Loquacious Set: 25,000 Hours of Transcribed and Diverse English Speech Recognition Data for Research and Commercial Use

Rogier Van Dalen; Shucong Zhang; Titouan Parcollet; Yuan Tseng

REVIEW 4 major objections 6 minor 23 references

Loquacious Set: 25,000 Hours of Transcribed and Diverse English Speech Recognition Data for Research and Commercial Use

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A 25,000-hour English speech corpus built from six open datasets gives researchers and industry a common, commercially usable benchmark.

desk verdict Useful curated 25k-hour commercial ASR dataset with sensible splits and recipes, but the headline benchmark claim rests on unaddressed train/eval overlap between Libriheavy/LibriSpeech and YODAS en003. read the letter →

arxiv 2505.21578 v1 pith:JD75TWRR submitted 2025-05-27 cs.CL cs.SDeess.AS

Titouan Parcollet , Yuan Tseng , Shucong Zhang , Rogier van Dalen This is my paper

classification cs.CLcs.SDeess.AS

keywords automaticspeechrecognitionEnglishcorpuscommercial-usedatasetcurationbenchmarkConformerworderrorratenormalizedtranscriptions

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that the speech community needs a successor to LibriSpeech—large enough for modern models, diverse in accents and recording conditions, and licensed so both academics and industry can use it—and that no existing dataset satisfies all those requirements. It introduces the Loquacious Set, a 25,000-hour English corpus assembled and curated from six permissively licensed sources, with normalized transcriptions, three nested training subsets (250, 2,500, and 25,000 hours), a clean subset, and dedicated development and test sets. The authors report that conformer models trained with the released recipes reach word error rates that are competitive with, or better than, much larger systems trained on undocumented data. If the claims hold, the corpus gives both academic and industrial researchers a shared, reproducible evaluation ground for English speech recognition.

What carries the argument

The load-bearing object is the curated corpus itself: six permissively licensed source collections are combined into nested training splits (small, medium, large, clean) and two evaluation sets, with all audio resampled to 16 kHz, segmented to 1–40 seconds (3–40 seconds for YODAS), and filtered by language and duration. Transcription normalization is the main curation step: numerals are converted to words, sentences containing non-English characters or more than four symbols are removed, punctuation and YouTube-style descriptors are stripped, and the remaining text is upper-cased and restricted to the 26 Latin letters plus the apostrophe. This machinery makes the dataset reproducible and gives every sample a standard audio-label pair, which is what allows a single training recipe to be applied across heterogeneous sources.

What would settle it

Compute the overlap at the segment, speaker, and book level between the training splits and the evaluation splits: check whether any Libriheavy training segment shares an audiobook or speaker with LibriSpeech test-other, and whether any YODAS training segment matches the YODAS-based dev/test segments. If overlap exists, rerun the reported word error rate table on a strictly disjoint split; a large increase in word error rate would show that the published numbers partly reflect in-domain test material.

Watch

Extended reading notes

Core claim

The central claim is that the Loquacious Set is the first large-scale English speech recognition corpus to combine a permissive commercial license, acoustic and linguistic diversity, normalized transcripts, defined training subsets, and dedicated evaluation sets. It pools audio from six existing datasets—Common Voice, VoxPopuli, Libriheavy, People's Speech, YODAS, and LibriSpeech—and applies a five-step text-normalization pipeline, duration filtering, and language identification, yielding 25,000 hours of curated speech in about 9.4 million segments with an average duration of 9.6 seconds. The paper reports that conformer models trained on this corpus achieve a 4.6% word error rate on the LibriSpeech test-other set with a 480M-parameter model, beating a widely used 1.5B-parameter open speech recognition system that scores 5.1%; the same model reaches 6.9% on the VoxPopuli test set and 12.0% on the Common Voice test set. The smaller subsets are designed so that 250-hour and 2,500-hour training runs can fit on four GPUs, making the benchmark accessible to groups without large compute budgets.

