Datasets:

47z
/

MoVE

language:
  - en
  - zh
license: cc-by-nc-4.0
pretty_name: MoVE — Multilingual Voice Emotion (paired EN/ZH)
size_categories:
  - 100K<n<1M
task_categories:
  - audio-to-audio
  - text-to-speech
  - translation
tags:
  - speech-translation
  - emotional-tts
  - paired-speech
configs:
  - config_name: 1hr
    data_files:
      - split: train
        path: metadata_1hr.tsv
  - config_name: 50hr
    data_files:
      - split: train
        path: metadata_50hr.tsv
  - config_name: 100hr
    data_files:
      - split: train
        path: metadata_100hr.tsv
  - config_name: 500hr
    data_files:
      - split: train
        path: metadata_500hr.tsv
  - config_name: 1000hr
    data_files:
      - split: train
        path: metadata_1000hr.tsv

MoVE — Multilingual Voice Emotion (paired EN/ZH)

Repo: 47z/MoVE

Paired English / Chinese emotional speech for speech-to-speech translation training. Every row is a (en_wav, zh_wav) pair belonging to one of five emotion categories: angry, crying, happy, laugh, sad.

Subsets (nested, emotion-balanced)

subset	en hours	pairs	per-emotion hours
1hr	1.00	839	0.20 each
50hr	50.00	42,467	10.00 each
100hr	99.99	85,004	20.00 each
500hr	500.00	425,952	100.00 each
1000hr	1055.67	896,477	95–133 (full)

Smaller subsets are strict subsets of larger ones. Hours are measured by English audio duration; the Chinese side is paired one-to-one and ~5% longer in total.

Repository layout

47z/MoVE/
├── README.md
├── metadata_1hr.tsv       # 5 columns: zh_path, en_path, zh_text, en_text, category
├── metadata_50hr.tsv
├── metadata_100hr.tsv
├── metadata_500hr.tsv
├── metadata_1000hr.tsv    # full
├── metadata_all.tsv       # all 896k pairs + subset column
├── data/                  # audio packed into tar shards
│   ├── s1hr_000.tar
│   ├── s50hr_*.tar
│   ├── s100hr_*.tar
│   ├── s500hr_*.tar
│   └── s1000hr_*.tar
└── scripts/
    └── tsv_to_metadata.py   # TSV -> Kimi-Audio training JSONL

Inside each tar, paths preserve the original tree:

en/<emotion>/<id>_en.wav
zh/<emotion>/<id>_zh.wav

So tar -xf data/*.tar reproduces a flat en/ + zh/ directory layout.

Quick start

Download just one subset (e.g. 100hr ≈ 34 GB audio)

huggingface-cli download 47z/MoVE \
  --repo-type dataset --local-dir ./move \
  --include "metadata_100hr.tsv" \
            "data/s1hr_*.tar" \
            "data/s50hr_*.tar" \
            "data/s100hr_*.tar" \
            "scripts/*"

cd move
for f in data/*.tar; do tar -xf "$f"; done   # extracts en/ and zh/

The wav paths in metadata_100hr.tsv (e.g. en/happy/happy_000001_en.wav) now resolve to local files.

Download the full 1000hr (~340 GB)

huggingface-cli download 47z/MoVE \
  --repo-type dataset --local-dir ./move
cd move && for f in data/*.tar; do tar -xf "$f"; done

Required tar shards per subset

subset	tar prefixes to download
1hr	`s1hr_*`
50hr	`s1hr_`, `s50hr_`
100hr	`s1hr_`, `s50hr_`, `s100hr_*`
500hr	`s1hr_`, `s50hr_`, `s100hr_`, `s500hr_`
1000hr	all (`s_`)

metadata.tsv schema

Each metadata_<size>.tsv is tab-separated with this header:

column	example	description
zh_path	`zh/happy/happy_000001_zh.wav`	path to Chinese wav (relative)
en_path	`en/happy/happy_000001_en.wav`	path to English wav (relative)
zh_text	`父亲。一个人出去约会...`	Chinese transcript
en_text	`Dater. A person who...`	English transcript
category	`happy`	one of {angry, crying, happy, laugh, sad}

metadata_all.tsv (overview)

Same 5 columns plus one subset column:

column	values	meaning
`subset`	`1hr` / `50hr` / `100hr` / `500hr` / `1000hr`	the smallest subset this row belongs to

Subsets are nested, so a row with subset="1hr" is also in 50hr, 100hr, 500hr and 1000hr; subset="1000hr" means the row is only in the full dataset. Subset sizes (cumulative):

subset value	cumulative includes	total rows
`1hr`	self	839
`50hr`	1hr + 50hr	42,467
`100hr`	1hr + 50hr + 100hr	85,004
`500hr`	+ 500hr	425,952
`1000hr`	full	896,477

Filter with pandas:

import pandas as pd
df = pd.read_csv("metadata_all.tsv", sep="\t")

# Build any subset by including this tier and all smaller ones:
order = ["1hr", "50hr", "100hr", "500hr", "1000hr"]
def subset(df, target):
    keep = order[:order.index(target) + 1]
    return df[df["subset"].isin(keep)]

df_50hr = subset(df, "50hr")   # equivalent to metadata_50hr.tsv

Convert to Kimi-Audio training format

The included script tsv_to_metadata.py converts any metadata TSV into the conversation-format JSONL used to replicate the Kimi-Audio speech-to-speech training pipeline described in our paper.

python scripts/tsv_to_metadata.py metadata_100hr.tsv \
  -o metadata_shard.jsonl

Output schema (one JSON object per line):

{
  "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/happy/happy_000001_en.wav"},
    {"role": "assistant", "message_type": "audio-text", "content": ["zh/happy/happy_000001_zh.wav", "中文文本..."]}
  ]
}

Each TSV row produces 2 conversations by default (en→zh + zh→en). Use --directions en2zh / --directions zh2en to keep one direction only.

How it was built

En/Zh paired by the <id>_en / <id>_zh suffix on the original entries_shard_*.jsonl records (2,801 unpaired entries dropped).
Tier assignment per emotion: shuffle (seed=42), accumulate English duration, label each pair with the smallest subset it falls into.
Tar shards are emotion-balanced (round-robin) within each tier and capped to keep individual tars manageable (≤ ~20 GB).

Citation

@article{chen2026move,
  title={MoVE: Translating Laughter and Tears via Mixture of Vocalization Experts in Speech-to-Speech Translation},
  author={Chen, Szu-Chi and Tsai, I-Ning and Lin, Yi-Cheng and Huang, Sung-Feng and Lee, Hung-yi},
  journal={arXiv preprint arXiv:2604.17435},
  year={2026}
}

License

CC BY-NC 4.0