/ Natural Language Processing

State-of-the-art text-to-speech toolkit for the open-source community

State-of-the-art text-to-speech toolkit for the open-source community

Parakeet

Parakeet aims to provide a flexible, efficient and state-of-the-art text-to-speech toolkit for the open-source community. It is built on PaddlePaddle Fluid dynamic graph and includes many influential TTS models proposed by Baidu Research and other research groups.

In particular, it features the latest WaveFlow model proposed by Baidu Research.

  • WaveFlow can synthesize 22.05 kHz high-fidelity speech around 40x faster than real-time on a Nvidia V100 GPU without engineered inference kernels, which is faster than WaveGlow and serveral orders of magnitude faster than WaveNet.
  • WaveFlow is a small-footprint flow-based model for raw audio. It has only 5.9M parameters, which is 15x smalller than WaveGlow (87.9M).
  • WaveFlow is directly trained with maximum likelihood without probability density distillation and auxiliary losses as used in Parallel WaveNet and ClariNet, which simplifies the training pipeline and reduces the cost of development.

Overview

In order to facilitate exploiting the existing TTS models directly and developing the new ones, Parakeet selects typical models and provides their reference implementations in PaddlePaddle. Further more, Parakeet abstracts the TTS pipeline and standardizes the procedure of data preprocessing, common modules sharing, model configuration, and the process of training and synthesis. The models supported here include Vocoders and end-to-end TTS models:

And more will be added in the future.

See the guide for details about how to build your own model and experiment in Parakeet.

Setup

Make sure the library libsndfile1 is installed, e.g., on Ubuntu.

sudo apt-get install libsndfile1

Install PaddlePaddle

See install for more details. This repo requires PaddlePaddle 1.7.0 or above.

Install Parakeet

git clone https://github.com/PaddlePaddle/Parakeet
cd Parakeet
pip install -e .

Install CMUdict for nltk

CMUdict from nltk is used to transform text into phonemes.

import nltk
nltk.download("punkt")
nltk.download("cmudict")

Examples

Entries to the introduction, and the launch of training and synthsis for different example models:

Pre-trained models and audio samples

Parakeet also releases some well-trained parameters for the example models, which can be accessed in the following tables. Each column of these tables lists resources for one model, including the url link to the pre-trained model, the dataset that the model is trained on and the total training steps, and several synthesized audio samples based on the pre-trained model.

  • Vocoders
WaveFlow ClariNet
LJSpeech, 2M LJSpeech, 500K
To be added soon



    Note: The input mel spectrogams are from validation dataset, which are not seen during training.

  • TTS models

Click each link to download, then you can get the compressed package which contains the pre-trained model and the yaml config describing how to train the model.

GitHub