Load-bearing premise

The reported benchmark results assume that the audio in the evaluation sets is not drawn from the same books, speakers, or segments as the training data: the Libriheavy training data and the LibriSpeech test-other set both derive from the same public-domain audiobook collection, and the YODAS-based dev/test segments come from a source subset that is also used in training, and the paper does not state that overlapping content was removed.

Editorial extensions

If this is right

Groups with four GPUs can train competitive models on the 250-hour or 2,500-hour subsets, while the full 25,000-hour set is designed for larger laboratories.
Researchers in industry can legally use the corpus for commercial speech recognition development, closing the licensing gap that blocks industrial use of several existing large datasets.
Models trained on the Loquacious Set beat a widely used 1.5B-parameter open speech recognition system on LibriSpeech test-other (4.6% versus 5.1% word error rate) and on VoxPopuli test (6.9% versus 7.5%) with fewer parameters, according to the paper's measurements.
Adding related but not identical data helps cross-domain performance: a 100M model trained on the corpus gets a 15.9% word error rate on the Common Voice test set versus 19.5% when trained on Common Voice alone.
The clean 13,000-hour subset matches the full 25,000-hour model on LibriSpeech test-other (6.0% word error rate with a 250M model), suggesting a cheaper compute path for clean-speech research.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A natural next step is to publish the full per-utterance language-identification decisions and the exact filtering statistics for each source; that would let other builders reproduce the curation and quantify how much of YODAS and People's Speech survives normalization.
The same curation recipe—normalization, duration bounds, language filtering, and nested subsets—could be ported to other permissively licensed multilingual corpora, though the language-identification step would need per-language validation.
Versioning will determine whether the Loquacious Set becomes a lasting benchmark: since Common Voice releases change over time, freezing a snapshot and releasing hashes of audio segments would preserve comparability across future work.
A stricter evaluation protocol would exclude any audiobook or YODAS source that appears in training; such a split would test generalization to unseen speakers and books more cleanly.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: the Loquacious Set is a genuinely useful consolidation of existing English ASR data into a commercially usable, normalized 25k-hour corpus with defined splits and released SpeechBrain recipes. That part holds up. The soft spot is the benchmark section: the paper claims state-of-the-art WERs without showing that evaluation sets are disjoint from training sources. Libriheavy and LibriSpeech both come from LibriVox audiobooks, and the Loquacious dev/test draw from YODAS en003 while en003 is among the YODAS subsets used in the large training split. No decontamination is reported. If books or speakers overlap, the reported 4.6% on LibriSpeech test-other and the dev/test numbers are partly in-domain, and the Whisper comparison is inflated. That concern is load-bearing, and the authors should fix it with explicit overlap checks and, if needed, recomputed numbers.

What the paper does well: it fills a real gap. GigaSpeech is non-commercial; YODAS and People's Speech have noisy transcriptions; CommonVoice is small and version-unstable. The selection criteria, normalization steps, and subset structure are sensible. Table 2 shows the expected scaling behavior, and the claim that a 100M model beats Whisper Small.en on LibriSpeech test-other at fewer parameters is plausible if the eval sets are clean. The authors are honest that People's Speech and YODAS transcriptions may still contain errors, which is why they provide a clean split. I also don't see the dev/test hour arithmetic error that one of our readers flagged; Table 1 sums correctly to 16.5 hours each.

Smaller issues: the "up to a certain threshold" for the large split is vague, transcription accuracy after normalization is not quantified beyond a small manual check, and the Whisper comparison relies on literature numbers. None of these are fatal, but they matter for a benchmark resource.

Bottom line: this paper deserves peer review. The dataset artifact is useful, and the flaws are fixable with documentation and an overlap analysis. I'd send it to review with a request for a decontamination section.

