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README.md

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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