kaggle/data.md

# Dataset Description

## Overview

The dataset contains **7,002 four-second WAV recordings** of English speech — half from real human speakers, half generated by neural TTS systems.

- **Train:** 5,748 clips (2,874 real, 2,874 fake)
- **Test:** 1,254 clips (627 real, 627 fake, labels withheld)
- **Audio:** 16 kHz, mono, 16-bit PCM WAV, exactly 4 seconds

The test set is **speaker-disjoint** (no test speaker appears in training) and includes audio from **at least one TTS system not seen during training**.

---

## Sources

**Real speech:**
- [VCTK Corpus](https://datashare.ed.ac.uk/handle/10283/3443) — 109 English speakers, various accents (CC BY 4.0)
- [LibriSpeech](https://www.openslr.org/12) — read speech from audiobooks (CC BY 4.0)

**Fake speech:**
- **Tacotron2** — attention-based seq2seq TTS (Shen et al., 2018)
- **VITS** — end-to-end TTS with normalizing flows (Kim et al., 2021)
- **SpeechT5** — unified-modal encoder-decoder (Ao et al., 2022)

---

## Files

| File | Description |
|------|-------------|
| `train/real/` | 2,874 real speech WAV files |
| `train/fake/` | 2,874 TTS-generated WAV files |
| `test/` | 1,254 test WAV files, flat directory, no labels |
| `train.csv` | Training labels: `id`, `label` |
| `test.csv` | Test file list: `id` (no label) |
| `sample_submission.csv` | Example submission with `score=0.5` for all |
| `solution.csv` | Ground truth labels (for local AUROC scoring) |
| `notebooks/` | 6 reference notebooks (starter + 5 graded approaches) |

---

## train.csv

```csv
id,label
train/real/real_00001.wav,real
train/real/real_00002.wav,real
train/fake/fake_00001.wav,fake
train/fake/fake_00002.wav,fake
...
```

- `label`: `"real"` or `"fake"`

---

## test.csv

```csv
id
test/voiceguard_00001.wav
test/voiceguard_00002.wav
...
```

---

## sample_submission.csv

```csv
id,score
test/voiceguard_00001.wav,0.5
test/voiceguard_00002.wav,0.5
...
```

Replace `0.5` with your model's P(fake) score for each clip.

---

## Directory Structure

```
train/
├── real/
│   ├── real_00001.wav    # VCTK / LibriSpeech recordings
│   └── ...               # 2,874 files total
└── fake/
    ├── fake_00001.wav    # Tacotron2 / VITS / SpeechT5 output
    └── ...               # 2,874 files total

test/
└── voiceguard_00001.wav ... voiceguard_01254.wav   # flat, no labels
```

---

## Loading Audio

```python
import librosa
import numpy as np
import pandas as pd

train_df = pd.read_csv("train.csv")
train_df["label_int"] = (train_df["label"] == "fake").astype(int)

y, sr = librosa.load(train_df.iloc[0]["id"], sr=16000)
# y.shape = (64000,) — exactly 4 seconds at 16 kHz

# Anti-spoofing features
flatness  = librosa.feature.spectral_flatness(y=y).mean()
mfcc      = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20)
zcr       = librosa.feature.zero_crossing_rate(y).mean()

# Harmonics-to-Noise Ratio (handle infinity!)
f0, voiced, _ = librosa.pyin(y, fmin=50, fmax=400, sr=sr)
# Note: autocorrelation-based HNR may return np.inf for very periodic signals
# Always apply: np.nan_to_num(hnr, nan=0.0, posinf=0.0, neginf=0.0)
```

---

## Key Acoustic Differences

Understanding why fake speech is detectable helps you build better features:

| Feature | Real Speech | TTS/Fake Speech |
|---------|-------------|-----------------|
| **Spectral flatness** | Low (~0.001–0.01) — peaky harmonic spectrum | Higher (~0.05–0.3) — smoother spectrum |
| **Pitch micro-variation** | Natural jitter (±1–3%) | Machine-perfect, overly smooth F0 |
| **HNR** | Moderate, with natural noise floor | Very high or infinite (no noise) |
| **LFCC smoothness** | Frame-to-frame variation | Over-smooth transitions |
| **Spectral envelope** | Slight roughness from vocal tract | Unnaturally clean |

---

## Important Notes

- All WAV files are **exactly 4 seconds** (64,000 samples). No padding needed.
- **Generalization:** the test set includes a TTS system not seen during training. Feature-based models (LFCC, spectral features) generalize better than models that memorize artifacts of specific synthesizers.
- **AUROC not accuracy:** always output probability scores (0–1), not binary predictions. See the Evaluation page.
- Both real and fake speech are **English only** in this dataset.
- Dataset is CC BY 4.0. Attribution: VCTK (Yamagishi et al.), LibriSpeech (Panayotov et al.), ASVspoof challenge.