README.md

---
license: apache-2.0
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: en
    dtype: string
  - name: vi
    dtype: string
  - name: audio_en
    dtype: audio
  - name: audio_vi
    dtype: audio
  - name: duration_en_s
    dtype: float64
  - name: duration_vi_s
    dtype: float64
  - name: duration_ratio_en_vi
    dtype: float64
  splits:
  - name: train
    num_bytes: 582098414656.2
    num_examples: 696243
  download_size: 564580628308
  dataset_size: 582098414656.2
---

# PhoMT EN–VI Parallel Speech

Synthetic parallel speech for English↔Vietnamese speech-to-speech translation research:
**696,243 utterance pairs (~1,200 hours of Vietnamese speech + comparable English)**, generated
from the sentence-aligned text pairs of [PhoMT](https://huggingface.co/datasets/ura-hcmut/PhoMT).
Each row carries the English and Vietnamese text plus a spoken rendition of each side.

| | |
|---|---|
| Rows | 696,243 |
| Vietnamese audio | 48 kHz mono WAV, ~1,228 h total (mean ≈ 6.4 s/clip) |
| English audio | 24 kHz mono WAV |
| Length balance | EN/VI duration ratio kept within 0.4–1.8 |

```python
from datasets import load_dataset
ds = load_dataset("anquachdev/PhoMT-en-vi-speech", split="train", streaming=True)
row = next(iter(ds))  # en, vi, audio_en, audio_vi, duration_en_s, duration_vi_s, duration_ratio_en_vi
```

## How it was generated

- **Vietnamese**: [VieNeu-TTS v3-Turbo](https://huggingface.co/pnnbao-ump/VieNeu-TTS) at 48 kHz.
- **English**: [Kokoro-82M](https://huggingface.co/hexgrad/Kokoro-82M) at 24 kHz.
- Voices, speaking speed, and target gender are assigned per row by a seeded deterministic RNG,
  so the corpus is exactly reproducible from the PhoMT indexes.

### Speaker diversity via voice cloning

The Vietnamese voice pool contains **40 voices**: 12 VieNeu presets plus **28 voices cloned from
[VIVOS](https://huggingface.co/datasets/AILAB-VNUHCM/vivos) speakers** using VieNeu's reference-audio
voice cloning. Every candidate voice passed a QA scorecard before entering the pool —
transcription accuracy (PhoWhisper-small CER on synthesized calibration text), clone fidelity
(speaker-embedding similarity to the reference), and speaking rate; 8 candidates that failed
(unstable CER or dragging pace) were excluded.

### Cross-lingual timbre matching

To keep speaker identity consistent across the language pair, each row's English voice is derived
from its Vietnamese voice: both sides are embedded with VieNeu's 192-d speaker encoder (from
synthesized calibration audio, not raw references), and the Vietnamese voice is matched to its
nearest same-gender English candidate by cosine similarity. The English candidate grid holds 34
timbres — 7 Kokoro voices plus pairwise same-gender blends at 25/50/75 mixing weights — of which
~14 are selected by the matching. Rows generated after this upgrade (~51% of the corpus, PhoMT
indexes ≥ 345600) use matched pairs; earlier rows pair voices independently.

## Quality control

Every clip was validated before upload:

- **Silence/corruption gate**: each waveform decode-checked — non-finite or near-silent
  (RMS < 1e-4) output is regenerated (this catches rare GPU-decoder faults).
- **Length-balance filter**: pairs outside the 0.4–1.8 EN/VI duration ratio band were
  regenerated once with fresh sampling; pairs still out of band (genuinely imbalanced
  translations) were excluded.
- Upload integrity is tracked per source index in `upload-state.json` — every row maps to a
  unique PhoMT pair, with no duplicates.

## Attribution

Text pairs from [PhoMT](https://github.com/VinAIResearch/PhoMT) (VinAI Research). Vietnamese
voice references from [VIVOS](https://huggingface.co/datasets/AILAB-VNUHCM/vivos) (AILAB, VNU-HCM).
Speech synthesized with [VieNeu-TTS](https://huggingface.co/pnnbao-ump/VieNeu-TTS) and
[Kokoro-82M](https://huggingface.co/hexgrad/Kokoro-82M). The audio is fully synthetic; no human
recordings are distributed in this dataset.