README.md

---
license: cc-by-sa-4.0
language: [eu]
task_categories: [text-to-speech, automatic-speech-recognition]
pretty_name: BSSD (Basque Synthetic Speech Dataset)
size_categories: [100K<n<1M]
tags: [synthetic, speech, basque, euskara, tts, omnivoice]
dataset_info:
  features:
  - name: id
    dtype: string
  - name: audio
    dtype:
      audio:
        sampling_rate: 24000
  - name: text
    dtype: string
  - name: text_norm
    dtype: string
  - name: text_src
    dtype: string
  - name: voice_mode
    dtype: string
  - name: voice_id
    dtype: string
  - name: prompt_src
    dtype: string
  - name: instruct
    dtype: string
  - name: duration
    dtype: float64
  - name: cer_whisper
    dtype: float64
  - name: cer_conformer
    dtype: float64
  - name: utmos
    dtype: float64
  - name: spk_sim
    dtype: float64
  - name: teacher
    dtype: string
  - name: num_step
    dtype: int64
  - name: guidance
    dtype: float64
  - name: status
    dtype: string
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: dev
    path: data/dev-*
  - split: test
    path: data/test-*
---

# BSSD: Basque Synthetic Speech Dataset

**364,870 utterances, 470.8 hours** of synthetic Basque (euskara) read speech, 24 kHz mono FLAC,
with normalised transcripts, voice metadata and per-utterance quality scores, in train / dev / test
splits with held-out voices and sentences. Version 1.0-rc, 2026-10-17. Built by [itzune](https://huggingface.co/itzune) as training data for Basque
text-to-speech research; also usable for ASR data augmentation.

| | |
|---|---|
| utterances / hours | 364,870 / 470.8 h (mean 4.65 s) |
| voice identities | ~18,400 ids, of which 180 have ≥ 1 h; 134 stable synthetic bank voices; 54 real speakers; 8 named default voices |
| intelligibility (ASR round trip) | CER median 0.0, p90 0.013, p99 0.027 on both Basque ASRs |
| naturalness proxy | UTMOS p10 3.16 / p50 3.51 / p90 3.90 |
| text | 236 h Wikipedia, 130 h Common Voice sentences, 75 h news (EusCrawl, CC BY-SA sites), 24 h generated numbers/dates, 4.5 h Wikisource, 0.5 h Tatoeba |

## How it was made

**Teacher.** All audio was generated with [OmniVoice](https://github.com/k2-fsa/OmniVoice)
(`k2-fsa/OmniVoice`, commit `c5fdb5cc`, package built from git on 2026-10-15; 32 decoding steps,
guidance 2.0, fp16). OmniVoice's code is Apache-2.0; its **weights are CC-BY-NC**
(the authors cite training-data constraints such as Emilia) and it bundles Boson's Higgs Audio V2
tokenizer under the Boson community license. We state this provenance so that downstream users can
make their own assessment; see "License" below.

**Voices (`voice_mode`).**
- `clone_bank` (213 h): 134 stable synthetic voices. OmniVoice generated 4,000 candidate voices in
  its auto and voice-design modes; candidates were kept only with CER ≤ 2 %, UTMOS in the top half,
  no clipping, then de-duplicated with WavLM x-vectors (cosine < 0.96, calibrated on real speakers) and
  balanced by pitch. Each kept clip is then used as the cloning prompt, so every bank voice is
  consistent across hundreds of sentences. Prompts are published in `voices.jsonl`.
- `clone_named` (77 h): eight named voices with a reserved share. **Maider** (16.4 h) and
  **Antton** (14.5 h) are the professional speakers of the
  [HiTZ-Aholab studio corpus](https://zenodo.org/records/17952596) (CC BY 4.0), cloned from a 5–7 s
  studio clip; **Nahia, Ane, Mikel, Jon, Unai, Aitor** (7–8 h each) are synthetic voices chosen by
  listening among 84 voice-design candidates. The named voices were given extra hours on purpose so
  that models trained on the data reproduce them reliably.
- `clone_real` (126 h): clones of 54 real speakers who recorded for TTS: the 52 volunteers of
  [OpenSLR 76](https://www.openslr.org/76/) (CC BY-SA 4.0) and the two HiTZ-Aholab speakers.
  **No Common Voice contributor was cloned.**
- `design` (33 h) and `auto` (22 h): one-off voices from OmniVoice's attribute-controlled and random
  voice modes.

**Text (`text_src`).** Sentences were filtered (4–30 words, Basque alphabet, a vocabulary check
against 380k trusted transcripts for web text, no abbreviations or foreign-letter tokens, no
duplicates) and normalised by a rule-based Basque front-end (`text_norm`: cardinals and ordinals
following Euskaltzaindia, case suffixes after digits such as *1990ean → mila bederatziehun eta
laurogeita hamarrean*, percentages, decimals, times, units, acronyms). Sentences with constructs the
normaliser refuses to guess (ranges, numeric dates, fractions) were dropped. Sources: Basque
Wikipedia (CC BY-SA), the Mozilla Common Voice Basque sentence pool (CC0), news and local media from
the [EusCrawl](https://ixa.ehu.eus/euscrawl/) free release restricted to documents the publishers
licensed CC BY-SA / CC BY (Berria, Goiena, Argia, Ataria, Guaixe, …), Basque Wikisource (CC BY-SA),
Tatoeba (CC BY) and template-generated number/date sentences. Basque Parliament transcripts were
**excluded** (lower-cased ASR fragments without punctuation).

