If you’re interested in building and utilizing advanced text-to-speech technologies, Mimic2 is an exciting project you can explore. Forked from the original keithito/tacotron, this project boasts enhancements and is continuously developed by the Mycroft AI team and community. In this guide, we’ll walk you through the steps to install, train, and synthesize speech using Mimic2.

Background

Google introduced a remarkable neural text-to-speech model in their paper, Tacotron: A Fully End-to-End Text-To-Speech Synthesis Model. Although Google didn’t provide the source code or training data, the Mimic2 project attempts to fill that gap by offering an open-source implementation. While the quality may not match Google’s latest demo, the community is dedicated to improving it.

Quick Start Guide

Installing Dependencies

Two methods are available for installing the necessary dependencies: via Docker (recommended) or manually.

Using Docker

1. Ensure you have Docker installed.
2. Build Docker images based on your deployment preference:

  
      # GPU version
      docker build -t mycroft/mimic2:gpu -f gpu.Dockerfile .

      # CPU version
      docker build -t mycroft/mimic2:cpu -f cpu.Dockerfile .
    

3. Run Docker using:

 
      # GPU version
      nvidia-docker run -it -p 3000:3000 mycroft/mimic2:gpu

      # CPU version
      docker run -it -p 3000:3000 mycroft/mimic2:cpu
    

Manually

1. Install Python 3.
2. Install the latest version of TensorFlow for your platform, preferably with GPU support.
3. Install the required packages:

  
      pip install -r requirements.txt
    

Training Your Model

To train a model, make sure you have at least 40GB of free disk space. Here’s how to get started:

1. Download a speech dataset. Supported datasets include:
• LJ Speech
• Blizzard 2012
• M-AILABS
2. Unpack the dataset: Organize it in the right folder structure.
3. Preprocess the data:

  
      python3 preprocess.py --dataset ljspeech
    

4. Train a model:

  
      python3 train.py
    

Monitor Your Training Process

Use TensorBoard to visualize the training logs:

  
  tensorboard --logdir ~/tacotron/logs-tacotron

Synthesize Speech from a Checkpoint

After training, you can generate speech samples using:

  
  python3 demo_server.py --checkpoint ~/tacotron/logs-tacotron/model.ckpt-185000

Open a web browser to localhost:3000 to enter text you want to be synthesized.

Troubleshooting Common Issues

• If you experience slow training speeds, consider installing TCMalloc, which can optimize your training time.
• For better pronunciation, use CMUDict during training.
• Check your training data setup; too long audio samples can cause errors, and you can adjust max iterations in parameters accordingly.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

An Analogy to Understand the Process

Imagine you are a chef aiming to perfect a new dish (speech synthesis). First, you need quality ingredients (datasets), which you can either source from local markets (download datasets) or grow yourself (record your own voice). After gathering the ingredients, you would organize them meticulously (unpacking the dataset), and carefully follow a recipe (training) to mix them just right.

As you cook, you might taste and adjust flavors (monitoring with TensorBoard), making sure every ingredient complements the others well. Once you have the dish created, you can serve it hot to your guests (synthesize speech) and get feedback on whether it met their expectations.

Lastly, if things go awry, don’t hesitate to tweak your ingredients or cooking methods until you return to the perfect recipe!

Conclusion

By following these steps and tips, you’ll be well on your way to developing your own text-to-speech synthesizer using Mimic2. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.