Philippine Language Dataset (PLD)

Ten Philippine languages, 980 speakers, 448 hours of prompted speech — one of the largest multilingual Philippine speech collections available as Parquet.

334,268 utterances · 448.2 hours · 980 speakers · 10 languages · 16kHz mono

Collected by the University of the Philippines Diliman Digital Signal Processing Laboratory. Every row is one prompted recording: the corpus ships pre-segmented WAVs with the prompt text stored inline in each session log, so no forced alignment or segmentation was applied here.

Most Philippine language speech data stops at Tagalog. This one covers Bikol, Cebuano, Kapampangan, Hiligaynon, Ilocano, Waray, Pangasinan and Tausug at scale — languages with tens of millions of speakers and almost no public ASR/TTS data.

Read text_is_prompt before you train. 2,586 rows carry an elicitation question instead of a transcript, and silently training on them will poison your model.

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30-second quickstart

from datasets import load_dataset

ds = load_dataset("sapinsapin/pld", split="train", streaming=True)
row = next(iter(ds))

print(row["language_name"], "|", row["sentence"])
print(row["speech_type"], row["duration"], "s")

The training-ready filter, in full:

ds = load_dataset("sapinsapin/pld", split="train")
ds = ds.filter(lambda x: not x["text_is_prompt"] and 0.3 <= x["duration"] <= 30.0)

One language at a time:

bikol = ds.filter(lambda x: x["language"] == "bcl")

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⚠️ Read this before training

text_is_prompt — 2,586 rows have no transcript

Rows where speech_type == "spontaneous" do not carry a transcript. The session logs store the elicitation question that was put to the speaker — e.g. "Saen an dream destination mo?" — while the audio is 20–90 seconds of their free-speech answer. The same question text repeats verbatim across different speakers.

They are kept because the audio is genuine spontaneous speech (32 hours of it, valuable for pretraining, VAD, diarization, or re-transcription), but they are poison for supervised (audio, text) training:

ds = ds.filter(lambda x: not x["text_is_prompt"])

Other things to know

Gotcha	Detail	What to do
Prompts, not transcripts	Text is what the speaker was asked to read; no one verified they read it exactly	Treat as weakly-supervised; round-trip ASR to score
Speaker overlap	Random 90/10 split over utterances, so speakers appear in both splits	Re-split on speaker_id for speaker-disjoint eval
eng is not native English	English word/sentence lists read by Filipino L2 speakers	Use corpus_language to see which collection they came from
Half the corpus is single words	164k isolated rows average 2.3 s	Filter on speech_type for sentence-level work

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What's inside

Languages

Code	Language	Utterances	Hours
bcl	Bikol	62,488	95.8
pam	Kapampangan	57,595	84.1
ceb	Cebuano	56,928	58.5
fil	Filipino	50,993	52.1
ilo	Ilocano	29,688	51.1
hil	Hiligaynon	30,965	39.8
war	Waray	21,526	31.4
eng	English	14,024	20.8
pag	Pangasinan	5,566	8.7
tsg	Tausug	4,495	5.9

English word and sentence lists (EngW.txt, EngSen.txt) were read by the same speakers. Those rows are labeled language = "eng", while corpus_language retains the Philippine language collection they came from, so per-language filters stay clean either way.

Speech types

Type	Utterances	Hours	Mean	What it is
read	158,121	302.9	6.9 s	Full prompted sentences — news, medical, literature, education, tourism. Best material for TTS.
isolated	164,447	107.3	2.3 s	Single words and short phrases from word lists
spontaneous	2,586	32.2	44.8 s	Free speech — prompt-only text, see the warning above
digits	9,114	5.9	2.3 s	Spoken digit strings

Splits

Split	Rows
train	300,842
test	33,426

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Schema

Field	Type	Description
audio	Audio(16000)	16 kHz mono, FLAC-compressed in storage
sentence	str	Prompt text read by the speaker (see text_is_prompt)
duration	float	Seconds
num_words	int	Whitespace word count
language	str	ISO 639-3 of the spoken content (eng for English lists)
language_name	str	Human-readable language name
corpus_language	str	Language collection the session belongs to
speech_type	str	read / isolated / digits / spontaneous
prompt_category	str	Prompt list, e.g. News, Medical, BodyParts
prompt_source	str	Original prompt filename
text_is_prompt	bool	true when text is an elicitation question, not a transcript
speaker_id	str	Language-namespaced speaker key, e.g. BIK_0800
gender	str	male / female / unknown
age	int	Speaker age, -1 when unrecorded
speaker_dialect	str	Self-reported dialect
mother_dialect / father_dialect	str	Parents' dialects — useful for contact/variation studies
profession	str	Self-reported profession
session_id	str	Recording session identifier
session_environment	str	Recording environment note
source_file	str	Original WAV stem

The dialect fields are unusually rich for a speech corpus — speaker, mother and father dialect are all recorded, which supports dialectometry and language-contact work that most corpora can't.

