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| "@id": "amh_asr_splits/split_name", |
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| "@type": "cr:Field", |
| "@id": "bau_tts_splits/split_name", |
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| "@id": "dag_asr_splits", |
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| "description": "Splits for the dag_asr config.", |
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| "@type": "cr:Field", |
| "@id": "dag_asr_splits/split_name", |
| "dataType": "sc:Text" |
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| "description": "Splits for the dga_asr config.", |
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| "@id": "dga_asr_splits/split_name", |
| "dataType": "sc:Text" |
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| "@type": "cr:RecordSet", |
| "dataType": "cr:Split", |
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| "@id": "yor_tts_splits", |
| "name": "yor_tts_splits", |
| "description": "Splits for the yor_tts config.", |
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| "@id": "yor_tts_splits/split_name", |
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| "conformsTo": "http://mlcommons.org/croissant/1.1", |
| "name": "WaxalNLP", |
| "description": "\n\t\n\t\t\n\t\tWaxal Datasets\n\t\n\nThe 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.\n\n\t\n\t\t\n\t\tDataset Description\n\t\n\nThe Waxal project provides datasets for both Automated Speech Recognition (ASR)\nand Text-to-Speech (TTS) for African languages. The goal of this dataset's\ncreation and release is to facilitate research that improves the accuracy and\nfluency of speech and language\u2026 See the full description on the dataset page: https://huggingface.co/datasets/google/WaxalNLP.", |
| "alternateName": [ |
| "google/WaxalNLP", |
| "Waxal NLP Datasets" |
| ], |
| "creator": { |
| "@type": "Organization", |
| "name": "Google", |
| "url": "https://huggingface.co/google" |
| }, |
| "keywords": [ |
| "automatic-speech-recognition", |
| "text-to-speech", |
| "creator_1", |
| "multilingual", |
| "UGSpeechData", |
| "DigitalUmuganda/AfriVoice", |
| "original", |
| "Acoli", |
| "Akan", |
| "Amharic", |
| "Bada (Nigeria)", |
| "Dagbani", |
| "Southern Dagaare", |
| "Ewe", |
| "Fanti", |
| "Fulah", |
| "Hausa", |
| "Igbo", |
| "Kikuyu", |
| "Ikposo", |
| "Lingala", |
| "Ganda", |
| "Luo (Kenya and Tanzania)", |
| "Masai", |
| "Malagasy", |
| "Nyankole", |
| "Oromo", |
| "Nigerian Pidgin", |
| "Sidamo", |
| "Shona", |
| "Sogdian", |
| "Swahili (macrolanguage)", |
| "Tigrinya", |
| "Twi", |
| "Wolaytta", |
| "Yoruba", |
| "cc-by-sa-4.0", |
| "cc-by-4.0", |
| "1M - 10M", |
| "parquet", |
| "Audio", |
| "Text", |
| "Datasets", |
| "Dask", |
| "Polars", |
| "Croissant", |
| "arxiv:2602.02734", |
| "\ud83c\uddfa\ud83c\uddf8 Region: US", |
| "audio", |
| "automatic-speech-recognition", |
| "text-to-speech" |
| ], |
| "license": "https://choosealicense.com/licenses/cc-by-sa-4.0/", |
| "url": "https://huggingface.co/datasets/google/WaxalNLP", |
| "rai:dataLimitations": [ |
| "Transcription Coverage: The current ASR release contains transcriptions for approximately 10\u201317% of the total audio collected by our partners (varying by language and partner).", |
| "Dialectal Representation: While efforts were made to recruit diverse speakers, the dataset may not capture the full dialectal and socio-linguistic variation present within each language.", |
| "Unintended Content: As the ASR data is unscripted, there is a small possibility of offensive or inappropriate speech, though this risk is mitigated by our manual quality control process.", |
| "Use-Case Specificity: The ASR dataset, with its diverse pool of speakers and environmental conditions, is not well-suited for training high-quality, single-speaker TTS models." |
| ], |
| "rai:dataBiases": [ |
| "Data collection aimed for gender balance and a wide distribution of speaker ages.", |
| "However, the dataset may not capture the full dialectal and socio-linguistic variation present within each language, which may lead to under-representation of certain dialects." |
| ], |
| "rai:personalSensitiveInformation": [ |
| "The intentionally collected attributes for the ASR dataset include speaker age, gender, language, and recording environment.", |
| "The collected attributes for the TTS dataset include speaker ID, gender, and language.", |
| "All personally identifiable information was removed from the transcripts and metadata. However, attributes such as ethnicity and race can likely be inferred from a speaker's language and accent." |
| ], |
| "rai:dataUseCases": [ |
| "The dataset is intended for training and evaluating Automatic Speech Recognition (ASR) models.", |
| "It has been used to benchmark state-of-the-art ASR models (Whisper-Large-v3, MMS-1B, Gemma3n-2B) in zero-shot and fine-tuned settings.", |
| "It enables domain-generalizable ASR adaptation for low-resource African languages, as demonstrated by cross-dataset transfer to FLEURS.", |
| "The TTS dataset is specifically designed for building high-quality, single-speaker synthetic voices.", |
| "The ASR dataset, with its diverse pool of speakers and environmental conditions, is not well-suited for training high-quality, single-speaker TTS models." |
| ], |
| "rai:dataSocialImpact": [ |
| "The release of the TTS dataset enables broad community benefit but carries the risk that the voices could be used in ways we did not foresee.", |
| "We believe the benefits of enabling technology for these languages outweigh the risks.", |
| "It aims to empower researchers and developers to build inclusive, practical speech technologies for the communities these languages represent." |
| ], |
| "rai:hasSyntheticData": false, |
| "prov:wasGeneratedBy": { |
| "@type": "prov:Activity", |
| "description": "Preprocessing: All personally identifiable information was removed from the transcripts and metadata. Transcriptions and audio underwent a quality control process to check for clarity, language accuracy, relevance to the prompt, and appropriate content. Data collection: The WAXAL dataset was acquired through a multi-year effort (Jan 2021 - Mar 2024) funded by Google with involvement from partners in Africa: Makerere University, University of Ghana, Digital Umuganda, and Media Trust. For the ASR dataset, participants were shown a diverse set of images and asked to describe them in their native language. For the TTS dataset, a phonetically balanced script of approximately 108,500 words was created for each of the 10 target languages. 72 male and female community participants were contracted, and recordings were conducted in a professional studio-like environment to ensure high audio fidelity. Data annotation: A subset corresponding to 10% of the total collected audio was transcribed by paid, local linguistic experts. All local language experts contracted for transcription were compensated at rates well above the local hourly average for their region." |
| } |
| } |
