Update README.md

README.md

@@ -20,13 +20,13 @@ size_categories:
 ## 📚 Related Resources
 
 - 🧾 **Paper:** [*SingMOS-Pro: A Comprehensive Benchmark for Singing Quality Assessment*](https://arxiv.org/abs/2510.01812)  
-    → Describes dataset design, annotation methodology, and experiments.
+  → Describes dataset design, annotation methodology, and experiments.
 
 - 🎶 **VoiceMOS 2024 Singing Track:** [SingMOS_v1](https://huggingface.co/datasets/TangRain/SingMOS_v1)  
-    → For reproducing or comparing with the official VoiceMOS 2024 track.
+  → For reproducing or comparing with the official VoiceMOS 2024 track.
 
 - 🤖 **Pretrained Model:** [Singing MOS Predictor](https://github.com/South-Twilight/SingMOS/tree/main)  
-    → Ready-to-use MOS prediction models trained on SingMOS and SingMOS-Pro.
+  → Ready-to-use MOS prediction models trained on SingMOS and SingMOS-Pro.
 
 ---
 
@@ -35,7 +35,7 @@ size_categories:
 **SingMOS-Pro** contains **7,981** Chinese and Japanese vocal clips, totaling **11.15 hours** of singing recordings.  
 Most samples are recorded at **16 kHz**, with a few at **24 kHz** or **44.1 kHz**.
 
-This dataset enables large-scale research on **singing quality assessment** for tasks such as  
+This dataset enables large-scale research on **singing quality assessment** for tasks such as:  
 - Singing voice synthesis (SVS)  
 - Voice conversion (SVC)  
 - MOS prediction and correlation modeling  
@@ -47,6 +47,7 @@ To use the dataset effectively, please refer to the following files:
 | `split.json` | Defines train/test partitions |
 | `score.json` | Provides system- and utterance-level MOS annotations |
 | `sys_info.json` | Describes system metadata (type, model, dataset, etc.) |
+| `metadata.csv` | Flat-format summary of all utterances and attributes |
 
 ---
 
@@ -59,10 +60,10 @@ SingMOS-Pro
 │   ├── sys0001-utt0001.wav
 │   ├── ...
 ├── info/                   # Metadata and annotations
-│   ├── split.json          # Dataset splits
-│   ├── score.json          # MOS annotations
-│   ├── sys_info.json       # System details
-└── metadata.csv            # Summary of all samples
+│   ├── split.json
+│   ├── score.json
+│   ├── sys_info.json
+└── metadata.csv
 
 ````
 
@@ -71,89 +72,190 @@ SingMOS-Pro
 ## 🧾 File Descriptions
 
 <details>
-<summary><b>1️⃣ split.json — dataset partition file</b></summary>
+<summary><b>1️⃣ split.json — Dataset Partition File</b></summary>
 
-Defines which utterances belong to the training and testing subsets.
+Defines the train/test splits for each dataset.
 
+**Example:**
 ```json
 {
     "dataset_name": {
-        "train": ["utt0001", "utt0002", ...],
-        "test": ["utt0101", "utt0102", ...]
+        "train": ["utt0001", "utt0002", "utt0003"],
+        "test": ["utt0101", "utt0102"]
     }
 }
 ````
 
-🔹 **Usage:** Load this file to organize your training and evaluation splits consistently across experiments.
+**Field Descriptions:**
+
+| Field          | Description                                             |
+| -------------- | ------------------------------------------------------- |
+| `dataset_name` | Name of the sub-dataset (e.g., `acesinger`, `opencpop`) |
+| `train`        | List of utterance IDs used for training                 |
+| `test`         | List of utterance IDs used for testing                  |
+
+🔹 **Usage:** Load this file to ensure consistent dataset splits across experiments.
 
 </details>
 
 ---
 
 <details>
-<summary><b>2️⃣ score.json — MOS annotation file</b></summary>
+<summary><b>2️⃣ score.json — MOS Annotation File</b></summary>
+
+Contains both **system-level** and **utterance-level** MOS (Mean Opinion Score) annotations.
 
-Contains both **system-level** and **utterance-level** MOS annotations.
+**Example:**
 
 ```json
 {
     "system": { 
-        "sys_id": {
-            "score": 3.85,       // average MOS of the system
-            "ci": 0.07           // confidence interval
+        "sys0001": {
+            "score": 3.85,
+            "ci": 0.07
         }
     },
     "utterance": {
-        "utt_id": {
+        "utt0001": {
             "sys_id": "sys0001",
             "wav": "wavs/sys0001-utt0001.wav",
             "score": {
-                "mos": 3.9,                // mean score for this utterance
-                "scores": [3.5, 4.0, 4.2], // judge scores
-                "judges": ["1", "2", "3"], // judge id
+                "mos": 3.9,
+                "scores": [3.5, 4.0, 4.2],
+                "judges": ["J01", "J02", "J03"]
             }
         }
     }
 }
 ```
 
-🔹 **Use cases:**
+**Field Descriptions:**
 
-* Compute correlations between system- and utterance-level MOS.
-* Analyze inter-rater variance or confidence intervals.
-* Build supervised MOS prediction models.
+| Field          | Description                                  |
+| -------------- | -------------------------------------------- |
+| `system`       | Stores system-level MOS results              |
+| `sys_id`       | Unique system identifier (e.g., `sys0001`)   |
+| `score`        | Average MOS of the system                    |
+| `ci`           | Confidence interval for the system-level MOS |
+| `utterance`    | Stores utterance-level annotations           |
+| `utt_id`       | Unique utterance identifier                  |
+| `wav`          | Relative path to the audio file              |
+| `score.mos`    | Mean MOS for the utterance                   |
+| `score.scores` | List of individual ratings from judges       |
+| `score.judges` | List of judge identifiers                    |
+
+🔹 **Usage:**
+
+* Evaluate system performance by comparing `system` and `utterance` levels.
+* Compute correlations, inter-rater consistency, or build MOS prediction models.
 
 </details>
 
 ---
 
 <details>
-<summary><b>3️⃣ sys_info.json — system metadata file</b></summary>
+<summary><b>3️⃣ sys_info.json — System Metadata File</b></summary>
+
+Describes each singing system’s **category**, **dataset source**, **model**, and **sampling rate**.
 
-Describes each singing system’s generation method, dataset, and configuration.
+**Example:**
 
