| --- |
| license: |
| - cc-by-sa-4.0 |
| - cc-by-4.0 |
| annotation_creators: |
| - human-annotated |
| - crowdsourced |
| language_creators: |
| - creator_1 |
| tags: |
| - audio |
| - automatic-speech-recognition |
| - text-to-speech |
| language: |
| - ach |
| - aka |
| - dag |
| - dga |
| - ewe |
| - fat |
| - ful |
| - hau |
| - ibo |
| - kpo |
| - lin |
| - lug |
| - mas |
| - mlg |
| - nyn |
| - sna |
| - sog |
| - swa |
| - twi |
| - yor |
| multilinguality: |
| - multilingual |
| pretty_name: Waxal NLP Datasets |
| task_categories: |
| - automatic-speech-recognition |
| - text-to-speech |
| source_datasets: |
| - UGSpeechData |
| - DigitalUmuganda/AfriVoice |
| - original |
| configs: |
| - config_name: asr |
| data_files: |
| - split: train |
| path: "data/ASR/**/*-train-*" |
| - split: validation |
| path: "data/ASR/**/*-validation-*" |
| - split: test |
| path: "data/ASR/**/*-test-*" |
| - split: unlabeled |
| path: "data/ASR/**/*-unlabeled-*" |
| - config_name: tts |
| data_files: |
| - split: train |
| path: "data/TTS/**/*-train-*" |
| - split: validation |
| path: "data/TTS/**/*-validation-*" |
| - split: test |
| path: "data/TTS/**/*-test-*" |
| dataset_info: |
| - config_name: asr |
| features: |
| - name: id |
| dtype: string |
| - name: speaker_id |
| dtype: string |
| - name: transcription |
| dtype: string |
| - name: language |
| dtype: string |
| - name: gender |
| dtype: string |
| - name: audio |
| dtype: audio |
| - config_name: tts |
| features: |
| - name: id |
| dtype: string |
| - name: speaker_id |
| dtype: string |
| - name: transcription |
| dtype: string |
| - name: locale |
| dtype: string |
| - name: gender |
| dtype: string |
| - name: audio |
| dtype: audio |
| --- |
| |
| # Waxal Datasets |
|
|
| ## Table of Contents |
|
|
| - [Dataset Description](#dataset-description) |
| - [ASR Dataset](#asr-dataset) |
| - [TTS Dataset](#tts-dataset) |
| - [How to Use](#how-to-use) |
| - [Dataset Structure](#dataset-structure) |
| - [ASR Data Fields](#asr-data-fields) |
| - [TTS Data Fields](#tts-data-fields) |
| - [Data Splits](#data-splits) |
| - [Dataset Curation](#dataset-curation) |
| - [Considerations for Using the Data](#considerations-for-using-the-data) |
| - [Additional Information](#additional-information) |
|
|
| ## Dataset Description |
|
|
| The Waxal project provides datasets for both Automated Speech Recognition (ASR) |
| and Text-to-Speech (TTS) for African languages. The goal of this dataset's |
| creation and release is to facilitate research that improves the accuracy and |
| fluency of speech and language technology for these underserved languages, and |
| to serve as a repository for digital preservation. |
|
|
| The Waxal datasets are collections acquired through partnerships with Makerere |
| University, The University of Ghana, Digital Umuganda, and Media Trust. |
| Acquisition was funded by Google and the Gates Foundation under an agreement to |
| make the dataset openly accessible. |
|
|
| ### ASR Dataset |
|
|
| The Waxal ASR dataset is a collection of data in 14 African languages. It |
| consists of approximately 1,250 hours of transcribed natural speech from a wide |
| variety of voices. The 14 languages in this dataset represent over 100 million |
| speakers across 40 Sub-Saharan African countries. |
|
|
| Provider | Languages | License |
| :------------------ | :--------------------------------------- | :------------: |
| Makerere University | Acholi, Luganda, Masaaba, Nyankole, Soga | `CC-BY-4.0` |
| University of Ghana | Akan, Ewe, Dagbani, Dagaare, Ikposo | `CC-BY-NC-4.0` |
| Digital Umuganda | Fula, Lingala, Shona, Malagasy | `CC-BY-4.0` |
|
|
| ### TTS Dataset |
|
|
| The Waxal TTS dataset is a collection of text-to-speech data in 10 African |
| languages. It consists of approximately 240 hours of scripted natural speech |
| from a wide variety of voices. |
|
|
| Provider | Languages | License |
