Hugging Face's logo

Datasets:
SilencioNetwork
/
cebuano-speech

Tasks:
Automatic Speech Recognition
Text-to-Speech
Modalities:
Audio
Text
Formats:
parquet
Languages:
Cebuano
Size:
< 1K
Tags:
cebuano
bisaya
binisaya
sugbuanon
philippines
visayas
Libraries:
Datasets
Dask
Polars
License:
Dataset card Data Studio Files
xet
 Community
cebuano-speech / README.md
ASMSIlencio's picture
ASMSIlencio
Upload README.md with huggingface_hub
20a32ee verified
preview code
|
Raw
History Blame Contribute Delete
13.6 kB
metadata 
license: cc-by-nc-4.0
language:
  - ceb
task_categories:
  - automatic-speech-recognition
  - text-to-speech
annotations_creators:
  - crowdsourced
language_creators:
  - crowdsourced
multilinguality: monolingual
source_datasets:
  - original
pretty_name: Cebuano (Bisaya) Spontaneous Speech — Silencio Philippines Pack
size_categories:
  - n<1K
tags:
  - cebuano
  - bisaya
  - binisaya
  - sugbuanon
  - philippines
  - visayas
  - cebu
  - philippine-languages
  - austronesian
  - low-resource
  - under-resourced-languages
  - spontaneous-speech
  - conversational
  - long-form
  - forced-alignment
  - word-timestamps
  - human-transcribed
  - asr
  - tts
  - crowdsourced
  - consented-data
configs:
  - config_name: default
    default: true
    data_files:
      - split: test
        path: data/test-*

Cebuano (Bisaya) Spontaneous Speech — Silencio Philippines Pack

Spontaneous long-form Cebuano with human transcription and word-level forced alignment. Fifteen speakers, mean clip length over two minutes, 27,000+ timestamped tokens. Part of the Silencio Philippines Pack.

Hours 3.48
Clips 90
Speakers 15
Countries 2
Speaker origin regions 4
L1 speakers of the recorded language 11 of 15 (65 clips)
Audio 48 kHz stereo WAV
Mean clip length 139.2 s
Transcripts human_validated: 90
Licence cc-by-nc-4.0

All 90 clips carry a human transcription.

Recordings are unscripted responses to open prompts, captured on contributors' own devices in their own environments. Mean clip length is 139 seconds — long-form spontaneous speech, not short read utterances.

Load it 

from datasets import load_dataset

ds = load_dataset("SilencioNetwork/cebuano-speech", split="train")
print(ds[0]["transcript"], ds[0]["dialect"], ds[0]["country"])

# datasets v4 returns a torchcodec AudioDecoder:
s = ds[0]["audio"].get_all_samples()
audio, sr = s.data, s.sample_rate

Requires pip install "datasets>=4.0" and FFmpeg ≥ 4.

Speaker and recording metadata

By country

Country Speakers %
Philippines 14 93.3%
Asia/Pacific Region 1 6.7%

Speaker origin / self-reported variety — this is the speaker's own background, not a dialect classification of the recorded language

Speaker origin Speakers %
Philippines - Manila (Tagalog) 11 73.3%
Philippines - Visayan English 2 13.3%
Philippines - Filipino English (Manila) 1 6.7%
United States - General American 1 6.7%

Demographics

Gender Speakers %
male 9 60.0%
female 6 40.0%
Age band Speakers %
25-34 5 33.3%
35-44 4 26.7%
18-24 4 26.7%
45-59 2 13.3%

Recording conditions

Device Clips %
Mobile 67 74.4%
Desktop 23 25.6%

Splits

Single split, test, 90 rows. No train/dev/test partition is provided: at this scale a partition would leave each part too small to be meaningful. Speaker identifiers are stable, so a speaker-disjoint split can be constructed at load time.

