How to Voice Clone With SoftVC VITS and Bert-VITS2

|

Introduction

Voice cloning, the process of replicating someone’s voice using artificial intelligence (AI) techniques, has seen remarkable advancements in recent years. SoftVC VITS and Bert-VITS2 are two state-of-the-art models that have gained attention for their ability to generate high-quality voice clones. In this guide, we’ll explore how to utilize these models for voice cloning, complete with coding examples.

Understanding SoftVC VITS and Bert-VITS2

SoftVC VITS (Variational Inference Transformers for Speech Synthesis) and Bert-VITS2 are both based on transformer architectures, a type of neural network particularly effective in sequence-to-sequence tasks like speech synthesis. These models leverage large-scale pretraining on audio data to understand and replicate various nuances of human speech.

SoftVC VITS employs variational inference techniques to generate high-quality and diverse speech samples, while Bert-VITS2 incorporates BERT (Bidirectional Encoder Representations from Transformers) architecture, which enhances the model’s understanding of context and semantics.

Setting Up Environment

Before diving into voice cloning with SoftVC VITS and Bert-VITS2, ensure you have the necessary libraries installed. You’ll need Python, PyTorch, Hugging Face’s Transformers library, and any additional dependencies required by the models.

python
# Install dependencies
!pip install torch
!pip install transformers

Voice Cloning with SoftVC VITS

SoftVC VITS offers an intuitive interface for voice cloning. Here’s a step-by-step guide to clone a voice using SoftVC VITS:

  1. Load the Model: Load the pretrained SoftVC VITS model.
python

from transformers import VitsForConditionalGeneration, VitsTokenizer

model_name = “ChenRocks/VITS-en-decoder”
tokenizer = VitsTokenizer.from_pretrained(model_name)
model = VitsForConditionalGeneration.from_pretrained(model_name)

  1. Encode Text: Tokenize and encode the text prompt you want to convert into speech.
python
text = "Hello, how are you?"
input_ids = tokenizer(text, return_tensors="pt").input_ids
  1. Generate Speech: Use the model to generate speech from the encoded text.
python
output = model.generate(input_ids)
  1. Decode Speech: Decode the generated speech into audio.
python

import soundfile as sf

audio = tokenizer.decode(output[0], skip_special_tokens=True)
sf.write(“output.wav”, audio, samplerate=22050)

Voice Cloning with Bert-VITS2

Bert-VITS2 provides another powerful option for voice cloning. Follow these steps to clone a voice using Bert-VITS2:

  1. Load the Model: Load the pretrained Bert-VITS2 model.
python

from transformers import BertVits2Tokenizer, BertVits2ForConditionalGeneration

model_name = “botlabs/bert-vits2-melgan-ljspeech”
tokenizer = BertVits2Tokenizer.from_pretrained(model_name)
model = BertVits2ForConditionalGeneration.from_pretrained(model_name)

  1. Encode Text: Tokenize and encode the text prompt.
python
text = "Can you please repeat this sentence?"
input_ids = tokenizer(text, return_tensors="pt").input_ids
  1. Generate Speech: Use the model to generate speech.
python
output = model.generate(input_ids)
  1. Decode Speech: Decode the generated speech into audio.
python
audio = tokenizer.decode(output[0], skip_special_tokens=True)
sf.write("output_bert-vits2.wav", audio, samplerate=22050)

Conclusion

Voice cloning technology, exemplified by SoftVC VITS and Bert-VITS2, represents a significant advancement in AI-driven synthesis of human speech. By combining transformer-based architectures with innovative techniques such as vector quantization and vocoders, these models are capable of producing remarkably realistic synthetic voices.

In this article, we’ve provided an overview of both SoftVC VITS and Bert-VITS2, along with coding examples demonstrating how to implement them for voice cloning tasks. However, it’s important to note that these models require substantial amounts of training data and computational resources to achieve optimal performance.

As AI continues to evolve, voice cloning technology is poised to revolutionize various industries, including entertainment, accessibility, and virtual assistants. With further advancements and refinements, we can expect even greater strides in the realism and quality of synthetic voices, opening up new possibilities for human-computer interaction.