**Filtering (`status = keep` for every published row).** Each clip was transcribed with
[`xezpeleta/whisper-large-v3-eu`](https://huggingface.co/xezpeleta/whisper-large-v3-eu) (CT2) and
[`HiTZ/stt_eu_conformer_transducer_large_v2`](https://huggingface.co/HiTZ/stt_eu_conformer_transducer_large_v2);
rows were kept with CER ≤ 3 % on both, hypothesis length ratio 0.7–1.3, 8–22 characters per second,
UTMOS ≥ 3.0, speaker similarity to the prompt ≥ 0.90 (cloned rows), no clipping, no internal
silence > 1.5 s, duration 1.5–20 s. About 85 % of generated rows passed; a per-voice cap of 1.5 h
was applied to non-named voices; two rows were removed after listening (`alegia` mispronounced,
one garbled utterance). Generated rows that failed are not included.

**Does OmniVoice reproduce real Common Voice speakers?** Common Voice (CC0) is in OmniVoice's
training data. We compared every auto and design voice (4,000 clips) with one clip from each of
the 9,678 Basque Common Voice v20 contributors using ECAPA-TDNN speaker embeddings. The nearest
contributor to any random voice scored at most 0.78 (auto) while the 99th percentile of nearest
*different* real contributors is 0.805; none of the 2,400 auto voices and 1 of 1,600 design voices
(0.06 %, below chance) exceeded that line. The random voices are statistically new speakers.

## Splits

| split | utterances | hours | contents |
|---|---|---|---|
| train | 347,559 | 448.0 | everything else |
| dev | 8,205 | 10.6 | 3 held-out bank voices (bank0020, bank0113, bank0026), 2 held-out real speakers (euf_07505, eum_08784), 0.5 % of one-off auto/design voices, 60 held-out sentences per named voice |
| test | 9,106 | 12.1 | 3 held-out bank voices (bank0027, bank0083, bank0070), 2 held-out real speakers (euf_05679, eum_08886), 0.5 % of one-off auto/design voices, 60 held-out sentences per named voice |

Every sentence occurs once in the dataset, so dev and test text is never seen in train. Held-out
voices never occur in train; the named voices do (their dev/test rows test unseen text on a seen
voice, the usual TTS preset condition). The plan is deterministic (`splits.json`, seed 2026).

## Columns

| column | meaning |
|---|---|
| `id` | `eu_syn<run>_<shard>_<n>` |
| `audio` | 24 kHz mono FLAC, trimmed, peak-normalised |
| `text` / `text_norm` | sentence as written / as sent to the teacher (verbalised numbers etc.) |
| `text_src` | `euwiki`, `cv`, `euscrawl`, `gen_numbers`, `wikisource`, `tatoeba` |
| `voice_mode`, `voice_id`, `prompt_src`, `instruct` | see above; `prompt_src` is `bank:*`, `named:*`, `oslr76:*`, `hitz:*` |
| `duration` | seconds |
| `cer_whisper`, `cer_conformer` | ASR round-trip character error rate vs `text_norm` |
| `utmos`, `spk_sim` | predicted MOS; WavLM cosine similarity to the prompt (cloned rows) |
| `teacher`, `num_step`, `guidance`, `status` | generation parameters; `keep` |

Extra files: `voices.jsonl` (bank voices and prompts), `splits.json` (split sizes and the held-out
voice plan), `manifest-{train,dev,test}-{a,b}.jsonl` (file name, duration, transcript; the data was
produced in two batches, `a` and `b`, hence two parquet series and two manifests per split).

## Known limitations

- Synthetic read speech: prosody and timbre variety are those of the teacher; no spontaneous speech.
- Occasional Spanish-style readings of Basque orthography survive ASR filtering (e.g. *ge/gi* read as
  /xe, xi/ in ~0.1 % of such rows; the name *Julen* read with a fricative). Numbers are verbalised by
  rules and may differ from a speaker's choice in rare cases.
- Mean duration is short (4.65 s); long-form prosody is under-represented.
- The non-named voices come from a model whose Basque training audio is mostly crowd-sourced, so
  their acoustic quality is "clean crowd microphone" rather than studio, except for Maider, Antton
  and the real-speaker clones.

## License and attribution

The dataset is released under **CC BY-SA 4.0**. Please credit: *BSSD (Basque Synthetic Speech
Dataset), itzune, 2026*, and the sources it builds on: k2-fsa OmniVoice (Zhu et al., 2026,
arXiv:2604.00688); Wikimedia (Basque Wikipedia and Wikisource); Mozilla Common Voice; the EusCrawl
corpus (Artetxe et al., 2022) and the publishers Berria, Goiena, Argia and others; Google /
OpenSLR 76 (Kjartansson et al., 2020); HiTZ Zentroa – Aholab (studio corpus, speakers Maider and
Antton); Tatoeba. Users should be aware that the teacher model's weights are licensed CC-BY-NC by
their authors; we document the full chain above rather than claim a cleaner one.

## Citation

```
@misc{bssd2026,
  title  = {BSSD: Basque Synthetic Speech Dataset},
  author = {itzune},
  year   = {2026},
  url    = {https://huggingface.co/datasets/itzune/bssd}
}
```