Fields

Column Description Values in this release
audio Audio payload. Stored at source rate; see the spec table for the exact distribution 48 kHz stereo WAV
speaker_id Pseudonymous speaker identifier. Coherent within this dataset; deliberately not linkable to other Silencio releases 15 distinct
language Language of the recording constant: Cebuano
transcript Human transcription of the recording 90 distinct
transcript_type Provenance of the transcript constant: human_validated
gender Self-reported female, male
country Speaker's country Asia/Pacific Region, Philippines
mother_tongue Speaker's self-reported first language English, Tagalog / Filipino
dialect Self-reported speaker origin / regional variety. This is the speaker's own background, NOT a dialect classification of the recorded language Philippines - Filipino English (Manila), Philippines - Manila (Tagalog), Philippines - Visayan English, United States - General American
os Operating system of the recording device Linux, Windows
device Recording device class Desktop, Mobile
duration Seconds 89 distinct
script_type Elicitation style constant: free_speech
words Word-level forced alignment: text, normalised text, start and end in seconds 26,618 entries across 90 clips
n_words Number of aligned tokens in this clip 82 distinct
transcript_model How the transcript text was produced constant: human
aligner Model used to produce the word timings constant: wav2vec2_mms
age_band Self-reported age, banded 18-24, 25-34, 35-44, 45-59
native_speaker True where mother_tongue matches the recorded language 2 distinct
proficiency Speaker's self-declared proficiency in the recorded language conversational, fluent, native

Related Cebuano and Philippine speech resources

Cebuano (Bisaya, Binisaya) has roughly 20 million speakers across the Central Visayas, Negros Oriental and much of Mindanao — the second most widely spoken language in the Philippines. Existing Hub coverage:

Resource Scale Type Licence
google/fleurs (ceb_ph) 4,027 utterances Read Wikipedia sentences, short utterances CC BY 4.0
sil-ai/bloom-speech Multilingual Children's book narration Varies
espnet/mms_ulab_v2 Multilingual Unlabelled audio —
This dataset 90 clips, 3.5 h, 139 s mean Spontaneous long-form, word-level alignment, speaker metadata CC BY-NC 4.0

There is no single-language Cebuano audio dataset on the Hub. FLEURS is the closest usable resource and is read speech in short utterances; this release is unscripted long-form speech with per-word timings. The two are complementary rather than competing — FLEURS for read-speech benchmarking, this for spontaneous-speech behaviour.

Also from Silencio. Tagalog / Filipino is published under this same protocol — spontaneous speech, human transcription, word-level alignment. Hiligaynon and expanded Cebuano follow; see SilencioNetwork.

Transcription and alignment

Two distinct provenances, kept separate because they carry different confidence.

Text — human. Every transcript was produced by a human annotator listening to the recording. The transcript_model column records this per clip.

Timings — machine. Word-level start and end times come from forced alignment with wav2vec2_mms, recorded per clip in the aligner column. On every clip in this release the aligner's token count matches the human reference token count exactly, and no word timing runs past the end of its audio file.

The words column holds one entry per token with text, normalized_text, start and end in seconds. Expand it for segment-level work:

ds = load_dataset("SilencioNetwork/cebuano-speech", split="test")
row = ds[0]
for w in row["words"][:5]:
    print(f"{w['start']:6.2f}-{w['end']:6.2f}  {w['text']}")

Speaker proficiency

Cebuano proficiency is taken from each contributor's own declared language profile, not inferred from a single primary-language field. Most contributors here are natively bilingual: their primary declared language is Tagalog, and they also declare Cebuano at native level.

Declared Cebuano level Speakers Clips Hours
native 11 65 2.16
fluent 3 19 0.82
conversational 1 6 0.50

Filter on native_speaker, or on proficiency for finer control.

What this is useful for

  • Spontaneous-speech ASR evaluation. Long-form unscripted Cebuano with human reference text. Models tuned on read speech typically degrade sharply here; that gap is the point.
  • Forced-alignment and VAD work. 27,000+ word-level timings over 3.5 hours.
  • Long-form segmentation. Mean clip length 139 s, with several clips over four minutes. Most Philippine-language audio on the Hub is short read utterances.
  • Code-switching and borrowing. Spontaneous Cebuano from bilingual speakers contains substantial Tagalog, Spanish and English material. Not annotated as such in this release.

