cagatayn/multi_accent_speech

Multi-Accent English Speech Corpus (Augmented & Speaker-Disjoint)

This dataset is a curated and augmented multi-accent English speech corpus designed for speech recognition, accent classification, and representation learning.
It consolidates multiple open-source accent corpora, converts all audio to a unified format, applies targeted data augmentation, and exports in a tidy, Hugging Face–ready structure.

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✨ Key Features

• Accents covered (12 total):
  american_english, british_english, indian_english, canadian_english, australian_english, scottish_english, irish_english, new_zealand_english, northernirish, african_english, welsh_english, south_african_english
• Speaker-disjoint splits: each speaker is assigned to exactly one split (train/validation/test).
• Augmentation strategy:
  • < 2.6k samples → expanded to 5k via augmentation
  • 2.6k–10k samples → expanded to 10k via augmentation

  • 10k samples → 50% replaced in place with augmented versions

  • Aug methods: time-stretch, pitch-shift, background noise injection, reverb/EQ
• Audio format (standardized): .wav, 16-bit PCM, 16 kHz sample rate, mono
• Metadata-rich: uid, text, accent, speaker_id, dataset, is_augmented, source_uid, aug_label, duration_s

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📂 Dataset Structure

hf_export/
├── data/
│   ├── train/
│   │   ├── 0000/uid.wav
│   │   └── ...
│   ├── validation/
│   └── test/
├── metadata/
│   ├── train.parquet
│   ├── validation.parquet
│   └── test.parquet
├── SPLIT_REPORT.md
├── QA_REPORT.md
├── splits_by_speaker.csv
└── README.md

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🗂 Metadata Schema

Column	Type	Description
uid	string	Unique ID per sample
path	string	Relative path to the .wav file
text	string	Transcript
accent	string	One of 17 whitelisted accents
speaker_id	string	Unique speaker ID (dataset-prefixed)
dataset	string	Source dataset (cv, vctk, accentdb, etc.)
is_augmented	bool	True if augmented
source_uid	string	UID of the original sample (for augmented rows)
aug_label	string	Applied augmentation method (e.g., pitch:+1.2)
duration_s	float32	Duration in seconds

Note: for accents with over 10k samples, is_augmented is false even though there's augmentation for half of their size. Use random sampling for these accents.

📊 Splitting Strategy

• Ratios: 78% train, 11% validation, 11% test
• Speaker-disjoint: a speaker appears in only one split
• Accent-aware: splits preserve global accent proportions
• Dataset balance: allocator prefers splits underrepresented for a dataset
• Augmentation inheritance: augmented samples inherit the split of their source speaker

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✅ Preflight Validations

Checks applied before release:

• Unique uid values
• All files exist and are readable
• Format: 16kHz / mono / PCM_16
• accent ∈ whitelist (17)
• Transcripts non-empty
• Augmented samples link back to valid originals
• Duration bounds: 0.2s–30s (flagged outliers)
• Speaker-disjointness across splits
• Accent & dataset distributions close to global ratios
• Duplicate detection (duration+text fingerprint)

Reports:

• SPLIT_REPORT.md: speaker allocation, accent/dataset balance
• QA_REPORT.md: split sizes, duration anomalies, duplicate candidates

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🔎 Example Usage

from datasets import load_dataset

ds = load_dataset("cagatayn/multi_accent_speech", split="train")

print(ds[0])
# {
#   'uid': 'abc123',
#   'path': 'data/train/0000/abc123.wav',
#   'text': "it's the philosophy that guarantees everyone's freedom",
#   'accent': 'american_english',
#   'speaker_id': 'cv_sample-000031',
#   'dataset': 'cv',
#   'is_augmented': False,
#   'source_uid': '',
#   'aug_label': '',
#   'duration_s': 3.21,
#   'audio': {'path': 'data/train/0000/abc123.wav', 'array': ..., 'sampling_rate': 16000}
# }

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📜 License

• Audio/data sourced from Common Voice, VCTK, L2 Arctic, Speech Accent Archive, AccentDB.
• Augmented versions generated as derivative works.
• Redistribution follows the most restrictive upstream license. Please review original dataset terms before commercial use.

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🙏 Acknowledgments

• Mozilla Common Voice
• VCTK Corpus
• Speech Accent Archive
• AccentDB
• L2-ARCTIC Corpus

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