Referee Report

4 major / 6 minor

Summary. The paper introduces the Loquacious Set, a curated English ASR corpus of roughly 25,000 hours built from six existing datasets (VoxPopuli, CommonVoice, People's Speech, YODAS, Libriheavy, and LibriSpeech), with text normalization, audio filtering, and defined small/medium/large/clean training splits plus dev/test sets. It ships SpeechBrain recipes and reports conformer encoder-decoder WERs on LibriSpeech test-other, VoxPopuli, CommonVoice, and its own dev/test, claiming state-of-the-art results that beat Whisper models at comparable or smaller parameter counts.

Significance. If the held-out evaluation is confirmed and the normalization quality is demonstrated, the dataset would be a valuable common resource for academic and industrial ASR: it has a permissive licensing profile, defined subsets of different sizes, and covers diverse acoustic conditions. The release of code and recipes is a concrete reproducibility strength. The main claims, however, depend on the evaluation sets being genuinely out-of-domain, which is not currently established.

major comments (4)

[3.1, Table 1] The paper does not address training/evaluation overlap. The large split includes 11,000 hours of Libriheavy, which is derived from LibriVox audiobooks, while the evaluation sets include LibriSpeech test-other (also LibriVox-derived); moreover, the YODAS dev/test segments are extracted from en003, which is among the YODAS subsets used to form the 6,100-hour training portion. The manuscript never states whether book-, speaker-, or segment-level decontamination was performed. Please state the overlap-removal procedure and report statistics on remaining overlap, or explicitly justify why overlap is absent. Without this, the reported WERs in Table 2 on LibriSpeech test-other and the Loquacious dev/test are not established as held-out, which weakens the comparison with Whisper and the claim of state-of-the-art performance.
[3.1] The selection rule for the large split is given only as 'up to a certain threshold to reach 25,000 hours of speech.' The per-source caps and any quality thresholds should be stated explicitly (e.g., a table of the exact hours taken from each source and the random seed). While the code may contain these details, the paper should provide them for reproducibility, especially because Table 1 reports 25,150 hours rather than 25,000.
[3.2] The normalization process is described but its resulting transcription quality is not quantified. The paper claims 'normalised transcriptions with few errors' but provides no measurement of residual errors after normalization (e.g., character error rate against a held-out reference subset, or the number of manually verified samples for YODAS and People's Speech). Since unreliable transcriptions are a stated limitation of the source corpora, please report a concrete accuracy figure for the normalized text, including for the manually verified YODAS dev/test segments.
[4.2] The Whisper comparison contains a factual error: the text states 'our 100M models obtains 5.7%' on LibriSpeech test-other, but Table 2 shows that the 250M model achieves 5.7%, while the 100M model achieves 6.0%. Please correct the parameter count in the comparison (and adjust the following sentence about '3 times more parameters' accordingly). This correction is needed to support the claim that the Loquacious recipes beat Whisper at smaller model sizes.

minor comments (6)

[4.1] The phrase 'height of them are used' should be 'eight of them are used'.
[3.1] The sentence 'most of the samples below this threshold correspond are just noises' contains a grammatical error; it should read 'correspond to noise' or 'are just noise.'
[Throughout] The dataset name appears as 'CommonV oice' in several places due to a LaTeX artifact; please correct to 'CommonVoice.'
[Table 1, 3.1] Table 1 gives the large split as 25,150 hours and the clean split as 13,150 hours, while the text in Section 3.1 says 25,000 and 13,000 hours; please harmonize these numbers.
[3.1] The text mentions 'YoutuBe videos' in the Acoustic conditions paragraph; this should be 'YouTube videos.'
[4.2] The literature numbers for Whisper (e.g., 6.7% on LibriSpeech test-other for Small.en) are cited without a reference; please add a source for these numbers.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the paper's claims are empirical dataset construction and reproducible ASR benchmarks; the evaluation-overlap concern is a data-hygiene issue, not a circularity step.

