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README.md

@@ -1,76 +1,104 @@
 ---
 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
-  - dga
   - dag
+  - dga
   - ewe
+  - fat
   - ful
+  - hau
+  - ibo
   - kpo
   - lin
   - lug
-  - mlg
   - mas
+  - mlg
   - nyn
   - sna
   - sog
+  - swa
+  - twi
+  - yor
 multilinguality:
 - multilingual
-pretty_name: Waxal Dataset
+pretty_name: Waxal Datasets
 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
-
+  - 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
+    splits:
+      - name: train
+      - name: validation
+      - name: test
+      - name: unlabeled
+  - 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: creator
+        dtype: string
+      - name: file_name
+        dtype: string
+      - name: audio
+        dtype: audio
+    splits:
+      - name: train
+      - name: validation
+      - name: test
+      - name: unlabeled
 ---
 
-# Waxal ASR Dataset
+# 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)
-    -   [Data Instances](#data-instances)
-    -   [Data Fields](#data-fields)
+    -   [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)
@@ -78,19 +106,23 @@ dataset_info:
 
 ## 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 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 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.
+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 |
 | :--- | :--- | :---: |
@@ -98,169 +130,123 @@ accessible.
 | 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
+### TTS Dataset
 
-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 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.
 
-The following language configurations may be used:
+| 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` |
 
-```python
-all, ach, aka, dga, dag, ewe, ful, kpo, lin, lug, mlg, mas, nyn, sna, sog
-```
+### How to Use
+
+The `datasets` library allows you to load and pre-process your dataset in pure
+Python, at scale.
 
-To download the config, specify the language code, (`all` for all languages,
-`<language>` code for a specific language).
+**Loading ASR Data**
 
-Downloading specific language data (e.g. Shona):
+To load ASR data, point to the `data/ASR` directory.
 
 ```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", data_dir="data/ASR")
+# Load Shona (sna) ASR dataset
+asr_data = load_dataset("google/WaxalNLP", "sna", data_dir="data/ASR")
 
 # 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']}")
+train = asr_data['train']
 ```
 
-Downloading ALL data (large):
+**Loading TTS Data**
+
+To load TTS data, point to the `data/TTS` directory.
 
 ```python
 from datasets import load_dataset
 
-all_data = load_dataset("google/WaxalNLP", "all", data_dir="data/ASR")
+# Load Swahili (swa) TTS dataset
+tts_data = load_dataset("google/WaxalNLP", "swa", data_dir="data/TTS")
+
+# Access splits
+train = tts_data['train']
 ```
 
 ## 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:
+### ASR Data Fields
 
 ```python
 {
   'id': 'sna_0',
-  'speaker_id': '2Eud8lyLlsMcciYhmlkwVRtBwi82',
+  'speaker_id': '...',
   'audio': {
     'array': [...],
     'sample_rate': 16_000
   },
-  'transcription': '<transcription | "">',
+  'transcription': '...',
   'language': 'sna',
   'gender': 'Female',
 }
 ```
 
-Field descriptions:
+*   **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).
 
--   **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).
+### TTS Data Fields
 
-### Data Splits
+```python
+{
+  'id': 'swa_0',
+  'creator': 'media_trust',
+  'speaker_id': '...',
+  'audio': {
+    'array': [...],
+    'sample_rate': 16_000
+  },
+  'transcription': '...',
+  'file_name': 'audio_file.wav',
+  'locale': 'swa',
+  'gender': 'Female',
+}
+```
 
-Each language configuration contains four data splits:
+*   **id**: Unique identifier.
+*   **creator**: Institution that provided the data.
+*   **speaker_id**: Unique identifier for the speaker.
+*   **audio**: Audio data.
+*   **transcription**: Transcription.
+*   **file_name**: Original filename.
+*   **locale**: ISO 639-2 language code.
+*   **gender**: Speaker gender.
 
-*   **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.
+### Data Splits
 
-The `all` configuration will load data from all languages, while other
-configurations (e.g., `sna`) will load data only for the specified language.
+Each language configuration contains four data splits: `train`, `validation`,
+`test`, and `unlabeled`.
 
 ## 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:
+The data was gathered by multiple partners:
 
-| Provider | Data Corpus | License |
+| 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
 
-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.
+Please check the license for the specific languages you are using, as they may
+differ between providers.
 
 **Affiliation:** Google Research
 
@@ -268,4 +254,3 @@ purposes.
 
 - **Current Version:** 1.0.0
 - **Last Updated:** 01/2026
-- **Release Date:** 01/2026