Limitations

  • Sample scale. 90 clips, 15 speakers, 3.5 hours. Enough for evaluation and for alignment work; not a training corpus.
  • Speaker origin is concentrated in this sample. Every contributor here records a Metro Manila or English-speaking origin — natively bilingual Cebuano speakers based outside the Cebuano-speaking regions. That is a real and commercially relevant population, but it is not in-region recording. Contributors based in the Central Visayas and Mindanao are present in Silencio's wider Cebuano inventory and reachable through the collection programme described below; they are simply not in this sample.
  • Proficiency is self-declared and not independently assessed.
  • Word timings are machine-generated. Forced alignment with wav2vec2_mms, not manually corrected. Token counts reconcile exactly against the human reference on every clip, but individual boundaries have not been human-verified.
  • Unbalanced contribution. Clips per speaker ranges from 5 to 7.
  • No acoustic annotation. Recording environment, background-noise class and SNR are not annotated. Available for commissioned collection.
  • Mixed audio format. Source audio is shipped untouched at its captured sample rate and channel count — see the spec table. Resample and downmix before batching.
  • No diarisation. Single speaker per clip. Multi-speaker material is part of the collection programme described below, not this release.
  • Pseudonymous speakers. speaker_id values are pseudonyms, coherent within this dataset, deliberately not linkable to speakers in other Silencio releases.
  • No baseline. No reference WER is published with this release.

Provenance and consent

Every recording is contributed by an opted-in participant through the Silencio app, under a consent record covering AI/ML training use. Contributors can request deletion, and deletion propagates to downstream releases. Full provenance documentation is available to licensees.

License

cc-by-nc-4.0 — free for research and non-commercial use with attribution.

Attribution string: Silencio Network, Cebuano (Bisaya) Spontaneous Speech, 2026. CC BY-NC 4.0.

Non-commercial covers research, evaluation and publication. Benchmarking a commercial product model against this data is a commercial use and needs a licence — ask, it is usually granted for evaluation. Model weights trained on this sample inherit the non-commercial restriction. Contributors may withdraw consent; withdrawal propagates to subsequent releases but places no retroactive obligation on an existing licensee.

Commercial licensing, including terms for models trained on this data: info@silencio.network

Citation 

@misc{silencio_cebuano_2026,
  title  = {Cebuano (Bisaya) Spontaneous Speech — Silencio Philippines Pack},
  author = {Silencio Network},
  year   = {2026},
  url    = {https://huggingface.co/datasets/SilencioNetwork/cebuano-speech}
}

The Silencio Philippines Pack

Philippine inventory as of August 2026:

Language / variety Hours Recordings Speakers
Tagalog / Filipino 2,135 192,264 5,507
Philippine English 2,705 129,433 2,340
Cebuano 505 31,668 614
Hiligaynon 12 1,600 51
Ilocano 11 1,186 50

In active collection: 7,500 hours. A collection and human-transcription programme covering 2,500 hours each of Cebuano, Tagalog and Hiligaynon, split per language into 1,000 hours single-speaker and 1,500 hours multi-speaker.

Language Single-speaker Multi-speaker Total
Cebuano 1,000 h 1,500 h 2,500 h
Tagalog 1,000 h 1,500 h 2,500 h
Hiligaynon 1,000 h 1,500 h 2,500 h
Total 3,000 h 4,500 h 7,500 h

Ilocano, Waray, Bikol, Kapampangan and Pangasinan are available through commissioned collection.

Silencio corpus and collection network

Two distinct figures, because they answer different questions.

Recorded and available off the shelf — audio already collected, with metadata, licensable today:

Hours recorded 127,793
Recordings 9,392,870
Contributors who recorded 222,145
Languages 156
Countries and territories of origin 216

Contributor network available for commissioned collection — registered, consented contributors who can be activated for a specific brief. These are not active contributors to the corpus above; they are the pool it is drawn from and extended through:

Registered contributors 2,000,000+
Countries 180+
Languages reachable 250+

For volume licensing, pre-release access to the Philippines programme, or commissioned collection in a language not listed: info@silencio.network