Creating New Voices

Introduction

This document describes the basic process in order to compile a new voice for Speect with Voicetools, given a number of basic resources. Due to the flexibility of Speect, this is not a comprehensive text on the topic, but rather a starting point describing the generic process and giving an example of how the accompanying scripts may be used to this end.

A voice in Speect consists of a collection of data resources, code modules or plugins and a single definition file describing how these are used during synthesis.

In this document we will assume pre-existing plugins and voice definition files and describe the creation of basic data resources.

Setting up basic resources

It is convention to store the voice definition and other resource files as a collection in a single directory. Thus, the following steps should be carried out:

1. Create a directory where voice definition and resource files will be stored ($VOICEDIR).

2. Create a voice definition file ($VOICEDEF) in $VOICEDIR. Examples of such a file can be found in ./examples/voice.*.json. See the voice definition example.

3. Define a phoneset (e.g. ./examples/phoneset.json), place this file in $VOICEDIR and ensure that it is specified in $VOICEDEF

4. Define a pronunciation lexicon and/or addendum (e.g. ./examples/lexicon.json and ./examples/addendum.json) by placing these files in $VOICEDIR and specifying them in $VOICEDEF

Compiling grapheme-to-phoneme rewrite rules

A simple utility (./scripts/compile_g2p.py) is provided to compile rewrite rules defined in semicolon delimited text files (e.g. as can be exported by DictionaryMaker - http://dictionarymaker.sourceforge.net/) to Speect format.

The following is an example of fields in such a semicolon delimited rules file:

a;;;a;0;5599
a;a;;0;1;2364
a;;a;A;2;2252
a;;0sie;A;3;174
a;;re;A;4;103

where a single field per line has the form:

grapheme;leftcontext;rightcontext;phoneme;ordinal;statistic

Empty fields denote any context and rules are ordered from most to least general. The statistic field is not important here. Other simple semicolon delimited mapping files can also be loaded to perform grapheme or phoneme representation mappings as well as specify rules for the insertion of graphemic nulls. These files all have fields of the form: inputmapping;outputmapping with one rule per line similar to the rules file example above.

The exact syntax and additional options can be obtained by running with the --help flag and an example of a metadata configuration file for use with this utility can be found in ./examples/g2p.conf

Performing phonetic alignments

Given a set of audio files in RIFF Wave format and a text file containing corresponding orthographic transcriptions (e.g. ./examples/utts.data), the TTS frontend can be used to perform automatic phonetic alignment in order to derive acoustic models for synthesis. For this purpose, the scripts ./scripts/do_build_setup.py and ./scripts/do_alignments.py can be used as described below.

Running:

do_build_setup.py -w $SOURCE_WAVDIR -u $SOURCE_TRANSCRFILE -o $BUILD_ROOTDIR

should set up a directory structure at $BUILD_ROOTDIR to contain a default configuration and downsampled, power normalised audio files which looks like this:

.
|-- etc
|   |-- feats.conf
|   |-- halign.conf
|   |-- halign_feats.conf
|   |-- halign_hmmproto
|   `-- utts.data
`-- wavs
    |-- ...
    |-- ...

The configuration files in $BUILD_ROOTDIR/etc can be edited if desired (an important speaker-specific parameter is the voice pitch range which can be set in feats.conf).

Once the configuration is satisfactory, the alignment process can be done by running the following in $BUILD_ROOTDIR:

do_alignments.py -v $VOICEDEF to_textgrid

This outputs alignments in Praat’s (http://www.fon.hum.uva.nl/praat/) TextGrid format. These files can be edited (as long as the word sequences stay intact) and then incorporated in aligned Speect format utterance files with the command:

do_alignments.py -v $VOICEDEF from_textgrid

If manual correction is not required, the above two commands can be replaced with the following:

do_alignments.py -v $VOICEDEF auto

Compiling acoustic representations

Unit selection catalogue

To compile a halfphone database given phonetic alignments, run the following in $BUILD_ROOTDIR:

do_halfphone_catalogue.py -v $VOICEDEF auto

This performs the relevant feature extraction and compiles a halfphone database file in Speect format. For more options run with --help

Hidden Markov Models (HTS)

A script (scripts/utt2lab.py) is provided to generate HTS labels from aligned Speect utterances. To train speech models, the HTS-demo scripts (available at http://hts.sp.nitech.ac.jp/?Download) can be used in conjunction with utt2lab.py