Step-by-step tutorial on how to clone your voice using python and ai
Cloning a voice using Python and AI involves training a model on your voice recordings and generating new speech based on the learned patterns. Here’s a step-by-step tutorial on how to achieve this using Tacotron 2 and WaveGlow, popular models for text-to-speech synthesis:
Step 1: Set up the Environment
- Install Python: Make sure you have Python 3.x installed on your system.
- Install the required packages: Open a terminal or command prompt and run the following commands to install the necessary packages:
pip install numpy torch==1.4.0 librosa unidecode inflect scipy
pip install git+https://github.com/NVIDIA/apex
pip install unidecode
pip install pillow
pip install tensorboardX
Install Tacotron 2 and WaveGlow: Clone the Tacotron 2 and WaveGlow repositories from GitHub using the following commands:
git clone https://github.com/NVIDIA/tacotron2.git
git clone https://github.com/NVIDIA/waveglow.git
Install additional requirements: Navigate to the cloned Tacotron 2 repository and install the additional requirements by running:
pip install -r requirements.txt
Step 2: Prepare the Training Data
- Collect voice recordings: Record a significant amount of your voice utterances. Aim for at least 1–2 hours of diverse speech.
- Preprocess the data: Convert your voice recordings to the WAV format and store them in a single directory. Ensure that the filenames follow a consistent naming convention.
- Create a file list: Create a text file that lists the filenames of your voice recordings, with each filename on a new line.
Step 3: Train Tacotron 2
- Preprocess the data: In the Tacotron 2 repository, create a folder named
datasetand place your file list and voice recordings in it.
- Run the preprocessing script: Execute the following command to preprocess the data:
python preprocess.py --dataset <dataset_folder_name>
<dataset_folder_name> with the name of the folder you created in the previous step.