  Video-to-speech is the process of reconstructing the audio speech from a
video of a spoken utterance. Previous approaches to this task have relied on a
two-step process where an intermediate representation is inferred from the
video, and is then decoded into waveform audio using a vocoder or a waveform
reconstruction algorithm. In this work, we propose a new end-to-end
video-to-speech model based on Generative Adversarial Networks (GANs) which
translates spoken video to waveform end-to-end without using any intermediate
representation or separate waveform synthesis algorithm. Our model consists of
an encoder-decoder architecture that receives raw video as input and generates
speech, which is then fed to a waveform critic and a power critic. The use of
an adversarial loss based on these two critics enables the direct synthesis of
raw audio waveform and ensures its realism. In addition, the use of our three
comparative losses helps establish direct correspondence between the generated
audio and the input video. We show that this model is able to reconstruct
speech with remarkable realism for constrained datasets such as GRID, and that
it is the first end-to-end model to produce intelligible speech for LRW (Lip
Reading in the Wild), featuring hundreds of speakers recorded entirely `in the
wild'. We evaluate the generated samples in two different scenarios -- seen and
unseen speakers -- using four objective metrics which measure the quality and
intelligibility of artificial speech. We demonstrate that the proposed approach
outperforms all previous works in most metrics on GRID and LRW.
