Wavenet
A TensorFlow implementation of DeepMind's WaveNet neural network for generating raw audio waveforms.
What is Wavenet?
TensorFlow-WaveNet is an open-source implementation of DeepMind's WaveNet generative neural network architecture for audio generation. It models the conditional probability of generating the next audio sample in a waveform, enabling high-quality synthesis of raw audio. The project is designed for tasks like text-to-speech and general audio generation, providing a practical codebase for training and generating audio with neural networks.
Machine learning researchers and developers working on audio synthesis, text-to-speech systems, or generative models who want to experiment with WaveNet architecture using TensorFlow.
It offers a faithful, well-documented TensorFlow implementation of the influential WaveNet paper, with features like global conditioning for multi-speaker generation and fast generation optimizations, making advanced audio synthesis accessible to the open-source community.
Overview
A TensorFlow implementation of DeepMind's WaveNet paper
Use Cases
Best For
- Implementing text-to-speech systems with neural networks
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