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README.md

@@ -1,36 +1,147 @@
 ---
-dataset_info:
-  features:
-  - name: audio
-    dtype:
-      audio:
-        sampling_rate: 16000
-  - name: sampling_rate
-    dtype: int32
-  - name: image
-    dtype: image
-  - name: same
-    dtype: bool
-  - name: emotion
-    dtype: string
-  - name: question
-    dtype: string
-  - name: answer
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 11858753276.0
-    num_examples: 24000
-  - name: test
-    num_bytes: 2964688319.0
-    num_examples: 6000
-  download_size: 14426301761
-  dataset_size: 14823441595.0
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+dataset_name: EMID-Emotion-Matching
+annotations_creators:
+- expert-generated
+language:
+- en
+license: cc-by-nc-sa-4.0
+pretty_name: EMID Music ↔ Image Emotion Matching Pairs
+tags:
+- audio
+- music
+- image
+- multimodal
+- emotion
+- contrastive-learning
+task_categories:
+- audio-classification
+- image-classification
+- visual-question-answering
 ---
+
+# EMID-Emotion-Matching
+
+`orrzohar/EMID-Emotion-Matching` is a derived dataset built on top of
+the **Emotionally paired Music and Image Dataset (EMID)** from ECNU (`ecnu-aigc/EMID`).
+It is designed for *music ↔ image emotion matching* with Qwen-Omni–style models.
+
+Each example contains:
+
+- `audio`: mono waveform stored as `datasets.Audio` (HF Hub preview can play it)
+- `sampling_rate`: sampling rate used when decoding (typically 16 kHz)
+- `image`: a single image (`datasets.Image`)
+- `same`: `bool`, whether the audio and image are labeled with the **same** emotion
+- `emotion`: normalized image emotion tag (e.g. `amusement`, `excitement`) for positive pairs; empty string for negatives
+- `question`: natural-language question used to prompt the model (several templates are mixed)
+- `answer`: canonical supervision text (`yes - {emotion}` for positives, `no` for negatives)
+
+| column         | type                            | description |
+| -------------- | ------------------------------- | ----------- |
+| `audio`        | `datasets.Audio (16k mono)`     | decoded waveform; HF UI can play it |
+| `sampling_rate`| `int32`                         | explicit sample rate mirrored beside the `Audio` column |
+| `image`        | `datasets.Image`                | PIL.Image-compatible object |
+| `same`         | `bool`                          | `True` if the pair is emotion-aligned |
+| `emotion`      | `string`                        | normalized emotion label for positives, `""` otherwise |
+| `question`     | `string`                        | user prompt template |
+| `answer`       | `string`                        | canonical supervision text (`yes - {emotion}` / `no`) |
+
+The original EMID row has one music clip and up to **three** tagged images
+(`Image1`, `Image2`, `Image3`). For each `(audio, image)` pair we create:
+
+- **1 positive example**: the audio and its own tagged image (`same = True`, `emotion = image_tag`)
+- **NEGATIVES_PER_POSITIVE = 1 negative example**: the same audio paired with an image drawn
+  from a *different* emotion tag (`same = False`, `emotion = ""`)
+
+With `MAX_SOURCE_ROWS = 4000`, this yields ~24,000 examples (positives + negatives),
+which we then split into:
+
+- `train`: 19,200 examples
+- `test`: 4,800 examples
+
+## Source Data (EMID)
+
+The base EMID dataset is described in:
+
+- **Emotionally paired Music and Image Dataset (EMID)**  
+  *Y. Guo, J. Li, et al.*  
+  arXiv:2308.07622 — "Emotionally paired Music and Image Dataset (EMID)"  
+  <https://arxiv.org/abs/2308.07622>
+
+EMID contains 10,738 unique music clips, each paired with three images in the same
+emotional category, plus rich annotations:
+
+- `Audio_Filename`: unique filename of the music clip
+- `genre`: letter A–M, one of 13 emotional categories
+- `feeling`: distribution of free-form feelings reported by listeners (% per feeling)
+- `emotion`: ratings on 11 emotional dimensions (1–9)
+- `Image{1,2,3}_filename`: matched image filenames
+- `Image{1,2,3}_tag`: image emotion category (e.g. `amusement`, `excitement`)
+- `Image{1,2,3}_text`: GIT-generated captions
+- `is_original_clip`: whether this is an original or expanded clip
+
+For more details, see the EMID README and the paper above.
+
+## How This Derived Dataset Was Built
+
+The script `prepare_emid_pairs.py` performs the following steps offline:
+
+1. Load `ecnu-aigc/EMID` (train split) and decode:
+   - `Audio_Filename` with `Audio(decode=True)`
+   - `Image{1,2,3}_filename` with `datasets.Image(decode=True)`
+2. Optionally cap the number of source rows with `MAX_SOURCE_ROWS` (default 4000).
+3. Build an **image pool** keyed by normalized emotion tags.
+4. For each EMID row and each available image (up to 3 per row):
+   - Create a positive pair `(audio, image, same=True, emotion=image_tag)`.
+   - Sample `NEGATIVES_PER_POSITIVE` images from *different* emotion tags to form negatives.
+5. Normalize the emotion strings (lowercase, replace spaces and punctuation with `_`).
+6. Draw a random question from a small set of Qwen-style templates and attach it as `question`.
+7. Store the mono waveform as `datasets.Audio` and the image as `datasets.Image` so
+   that downstream scripts can call `datasets.load_dataset` without extra decoding logic.
+8. Split into train/test with `TRAIN_FRACTION = 0.8`.
+
+This yields a simple, flat structure that is convenient for SFT / contrastive training
+with Qwen2.5-Omni (or other multimodal LMs), without re-doing negative sampling or
+audio/image decoding inside notebooks.
+
+## Suggested Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("orrzohar/EMID-Emotion-Matching")
+train_ds = ds["train"]
+test_ds = ds["test"]
+
+ex = train_ds[0]
+audio = ex["audio"]        # dict with "array" + "sampling_rate"
+sr = ex["sampling_rate"]   # int
+image = ex["image"]        # PIL.Image.Image
+same = ex["same"]          # bool
+emotion = ex["emotion"]    # str
+question = ex["question"]  # str
+answer = ex["answer"]      # str
+```
+
+In the Qwen-Omni demos, we typically:
+
+- Use `question` as the user prompt,
+- Provide `audio` and `image` as multimodal inputs, and
+- Supervise the model with the provided `answer` (or regenerate your own phrasing from `same`/`emotion`).
+
+## License
+
+This derived dataset **inherits the license** from EMID:
+
+- **CC BY-NC-SA 4.0** (Attribution–NonCommercial–ShareAlike 4.0 International)
+
+You **must**:
+
+- Use the data only for **non-commercial** purposes.
+- Provide appropriate **attribution** to the EMID authors and this derived dataset.
+- Distribute derivative works under the **same license**.
+
+Please refer to the full license text for details:
+<https://creativecommons.org/licenses/by-nc-sa/4.0/>
+
+If you use this dataset in academic work, please cite the EMID paper and, if appropriate,
+this derived dataset as well.