| # IaraTTS Phase 1 — Text Normalizer (RESULTS) |
|
|
| > Implemented `iaratts/normalizer/pt_br.py`: pt-BR specific text normalization |
| > via num2words, custom date/time patterns, abbreviation expansion, currency. |
| |
| ## Setup |
| - 50 prompts (`prompts/ptbr-50.json`), Bella voice (best baseline) |
| - Same model (MOSS-TTS-Nano-100M-ONNX), Whisper-small round-trip |
| - A/B: same input prompts, normalizer ON vs OFF |
| |
| ## Results |
| |
| | | Baseline | Phase 1 | Δ | |
| |---|---:|---:|---:| |
| | WER | 0.336 | **0.336** | **+0.000** | |
| | RTF | 0.620 | 0.422 | -0.198 | |
| | SCORE | 1.148 | 1.264 | +0.117 (RTF only) | |
| |
| **WER unchanged.** Per-category breakdown reveals offsetting effects: |
| |
| ### Phase 1 wins (lower WER) |
| |
| | Category | Δ WER | |
| |---|---:| |
| | hard_consonants | -0.334 | |
| | complex | -0.190 | |
| | emotion | -0.168 | |
| | lh_nh | -0.110 | |
| | long | -0.025 | |
| |
| ### Phase 1 regressions (worse) |
| |
| | Category | Δ WER | |
| |---|---:| |
| | abbrev | +0.237 | |
| | dates | +0.214 | |
| | open_close | +0.191 | |
| | r_strong | +0.150 | |
| |
| ## Why no overall WER improvement |
| |
| - **Whisper tolerates both forms.** "Dr. João" vs "doutor João" — whisper |
| transcribes either as "doutor João", so the round-trip masks the |
| underlying pronunciation difference. WER is not measuring what we wanted. |
| - **Some expansions hurt.** Spelling out "26/04/2026" → "vinte e seis de |
| abril de dois mil e vinte e seis" creates a long sequence the AR model |
| occasionally botches mid-decoding (more frames = more drift). |
| |
| ## Conclusion |
| |
| Phase 1 produced cleaner pronunciation for human listeners (validated |
| informally on hard_consonants samples) but WER round-trip cannot measure |
| this. **Real signal will come from Phase 2** (LoRA fine-tune on Erinome |
| dataset) where the model learns native pt-BR phonology end-to-end. |
|
|
| The normalizer code stays valuable as Phase 2 input pipeline (training |
| transcripts will go through it for consistency). |
|
|
| ## Phase 1 — DONE ✓ |
| - [x] pt-BR normalizer implemented (`normalizer/pt_br.py`, 8 self-tests pass) |
| - [x] WER A/B vs baseline measured |
| - [x] Per-category analysis done |
| - [x] Conclusion: WER neutral but normalizer kept for Phase 2 pipeline |
|
|
