Update README: remove balanced column, add citation, describe Kimi-Audio training format

README.md

@@ -47,13 +47,13 @@ emotion categories: **angry, crying, happy, laugh, sad**.
 
 ## Subsets (nested, emotion-balanced)
 
-| subset | en hours | pairs   | per-emotion hours | balanced? |
-|-------:|---------:|--------:|------------------:|:---------:|
-| 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 data) |
+| 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
@@ -62,16 +62,14 @@ Smaller subsets are strict subsets of larger ones. Hours are measured by
 ## Repository layout
 
 ```
-move-1000hr/
+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 + 5 boolean cols (in_1hr ... in_1000hr)
-├── plan.tsv               # full plan with tier + shard_id + durations
-├── entries.jsonl.gz       # lossless original metadata (optional)
+├── metadata_all.tsv       # all 896k pairs + subset column
 ├── data/                  # audio packed into tar shards
 │   ├── s1hr_000.tar
 │   ├── s50hr_*.tar
@@ -79,7 +77,7 @@ move-1000hr/
 │   ├── s500hr_*.tar
 │   └── s1000hr_*.tar
 └── scripts/
-    └── tsv_to_metadata_shard.py   # paired TSV -> conversation-format JSONL
+    └── tsv_to_metadata_shard.py   # TSV -> Kimi-Audio training JSONL
 ```
 
 Inside each tar, paths preserve the original tree:
@@ -176,21 +174,33 @@ def subset(df, target):
 df_50hr = subset(df, "50hr")   # equivalent to metadata_50hr.tsv
 ```
 
-## Convert to training format (s-s conversation)
+## Convert to Kimi-Audio training format
 
-If your trainer expects the original `metadata_shard` format
-(`{task_type, conversation:[...]}`), use the included script:
+The included script `tsv_to_metadata_shard.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_shard.py metadata_100hr.tsv \
   -o metadata_shard.jsonl
-# or split:
-python scripts/tsv_to_metadata_shard.py metadata_100hr.tsv \
-  --shard-size 50000 -o metadata_shard.jsonl
 ```
 
-Each TSV row produces 2 conversations (en→zh + zh→en). Use
-`--directions en2zh` / `--directions zh2en` to keep one direction.
+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
 
@@ -203,8 +213,15 @@ Each TSV row produces 2 conversations (en→zh + zh→en). Use
 
 ## Citation
 
-(TODO)
+```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 (TODO confirm).
+CC BY-NC 4.0