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Datasets:
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en-accent-common-accent

Tasks:
Automatic Speech Recognition
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Languages:
English
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common_accent

ASR-ready version of DTU54DL/common-accent (license: MIT).

Non-US/UK subset of Common Voice: India/South Asia, German, South African, Filipino, Singaporean, Hong Kong, Malaysian. Overlaps globe_v2.

Format: parquet, 500-row row groups (Cohere training format). Columns: audio (bytes, 16 kHz mono FLAC), id, text, duration, accent, speaker_id, gender, source, lang, reference_text, meta (JSON string). Clips: 0.5–30 s, non-empty text. No judge/quality filtering applied.

Splits: test: 451 clips / 0.7 h, train: 10,000 clips / 16.8 h

Top accent labels: India and South Asia (India, Pakistan, Sri Lanka) 6,077, German English,Non native speaker 3,024, Southern African (South Africa, Zimbabwe, Namibia) 332, Filipino 302, Hong Kong English 219, Singaporean English 214, Malaysian English 88, West Indies and Bermuda (Bahamas, Bermuda, Jamaica, Trinidad) 36, Eastern European English 8, East African Khoja 8, Austrian 7, Dutch English 7, Dutch 7, European,German,Foreign,Non-native 7, Thai 7
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