This change reduces the algorithm latency of the video encoderfrom 120 ms (3 frames) to 40 ms (1 frame)

v

j

first training anaudio-only SSL model, which is used to gener-ate unit targets for audio-visual SSL pre-training.

We improve AV-HuBERT (Shi et al., 2022a) with simplified trainingprotocol and updated model architecture, and scalearXiv:2403.14402v1 [cs.CL] 21 Mar 2024model size up to 2B parameters for performance inthe multilingual setting.

The decoder optimises the first loss term only, the encoder optimises the first and the last loss terms,and the embeddings are optimised by the middle loss term

C

BigVGAN-base (without filter & snake) refers to the vanilla HiFi-GAN architecturetrained with MRD replacing MSD.

α = 1 and β = 0

(A-data2vec)

val-idating that Lmse improves the convergence

t has been found in the literature [32, 31] that wave-form reconstruction from VQ acoustic feature needs additionalprosody feature

In particular, txt2vec basically becomes aclassification model rather than a traditional regression model

In practice, we use an equallyweighted combination of two spectrogram losses for 16kHz au-dio: one with large FFT window size of 2048 and hop size of512, and one with small FFT window size of 512 and hop size of128. The larger one gives more frequency resolution, while thesmaller one gives more temporal resolution.

First, we train the feed-forward WaveNet for 500K steps with learning rate 0.001, usingL1 loss and spectrogram loss.

For this purpose, we use the L1 loss between theSTFT magnitudes of the real and synthesized audio as follows

We did not attempt batch normalization, which worked wellfor the generator, since this interferes with the gradient penaltyfor our adversarial loss [16].

To discriminate the realfrom the synthesized waveforms, we adapt the critic from[25].

In an attempt to alleviatethe issue of abrupt frame transitions, we use an overlap of50 % between the generated waveform frames

we use thespeech representation Z without pitch supervision to predictpitch

WaveGAN including VAE and GAN,

λ3Lrecons +λ4Ladv g +λ5Lf m

we do not directly useany predesigned acoustic features such as Mel-spectrum. Thisis because the estimated approximations of the Mel distributionfrom the acoustic model are still different from those inputs toa neural vocoder, which may cause artifacts i.e., metallic ef-fects in synthesized audios.

(i) content encoder; (ii) F0 encoder;(iii) speaker identity encoder; and a decoder network.

The study by [23] is the closest toour work. In which, the authors suggest training a VQ-VAEwith two encoders, one for the waveform and the other for F0

, LLMs employing StreamingLLM maintainreasonable accuracy even as input lengths approach 120K tokens

hile the window attention method worksefficiently, it exhibits low accuracy due to random outputs when the input length exceeds the cachesize. Conversely, StreamingLLM excels by efficiently handling the streaming format, aligning withthe one-shot, sample-by-sample baseline accuracy.

Why do LLMs break when removing initial tokens’ KV?

and the speaker embedding[16, 18] is obtained by encoding random short audio clip ofthe same speaker with another 3 layered 1D convolutions with128, 256, and 256 hidden dimensions and a kernel size of7.