Project Title

VoiceCraft — Zero-Shot Speech Editing and Text-to-Speech in the Wild

Overview

VoiceCraft is a token infilling neural codec language model that excels in speech editing and zero-shot text-to-speech (TTS) on in-the-wild data, including audiobooks, internet videos, and podcasts. It requires only a few seconds of reference to clone or edit an unseen voice, offering state-of-the-art performance in these areas.

Key Features

State-of-the-art performance in speech editing and zero-shot TTS
Minimal reference audio needed (a few seconds)
Flexible inference options including Google Colab, Docker, and command line
Integration with HuggingFace Spaces for easy model deployment

Use Cases

Content creators can use VoiceCraft to edit and manipulate voiceovers in videos and podcasts.
Developers can integrate VoiceCraft into applications for real-time speech synthesis and editing.
Researchers can leverage VoiceCraft for experiments in speech processing and TTS on in-the-wild data.

Advantages

Achieves high performance with minimal reference audio, making it highly efficient.
Provides multiple ways to run inference, catering to different user preferences and environments.
Offers integration with HuggingFace Spaces, simplifying model deployment and accessibility.

Limitations / Considerations

The model's performance may degrade with very short or very long input audio sequences.
The project is relatively new, and there may be a learning curve for new users.
The license is currently unknown, which could affect how the software can be used commercially.

LibriTTS: A TTS dataset and model focused on high-quality audiobooks, but not specifically designed for zero-shot TTS.
Tacotron 2: A popular TTS model that requires a dataset for training, unlike VoiceCraft's zero-shot capability.
WaveNet: A deep neural network for generating raw audio waveforms, which can be used for TTS but requires more computational resources.