tacotron

Tacotron: A Dive into Open-Source Speech Synthesis

Tacotron is a popular project in the field of speech synthesis, known for its implementation using TensorFlow. The project aims to recreate the capabilities of Google's text-to-speech model described in their 2017 paper, "Tacotron: Towards End-to-End Speech Synthesis." While the original research didn't provide source code or training data, Tacotron serves as an open-source alternative to explore this technology.

Audio Samples

For those interested in hearing Tacotron in action, there are several audio samples available for listening. The first collection was trained for 441,000 steps on the LJ Speech Dataset. Interestingly, the synthesized speech began to be understandable after around 20,000 steps. Another set was trained by an enthusiast, @MXGray, who worked on the Nancy Corpus for 140,000 steps, further showcasing Tacotron's versatility.

Recent Updates and Enhancements

The community surrounding Tacotron is active and continually working to improve the model. Recent contributions include bug fixes and enhancements such as incorporating location-sensitive attention and the stop token features from Tacotron 2. These updates can significantly reduce the data requirements for training an effective model, making Tacotron more accessible to a broader range of users.

Getting Started with Tacotron

Installing Dependencies

To start using Tacotron, you need to set up the following on your system:

1. Install Python 3.
2. Install TensorFlow, preferably with GPU support for enhanced performance.
3. Use the following command to install other necessary packages:

   pip install -r requirements.txt
   

Using a Pre-Trained Model

To test Tacotron quickly, you can use a pre-trained model by following these steps:

1. Download and unpack the model:

   curl https://data.keithito.com/data/speech/tacotron-20180906.tar.gz | tar xzC /tmp
   

2. Run the demo server:

   python3 demo_server.py --checkpoint /tmp/tacotron-20180906/model.ckpt
   

3. Open your browser and navigate to localhost:9000, where you can type text to synthesize speech.

Training Your Own Model

Training a model from scratch is a more in-depth process requiring at least 40GB of disk space. Here’s a streamlined guide to get started:

1. Download a speech dataset, such as LJ Speech or Blizzard 2012.
2. Unpack the dataset into the ~/tacotron directory, organizing files into the required structure.
3. Preprocess the data by running:

   python3 preprocess.py --dataset ljspeech
   

4. Initiate training with:

   python3 train.py
   

   Adjust hyperparameters using the --hparams flag as needed. Default settings are optimized for English-language data.
5. Monitor progress using Tensorboard:

   tensorboard --logdir ~/tacotron/logs-tacotron
   

6. Synthesize speech using a trained checkpoint:

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

Common Issues and Solutions

Tacotron users may encounter various challenges, such as memory allocation issues and training loss spikes. Utilizing tools like TCMalloc and carefully monitoring training checkpoints can mitigate these problems. Fine-tuning parameters such as max_iters can also resolve errors related to audio length during evaluation.

Community and Contributions

Tacotron's open-source nature invites contributions, offering a chance for developers and researchers to experiment and enhance speech synthesis technology. Other implementations, such as those by Alex Barron and Kyubyong Park, underscore the project's influence and collaborative spirit in the AI community.