 ```json
 {
-    "sys_id": {
-        "type": "svs",             // "svs" (singing voice synthesis), "svc"(singing voice conversion), "svr" (singing voice resynthesis), or "gt" (ground truth)
-        "dataset": "Opencpop",     // original dataset source
-        "model": "DiffSinger",     // model name
-        "sample_rate": 16000,      // sample rate in Hz
+    "sys0001": {
+        "type": "svs",
+        "dataset": "Opencpop",
+        "model": "DiffSinger",
+        "sample_rate": 16000,
         "tag": {
-            "domain_id": "1", // annotation batch or domain identifier
-            "other_info": "default" // additional metadata (e.g., codec codebook size, speaker transfer info, ...)
+            "domain_id": "batch1",
+            "other_info": "default"
         }
     }
 }
 ```
 
-> 💡 `"other_info": "default"` means no additional metadata is available for that system.
+**Field Descriptions:**
+
+| Field            | Description                                                                              |
+| ---------------- | ---------------------------------------------------------------------------------------- |
+| `sys_id`         | Unique system identifier                                                                 |
+| `type`           | System type: `svs` (singing synthesis), `svc` (voice conversion), or `gt` (ground truth) |
+| `dataset`        | Original dataset source                                                                  |
+| `model`          | Model or architecture name used for generation                                           |
+| `sample_rate`    | Audio sampling rate (Hz)                                                                 |
+| `tag.domain_id`  | Batch ID or annotation domain                                                            |
+| `tag.other_info` | Extra information (e.g., codec codebook, speaker transfer, etc.)                         |
+
+> 💡 `"other_info": "default"` means no additional metadata is available.
+
+🔹 **Usage:**
+
+* Filter systems by type or dataset.
+* Analyze system-level trends and quality differences.
+
+</details>
+
+---
+
+<details>
+<summary><b>4️⃣ metadata.csv — Sample-Level Summary Table</b></summary>
+
+Provides a **flat-format summary** of all utterances, integrating data from the JSON files.
+Ideal for quick indexing, filtering, and statistical analysis (e.g., via `pandas`).
 
-🔹 **Use cases:**
+**Example:**
+
+```json
+{
+    "dataset": "acesinger",
+    "domain_id": 1,
+    "id": "sys0001-utt0001",
+    "judge_id": [1, 2, 3, 4, 5],
+    "judge_lyrics_score": [],
+    "judge_melody_score": [],
+    "judge_score": [4.0, 4.0, 4.0, 4.0, 4.0],
+    "language": "Chinese",
+    "lyrics": "",
+    "model_name": "ace",
+    "other_info": "default",
+    "raw_wav_id": "22#2100003752",
+    "sample_rate": 16000,
+    "split": "test",
+    "system": "acesinger@ace@default",
+    "system_id": "sys0001",
+    "type": "svs",
+    "wav": "wav/sys0001-utt0001.wav"
+}
+```
 
-* Filter systems by type (e.g., SVC vs. SVS).
-* Analyze how model architecture affects MOS.
-* Link metadata with subjective evaluation results.
+**Field Descriptions:**
+
+| Field                                       | Description                                               |
+| ------------------------------------------- | --------------------------------------------------------- |
+| `dataset`                                   | Original dataset name                                     |
+| `domain_id`                                 | Annotation batch or domain index                          |
+| `id`                                        | Unique utterance identifier (`sysID-uttID`)               |
+| `judge_id`                                  | List of judge IDs who rated this utterance                |
+| `judge_lyrics_score` / `judge_melody_score` | Optional sub-dimension ratings (may be empty)             |
+| `judge_score`                               | List of overall MOS ratings from judges                   |
+| `language`                                  | Singing language (`Chinese` or `Japanese`)                |
+| `lyrics`                                    | Transcribed lyrics text (if available)                    |
+| `model_name`                                | Model or architecture name used to generate audio         |
+| `other_info`                                | Additional configuration info (e.g., codec, speaker info) |
+| `raw_wav_id`                                | Original recording or dataset identifier                  |
+| `sample_rate`                               | Sampling rate in Hz                                       |
+| `split`                                     | Dataset partition (`train` / `test`)                      |
+| `system`                                    | Full system identifier (`dataset@model@info`)             |
+| `system_id`                                 | System-level ID (matches `sys_info.json`)                 |
+| `type`                                      | System type: `svs`, `svc`, or `gt`                        |
+| `wav`                                       | Relative path to waveform file                            |
+
+🔹 **Usage:**
+
+* Load with `pandas.read_csv` for analysis.
+* Merge by `system_id` or filter by language/type.
+* Perform judge-level or system-level statistical analysis.
 
 </details>