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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
+language:
+  - ach
+  - aka
+  - dga
+  - dag
+  - ewe
+  - ful
+  - kpo
+  - lin
+  - lug
+  - mlg
+  - mas
+  - nyn
+  - sna
+  - sog
+multilinguality:
+- multilingual
+pretty_name: Waxal Dataset
+task_categories:
+  - automatic-speech-recognition
+  - text-to-speech
+  - text-to-audio
+source_datasets:
+  - UGSpeechData
+  - DigitalUmuganda/AfriVoice
+  - original
+dataset_info:
+  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: all
+  splits:
+    - name: train
+    - name: validation
+    - name: test
+    - name: unlabeled
+
+--->
+
+# Waxal ASR Dataset
+
+## Table of Contents
+
+-   [Dataset Description](#dataset-description)
+    -   [How to Use](#how-to-use)
+-   [Dataset Structure](#dataset-structure)
+    -   [Data Instances](#data-instances)
+    -   [Data Fields](#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 dataset is a collection of automated speech recognition (ASR) data in
+14 African languages. It consists of approximately 1,250 hours of transcribed
+natural speech from a wide variety of voices, suitable for ASR. The 14 languages
+in this dataset represent over 100 million speakers across 40 Sub-Saharan
+African countries. 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 dataset is a collection acquired through partnerships with Makerere
+University, The University of Ghana and Digital Umuganda. Acquisition was funded
+by Google and the Gates Foundation under an agreement to make the dataset openly
+accessible.
+
+| 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` |
+
+### How to Use
+
+The `datasets` library allows you to load and pre-process your dataset in pure
+Python, at scale. The dataset can be downloaded and prepared in one call to your
+local drive by using the `load_dataset` function.
+
+The following language configurations may be used:
+
+```python
+all, ach, aka, dga, dag, ewe, ful, kpo, lin, lug, mlg, mas, nyn, sna, sog
+```
+
+To download the config, specify the language code, (`all` for all languages,
+`<language>` code for a specific language).
+
+Downloading specific language data (e.g. Shona):
+
+```python
+from datasets import load_dataset
+
+# Ensure you have the audio dependencies installed:
+# pip install datasets[audio]
+
+# Load Shona (sna) dataset
+shona_data = load_dataset("google/WaxalNLP", "sna")
+
+# Access splits
+train = shona_data['train']
+val = shona_data['validation']
+test = shona_data['test']
+unlabeled = shona_data['unlabeled']
+
+# The 'audio' column is automatically decoded when accessed.
+# It returns a dictionary containing 'path', 'array', and 'sampling_rate'.
+example = train[0]
+audio_data = example['audio']
+
+print(f"Transcription: {example['transcription']}")
+print(f"Audio array shape: {audio_data['array'].shape}")
+print(f"Sampling rate: {audio_data['sampling_rate']}")
+```
+
+Downloading ALL data (large):
+
+```python
+from datasets import load_dataset
+
+all_data = load_dataset("google/WaxalNLP", "all")
+```
+
+## Dataset Structure
+
+The Waxal ASR dataset is a collection of recordings of speakers in 14 African
+languages with a human-written transcription of each recording. Each data point
+consists of a recording of at least 15 seconds, with a transcript. Each data
+point includes speaker-id, name of the language (one of 14 languages), speaker
+age, speaker gender, and speaker environment (Indoor, Outdoor, Other, In a car,
+Office or Studio).
+
+### Data Instances
+
+*   **Size of ASR dataset:** 1.7T
+*   **Number of Instances:** 1,937,031
+*   **Number of Fields:** 6
+*   **Labeled Classes:** N/A (Each label is a manually written transcription of
+    the audio, no classes apply)
+*   **Number of Labels:** 224,767
+*   **Percentage labeled instances:** 11.62%
+*   **Algorithmic Labels:** 0
+*   **Human Labels:** 224,767
+*   **Hours:** 10960
+
+#### Language Codes
+
+The data entries are grouped by ISO 639-2 language codes. This is so that the
+audio has a single universal name according to international standards removing
+ambiguity for languages that have multiple names.
