tacotron-cli 0.0.5
Command-line interface (CLI) to train Tacotron 2 using .wav <=> .TextGrid pairs.
License: MIT
Requires Python: <3.12,>=3.8
Keywords: Text-to-speech,Speech synthesis,Corpus,Utils,Language,Linguistics

# tacotron-cli

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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

```sh
pip install tacotron-cli --user
```

## 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](https://pypi.org/project/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

- English
  - [LJ Speech English TTS](https://zenodo.org/records/10200955)
  - [LJ Speech English TTS with explicit duration markers](https://zenodo.org/records/10107104)
- Chinese
  - [THCHS-30 Chinese TTS](https://zenodo.org/records/10210310)
  - [THCHS-30 Chinese TTS with explicit duration markers](https://zenodo.org/records/10209990)

## 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](https://tuc.cloud/index.php/s/gzaYDNKinHw6GCz) (headphones recommended)

## Example Synthesis

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

```sh
# 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

```sh
# 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

```sh
# 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:

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

## License

MIT License

## Acknowledgments

Model code adapted from [Nvidia](https://github.com/NVIDIA/tacotron2).

Papers:

- [Tacotron: Towards End-to-End Speech Synthesis](https://www.isca-speech.org/archive/interspeech_2017/wang17n_interspeech.html)
- [Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions](https://ieeexplore.ieee.org/document/8461368)

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*).

```txt
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](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](https://doi.org/10.1109/CIVEMSA53371.2022.9853712)