WaveNet: A Generative Model for Raw Audio
📜 Abstract
This paper introduces WaveNet, a deep neural network for generating raw audio waveforms. We show that WaveNet models are able to generate speech which mimics any human voice and which can be conditioned on text input to output text-to-speech, and that the same network architecture can be used to synthesize other audio modalities such as music. WaveNets auto-regressive nature allows them to produce excellent high fidelity audio, which is mostly better than other systems in subjective tests of human speech quality, and retains the capability to generate a rich variety of realistic synthetic audio textures, which are not possible with conventional methods.
✨ Summary
WaveNet, introduced by DeepMind researchers in a 2016 paper, is a groundbreaking architecture for generating raw audio waveforms. Using deep neural networks, WaveNet can produce high-quality audio samples, with the ability to mimic human speech and synthesize various types of audio, including text-to-speech (TTS) and music. This innovative model leverages an auto-regressive approach for probabilistic audio generation. The introduction of temporal convolutions within a neural network facilitated a significant advancement in audio synthesis, outperforming conventional methods like Hidden Markov Models in subjective audio quality tests.
WaveNet’s impact spans various fields. It has notably transformed the domain of text-to-speech synthesis, contributing to the development of refined speech synthesis systems by companies like Google and Apple. It served as a direct precursor to Google’s Tacotron, a more efficient TTS model. In academia and industry, WaveNet is frequently referenced and its principles are foundational in ongoing research on neural audio synthesis.
Key references include the development of Google’s WaveNet-based voice in Google Assistant (source), and advancements by researchers improving neural network efficiency through pruning and quantization strategies inspired by WaveNet (source). The model has also been influential in real-time implementations via parallel WaveNets, explored by DeepMind to cope with its high computational demands (source). Overall, WaveNet’s introduction has led to substantial improvements in how machines can understand and generate audio data.