nonverbalspeech/nonverbalspeech38k

🎉 NonVerbalSpeech-38K: A Scalable Pipeline for Enabling Non-Verbal Speech Generation and Understanding

The official repository for NonVerbalSpeech-38K (NVS-38K) dataset.

The NVS-38K dataset is constructed from in-the-wild audio sources, such as movies, cartoons, and audiobooks (see Section: Source Distribution of NVS-38K). It contains a total of 38,718 samples spanning approximately 131 hours, annotated with 10 non-verbal categories (see Section: Special Tag in the NVS-38K). NVS-38K is designed to support both non-verbal speech generation and non-verbal speech understanding tasks (see Figure below).

Demo Page

📊 Dataset Overriew

Source Distribution of NVS-38K

Source	Crawled (hrs)	NVS-38K (#)
Radio Dramas	17,400	21,668
Comedy Sketches	7,200	7,273
Cartoon	3,600	5,019
Variety Shows	1,400	1,217
Short Plays	1,200	809
Speeches	1,079	158
Documentaries	600	105
Movies	500	1,090
Audiobooks	263	1,375
Toy Unboxing	9	4

Note: “Crawled (hrs)” shows the duration of the original crawled data; “NVS-38K (#)” shows the final sample counts in the proposed NonVerbalSpeech-38K dataset.

Distributions of Languages, Labels, and Durations in NVS-38K

Special Tag in the NVS-38K

• [snore], [throatclearing], [crying], [breath], [sniff], [laughing], [coughing], [gasp], [yawn], [sigh]
• <B> and </B>. The <B> and </B> tags indicate that non-verbal expressions overlap with spoken words.

🔧 Usage

from datasets import load_dataset
ds = load_dataset("nonverbalspeech/nonverbalspeech38k")
ds.save_to_disk("./nonverbalspeech38k")

>>> from datasets import load_from_disk
>>> ds = load_from_disk("./nonverbalspeech38k")
Loading dataset from disk: 100%|████████████████████████████████████████████████████████████████████████████████████| 46/46 [00:00<00:00, 6769.04it/s]
>>> ds["train"][0]
{'duration': 17.854, 'non_verbal_region': [9.379500124000003, 10.124500248000004], 'caption': '杨总，我知道数的都是雷政委的名字，没关系，以我的身份没
  权利，拥有什么研究成果的，让你受委屈了。没事的， [sigh]<B> 能在红暗基地做研究， </B> 能看到这么丰富的资料。', 'source': 'Radio Dramas', 'language': 'ZH', 'label': 'sigh', 'audio': {'path': None, 'array': array([0.00048828, 0.00057983, 0.00094604, ..., 0.00595093, 0.00473022,
       0.        ]), 'sampling_rate': 24000}}

📢 Notes [[IMPORTANT!!!]]

Disclaimer: The NVS-38K dataset does not own the copyrights of the audio files it contains; copyright ownership resides with the original creators. The dataset is made available solely for non-commercial research purposes under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.

🚫 Limitation and Future Work

• Due to limitations in ASR-based timestamps, slight misalignments may exist between the audio and captions. However, the detected non-verbal segments themselves remain accurate to a certain extent. We plan to improve alignment quality in future updates. (Demo Page)

• In the current version, we extend overlapping non-verbal expressions to the sub-sentence level to reduce misalignment issues.

  For example, we modify:

  "李渊看着秦琼心说，这是我的金殿哪，他在这儿就指着我两个儿子，让我两个儿子心服口服。哎呀。李渊心说，大唐<sniff><B>江山</B>要紧，我也不能再包庇我的两个儿子了。哎呀，是世民，你也给我跪下。"

  to:

  "李渊看着秦琼心说，这是我的金殿哪，他在这儿就指着我两个儿子，让我两个儿子心服口服。哎呀。李渊心说，<sniff><B>大唐江山要紧</B>，我也不能再包庇我的两个儿子了。哎呀，是世民，你也给我跪下。"

  This strategy aims to improve alignment between captions and audio. However, we observe that such modifications do not significantly enhance alignment quality.

  To support further research, we also release the Non-Verbal Regions detected by our model, which can be used to re-annotate the dataset with more accurate timestamps.

📖 Reference

If you use the NVS-38K dataset, please cite the following papers:

Coming Soon...