| :------------------ | :----------------------------------- | :------------: |
| Makerere University | Acholi, Luganda, Kiswahili, Nyankole | `CC-BY-4.0` |
| University of Ghana | Akan (Fante, Twi) | `CC-BY-NC-4.0` |
| Media Trust | Fula, Igbo, Hausa, Yoruba | `CC-BY-4.0` |
|
|
| ### How to Use |
|
|
| The `datasets` library allows you to load and pre-process your dataset in pure |
| Python, at scale. |
|
|
| First, ensure you have the necessary dependencies installed to handle audio |
| data: |
|
|
| ```bash |
| pip install datasets[audio] |
| ``` |
|
|
| **Loading ASR Data** |
|
|
| To load ASR data, point to the `data/ASR` directory. |
|
|
| ```python |
| from datasets import load_dataset, Audio |
| |
| # Load Shona (sna) ASR dataset |
| asr_data = load_dataset("google/WaxalNLP", "sna", data_dir="data/ASR") |
| |
| # Access splits |
| train = asr_data['train'] |
| val = asr_data['validation'] |
| test = asr_data['test'] |
| |
| # Example: Accessing audio bytes and other fields |
| example = train[0] |
| print(f"Transcription: {example['transcription']}") |
| print(f"Sampling Rate: {example['audio']['sampling_rate']}") |
| # 'array' contains the decoded audio bytes as a numpy array |
| print(f"Audio Array Shape: {example['audio']['array'].shape}") |
| ``` |
|
|
| **Loading TTS Data** |
|
|
| To load TTS data, point to the `data/TTS` directory. |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Load Swahili (swa) TTS dataset |
| tts_data = load_dataset("google/WaxalNLP", "swa", data_dir="data/TTS") |
| |
| # Access splits |
| train = tts_data['train'] |
| ``` |
|
|
| ## Dataset Structure |
|
|
| ### ASR Data Fields |
|
|
| ```python |
| { |
| 'id': 'sna_0', |
| 'speaker_id': '...', |
| 'audio': { |
| 'array': [...], |
| 'sample_rate': 16_000 |
| }, |
| 'transcription': '...', |
| 'language': 'sna', |
| 'gender': 'Female', |
| } |
| ``` |
|
|
| * **id**: Unique identifier. |
| * **speaker_id**: Unique identifier for the speaker. |
| * **audio**: Audio data. |
| * **transcription**: Transcription of the audio. |
| * **language**: ISO 639-2 language code. |
| * **gender**: Speaker gender ('Male', 'Female', or empty). |
| |
| ### TTS Data Fields |
| |
| ```python |
| { |
| 'id': 'swa_0', |
| 'speaker_id': '...', |
| 'audio': { |
| 'array': [...], |
| 'sample_rate': 16_000 |
| }, |
| 'transcription': '...', |
| 'locale': 'swa', |
| 'gender': 'Female', |
| } |
| ``` |
| |
| * **id**: Unique identifier. |
| * **speaker_id**: Unique identifier for the speaker. |
| * **audio**: Audio data. |
| * **transcription**: Transcription. |
| * **locale**: ISO 639-2 language code. |
| * **gender**: Speaker gender. |
|
|
| ### Data Splits |
|
|
| For the **ASR Dataset**, the data with transcriptions is split as follows: * |
| **train**: 80% of labeled data. * **validation**: 10% of labeled data. * |
| **test**: 10% of labeled data. |
|
|
| The **unlabeled** split contains all samples that do not have a corresponding |
| transcription. |
|
|
| The **TTS Dataset** follows a similar structure, with data split into `train`, |
| `validation`, and `test` sets. |
|
|
| ## Dataset Curation |
|
|
| The data was gathered by multiple partners: |
|
|
| Provider | Dataset | License |
| :------------------ | :------------------------------------------------------- | :------ |
| University of Ghana | [UGSpeechData](https://doi.org/10.57760/sciencedb.22298) | `CC BY 4.0` |
| Digital Umuganda | [AfriVoice](DigitalUmuganda/AfriVoice) | `CC-BY 4.0` |
| Makerere University | [Yogera Dataset](https://doi.org/10.7910/DVN/BEROE0) | `CC-BY 4.0` |
| Media Trust | | `CC-BY 4.0` |
|
|
| ## Considerations for Using the Data |
|
|
| Please check the license for the specific languages you are using, as they may |
| differ between providers. |
|
|
| **Affiliation:** Google Research |
|
|
| ## Version and Maintenance |
|
|
| - **Current Version:** 1.0.0 |
| - **Last Updated:** 01/2026 |
|
|
