README.md

---
language:
  - en
  - zh
license: cc-by-nc-4.0
task_categories:
  - translation
  - automatic-speech-recognition
pretty_name: MoVE — Mixture of Vocalization Experts Dataset
size_categories:
  - 100K<n<1M
tags:
  - speech-to-speech-translation
  - expressive-speech
  - emotion
  - bilingual
  - tts
---

# MoVE Dataset

Bilingual (EN↔ZH) expressive speech-to-speech translation dataset.  
858,312 pairs · 5 emotion categories: `angry`, `happy`, `sad`, `laugh`, `crying`.

> **Paper:** [MoVE: Translating Laughter and Tears via Mixture of Vocalization Experts in Speech-to-Speech Translation](https://arxiv.org/abs/2604.17435) (Interspeech 2026)

## Directory Structure

```
├── en/{emotion}/*.flac       # English TTS audio (FLAC, lossless)
├── zh/{emotion}/*.flac       # Chinese TTS audio (FLAC, lossless)
├── metadata.tsv              # Pair metadata (see below)
└── make_kimi_train.py        # Convert to Kimi-Audio training format
```

## metadata.tsv

Columns: `zh_path`, `en_path`, `zh_text`, `en_text`, `category`

All paths are relative to this directory and use `.flac` extension.

```bash
python make_metadata_tsv.py
```

## Kimi-Audio Training Format

> ⚠️ **WARNING**: `make_kimi_train.py` converts all `.flac` files to `.wav` **in-place**
> and deletes the original `.flac` files. WAV files are approximately **2–3× larger**
> than FLAC. Run this only when you intend to use the data for local Kimi-Audio training
> and no longer need the FLAC files.

Produces `metadata_kimi_train.jsonl` in Kimi-Audio conversation format:

```json
{"task_type": "s-s", "conversation": [
  {"role": "user", "message_type": "text", "content": "Translate the given English speech into Chinese while preserving its expressiveness."},
  {"role": "user", "message_type": "audio", "content": "en/angry/angry_000001_en.wav"},
  {"role": "assistant", "message_type": "audio-text", "content": ["zh/angry/angry_000001_zh.wav", "..."]}
]}
```

Each pair generates two entries (EN→ZH and ZH→EN).

```bash
python make_kimi_train.py
```