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+---
+license:
+  - cc-by-sa-4.0
+  - cc-by-nc-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-NC-ND 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