s appendix.

Appendix

Domain	Videos ∗	Hours
Audio-book	277	254.37
Talk-show	176	142.40
Movie	74	122.18
Natok (Drama)	95	78.66
Podcast	62	57.59
Waz (Sermons)	66	47.89

Appendix

Table 10: Domain distribution of Lipi-Ghor. ∗ Per-domain counts are raw video counts before cross-domain deduplication (1,019 total); deduplication yields 998 unique videos. Hours are on the deduplicated corpus.

Caption Type	Videos	Percentage
Manually created captions	102	10.22%
Auto-generated captions	896	89.78%
Total	998	100.00%

Appendix

Table 11: Distribution of YouTube Caption Types in Lipi-Ghor

Figure 7: The Lipi-Ghor annotation interface. Clockwise from top left: the embedded video player, the waveform with a per-speaker diarization ribbon beneath it, the speaker roster for the recording, and the segment list where each row carries a speaker assignment, timestamps, and an editable Bengali transcript. Annotators can split, merge, or delete segments and reassign speakers from the dropdown. Identifying details of the source video, including the video identifier and all channel branding, are masked for double-blind review; annotators saw the unmasked frame.

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Feb 26, 2026J/K move · Enter open · S save

Make It Hard to Hear, Easy to Learn: Long-Form Bengali ASR and Speaker Diarization via Extreme Augmentation and Perfect Alignment

Sanjid Hasan, Risalat Labib, A H M Fuad, Bayazid Hasan

EEE, KUET · CSE, BUET

Long-Form Speech Recognition Automatic Speech Recognition Speaker Diarization Low-Resource Speech Recognition

Open paper AskPDF arXiv

Although Automatic Speech Recognition (ASR) in Bengali has seen significant progress, processing long-duration audio and performing robust speaker diarization remain critical research gaps. To address the severe scarcity of joint ASR and diarization resources for this language, we introduce Lipi-Ghor-882, a comprehensive 882-hour multi-speaker Bengali dataset. In this paper, detailing our submission to the DL Sprint 4.0 competition, we systematically evaluate various architectures and approaches for long-form Bengali speech. For ASR, we demonstrate that raw data scaling is ineffective; instead, targeted fine-tuning utilizing perfectly aligned annotations paired with synthetic acoustic degradation (noise and reverberation) emerges as the singular most effective approach. Conversely, for speaker diarization, we observed that global open-source state-of-the-art models (such as Diarizen) performed surprisingly poorly on this complex dataset. Extensive model retraining yielded negligible improvements; instead, strategic, heuristic post-processing of baseline model outputs proved to be the primary driver for increasing accuracy. Ultimately, this work outlines a highly optimized dual pipeline achieving a

\sim

0.019 Real-Time Factor (RTF), establishing a practical, empirically backed benchmark for low-resource, long-form speech processing.

Abstract

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