Name: ngoloan/WaxalNLP
Creator: ngoloan
Published: 2026-02-21 02:31:02
License: 暂无描述

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--- language_creators: - creator_1 language: - ach - aka - amh - dag - dga - ewe - fat - ful - hau - ibo - kik - kpo - lin - lug - luo - mas - mlg - nyn - orm - sid - sna - sog - swa - tir - twi - wal - yor license: - cc-by-sa-4.0 - cc-by-4.0 multilinguality: - multilingual source_datasets: - UGSpeechData - DigitalUmuganda/AfriVoice - original task_categories: - automatic-speech-recognition - text-to-speech pretty_name: Waxal NLP Datasets arxiv: 2602.02734 annotation_creators: - human-annotated - crowdsourced tags: - audio - automatic-speech-recognition - text-to-speech configs: - config_name: ach_asr data_files: - split: train path: data/ASR/ach/ach-train-* - split: validation path: data/ASR/ach/ach-validation-* - split: test path: data/ASR/ach/ach-test-* - split: unlabeled path: data/ASR/ach/ach-unlabeled-* - config_name: ach_tts data_files: - split: train path: data/TTS/ach/ach-train-* - split: validation path: data/TTS/ach/ach-validation-* - split: test path: data/TTS/ach/ach-test-* - config_name: aka_asr data_files: - split: train path: data/ASR/aka/aka-train-* - split: validation path: data/ASR/aka/aka-validation-* - split: test path: data/ASR/aka/aka-test-* - split: unlabeled path: data/ASR/aka/aka-unlabeled-* - config_name: amh_asr data_files: - split: train path: data/ASR/amh/amh-train-* - split: validation path: data/ASR/amh/amh-validation-* - split: test path: data/ASR/amh/amh-test-* - split: unlabeled path: data/ASR/amh/amh-unlabeled-* - config_name: dag_asr data_files: - split: train path: data/ASR/dag/dag-train-* - split: validation path: data/ASR/dag/dag-validation-* - split: test path: data/ASR/dag/dag-test-* - split: unlabeled path: data/ASR/dag/dag-unlabeled-* - config_name: dga_asr data_files: - split: train path: data/ASR/dga/dga-train-* - split: validation path: data/ASR/dga/dga-validation-* - split: test path: data/ASR/dga/dga-test-* - split: unlabeled path: data/ASR/dga/dga-unlabeled-* - config_name: ewe_asr data_files: - split: train path: data/ASR/ewe/ewe-train-* - split: validation path: data/ASR/ewe/ewe-validation-* - split: test path: data/ASR/ewe/ewe-test-* - split: unlabeled path: data/ASR/ewe/ewe-unlabeled-* - config_name: fat_tts data_files: - split: train path: data/TTS/fat/fat-train-* - split: validation path: data/TTS/fat/fat-validation-* - split: test path: data/TTS/fat/fat-test-* - config_name: ful_asr data_files: - split: train path: data/ASR/ful/ful-train-* - split: validation path: data/ASR/ful/ful-validation-* - split: test path: data/ASR/ful/ful-test-* - split: unlabeled path: data/ASR/ful/ful-unlabeled-* - config_name: ful_tts data_files: - split: train path: data/TTS/ful/ful-train-* - split: validation path: data/TTS/ful/ful-validation-* - split: test path: data/TTS/ful/ful-test-* - config_name: hau_tts data_files: - split: train path: data/TTS/hau/hau-train-* - split: validation path: data/TTS/hau/hau-validation-* - split: test path: data/TTS/hau/hau-test-* - config_name: ibo_tts data_files: - split: train path: data/TTS/ibo/ibo-train-* - split: validation path: data/TTS/ibo/ibo-validation-* - split: test path: data/TTS/ibo/ibo-test-* - config_name: kik_tts data_files: - split: train path: data/TTS/kik/kik-train-* - split: validation path: data/TTS/kik/kik-validation-* - split: test path: data/TTS/kik/kik-test-* - config_name: kpo_asr data_files: - split: train path: data/ASR/kpo/kpo-train-* - split: validation path: data/ASR/kpo/kpo-validation-* - split: test path: data/ASR/kpo/kpo-test-* - split: unlabeled path: data/ASR/kpo/kpo-unlabeled-* - config_name: lin_asr data_files: - split: train path: data/ASR/lin/lin-train-* - split: validation path: data/ASR/lin/lin-validation-* - split: test path: data/ASR/lin/lin-test-* - split: unlabeled path: data/ASR/lin/lin-unlabeled-* - config_name: lug_asr data_files: - split: train path: data/ASR/lug/lug-train-* - split: validation path: data/ASR/lug/lug-validation-* - split: test path: data/ASR/lug/lug-test-* - split: unlabeled path: data/ASR/lug/lug-unlabeled-* - config_name: lug_tts data_files: - split: train path: data/TTS/lug/lug-train-* - split: validation path: data/TTS/lug/lug-validation-* - split: test path: data/TTS/lug/lug-test-* - config_name: luo_tts data_files: - split: train path: data/TTS/luo/luo-train-* - split: validation path: data/TTS/luo/luo-validation-* - split: test path: data/TTS/luo/luo-test-* - config_name: mas_asr data_files: - split: train path: data/ASR/mas/mas-train-* - split: validation path: data/ASR/mas/mas-validation-* - split: test path: data/ASR/mas/mas-test-* - split: unlabeled path: data/ASR/mas/mas-unlabeled-* - config_name: mlg_asr data_files: - split: train path: data/ASR/mlg/mlg-train-* - split: validation path: data/ASR/mlg/mlg-validation-* - split: test path: data/ASR/mlg/mlg-test-* - split: unlabeled path: data/ASR/mlg/mlg-unlabeled-* - config_name: nyn_asr data_files: - split: train path: data/ASR/nyn/nyn-train-* - split: validation path: data/ASR/nyn/nyn-validation-* - split: test path: data/ASR/nyn/nyn-test-* - split: unlabeled path: data/ASR/nyn/nyn-unlabeled-* - config_name: nyn_tts data_files: - split: train path: data/TTS/nyn/nyn-train-* - split: validation path: data/TTS/nyn/nyn-validation-* - split: test path: data/TTS/nyn/nyn-test-* - config_name: orm_asr data_files: - split: train path: data/ASR/orm/orm-train-* - split: validation path: data/ASR/orm/orm-validation-* - split: test path: data/ASR/orm/orm-test-* - split: unlabeled path: data/ASR/orm/orm-unlabeled-* - config_name: sid_asr data_files: - split: train path: data/ASR/sid/sid-train-* - split: validation path: data/ASR/sid/sid-validation-* - split: test path: data/ASR/sid/sid-test-* - split: unlabeled path: data/ASR/sid/sid-unlabeled-* - config_name: sna_asr data_files: - split: train path: data/ASR/sna/sna-train-* - split: validation path: data/ASR/sna/sna-validation-* - split: test path: data/ASR/sna/sna-test-* - split: unlabeled path: data/ASR/sna/sna-unlabeled-* - config_name: tir_asr data_files: - split: train path: data/ASR/tir/tir-train-* - split: validation path: data/ASR/tir/tir-validation-* - split: test path: data/ASR/tir/tir-test-* - split: unlabeled path: data/ASR/tir/tir-unlabeled-* - config_name: sog_asr data_files: - split: train path: data/ASR/sog/sog-train-* - split: validation path: data/ASR/sog/sog-validation-* - split: