Dataset card written from measured statistics

README.md

@@ -1,28 +1,88 @@
 ---
-dataset_info:
-  features:
-  - name: name
-    dtype: string
-  - name: path
-    dtype: string
-  - name: speech
-    sequence: float64
-  - name: text
-    dtype: string
-  - name: emotion
-    dtype: string
-  - name: text_emotions
-    sequence: string
-  splits:
-  - name: train
-    num_bytes: 866818029
-    num_examples: 1116
-  - name: test
-    num_bytes: 212496333
-    num_examples: 280
-  download_size: 615228382
-  dataset_size: 1079314362
+license: mit
+language:
+- ru
+annotations_creators:
+- expert-generated
+language_creators:
+- expert-generated
+multilinguality:
+- monolingual
+pretty_name: RESD (Annotated, multi)
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- audio-classification
+task_ids:
+- audio-emotion-recognition
+tags:
+- emotion-recognition
+- russian
+- speech
 ---
-# Dataset Card for "resd_annotated_multi"
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+![RESD (Annotated, multi)](assets/banner.svg)
+
+# RESD (Annotated, multi)
+
+RESD with transcripts and separate text-emotion labels.
+
+## How it was recorded
+
+RESD was recorded in a studio by **20 voice actors**. There was no script: the actors were not handed lines to read. Instead each actor in a pair was privately given an emotion to play, and the dialogue was improvised from there. So the words are spontaneous while the emotion is deliberate — which is the point, and also the limit. The label describes what the actor was told to convey, not what a listener independently judged.
+
+## Splits
+
+| Split | Rows | Hours | Mean clip |
+|---|---:|---:|---:|
+| `train` | 1116 | 1.88 | 6.1 s |
+| `test` | 280 | 0.46 | 5.9 s |
+
+<img src="assets/classes.svg" alt="Class distribution" width="760">
+
+## Fields
+
+| Column | Meaning |
+|---|---|
+| `name` | Clip identifier |
+| `path` | Original file path |
+| `speech` | Audio |
+| `text` | Transcript of the utterance |
+| `emotion` | Emotion label of the recording |
+| `text_emotions` | Emotions judged from the transcript alone, multi-label |
+
+> [!NOTE]
+> `emotion` and `text_emotions` disagree by design. The audio labels are near-balanced across seven classes, while the text labels are dominated by `neutral` (717 of 1378 in `train`). The same clip is often flat on the page and loud in the voice, which is the whole argument for listening rather than reading.
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("Aniemore/resd_annotated_multi")
+print(ds["train"][0]["emotion"])
+```
+
+## Limitations
+
+Acted emotion is not spontaneous emotion. The classes here are near-balanced, while unscripted Russian speech is overwhelmingly neutral, so a model that scores well on this test set can still miss most of the neutral speech it meets in production. Treat a RESD score as a comparison between models, not as a readiness signal.
+
+## Citation
+
+```bibtex
+@misc{Aniemore,
+  author = {Артем Аментес, Илья Лубенец, Никита Давидчук},
+  title = {Открытая библиотека искусственного интеллекта для анализа и выявления эмоциональных оттенков речи человека},
+  year = {2022},
+  publisher = {Hugging Face},
+  journal = {Hugging Face Hub},
+  howpublished = {\url{https://huggingface.com/aniemore/Aniemore}},
+  email = {hello@socialcode.ru}
+}
+```
+
+## License
+
+MIT.