# 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
```

---

## 🗂 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  

---

## 🔎 Example Usage

```python
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](https://commonvoice.mozilla.org/)  
- [VCTK Corpus](https://datashare.ed.ac.uk/handle/10283/3443)  
- [Speech Accent Archive](https://accent.gmu.edu/)  
- [AccentDB](https://github.com/AI4Bharat/AccentDB)  
- [L2-ARCTIC Corpus](https://psi.engr.tamu.edu/l2-arctic-corpus/) 

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