StyleSpeech - PyTorch Implementation

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

Branch

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

Quickstart

Dependencies

You can install the Python dependencies with

pip3 install -r requirements.txt

Inference

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

For English multi-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

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.

Controllability

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.

Training

Datasets

The supported datasets are

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

Preprocessing

Run

python3 prepare_align.py config/LibriTTS/preprocess.yaml

for some preparations.

For the forced alignment, Montreal Forced Aligner (MFA) is used to obtain the alignments between the utterances and the phoneme sequences. Pre-extracted alignments for the datasets are provided here. You have to unzip the files in preprocessed_data/LibriTTS/TextGrid/. Alternately, you can run the aligner by yourself.

After that, run the preprocessing script by

python3 preprocess.py config/LibriTTS/preprocess.yaml

Training

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.

TensorBoard

Use

tensorboard --logdir output/log/LibriTTS

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

Implementation Issues

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.

Citation

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

References

• 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