tacotron-cli

Command-line interface (CLI) to train Tacotron 2 using .wav <=> .TextGrid pairs.

Features

train phoneme stress separately (ARPAbet/IPA)
train phoneme tone separately (IPA)
train phoneme duration separately (IPA)
train single/multi-speaker
train/synthesize on CPU or GPU
synthesis of paragraphs
copy embeddings from one checkpoint to another
train using embeddings or one-hot encodings

Installation

pip install tacotron-cli --user

Usage

Click to unfold usage

```txt usage: tacotron-cli [-h] [-v] {create-mels,train,continue-train,validate,synthesize,synthesize-grids,analyze,add-missing-symbols} ... Command-line interface (CLI) to train Tacotron 2 using .wav <=> .TextGrid pairs. positional arguments: {create-mels,train,continue-train,validate,synthesize,synthesize-grids,analyze,add-missing-symbols} description create-mels create mel-spectrograms from audio files train start training continue-train continue training from a checkpoint validate validate checkpoint(s) synthesize synthesize lines from a file synthesize-grids synthesize .TextGrid files analyze analyze checkpoint add-missing-symbols copy missing symbols from one checkpoint to another options: -h, --help show this help message and exit -v, --version show program's version number and exit ```

Training

The dataset structure need to follow the generic format of speech-dataset-parser, i.e., each TextGrid need to contain a tier in which all phonemes are separated into single intervals, e.g., T|h|i|s| |i|s| |a| |t|e|x|t|..

Tips:

place stress directly to the vowel of the syllable, e.g. b|ˈo|d|i instead of ˈb|o|d|i (body)
place tone directly to the vowel of the syllable, e.g. ʈʂʰ|w|a˥˩|n instead of ʈʂʰ|w|a|n˥˩ (串)
- tone-characters which are considered: ˥ ˦ ˧ ˨ ˩, e.g., ɑ˥˩
duration-characters which are considered: ˘ ˑ ː, e.g., ʌː
normalize the text, e.g., numbers should be written out
substituted space by either SIL0, SIL1 or SIL2 depending on the duration of the pause
- use SIL0 for no pause
- use SIL1 for a short pause, for example after a comma ...|v|i|ˈɛ|n|ʌ|,|SIL1|ˈɔ|s|t|ɹ|i|ʌ|...
- use SIL2 for a longer pause, for example after a sentence: ...|ˈɝ|θ|.|SIL2
Note: only phonemes occurring in the TextGrids (on the selected tier) are possible to synthesize

Synthesis

To prepare a text for synthesis, following things need to be considered:

each line in the text file will be synthesized as a single file, therefore it is recommended to place each sentence onto a single line
paragraphs can be separated by a blank line
each symbol needs can be separated by an separator like |, e.g. s|ˌɪ|ɡ|ɝ|ˈɛ|t
- this is useful if the model contains phonemes/symbols that consist of multiple characters, e.g., ˈɛ

Example valid sentence: "As the overlying plate lifts up, it also forms mountain ranges." => ˈæ|z|SIL0|ð|ʌ|SIL0|ˌoʊ|v|ɝ|l|ˈaɪ|ɪ|ŋ|SIL0|p|l|ˈeɪ|t|SIL0|l|ˈɪ|f|t|s|SIL0|ˈʌ|p|,|SIL1|ɪ|t|SIL0|ˈɔ|l|s|oʊ|SIL0|f|ˈɔ|ɹ|m|z|SIL0|m|ˈaʊ|n|t|ʌ|n|SIL0|ɹ|ˈeɪ|n|d͡ʒ|ʌ|z|.|SIL2

Example invalid sentence: "Digestion is a vital process which involves the breakdown of food into smaller and smaller components, until they can be absorbed and assimilated into the body." => daɪˈʤɛsʧʌn ɪz ʌ ˈvaɪtʌl ˈpɹɑˌsɛs wɪʧ ɪnˈvɑlvz ðʌ ˈbɹeɪkˌdaʊn ʌv fud ˈɪntu ˈsmɔlɝ ænd ˈsmɔlɝ kʌmˈpoʊnʌnts, ʌnˈtɪl ðeɪ kæn bi ʌbˈzɔɹbd ænd ʌˈsɪmʌˌleɪtɪd ˈɪntu ðʌ ˈbɑdi.

Pretrained Models

Audio Example

"The North Wind and the Sun were disputing which was the stronger, when a traveler came along wrapped in a warm cloak." Listen here (headphones recommended)

Example Synthesis

