Synthetic Spanish–English Code-Switching Speech (DS-3)

DS-3 — Synthetic Spanish–English code-switching speech (synthetic voices only)

Created by participant inyongkhafid for the Lost in Transcription — Spanish–English challenge (Mozilla Data Collective / DrivenData, 2026) and published on MDC because the competition requires external training data to be published on MDC. License: GPL-3.0-or-later.

This dataset contains no voices of real people. All voices were designed from text descriptions with a TTS model.

Summary

DS-3 is a fully synthetic corpus of informal Spanish–English code-switched speech. 86 % of utterances contain both Spanish and English function words. It was built to add intra-utterance code-switching and conversational voice-note style speech to the training data. A processed copy of it was used to fine-tune Whisper large-v3, Parakeet-TDT-0.6B-v3, Cohere Transcribe 03-2026 and Qwen3-Omni-30B-A3B in the Spanish–English submission that placed 1st on the competition's private leaderboard. That processed copy is not distributed.

Size

• 2,233 utterances · 5.78 hours · 66,599 words (median 29 words per utterance)
• Duration 1.2–24.9 s (median 9.0 s): < 5 s: 321 · 5–10 s: 993 · 10–20 s: 886 · 20–29.5 s: 33
• 231 synthetic conversations · 2 turn roles (A 1,141 · B 1,092) · 62 synthetic TTS voice personas

Creation pipeline

Text statistics (step 1) were taken from aggregate, clip-level statistics of the competition development set (language-mix ratios). No audio, transcripts or excerpts of competition data are contained in DS-3 (verified: 0 eight-word sequences shared with the development transcripts).

Structure

ds3_synthetic_es_en_no_vc/
  wav/syn_{conv}_{turn}_{persona}_16k.wav     # 2,233 files, 16 kHz mono
  manifest.jsonl                              # one line per utterance
  LICENSE (GPL-3.0-or-later) · NOTICE · README.md (this datasheet) · SHA256SUMS

Manifest fields: audio (relative path) · transcript (orthographic text) · duration (s) · conversation · turn_role (A/B) · tts_persona.

Orthography

Standard orthography with case, accents and punctuation as generated (e.g. ¿…?, ¡…!). Spanish and English words are written in their own spelling. There are no language tags inside the text. Disfluencies and fillers appear as words when the LLM produced them (≈ 8 per 1,000 words). Numbers are mostly written as words (0.9 % of utterances contain digits).

Samples (synthetic text)

1. syn_0012_01_meksiko0a_16k.wav · 4.2 s — I think the el precio es but it vale la pena maybe you agree
2. syn_0284_06_meksiko2b_16k.wav · 5.2 s — ¿Tú traerás also cheese? ¿me lo dices? anyway now here
3. syn_0001_03_tengah1b_16k.wav · 5.2 s — I think you should but no te olvides to update your résumé porque los recruiters love details.
4. syn_0238_00_meksiko0a_16k.wav · 5.8 s — ¿Sabes que perdí las llaves mami y ahora estoy atrasada porque el metro está lento y no sé si me puedes ayudar para ah tú me...?
5. syn_0092_89_karibia7b_16k.wav · 6.7 s — Sí pienso que el plan es good pero me preocupa la tasa de interés, y el hecho de que no incluyan garantía
6. syn_0331_03_selatan2b_16k.wav · 12.3 s — I can check the circuit board, but primero I need the manual y si no lo tienes we can also call the power company y si no ¿puedes pasarme el número del servicio de energía para llamar?
7. syn_0128_07_tengah7b_16k.wav · 10.4 s — Oye ¿tienes idea si el doctor está en el consultorio ahora but the waiting room es grande y I think we might need to wait y qué te parece si nos encontramos antes de hoy?
8. syn_0334_00_tengah2a_16k.wav · 15.2 s — Hey mijo I saw the shift chart and I think we got schedule today so mi turno es de ocho a diez pero que me preocupa si el día viernes es de doce a diez, I think this might cause a problem for me y si me dan el día libre, podría viajar tarde para la casa de mi abuela de nuevo.

Intended use and limitations

• For ASR training and augmentation. The speech is synthetic: prosody and spontaneity are approximations, and the LLM text can be grammatically odd at switch points.
• Not suitable for linguistic analysis of natural code-switching, for speaker recognition, or for any biometric purpose.
• QC used round-trip ASR (corpus WER 5.35 % on the TTS audio). This number includes the QC model's own recognition errors, so it is an upper bound on label–audio mismatch, not an exact rate.
• Request (not a licence condition): please do not use this dataset for speaker identification, voice cloning, impersonation, or any other biometric purpose.

Sources and licences

• This dataset contains no audio from any real person or from any speech corpus.
• gpt-oss-20b (Apache-2.0) · Qwen3-TTS-12Hz-1.7B VoiceDesign/Base (Apache-2.0) · llama.cpp (MIT).