full rationale

The Loquacious Set paper contains no derivation chain in which an output quantity is defined in terms of the quantity it claims to predict. The central outputs are (i) a curated 25,000-hour corpus assembled by subsampling six existing datasets and (ii) measured WERs from SpeechBrain conformer recipes. These are empirical measurements, not predictions derived from a fitted parameter. The SpeechBrain citation [14] is a toolkit reference whose authors overlap with the present paper, but the experimental hyperparameters and table of results are fully specified in the paper, so the citation is not load-bearing for the WER claims. The language-identification filtering of YODAS uses a published SpeechBrain model as an external tool; this is a data-cleaning step, not an argument whose conclusion is assumed. The strongest integrity concern is that Loquacious dev/test are drawn from YODAS en003 while the large training split includes YODAS audio, and that Libriheavy training and LibriSpeech test-other share the LibriVox domain; without an explicit decontamination statement the reported WERs could be optimistic. However, this is potential train/evaluation leakage, not circularity: the WER numbers are not equivalent by construction to the training data, and the paper does not claim a derivation that reduces to its inputs. No fitted input is renamed as a prediction, no uniqueness theorem is imported, and no ansatz is smuggled in via citation. Under the stated criteria, the appropriate finding is no significant circularity (score 0).

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

No new physical entities are introduced. The central resource depends on hand-set curation thresholds and unstated disjointness assumptions between training and evaluation audio.

free parameters (3)

Large split size caps = Libriheavy 11,000h, YODAS 6,100h, People's Speech 5,900h, CommonVoice 1,600h, VoxPopuli 550h
The paper says large contains data 'up to a certain threshold' to reach 25,000 hours, but does not justify the per-source caps; these are hand-set choices.
YODAS minimum segment duration = 3 seconds
Introduced ad hoc because the paper says most YODAS samples below this threshold are noises, while other sources use a 1-second lower bound.
Symbol removal threshold = more than 4 symbols triggers removal
Hand-chosen cutoff in the text normalization pipeline; sentences with more than four symbols are removed as uncertain.

assumptions (4)

domain assumption Source datasets' licenses permit commercial combination
The licensing claim for all six sources is asserted from Table 1; the paper does not independently verify the provenance of every YouTube audio file in YODAS.
domain assumption SpeechBrain VoxLingua107 language ID correctly identifies English in YODAS
The 9,200-hour English estimate depends on this model; only 'a few random samples' were manually checked.
domain assumption Libriheavy training data do not overlap LibriSpeech test-other
Both come from LibriVox audiobooks, but the paper never states that overlapping books or speakers were removed; this is essential for the external LibriSpeech WER comparisons.
domain assumption YODAS dev and test segments are disjoint from the large training split
Not stated in Section 3.1; if the 1.5-hour dev and test segments come from en003, which is also a training source, contamination is possible.

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Cite this review

Pith. "Pith review of Loquacious Set: 25,000 Hours of Transcribed and Diverse English Speech Recognition Data for Research and Commercial Use." pith.science (2026). https://pith.science/paper/JD75TWRR

@misc{pith2026250521578,
  author       = {Pith},
  title        = {Pith review of: Loquacious Set: 25,000 Hours of Transcribed and Diverse English Speech Recognition Data for Research and Commercial Use},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JD75TWRR}},
  note         = {Machine review of arXiv:2505.21578}
}

read the original abstract

Automatic speech recognition (ASR) research is driven by the availability of common datasets between industrial researchers and academics, encouraging comparisons and evaluations. LibriSpeech, despite its long success as an ASR benchmark, is now limited by its size and focus on clean, read speech, leading to near-zero word error rates. More recent datasets, including MOSEL, YODAS, Gigaspeech, OWSM, Libriheavy or People's Speech suffer from major limitations including licenses that researchers in the industry cannot use, unreliable transcriptions, incorrect audio data, or the lack of evaluation sets. This work presents the Loquacious Set, a 25,000-hour curated collection of commercially usable English speech. Featuring hundreds of thousands of speakers with diverse accents and a wide range of speech types (read, spontaneous, talks, clean, noisy), the Loquacious Set is designed to work for academics and researchers in the industry to build ASR systems in real-world scenarios.

