| --- |
| 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`](https://huggingface.co/datasets/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) |
|
|
| ```bash |
| 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 "$f"; done # extracts en/ and zh/ |
| ``` |
|
|
| The wav paths in `metadata_100hr.tsv` (e.g. `en/happy/happy_000001_en.wav`) |
| now resolve to local files. |
|
|
| ### Download the full 1000hr (~340 GB) |
|
|
| ```bash |
| huggingface-cli download 47z/MoVE \ |
| --repo-type dataset --local-dir ./move |
| cd move && for f in data/*.tar; do tar -xf "$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: |
| |
| ```python |
| 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](https://github.com/MoonshotAI/Kimi-Audio) speech-to-speech |
| training pipeline described in our paper. |
| |
| ```bash |
| python scripts/tsv_to_metadata.py metadata_100hr.tsv \ |
| -o metadata_shard.jsonl |
| ``` |
| |
| Output schema (one JSON object per line): |
| |
| ```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/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 |
|
|
| ```bibtex |
| @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 |
|
|
