Datasets:

cagatayn
/

multi_accent_speech

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):
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

📂 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

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

✅ 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.

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