StyleSpeech - PyTorch Implementation
PyTorch Implementation of Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation.
Status (2021.06.09)
- StyleSpeech
- Meta-StyleSpeech
Quickstart
Dependencies
You can install the Python dependencies with
pip3 install -r requirements.txt
Inference
You have to download the pretrained models and put them in output/ckpt/LibriTTS/.
For English single-speaker TTS, run
python3 synthesize.py --text "YOUR_DESIRED_TEXT" --ref_audio path/to/reference_audio.wav --speaker_id <SPEAKER_ID> --restore_step 100000 --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 spoken by speaker_id speaker. Note that the controllability of speakers is not a vital interest of StyleSpeech.
Batch Inference
Batch inference is also supported, try
python3 synthesize.py --source preprocessed_data/LibriTTS/val.txt --restore_step 100000 --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 100000 --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.
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
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
Training
Train your model with
python3 train.py -p config/LibriTTS/preprocess.yaml -m config/LibriTTS/model.yaml -t config/LibriTTS/train.yaml
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
- Use
22050Hzsampling rate instead of16kHz. - Add one fully connected layer at the beginning of Mel-Style Encoder to upsample input mel-spectrogram from
80to128. - The Paper doesn't mention speaker embedding for the Generator, but I add it as a normal multi-speaker TTS. And the
style_prototypeof Meta-StyleSpeech can be seen as a speaker embedding space. - 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}}
}
This is what my loss curve looks like.
Can you help me with what can I do now to improve my synthesized audio results?
21 Aug 12, 2022
1 Jan 20, 2022
475 Jan 04, 2023
1.2k Jan 08, 2023
5 Oct 01, 2022
1 Jan 06, 2022
2 Jan 12, 2022
34 Nov 24, 2022
11 Nov 17, 2022
164 Jan 02, 2023
3.1k Dec 29, 2022
3 Oct 15, 2021
4 Nov 11, 2021
2 Jun 24, 2022
1 Feb 07, 2022
1.6k Dec 29, 2022
1 Dec 23, 2021
61 Sep 18, 2022
62 Oct 02, 2022
1 Dec 22, 2021