/IdiapTTS

A Python-based modular toolbox for building Deep Neural Network models (using PyTorch) for statistical parametric speech synthesis

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IdiapTTS

This repository contains the Idiap Text-to-Speech system developed at the Idiap Research Institute, Martigny, Switzerland.
Contact: bastian.schnell@idiap.ch

It is an almost purely Python-based modular toolbox for building Deep Neural Network models (using PyTorch) for statistical parametric speech synthesis. It provides scripts for feature extraction and preparation which are based on third-party tools (e.g. Festival). It uses the WORLD vocoder (i.e. its Python wrapper) for waveform generation. The framework was highly inspired by Merlin and reuses some of its data preparation functionalities. In contrast to Merlin it is intended to be more modular and allowing prototyping purely in Python.

It comes with recipes in the spirit of Kaldi located in separate repositories.

IdiapTTS is distributed under the MIT license, allowing unrestricted commercial and non-commercial use.

IdiapTTS is tested with: Python 3.6

Installation

Follow the instructions given in INSTALL.md.

Experiments

Instructions to run specific experiments are in the README files of the respective egs repositories:

Publications

Interspeech '18: A Neural Model to Predict Parameters for a Generalized Command Response Model of Intonation

Instructions to produce results similar to those reported in the paper can be found at https://github.com/idiap/idiaptts_egs_blizzard08_roger s1/.

Icassp'19: An End-to-End Network to Synthesize Intonation using a Generalized Command Response Model

Instructions to reproduce the results of the paper can be found in https://github.com/idiap/idiaptts_egs_blizzard08_roger s2/.

SSW'10: Neural VTLN for Speaker Adaptation in TTS

Instructions to produce results similar to those in the paper can be found at https://github.com/idiap/idiaptts_egs_vctk s1/.