Aniemore/resd_annotated

RESD (Annotated)

RESD with a transcript for every clip.

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

Fields

Column	Meaning
name	Clip identifier
path	Original file path
speech	Audio
text	Transcript of the utterance
emotion	Emotion label of the recording

The sample rate is not uniform. In train 565 clips are 16000 Hz, 551 clips are 44100 Hz. Ask for one rate when you load, and do not tell a feature extractor the audio is 16 kHz when it is 44.1 kHz: that stretches time 2.8x and silently changes the answer instead of raising.

datasets does the resampling itself — declare the rate on the column and every clip arrives at it:

from datasets import load_dataset, Audio

ds = load_dataset("Aniemore/resd_annotated")
ds = ds.cast_column("speech", Audio(sampling_rate=16000))
wav = ds["train"][0]["speech"]["array"]   # 16 kHz, float32

If you are handling files yourself instead, any of these will do the same job: librosa.load(path, sr=16000, mono=True), torchaudio.load followed by torchaudio.transforms.Resample, or torchcodec.decoders.AudioDecoder with a target rate.

Usage

from datasets import load_dataset

ds = load_dataset("Aniemore/resd_annotated")
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

@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://hg.176671.xyzm/aniemore/Aniemore}},
  email = {hello@socialcode.ru}
}

License

MIT.