test path: data/ASR/sog/sog-test-* - split: unlabeled path: data/ASR/sog/sog-unlabeled-* - config_name: swa_tts data_files: - split: train path: data/TTS/swa/swa-train-* - split: validation path: data/TTS/swa/swa-validation-* - split: test path: data/TTS/swa/swa-test-* - config_name: twi_tts data_files: - split: train path: data/TTS/twi/twi-train-* - split: validation path: data/TTS/twi/twi-validation-* - split: test path: data/TTS/twi/twi-test-* - config_name: yor_tts data_files: - split: train path: data/TTS/yor/yor-train-* - split: validation path: data/TTS/yor/yor-validation-* - split: test path: data/TTS/yor/yor-test-* - config_name: wal_asr data_files: - split: train path: data/ASR/wal/wal-train-* - split: validation path: data/ASR/wal/wal-validation-* - split: test path: data/ASR/wal/wal-test-* - split: unlabeled path: data/ASR/wal/wal-unlabeled-* dataset_info: - config_name: ach_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: ach_tts features: - name: id dtype: string - name: speaker_id dtype: string - name: text dtype: string - name: locale dtype: string - name: gender dtype: string - name: audio dtype: audio - config_name: aka_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: dag_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: dga_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: ewe_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: fat_tts features: - name: id dtype: string - name: speaker_id dtype: string - name: text dtype: string - name: locale dtype: string - name: gender dtype: string - name: audio dtype: audio - config_name: ful_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: ful_tts features: - name: id dtype: string - name: speaker_id dtype: string - name: text dtype: string - name: locale dtype: string - name: gender dtype: string - name: audio dtype: audio - config_name: hau_tts features: - name: id dtype: string - name: speaker_id dtype: string - name: text dtype: string - name: locale dtype: string - name: gender dtype: string - name: audio dtype: audio - config_name: ibo_tts features: - name: id dtype: string - name: speaker_id dtype: string - name: text dtype: string - name: locale dtype: string - name: gender dtype: string - name: audio dtype: audio - config_name: kik_tts features: - name: id dtype: string - name: speaker_id dtype: string - name: text dtype: string - name: locale dtype: string - name: gender dtype: string - name: audio dtype: audio - config_name: kpo_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: lin_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: lug_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: lug_tts features: - name: id dtype: string - name: speaker_id dtype: string - name: text dtype: string - name: locale dtype: string - name: gender dtype: string - name: audio dtype: audio - config_name: luo_tts features: - name: id dtype: string - name: speaker_id dtype: string - name: text dtype: string - name: locale dtype: string - name: gender dtype: string - name: audio dtype: audio - config_name: mas_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: mlg_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: nyn_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: nyn_tts features: - name: id dtype: string - name: speaker_id dtype: string - name: text dtype: string - name: locale dtype: string - name: gender dtype: string - name: audio dtype: audio - config_name: sna_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: sog_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: swa_tts features: - name: id dtype: string - name: speaker_id dtype: string - name: text dtype: string - name: locale dtype: string - name: gender dtype: string - name: audio dtype: audio - config_name: twi_tts features: - name: id dtype: string - name: speaker_id dtype: string - name: text dtype: string - name: locale dtype: string - name: gender dtype: string - name: audio dtype: audio - config_name: yor_tts features: - name: id dtype: string - name: speaker_id dtype: string - name: text dtype: string - name: locale dtype: string - name: gender dtype: string - name: audio dtype: audio --- # Waxal Datasets The WAXAL dataset is a large-scale multilingual speech corpus for African languages, introduced in the paper [WAXAL: A Large-Scale Multilingual African Language Speech Corpus](https://huggingface.co/papers/2602.02734). ## 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) - [Citation](#citation) ## 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 19 African languages. It consists of approximately 1,250 hours of transcribed natural speech from a wide variety of voices. The 19 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-SA-4.0` University of Ghana | Akan, Ewe, Dagbani, Dagaare, Ikposo | `CC-BY-4.0` Digital Umuganda | Fula, Lingala, Shona, Malagasy, Amharic, Oromo, Sidama, Tigrinya, Wolaytta | `CC-BY-SA-4.0` ### TTS Dataset The Waxal TTS dataset is a collection of text-to-speech data in 16 African languages. It consists of over 180 hours of high-quality, single-speaker recordings reading phonetically balanced scripts. Provider | Languages | License :------------------ | :----------------------------------- | :------------: Makerere University | Acholi, Luganda, Kiswahili, Nyankole | `CC-BY-SA-4.0` University of Ghana | Akan (Fante, Twi) | `CC-BY-4.0` Media Trust | Fula, Igbo, Hausa, Yoruba | `CC-BY-SA-4.0` Loud and Clear | Kikuyu, Luo | `CC-BY-SA-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. You will need `ffmpeg` installed on your system. **Google Colab / Ubuntu** ```bash sudo apt-get install ffmpeg pip install datasets[audio] ``` **macOS** ```bash brew install ffmpeg pip install datasets[audio] ``` **Windows** Download and install from [ffmpeg.org](https://ffmpeg.org/download.html) and ensure it's in your PATH. ```bash pip install datasets[audio] ``` If you encounter `RuntimeError: Could not load libtorchcodec`, please ensure `ffmpeg` is correctly installed or check for compatibility between your `torch`, `torchaudio`, and `torchcodec` versions. **Loading ASR Data** To load ASR data for a specific language, specify the configuration name, e.g. `sna_asr` for Shona ASR data. ```python from datasets import load_dataset, Audio # Load Shona (sna) ASR dataset asr_data = load_dataset("google/WaxalNLP", "sna_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 for a specific language, specify the configuration name, e.g. `swa_tts` for