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wav2vec_LittleBeats_LENA

Audio Classification
Fairseq
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wav2vec_LittleBeats_LENA / README.md
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metadata
license: openrail
language:
  - en
metrics:
  - f1
library_name: fairseq
pipeline_tag: audio-classification

Model Card for Model ID

We explore benefits of unsupervised pretraining of wav2vec 2.0 (W2V2) using large-scale unlabeled home recordings collected using LittleBeats and LENA (Language Environment Analysis) devices. LittleBeats (LB) is a new infant wearable multi-modal device that we developed, which simultaneously records audio, movement of the infant, as well as heart-rate variablity. We use W2V2 to advance LB audio pipeline such that it automatically provides reliable labels of speaker diarization and vocalization classifications for family members, including infants, parents, and siblings, at home. We show that W2V2 pretrained on thousands hours of large-scale unlabeled home audio outperforms oracle W2V2 pretrained on 52k-hours released by Facebook/Meta in terms of automatic family audio analysis tasks.

Model Details

Model Description

Two versions of pretrained W2V2 models are available:

  • LB1100/checkpoint_best.pt pretrained using 1100-hour of LB home recordings collected from 110 families of children under 5-year-old
  • LL4300/checkpoint_best.pt pretrained using 1100-hour of LB home recordings collected from 110 families + 3200-hour of LENA home recordings from 275 families of children under 5-year-old

Model Sources [optional]

For more information regarding this model, please checkout our paper

  • Paper [optional]: [More Information Needed]

Uses

We develop fine-tuning recipe using SpeechBrain toolkit available at

Quick Start [optional]

If you wish to use fairseq framework, the following code snippet can be used to load the pretrained model

[More Information Needed]

Evaluation

We test 4 unlabeled datasets on unsupervised pretrained W2V2-base models:

  • base (oracle version): originally released version pretrained on ~52k-hour unlabeled audio
  • Libri960h: oracle version fine-tuned using 960h Librispeech
  • LB1100h: pretrain W2V2 using 1100h LB home recordings
  • LL4300h: pretrain W2V2 using 4300h LB+LENA home recordings We then fine-tune pretrained models on 11.7h of LB labeled home recordings, the f1 scores across three tasks are

For more details, please refer to our paper.

Citation

If you found this model helpful to you, please cite us as BibTeX:

Model Card Contact

Jialu Li (she, her, hers)

Ph.D candidate @ Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign

E-mail: [email protected]

Homepage: https://sites.google.com/view/jialuli/