| ---
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| task_categories:
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| - automatic-speech-recognition
|
| ---
|
| # WikIPA |
|
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| ## Dataset Description |
|
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| **WikIPA** is a multilingual benchmark dataset designed for **speech-to-IPA (STIPA) transcription**, linking spoken audio with International Phonetic Alphabet (IPA) transcriptions. |
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| The dataset integrates two large-scale community-driven resources: |
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| * **WikiPron** — human-curated IPA pronunciations extracted from Wiktionary |
| * **Lingua Libre** — crowdsourced recordings of spoken lexical items |
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| By connecting these two resources, WikIPA provides a dataset that links **speech audio to phonetic representations**, enabling evaluation of models that transcribe speech directly into IPA. |
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| The dataset supports both: |
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| * **Broad (phonemic) IPA transcriptions** |
| * **Narrow (phonetic) IPA transcriptions** |
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| WikIPA is introduced in the paper: |
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| > **WikIPA: Integrating WikiPron and Lingua Libre for Multilingual IPA Transcription** |
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| The dataset contains **289,694 audio–IPA pairs across 78 languages**, making it one of the largest multilingual resources for speech-to-IPA evaluation. |
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|
|
| ## Citation |
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| If you use WikIPA or this repository, please cite: |
|
|
| ```bibtex |
| @inproceedings{cassotti2026wikipa, |
| title={WikIPA: Integrating WikiPron and Lingua Libre for Multilingual IPA Transcription}, |
| author={Cassotti, Pierluigi and Suchardt, Jacob Lee and De Cristofaro, Domenico}, |
| booktitle={Proceedings of LREC 2026}, |
| year={2026} |
| } |
| ``` |
|
|
| --- |
|
|
| # Dataset Summary |
|
|
| | Property | Value | |
| | -------------------- | -------------------------- | |
| | Languages | 78 | |
| | Total samples | 289,694 | |
| | Audio duration (avg) | 1.15 seconds | |
| | Speakers | 962 | |
| | Task | Speech → IPA transcription | |
|
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| Each entry corresponds to a **single spoken lexical item** recorded in Lingua Libre and linked to **IPA pronunciations from WikiPron**. |
|
|
| --- |
|
|
| # Dataset Structure |
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| The dataset consists of **audio recordings paired with IPA transcriptions and metadata**. |
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| Typical fields include: |
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| * **audio** – speech recording |
| * **lexical_item** – word or phrase spoken |
| * **ipa_broad** – phonemic transcription (when available) |
| * **ipa_narrow** – phonetic transcription (when available) |
| * **language** – language code |
| * **speaker** – identifier of the speaker |
| * **dialect** – dialect information when available |
| |
| Example entry: |
| |
| ```json |
| { |
| "audio": "...", |
| "lexical_item": "domingo", |
| "ipa_broad": "[d o m i N g o]", |
| "ipa_narrow": "[d̪õmiŋgo]", |
| "language": "spa", |
| "speaker": "Eavqwiki" |
| } |
| ``` |
| |
| A lexical item may have **multiple possible IPA transcriptions**, reflecting pronunciation variants or dialectal differences. |
| |
| --- |
| |
| # Data Splits |
| |
| The dataset is divided into **training and test splits**. |
| |
| | Split | Examples | |
| | ----- | -------- | |
| | train | 231,755 | |
| | test | 57,939 | |
| | total | 289,694 | |
| |
| The **test split is stratified by language** to ensure balanced evaluation across languages. |
|
|
| --- |
|
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| # Languages |
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| WikIPA covers **78 languages** from multiple language families. |
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| The dataset includes languages with varying phonological complexity and different levels of phonetic annotation detail. |
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| Both **broad and narrow IPA transcriptions** are available depending on the language and Wiktionary annotations. |
|
|
| --- |
|
|
| # Usage |
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| Load the dataset using `datasets`: |
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| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset("pierluigic/WikIPA") |
| ``` |
|
|
| Example: |
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|
| ```python |
| sample = dataset["train"][0] |
| |
| print(sample["audio"]) |
| print(sample["ipa_broad"]) |
| print(sample["language"]) |
| ``` |
|
|
| --- |
|
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| # Tasks |
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| The dataset is intended for: |
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| * **Speech-to-IPA transcription (STIPA)** |
| * **Universal phone recognition** |
| * **Phonetic modeling** |
| * **Cross-lingual speech modeling** |
| * **Evaluation of multilingual phonetic transcription systems** |
|
|
| --- |