Create your AI digital voice clone locally with Piper TTS | Tutorial

Summary

This tutorial walks viewers through the process of creating a custom AI-driven voice clone using Piper Text-to-Speech (TTS). It explains the steps involved, including setting up a Linux environment, installing necessary dependencies, and training a TTS model with a personal voice dataset. The video covers everything from preparing the data to fine-tuning a pre-trained model and exporting it for use in Piper. The tutorial is designed for those interested in generating high-quality synthetic speech locally on their machine, even on devices like Raspberry Pi, offering tips and troubleshooting along the way.

Takeaways

• 😀 Piper TTS is a local text-to-speech service that runs efficiently even on small devices like Raspberry Pi, supporting multiple languages and quality levels.
• 😀 The tutorial demonstrates how to create a personalized AI voice clone using Piper TTS, focusing on training a high-quality voice model using your own voice dataset.
• 😀 For training, a Linux system with Python 3 development tools is required. The tutorial uses Ubuntu 20.04 as an example.
• 😀 The first step involves cloning the Piper TTS repository, setting up a Python virtual environment, and installing necessary dependencies from the requirements.txt file.
• 😀 The dataset used in the tutorial is the German trust voice dataset, structured in the LJ Speech format, which is compatible with Piper TTS for training.
• 😀 The pre-processing of the dataset is essential and involves running a Python script that prepares the audio files and metadata for training.
• 😀 Fine-tuning an existing pre-trained model (instead of training from scratch) is recommended to speed up the training process and improve results.
• 😀 GPU-based training is preferred over CPU for faster and more efficient results, especially when training with high-quality models.
• 😀 Tensorboard can be used during the training process to monitor progress, visualizing metrics like loss values to track the improvement of the trained model.
• 😀 Once training is complete, the trained model can be exported to an ONNX format, making it ready for use in Piper TTS for speech synthesis.
• 😀 The tutorial emphasizes the importance of testing the synthesized audio regularly during training to ensure that the model is producing natural-sounding speech.

Q & A

What is Piper TTS, and how is it different from other text-to-speech services?

Piper TTS is a locally running text-to-speech service that can operate on small compute devices, such as a Raspberry Pi. Unlike many cloud-based services, it runs locally on your computer and offers multiple quality levels (low, medium, high) for generating speech in various languages.

What are the main quality levels available in Piper TTS?

Piper TTS offers three quality levels for text-to-speech models: low (16kHz sample rate), medium (22kHz sample rate), and high (also 22kHz but with a larger voice model).

What are the first steps to set up Piper TTS on a Linux system?

The first steps involve setting up a Python virtual environment, cloning the Piper repository, installing necessary dependencies, and ensuring that tools like `espeak-ng` are installed. After that, you need to configure your environment and dependencies as per the instructions in the Piper documentation.

Why is it recommended to use an existing pre-trained model for fine-tuning?

Starting with an existing pre-trained model speeds up the training process, as you're essentially fine-tuning it rather than training a model from scratch. This saves considerable time and computational resources while still allowing you to create a personalized voice model.

How do you prepare the dataset for training a personal voice model?

You need a structured dataset, such as the LJ Speech dataset, which includes a metadata CSV file linking text with audio files. The dataset should be pre-processed using the provided Python script to ensure it's in the correct format for training, including phonetic adjustments.

What is the role of `tensorboard` during the training process?

TensorBoard is used to monitor the training progress by displaying graphical representations of metrics like loss values. It helps ensure that the training is proceeding correctly and allows you to observe if the quality of the model is improving over time.

What hardware is required to train a Piper TTS model efficiently?

While it's possible to train using a CPU, using a GPU is highly recommended for faster training times and better performance. The GPU accelerates the training process significantly compared to using just a CPU.

How do you export a trained model for use with Piper TTS?

After completing the training, you can export the trained model to the ONNX format, which makes it compatible with Piper TTS. This involves running an export command, specifying the trained model and its configuration, and ensuring both the model file and the configuration are in the correct format.

Can you use your own voice dataset for training in Piper TTS?

Yes, you can use your own voice dataset for training. However, it's easier to start with a well-known dataset structure like the LJ Speech dataset, which is already supported by Piper. Your dataset should include audio files and a metadata file with corresponding transcriptions.

What should you do if you encounter issues with missing modules during installation?

If you run into issues with missing modules, a possible solution is to run `pip install -e .` to build the Piper TTS package from the source, which may resolve dependency issues that prevent the training steps from executing properly.