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README.md

@@ -34,7 +34,7 @@ language:
   - twi
   - yor
 multilinguality:
-- multilingual
+  - multilingual
 pretty_name: Waxal NLP Datasets
 task_categories:
   - automatic-speech-recognition
@@ -43,49 +43,27 @@ source_datasets:
   - UGSpeechData
   - DigitalUmuganda/AfriVoice
   - original
-dataset_info:
+configs:
   - 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
+    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
-    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
+    data_files:
+      - split: train
+        path: "data/TTS/**/train-*"
+      - split: validation
+        path: "data/TTS/**/validation-*"
+      - split: test
+        path: "data/TTS/**/test-*"
+      - split: unlabeled
+        path: "data/TTS/**/unlabeled-*"
 ---
 
 # Waxal Datasets
@@ -124,11 +102,11 @@ 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` |
+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
 
@@ -136,18 +114,19 @@ 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` |
+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:
+First, ensure you have the necessary dependencies installed to handle audio
+data:
 
 ```bash
 pip install datasets[audio]
@@ -244,10 +223,9 @@ train = tts_data['train']
 
 ### 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.
+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.
@@ -259,12 +237,12 @@ The **TTS Dataset** follows a similar structure, with data split into `train`,
 
 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` |
+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
 
@@ -275,5 +253,5 @@ differ between providers.
 
 ## Version and Maintenance
 
-- **Current Version:** 1.0.0
-- **Last Updated:** 01/2026
+-   **Current Version:** 1.0.0
+-   **Last Updated:** 01/2026