Blame for writeup/writeup.md

# LinguaWave — Competition Writeup

**Task:** Language Identification — 8-class audio classification  
**Metric:** Macro F1-score  
**Dataset:** 8,000 train samples, 4,000 test samples (1,000 / 500 per language)  
**Source:** Google FLEURS (CC BY 4.0)  
**Languages:** Indonesian (id), Malay (ms), Vietnamese (vi), Thai (th), English (en), Chinese (zh), Arabic (ar), French (fr)

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## Approach Progression

| # | Approach | Val Macro F1 | Description |
|---|----------|-------------|-------------|
| 1 | MFCC + SVM | **0.9775** | 40-dim MFCC mean+std → SVM RBF, C=10 |
| 2 | Pitch + LightGBM | **0.9594** | ~80-dim acoustic + prosody → LGBM |
| 3 | Bag of Codewords | **0.6290** | k-means codebook (k=64) + histogram → LogReg |
| 4 | CNN on Log-Mel | **0.8960** | 128×625 log-mel → 4-block CNN, BATCH=64, 5 epochs |
| 5 | Multi-Scale CNN | **0.9682** | 3-branch CNN (n_fft=512/1024/2048) + hard-neg mining on id/ms, precomputed mel cache |

### Key Takeaways

- **Language ID is surprisingly easy with MFCCs**: at 0.9775, approach 1 already nears the ceiling. The 8 languages span distinct phoneme inventories, rhythm, and prosody, making them linearly separable in MFCC space.
- **More features ≠ better (approach 2 < 1)**: adding pitch and prosody (F0, jitter, shimmer, HNR) slightly *hurts* because these features are noisier over 10-second clips with variable speech content. The MFCC SVM is more robust.
- **Codebook approach underperforms (0.63)**: bag-of-codewords with k=64 compresses too aggressively. Language ID benefits from temporal dynamics (captured by delta-MFCC in approach 1) that flat histograms discard.
- **Hard negative mining for id/ms**: Indonesian and Malay share ~60% vocabulary and acoustic similarity. The multi-scale CNN addresses this by oversampling these pairs during training (2× factor).
- **Multi-scale spectrograms**: using n_fft=512/1024/2048 captures short-term phonemes (512), syllable-level rhythm (1024), and prosodic contours (2048) simultaneously. However, the multi-branch architecture converges unstably (F1 oscillating 0.30–0.78) — a lower learning rate and warm-up schedule are recommended.

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## Dataset Details

- All clips from FLEURS dataset, filtered to 3–12 seconds duration
- Padded/trimmed to exactly 10 seconds (160,000 samples at 16 kHz)
- Stratified 80/20 split: 1,000 train / 500 test per language
- Class balance is exact — macro F1 = accuracy in this case

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## Tips for Participants

1. **Approach 1 sets a very high bar**: if your CNN underperforms the MFCC SVM (0.9775), your model is not learning — check preprocessing and batch size.
2. **Memory-aware mel computation**: the mel shape is (1, 128, 625) at 16 kHz / 10s. With `BATCH=64` this is ~64 MB/batch — safe on Colab T4. Do NOT use batch >128 without checking memory.
3. **id vs ms is the hard pair**: confusion between Indonesian and Malay is the main error source. Consider adding language pair augmentation or a dedicated hard-negative loss.
4. **Cache your mel spectrograms**: approach 5 precomputes all mels to numpy arrays before training. This cuts training time from 27 hours (on-the-fly) to ~30 minutes.
5. **LabelEncoder order matters**: ensure consistent label encoding between train/val/test splits. Always fit the encoder on training data only.
6. **For approach 3 improvement**: try k=256 codebook, Fisher vectors instead of BoW histograms, and SVM classifier — this should recover 10–15 points vs. k=64 + LogReg.

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## Hardest Language Pairs (id/ms)

Indonesian and Malay are the most commonly confused pair. Strategies that help:
- Train on more diverse speakers per language
- Use character n-gram language models on ASR transcripts
- Temporal pooling over the full 10-second clip rather than just mean/std

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## Reproducibility

All scripts use `SEED = 42`. Feature extraction caches are keyed by script number (e.g., `lw_01_train.npy`). The CNN uses `torch.manual_seed(42)`. joblib parallel feature extraction is thread-based (prefer="threads") for librosa compatibility.

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## Baseline Comparison

| Method | Val Macro F1 | Notes |
|--------|-------------|-------|
| Majority class | 0.125 | 8 balanced classes |
| Approach 3 (BoW k=64) | 0.6290 | Histogram loses temporal dynamics |
| Approach 4 (CNN Log-Mel) | 0.8960 | 5 epochs; converged well at epoch 5 |
| Approach 5 (MultiScale CNN) | 0.9682 | Best epoch 5; precomputed mel cache |
| Approach 2 (Pitch LGBM) | 0.9594 | Prosody features + gradient boosting |
| Approach 1 (MFCC SVM) | 0.9775 | Best overall — classical ML wins here |
| Human (expert) | ~0.98 | Native speakers, clean recordings |