Zero-shot voice cloning refers to the process of synthesizing speech in the voice of a target speaker using only a small amount of training data or even without any specific training data from that speaker. Unlike traditional voice cloning methods that require a substantial amount of high-quality training data from the target speaker, zero-shot voice cloning aims to generate speech that mimics the voice characteristics of the target speaker using a different approach. In zero-shot voice cloning, the focus is on capturing the [[speaker's voice]]'s voice and [[speaking style]] and characteristics from a limited amount of available data. Instead of relying on a large speaker-specific dataset, zero-shot voice cloning leverages transfer learning techniques and the knowledge learned from a pre-existing voice model that has been trained on a diverse dataset containing multiple speakers. The process typically involves two steps: 1. Pre-training: A voice model is initially trained on a large dataset that includes speech samples from various speakers. This pre-training step allows the [[learning style embeddings]] and captures common voice characteristics shared across speakers. 2. Fine-tuning: The pre-trained model is then fine-tuned using a small amount of the target speaker's data, which can be as little as a few minutes or even shorter. During fine-tuning, the model learns to adapt its learned representations to match the unique voice characteristics of the target speaker, capturing their vocal style, intonation, and other distinctive features. ![[zero-shot-tts.png]] Speaker adaptation and speaker encoding approaches for voice cloning ([Arik2019](https://arxiv.org/abs/1802.06006))