PyTorch Implementation of Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation.

• StyleSpeech (naive branch)
• Meta-StyleSpeech (main branch)

You can install the Python dependencies with

pip3 install -r requirements.txt

You have to download pretrained models and put them in output/ckpt/LibriTTS_meta_learner/.

For English single-speaker TTS, run

python3 synthesize.py --text "YOUR_DESIRED_TEXT" --ref_audio path/to/reference_audio.wav --restore_step 200000 --mode single -p config/LibriTTS/preprocess.yaml -m config/LibriTTS/model.yaml -t config/LibriTTS/train.yaml

The generated utterances will be put in output/result/. Your synthesized speech will have ref_audio's style.

Batch inference is also supported, try

python3 synthesize.py --source preprocessed_data/LibriTTS/val.txt --restore_step 200000 --mode batch -p config/LibriTTS/preprocess.yaml -m config/LibriTTS/model.yaml -t config/LibriTTS/train.yaml

to synthesize all utterances in preprocessed_data/LibriTTS/val.txt. This can be viewed as a reconstruction of validation datasets referring to themselves for the reference style.

The pitch/volume/speaking rate of the synthesized utterances can be controlled by specifying the desired pitch/energy/duration ratios. For example, one can increase the speaking rate by 20 % and decrease the volume by 20 % by

python3 synthesize.py --text "YOUR_DESIRED_TEXT" --restore_step 200000 --mode single -p config/LibriTTS/preprocess.yaml -m config/LibriTTS/model.yaml -t config/LibriTTS/train.yaml --duration_control 0.8 --energy_control 0.8

Note that the controllability is originated from FastSpeech2 and not a vital interest of StyleSpeech. Please refer to STYLER [demo, code] for the controllability of each style factor.

The supported datasets are

• LibriTTS: a multi-speaker English dataset containing 585 hours of speech by 2456 speakers.
• (will be added more)

First, run

python3 prepare_align.py config/LibriTTS/preprocess.yaml

for some preparations.

In this implementation, Montreal Forced Aligner (MFA) is used to obtain the alignments between the utterances and the phoneme sequences.

Download the official MFA package and run

./montreal-forced-aligner/bin/mfa_align raw_data/LibriTTS/ lexicon/librispeech-lexicon.txt english preprocessed_data/LibriTTS

or

./montreal-forced-aligner/bin/mfa_train_and_align raw_data/LibriTTS/ lexicon/librispeech-lexicon.txt preprocessed_data/LibriTTS

to align the corpus and then run the preprocessing script.

python3 preprocess.py config/LibriTTS/preprocess.yaml

Train your model with

python3 train.py -p config/LibriTTS/preprocess.yaml -m config/LibriTTS/model.yaml -t config/LibriTTS/train.yaml

As described in the paper, the script will start from pre-training the naive model until meta_learning_warmup steps and then meta-train the model for additional steps via episodic training.

Use

tensorboard --logdir output/log/LibriTTS

to serve TensorBoard on your localhost. The loss curves, synthesized mel-spectrograms, and audios are shown.

1. Use 22050Hz sampling rate instead of 16kHz.
2. Add one fully connected layer at the beginning of Mel-Style Encoder to upsample input mel-spectrogram from 80 to 128.
3. The model size including meta-learner is 28.197M.
4. Use a maximum 16 batch size on training instead of 48 or 20 mainly due to the lack of memory capacity with a single 24GiB TITAN-RTX. This can be achieved by the following script to filter out data longer than max_seq_len:

   python3 filelist_filtering.py -p config/LibriTTS/preprocess.yaml -m config/LibriTTS/model.yaml
   

   This will generate train_filtered.txt in the same location of train.txt.
5. Since the total batch size is decreased, the number of training steps is doubled compared to the original paper.
6. Use HiFi-GAN instead of MelGAN for vocoding.

@misc{lee2021stylespeech,
  author = {Lee, Keon},
  title = {StyleSpeech},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/keonlee9420/StyleSpeech}}
}

• Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation
• A Style-Based Generator Architecture for Generative Adversarial Networks
• Matching Networks for One Shot Learning
• Prototypical Networks for Few-shot Learning
• TADAM: Task dependent adaptive metric for improved few-shot learning
• ming024's FastSpeech2