Figures

Figures reproduced from arXiv: 2505.21578 by the authors.

**Figure 1.** Durations of the 9.6M samples of the large training split of the Loquacious Set. The average is 9.6 seconds. symbols are removed as the pronunciation may again be too uncertain. The fifth step relies on the nltk library to remove any remaining YouTube descriptors like [explosion] or [light music]. Finally, all words are upper-cased and checked to only contain the 26 Latin letters or the apostrophe. Due to imperfect … view at source ↗

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Reference graph

Works this paper leans on

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

open-source

Introduction The success of deep learning in automatic speech recognition (ASR) hinges on access to standardised datasets. Corpus like the Wall Street Journal (WSJ) [1] and TIMIT [2] were crucial for early ASR research, but their limited size and price rapidly became limiting issues. The advent of LibriSpeech [3], with its open CC-BY license and 1,000 hou...

work page

[2] [2]

Loquacious Set: 25,000 Hours of Transcribed and Diverse English Speech Recognition Data for Research and Commercial Use

Large Scale English ASR Datasets The Loquacious Set is not the first attempt to create a standard- ised large-scale ASR dataset for the English language. This sec- tion discusses others relevant initiatives. The CommonV oice dataset [13] is based on crowd- sourcing, and can theoretically leverage millions of users recording each a few samples on their dev...

work page Pith review arXiv 2025

[3] [3]

dev-other

The Loquacious Set This section describes the data selection and corpus structure of the Loquacious Set (section 3.1) as well as the process used to normalise the transcriptions (section 3.2). License considerations. The Loquacious dataset uses existing data and does not impose any new licensing restrictions. Each component of the original datasets keeps ...

work page

[4] [4]

Speech Recognition Experiments This section details the experimental protocol (section 4.1) as well as the speech recognition results (section 4.2) ob- tained with the Loquacious Set on various benchmarking speech recognition datasets. 4.1. Experimental protocol Models are trained on the three subsets of the Loquacious Set, hence containing either 250, 25...

work page

[5] [5]

The dataset is easy to reproduce thanks to the SpeechBrain re- leased source code and can be loaded in a single line of code

Conclusion The Loquacious Set offers 25,000 hours of normalised tran- scribed and diverse English speech recognition data available for academics, researchers in the industry and commercial use. The dataset is easy to reproduce thanks to the SpeechBrain re- leased source code and can be loaded in a single line of code. It introduces a new common ground fo...

work page

[6] [6]

Mosel: 950,000 hours of speech data for open-source speech foundation model train- ing on eu languages,

M. Gaido, S. Papi, L. Bentivogli, A. Brutti, M. Cettolo, R. Gretter, M. Matassoni, M. Nabih, and M. Negri, “Mosel: 950,000 hours of speech data for open-source speech foundation model train- ing on eu languages,” in Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , 2024, pp. 13 934–13 947

work page 2024

[7] [7]

The design for the wall street journal- based csr corpus,

D. B. Paul and J. Baker, “The design for the wall street journal- based csr corpus,” in Speech and Natural Language: Proceed- ings of a Workshop Held at Harriman, New York, February 23-26, 1992, 1992

work page 1992

[8] [8]

Darpa timit acoustic-phonetic continous speech corpus cd-rom. nist speech disc 1-1.1,

J. S. Garofolo, L. F. Lamel, W. M. Fisher, J. G. Fiscus, and D. S. Pallett, “Darpa timit acoustic-phonetic continous speech corpus cd-rom. nist speech disc 1-1.1,”NASA STI/Recon technical report n, vol. 93, p. 27403, 1993

work page 1993