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Models trained on this data

Eleven reference finetunes — a TTS model per language plus one multilingual speech-to-speech model — each with listen-test samples in its samples/ directory. All were trained on a single 8 GB GPU with finetune_tts.py / finetune_s2s.py, so they are baselines to hear and beat, not state-of-the-art:

Language	TTS model (microsoft/speecht5_tts base)
Bikol	speecht5_tts-pld-bcl
Cebuano	speecht5_tts-pld-ceb
English (PH)	speecht5_tts-pld-eng
Filipino	speecht5_tts-pld-fil
Hiligaynon	speecht5_tts-pld-hil
Ilocano	speecht5_tts-pld-ilo
Pangasinan	speecht5_tts-pld-pag
Kapampangan	speecht5_tts-pld-pam
Tausug	speecht5_tts-pld-tsg
Waray	speecht5_tts-pld-war

Speech-to-speech: speecht5_vc-pld — any-to-any voice conversion across all ten languages, trained on same-sentence cross-speaker pairs mined from PLD's shared prompt lists (the corpus has no parallel translations, but many speakers reading the same prompt is exactly the parallel data voice conversion needs).

Reproduce any of them in one command:

python finetune_tts.py --dataset pld --language ceb --push
python finetune_s2s.py --push

An ASR baseline (whisper-small per language) has not been trained yet — a Bikol or Cebuano one would be the first of its kind in public. If you train something on PLD, tag this dataset in your model card and it will appear here.

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How it was built

1. Walk every session directory; parse the per-session .log (speaker demographics header, then one row per utterance: WAV name, prompt list, prompt text).
2. Classify each utterance's speech_type. Explicit markers (_Iso_, _Utt_, Spontaneous, digits) are used where present; the corpus uses at least five naming conventions, so the ~55k rows with no marker are typed by the measured median word count of their prompt list rather than by guessing from the filename.
3. Repair double-encoded UTF-8 in transcripts (hapúnan → hapúnan) — 150 of 166 affected lines recover; the rest are left intact rather than risk a worse string.
4. Resample to 16 kHz mono, encode FLAC, shard to Parquet, 90/10 random split.

1,943 rows (0.6%) reference WAVs that are not present in the archive and were skipped.

Pipeline source: process_pld_parquet.py · parser: halolib/pld.py

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Limitations

• Prompted, not conversational. Except for the 32 h spontaneous portion, this is people reading from lists. Prosody and vocabulary reflect that.
• Transcripts are unverified prompts. Nobody checked that speakers read the prompt exactly; expect a residual mismatch rate.
• Coverage is uneven — Bikol has 95.8 h, Tausug 5.9 h. Don't expect balanced multilingual behaviour without resampling.
• Recording conditions vary by session and are only loosely described in session_environment.
• No held-out speaker split is provided by default.
• Language codes follow ISO 639-3; fil and tgl distinctions in the wild are inconsistent, so filter on both if you merge with other corpora.

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Related datasets

Part of the halohalo Philippine-language speech family:

Dataset	What it covers	Scale
pld (this one)	10 Philippine languages, prompted	334k utterances · 448 h
sapinsapin/filipinospeechcorpus	Filipino studio read + spontaneous	305k segments · 65 h
sapinsapin/halo-livestream	Taglish code-switched livestream speech	seed release

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License, source and citation

Collected by the UP Diliman Digital Signal Processing Laboratory. This is a repackaging for research use; the underlying corpus terms are those of UP-DSP. Please credit the original collectors, and contact UP-DSP for terms covering uses beyond research.

If you represent UP-DSP and want attribution, terms, or access changed, please open a discussion on this repo.

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Contributing

Eight of these ten languages have essentially no public ASR or TTS baseline. That is the opportunity here.

• Train a baseline on any single language and tag this dataset in your model card
• Report bad rows via the Community tab (include source_file and session_id)
• Improve the pipeline: github.com/sapinsapin/halohalo