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

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.