To reproduce the audio example from above, you can use the following commands:

# Create example directory
mkdir ~/example

# Download pre-trained Tacotron model checkpoint
wget https://tuc.cloud/index.php/s/xxFCDMgEk8dZKbp/download/LJS-IPA-101500.pt -O ~/example/checkpoint-tacotron.pt

# Download pre-trained Waveglow model checkpoint
wget https://tuc.cloud/index.php/s/yBRaWz5oHrFwigf/download/LJS-v3-580000.pt -O ~/example/checkpoint-waveglow.pt

# Create text containing phonetic transcription of: "The North Wind and the Sun were disputing which was the stronger, when a traveler came along wrapped in a warm cloak."
cat > ~/example/text.txt << EOF
ð|ʌ|SIL0|n|ˈɔ|ɹ|θ|SIL0|w|ˈɪ|n|d|SIL0|ˈæ|n|d|SIL0|ð|ʌ|SIL0|s|ˈʌ|n|SIL0|w|ɝ|SIL0|d|ɪ|s|p|j|ˈu|t|ɪ|ŋ|SIL0|h|w|ˈɪ|t͡ʃ|SIL0|w|ˈɑ|z|SIL0|ð|ʌ|SIL0|s|t|ɹ|ˈɔ|ŋ|ɝ|,|SIL1|h|w|ˈɛ|n|SIL0|ʌ|SIL0|t|ɹ|ˈæ|v|ʌ|l|ɝ|SIL0|k|ˈeɪ|m|SIL0|ʌ|l|ˈɔ|ŋ|SIL0|ɹ|ˈæ|p|t|SIL0|ɪ|n|SIL0|ʌ|SIL0|w|ˈɔ|ɹ|m|SIL0|k|l|ˈoʊ|k|.|SIL2
EOF

# Synthesize text to mel-spectrogram
tacotron-cli synthesize \
  ~/example/checkpoint-tacotron.pt \
  ~/example/text.txt \
  --sep "|"

# Install waveglow-cli for synthesis of mel-spectrograms
pip install waveglow-cli --user

# Synthesize mel-spectrogram to wav
waveglow-cli synthesize \
  ~/example/checkpoint-waveglow.pt \
  ~/example/text -o

# Resulting wav is written to: ~/example/text/1-1.npy.wav

Roadmap

Outsource method to convert audio files to mel-spectrograms before training
Better logging
Provide more pre-trained models
Adding tests

Development setup

# update
sudo apt update
# install Python 3.8-3.11 for ensuring that tests can be run
sudo apt install python3-pip \
  python3.8 python3.8-dev python3.8-distutils python3.8-venv \
  python3.9 python3.9-dev python3.9-distutils python3.9-venv \
  python3.10 python3.10-dev python3.10-distutils python3.10-venv \
  python3.11 python3.11-dev python3.11-distutils python3.11-venv
# install pipenv for creation of virtual environments
python3.8 -m pip install pipenv --user

# check out repo
git clone https://github.com/stefantaubert/tacotron.git
cd tacotron
# create virtual environment
python3.8 -m pipenv install --dev

Running the tests

# first install the tool like in "Development setup"
# then, navigate into the directory of the repo (if not already done)
cd tacotron
# activate environment
python3.8 -m pipenv shell
# run tests
tox

Final lines of test result output:

py38: commands succeeded
py39: commands succeeded
py310: commands succeeded
py311: commands succeeded
congratulations :)

License

MIT License

Acknowledgments

Model code adapted from Nvidia.

Papers:

Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 416228727 – CRC 1410

Citation

If you want to cite this repo, you can use the BibTeX-entry generated by GitHub (see About => Cite this repository).

Taubert, S. (2024). tacotron-cli (Version 0.0.5) [Computer software]. [https://doi.org/10.5281/zenodo.10568731](https://doi.org/10.5281/zenodo.10568731)

Cited by

Taubert, S., Sternkopf, J., Kahl, S., & Eibl, M. (2022). A Comparison of Text Selection Algorithms for Sequence-to-Sequence Neural TTS. 2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), 1–6. https://doi.org/10.1109/ICSPCC55723.2022.9984283
Albrecht, S., Tamboli, R., Taubert, S., Eibl, M., Rey, G. D., & Schmied, J. (2022). Towards a Vowel Formant Based Quality Metric for Text-to-Speech Systems: Measuring Monophthong Naturalness. 2022 IEEE 9th International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), 1–6. https://doi.org/10.1109/CIVEMSA53371.2022.9853712

stefantaubert / tacotron

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