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.

✨ Key Features

• —Accents covered (12 total): americanenglish, britishenglish, indianenglish, canadianenglish, australianenglish, scottishenglish, irishenglish, newzealandenglish, northernirish, africanenglish, welshenglish, southafrican_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

📂 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

🗂 Metadata Schema

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

✅ 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

🔎 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}
# }

📜 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.

🙏 Acknowledgments

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