---
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

```python
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`](https://huggingface.co/datasets/google/fleurs) (`ceb_ph`) | 4,027 utterances | Read Wikipedia sentences, short utterances | CC BY 4.0 |
| [`sil-ai/bloom-speech`](https://huggingface.co/datasets/sil-ai/bloom-speech) | Multilingual | Children's book narration | Varies |
| [`espnet/mms_ulab_v2`](https://huggingface.co/datasets/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](https://huggingface.co/datasets/SilencioNetwork/tagalog-filipino-speech)
is published under this same protocol — spontaneous speech, human transcription, word-level
alignment. Hiligaynon and expanded Cebuano follow; see
[SilencioNetwork](https://huggingface.co/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:

```python
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



```bibtex
@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**