VocaLiST (acronym for Vocal Lip Synchronisation Transformer), is a novel audio-visual transformer-based lip-voice synchronisation model that estimates the extent of synchronisation between the lips motion and the voice in a given voice video. Fig. 1 shows the high-level architecture of the entire model.
Fig 1. High level architecture of our lip synchronisation model.

Audio Encoder

Our audio encoder is very similar to the audio encoder used in the lip-sync expert discriminator [1]. The architecture details are shown in Fig. 2 (left). The audio encoder is a stack of 2D convolutional layers with residual skip-connections that operate on the mel-spectrogram input of dimensions 1 × 80 × ta . The mel-spectrograms are obtained using 80 mel-filterbanks with a hop size of 200 and window size of 800. The audios have a 16kHz sampling rate. The audio features are of the dimension 512 × ta. The audio encoder conserves the temporal resolution of the input.
Fig 2. Our audio encoder (left) and visual encoder (right).

Visual Encoder

The visual encoder ingests a sequence of RGB images cropped around the mouth having dimensions 3×48×96×tv. Its architecture is inspired by the visual encoder of the lip-sync expert discriminator. Unlike in the latter, we apply 3D convolutions and conserve the temporal resolution in the feature maps. The output visual features are of dimension 512 × tv . The conservation of temporal resolution in both the audio and visual features is helpful for learning the synchronisation patterns between the two modalities spread across the temporal dimension when we feed them into the synchronisation module. The visual frames are sampled from videos of 25 fps.

Synchronisation Block

We design a powerful cross-modal audio-visual transformer that can use the audio-visual representations learned in its cross-modal attention modules to deduce the inherent audio-visual correspondence in a synchronised voice and lips motion pair. We refer to our transformer model as VocaLiST, the Vocal Lip Sync Transformer. Its design is inspired by the cross-modal transformer from [2]. The cross-modal attention blocks track correlations between signals across modalities.

Fig 3. Architecture of Synchronisation block of VocaLiST

The synchronisation block contains three cross-modal transformer encoders, each made up of 4 layers, 8 attention heads and the hidden unit dimension of 512. The A→V unit takes in audio features as the query and the visual features as the key and values. The roles of these audio and visual features is swapped in the V→A unit. The output of the A→V unit forms the query to the hybrid fusion transformer unit, while its key and values are sourced from the output of the V→A unit. We max-pool the output of this hybrid fusion unit along the temporal dimension and pass it through tanh activation. Fig. 1 shows the architecture of VocaLiST.

Finally, there is a fully-connected layer acting as a classifier which outputs a score indicating if the voice and lips motion are synchronised or not. The whole architecture can handle any length of audio and visual inputs.


[1] K. Prajwal, R. Mukhopadhyay, V. P. Namboodiri, and C. Jawahar, “A lip sync expert is all you need for speech to lip generation in the wild,” in Proceedings of the 28th ACM International Conference on Multimedia, 2020, pp. 484–492.
[2] Y.-H. H. Tsai, S. Bai, P. P. Liang, J. Z. Kolter, L.-P. Morency, and R. Salakhutdinov, “Multimodal transformer for unaligned multimodal language sequences,” in Proceedings of the conference. Association for Computational Linguistics. Meeting, vol. 2019. NIH Public Access, 2019, p. 6558.