YODAS2 is the long-form dataset from YODAS dataset.

It provides the same dataset as espnet/yodas but YODAS2 has the following new features:

• formatted in the long-form (video-level) where audios are not segmented.
• audios are encoded using higher sampling rates (i.e. 24k)

For detailed information about YODAS dataset, please refer to our paper and the espnet/yodas repo.

Usage:

Each data point corresponds to an entire video on YouTube, it contains the following fields:

• video_id: unique id of this video (note this id is not the video_id in Youtube)
• duration: total duration in seconds of this video
• audio
  • path: local path to wav file if in standard mode, otherwise empty in the streaming mode
  • sampling_rate: fixed to be 24k. (note that the sampling rate in espnet/yodas is 16k)
  • array: wav samples in float
• utterances
  • utt_id: unique id of this utterance
  • text: transcription of this utterance
  • start: start timestamp in seconds of this utterance
  • end: end timestamp in seconds of this utterance

YODAS2 also supports two modes:

standard mode: each subset will be downloaded to the local dish before first iterating.

from datasets import load_dataset

# Note this will take very long time to download and preprocess
# you can try small subset for testing purpose
ds = load_dataset('espnet/yodas2', 'en000')
print(next(iter(ds['train'])))

streaming mode most of the files will be streamed instead of downloaded to your local deivce. It can be used to inspect this dataset quickly.

from datasets import load_dataset

# this streaming loading will finish quickly
ds = load_dataset('espnet/yodas2', 'en000', streaming=True)

Reference

@inproceedings{li2023yodas,
  title={Yodas: Youtube-Oriented Dataset for Audio and Speech},
  author={Li, Xinjian and Takamichi, Shinnosuke and Saeki, Takaaki and Chen, William and Shiota, Sayaka and Watanabe, Shinji},
  booktitle={2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)},
  pages={1--8},
  year={2023},
  organization={IEEE}
}

Contact

If you have any questions, feel free to contact us at the following email address.

We made sure that our dataset only consisted of videos with CC licenses during our downloading. But in case you find your video unintentionally included in our dataset and would like to delete it, you can send a delete request to the following email.

Remove the parenthesis () from the following email address

(lixinjian)(1217)@gmail.com