+
+```
+ach, aka, dga, dag, ewe, ful, kpo, lin, lug, mlg, mas, nyn, sna, sog
+```
+
+The dataset includes 14 African languages:
+
+| ASR Language | ISO 639-2 | Audio Files | Transcribed Hours | Untranscribed Hours | Total Hours |
+| :--- | :---: | ---: | ---: | ---: | ---: |
+| Acholi | ach | 114,308 | 32.32 | 659.49 | 691.81 |
+| Akan | aka | 195,285 | 101.93 | 941.37 | 1,043.30 |
+| Dagaare | dga | 191,404 | 104.66 | 949.80 | 1,054.47 |
+| Dagbani | dag | 188,808 | 98.54 | 962.28 | 1,060.82 |
+| Ewe | ewe | 203,391 | 99.77 | 976.58 | 1,076.35 |
+| Fulani | ful | 100,827 | 124.24 | 403.21 | 527.45 |
+| Ikposo | kpo | 191,984 | 103.81 | 941.22 | 1,045.03 |
+| Lingala | lin | 100,226 | 101.53 | 415.61 | 517.14 |
+| Luganda | lug | 98,475 | 45.96 | 631.44 | 677.40 |
+| Malagasy | mlg | 101,183 | 182.51 | 333.71 | 516.22 |
+| Masaaba | mas | 116,102 | 48.82 | 645.09 | 693.90 |
+| Nyankole | nyn | 131,743 | 50.87 | 754.51 | 805.38 |
+| Shona | sna | 102,969 | 99.23 | 474.94 | 574.16 |
+| Soga | sog | 120,172 | 50.34 | 736.45 | 786.79 |
+| **Total** | **all** | **1,956,877** | **1,244.52** | **9,825.71** | **11,070.23** |
+
+### Data Fields
+
+The data is structured as follows:
+
+```python
+{
+  'id': 'sna_0',
+  'speaker_id': '2Eud8lyLlsMcciYhmlkwVRtBwi82',
+  'audio': {
+    'array': [...],
+    'sample_rate': 16_000
+  },
+  'transcription': '<transcription | "">',
+  'language': 'sna',
+  'gender': 'Female',
+}
+```
+
+Field descriptions:
+
+-   **id**: `(string)` unique identifier for the record.
+-   **speaker_id**: `(string)` unique identifier for every speaker.
+-   **audio**: `(Audio)` audio data for each example as a sound array.
+-   **transcription**: `(string)` the transcription of the audio file if
+    labeled, otherwise empty string.
+-   **language**: `(string)` language code for the language in ISO 639-2 format.
+-   **gender**: `(string)` represents the gender of the speaker if present,
+    ('Male', 'Female' or empty if not present).
+
+### Data Splits
+
+Each language configuration contains four data splits:
+
+*   **train**: Contains 80% of samples with transcriptions.
+*   **validation**: Contains 10% of samples with transcriptions.
+*   **test**: Contains 10% of samples with transcriptions.
+*   **unlabeled**: Contains all samples that do not have a transcription.
+
+The `all` configuration will load data from all languages, while other
+configurations (e.g., `sna`) will load data only for the specified language.
+
+## Dataset Curation
+
+The data is a curation of data that was gathered by multiple partners into one
+collective data collection with a standard interface to make it more universally
+accessible and useful for model training.
+
+The original data sources and providers are listed below:
+
+| Provider | Data Corpus | 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` |
+
+## Considerations for Using the Data
+
+When using this data corpus please keep in mind that data from different
+providers may license their data differently. Please check the license for
+the specific languages that you are using to make sure it is fit for your
+purposes.
+
+**Affiliation:** Google Research
+
+## Version and Maintenance
+
+- **Current Version:** 1.0.0
+- **Last Updated:** 01/2026
+- **Release Date:** 01/2026