Swahili TTS data. ```python from datasets import load_dataset # Load Swahili (swa) TTS dataset tts_data = load_dataset("google/WaxalNLP", "swa_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 }, 'text': '...', 'locale': 'swa', 'gender': 'Female', } ``` * **id**: Unique identifier. * **speaker_id**: Unique identifier for the speaker. * **audio**: Audio data. * **text**: Text script. * **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 4.0` Digital Umuganda | [AfriVoice](DigitalUmuganda/AfriVoice) | `CC-BY-SA 4.0` Makerere University | [Yogera Dataset](https://doi.org/10.7910/DVN/BEROE0) | `CC-BY-SA 4.0` Media Trust | | `CC-BY-SA 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 ## Citation ```bibtex @article{waxal2026, title={WAXAL: A Large-Scale Multilingual African Language Speech Corpus}, author={Anonymous}, journal={arXiv preprint arXiv:2602.02734}, year={2026} } ```

### 数据集元数据语言创建者： - creator_1 覆盖语言： - ach - aka - amh - dag - dga - ewe - fat - ful - hau - ibo - kik - kpo - lin - lug - luo - mas - mlg - nyn - orm - sid - sna - sog - swa - tir - twi - wal - yor 许可协议： - CC-BY-SA-4.0（知识共享署名-相同方式共享4.0） - CC-BY-4.0（知识共享署名4.0）多语言属性： - 多语言源数据集： - UGSpeechData - DigitalUmuganda/AfriVoice - 原始数据集任务类别： - 自动语音识别（Automatic Speech Recognition） - 文本转语音（Text-to-Speech）美观名称：Waxal NLP 数据集 arXiv编号：2602.02734 标注创建者： - 人工标注 - 众包标注标签： - 音频 - 自动语音识别 - 文本转语音配置项： - 配置名称：ach_asr 数据文件： - 划分：train 路径：data/ASR/ach/ach-train-* - 划分：validation 路径：data/ASR/ach/ach-validation-* - 划分：test 路径：data/ASR/ach/ach-test-* - 划分：unlabeled 路径：data/ASR/ach/ach-unlabeled-* - 配置名称：ach_tts 数据文件： - 划分：train 路径：data/TTS/ach/ach-train-* - 划分：validation 路径：data/TTS/ach/ach-validation-* - 划分：test 路径：data/TTS/ach/ach-test-* - 其余配置项遵循相同结构，保留原配置名称与文件路径数据集信息： - 配置名称：ach_asr 字段： - 名称：id 数据类型：字符串 - 名称：speaker_id 数据类型：字符串 - 名称：transcription 数据类型：字符串 - 名称：language 数据类型：字符串 - 名称：gender 数据类型：字符串 - 名称：audio 数据类型：音频数据 - 其余数据集信息字段遵循相同结构 # Waxal 数据集 WAXAL数据集是面向非洲语言的大规模多语言语音语料库，相关论文为《WAXAL: A Large-Scale Multilingual African Language Speech Corpus》（链接：https://huggingface.co/papers/2602.02734）。 ## 目录 - [数据集概述](#dataset-description) - [自动语音识别数据集](#asr-dataset) - [文本转语音数据集](#tts-dataset) - [使用方法](#how-to-use) - [数据集结构](#dataset-structure) - [ASR数据字段](#asr-data-fields) - [TTS数据字段](#tts-data-fields) - [数据划分](#data-splits) - [数据集构建](#dataset-curation) - [数据使用注意事项](#considerations-for-using-the-data) - [附加信息](#additional-information) - [引用](#citation) ## 数据集概述 Waxal项目提供面向非洲语言的自动语音识别（Automatic Speech Recognition, ASR）与文本转语音（Text-to-Speech, TTS）数据集。本数据集的开发与发布旨在推动针对这些服务不足语言的语音及语言技术精度、流畅度优化研究，并作为数字保存库使用。 Waxal数据集由马克雷雷大学、加纳大学、Digital Umuganda及Media Trust合作采集，采集工作由谷歌与盖茨基金会资助，依据相关协议将数据集公开开放获取。 ### 自动语音识别数据集 Waxal自动语音识别数据集涵盖19种非洲语言，包含约1250小时的多发声者自然语音转录数据。该数据集覆盖的19种语言拥有超1亿使用者，分布在撒哈拉以南非洲的40个国家。 | 合作机构 | 覆盖语言 | 许可协议 | |:---------------------- |:----------------------------------------------------------------------- |:--------------------:| | 马克雷雷大学 | 阿乔利语（ach）、卢干达语（lug）、马萨巴语（mas）、恩扬科莱语（nyn）、索加语 | CC-BY-SA-4.0 | | 加纳大学 | 阿肯语（aka）、埃维语（ewe）、达格班尼语（dag）、达加雷语（dga）、伊克波索语（kpo） | CC-BY-4.0 | | Digital Umuganda | 富拉语（ful）、林加拉语（lin）、绍纳语（sna）、马拉加西语（mlg）、阿姆哈拉语（amh）、奥罗莫语（orm）、西达莫语（sid）、提格雷尼亚语（tir）、沃莱塔语（wal） | CC-BY-SA-4.0 | ### 文本转语音数据集 Waxal文本转语音数据集涵盖16种非洲语言，包含超180小时的高质量单发声者朗读语音，朗读内容为语音平衡脚本。 | 合作机构 | 覆盖语言 | 许可协议 | |:---------------------- |:----------------------------------------------------------------------- |:--------------------:| | 马克雷雷大学 | 阿乔利语（ach）、卢干达语（lug）、斯瓦西里语（swa）、恩扬科莱语（nyn） | CC-BY-SA-4.0 | | 加纳大学 | 阿肯语（aka，含方蒂语、特维语） | CC-BY-4.0 | | Media Trust | 富拉语（ful）、伊博语（ibo）、豪萨语（hau）、约鲁巴语（yor） | CC-BY-SA-4.0 | | Loud and Clear | 基库尤语（kik）、卢奥语（luo） | CC-BY-SA-4.0 | ### 使用方法使用`datasets`库可通过纯Python代码规模化加载与预处理该数据集。首先需安装处理音频数据所需的依赖，需在系统中安装`ffmpeg`。 **Google Colab / Ubuntu** bash sudo apt-get install ffmpeg pip install datasets[audio] **macOS** bash brew install ffmpeg pip install datasets[audio] **Windows** 从[ffmpeg.org](https://ffmpeg.org/download.html)下载并安装ffmpeg，并将其添加至系统PATH环境变量，随后执行： bash pip install datasets[audio] 若遇到`RuntimeError: Could not load libtorchcodec`错误，请确认`ffmpeg`是否正确安装，或检查`torch`、`torchaudio`与`torchcodec`版本兼容性。 **加载ASR数据** 若需加载特定语言的ASR数据，请指定配置名称，例如使用`sna_asr`加载绍纳语（sna）ASR数据集。 python from datasets import load_dataset, Audio # 加载绍纳语（sna）ASR数据集 asr_data = load_dataset("google/WaxalNLP", "sna_asr") # 访问数据集划分 train = asr_data['train'] val = asr_data['validation'] test = asr_data['test'] # 示例：访问音频字节与其他字段 example = train[0] print(f"转录文本: {example['transcription']}") print(f"采样率: {example['audio']['sampling_rate']}") # 'array' 字段存储了解码后的音频numpy数组 print(f"音频数组形状: {example['audio']['array'].shape}") **加载TTS数据** 若需加载特定语言的TTS数据，请指定配置名称，例如使用`swa_tts`加载斯瓦西里语（swa）TTS数据集。 python from datasets import load_dataset # 加载斯瓦西里语（swa）TTS数据集 tts_data = load_dataset("google/WaxalNLP", "swa_tts") # 访问数据集划分 train = tts_data['train'] ## 数据集结构 ### ASR数据字段 python { 'id': 'sna_0', 'speaker_id': '...', 'audio': { 'array': [...], 'sample_rate': 16_000 }, 'transcription': '...', 'language': 'sna', 'gender': 'Female', } * **id**: 唯一标识符。 * **speaker_id**: 发声者唯一标识符。 * **audio**: 音频数据。 * **transcription**: 音频对应的转录文本。 * **language**: ISO 639-2 语言代码。 * **gender**: 发声者性别（'Male'为男性，'Female'为女性，或为空）。 ### TTS数据字段 python { 'id': 'swa_0', 'speaker_id': '...', 'audio': { 'array': [...], 'sample_rate': 16_000 }, 'text': '...', 'locale': 'swa', 'gender': 'Female', } * **id**: 唯一标识符。 * **speaker_id**: 发声者唯一标识符。 * **audio**: 音频数据。 * **text**: 朗读文本脚本。 * **locale**: ISO 639-2 语言代码。 * **gender**: 发声者性别。 ### 数据划分对于**ASR数据集**，带转录的标注数据划分如下： * **train**: 80%的标注数据。 * **validation**: 10%的标注数据。 * **test**: 10%的标注数据。 **unlabeled**划分包含所有无对应转录文本的样本。 **TTS数据集**采用类似划分逻辑，数据被划分为`train`、`validation`与`test`集。 ## 数据集构建本数据由多家合作机构采集： | 合作机构 | 数据集来源 | 许可协议 | |:---------------------- |:----------------------------------------------------------------------- |:--------------------:| | 加纳大学 | [UGSpeechData](https://doi.org/10.57760/sciencedb.22298) | CC BY 4.0 | | Digital Umuganda | [AfriVoice](DigitalUmuganda/AfriVoice) | CC-BY-SA 4.0 | | 马克雷雷大学 | [Yogera Dataset](https://doi.org/10.7910/DVN/BEROE0) | CC-BY-SA 4.0 | | Media Trust | 无指定来源 | CC-BY-SA 4.0 | ## 数据使用注意事项请确认您使用的特定语言对应的许可协议，不同合作机构的许可协议可能存在差异。 **所属机构**: 谷歌研究院 ## 版本与维护 - **当前版本**: 1.0.0 - **最后更新**: 2026年1月 ## 引用 bibtex @article{waxal2026, title={WAXAL: A Large-Scale Multilingual African Language Speech Corpus}, author={Anonymous}, journal={arXiv preprint arXiv:2602.02734}, year={2026} }

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