README.md

metadata

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 (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.

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:

{"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).

python make_kimi_train.py