Comprehensive Tacotron2 - PyTorch Implementation
PyTorch Implementation of Google's Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions. Unlike many previous implementations, this is kind of a Comprehensive Tacotron2 where the model supports both single-, multi-speaker TTS and several techniques such as reduction factor to enforce the robustness of the decoder alignment. The model can learn alignment only in 5k.
The validation logs up to 70K of synthesized mel and alignment are shown below (LJSpeech_val_LJ038-0050 and VCTK_val_p323_008 from top to bottom).
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/LJSpeech/ or output/ckpt/VCTK/.
For a single-speaker TTS, run
python3 synthesize.py --text "YOUR_DESIRED_TEXT" --restore_step RESTORE_STEP --mode single --dataset LJSpeech
For a multi-speaker TTS, run
python3 synthesize.py --text "YOUR_DESIRED_TEXT" --speaker_id SPEAKER_ID --restore_step RESTORE_STEP --mode single --dataset VCTK
The generated utterances will be put in output/result/.
Batch Inference
Batch inference is also supported, try
python3 synthesize.py --source preprocessed_data/LJSpeech/val.txt --restore_step RESTORE_STEP --mode batch --dataset LJSpeech
to synthesize all utterances in preprocessed_data/LJSpeech/val.txt. You can replace LJSpeech with VCTK. Note that only 1 batch size is supported currently due to the autoregressive model architecture.
Training
Datasets
The supported datasets are
- LJSpeech: a single-speaker TTS English dataset consists of 13100 short audio clips of a female speaker reading passages from 7 non-fiction books, approximately 24 hours in total.
- VCTK: The CSTR VCTK Corpus includes speech data uttered by 110 English speakers (multi-speaker TTS ) with various accents. Each speaker reads out about 400 sentences, which were selected from a newspaper, the rainbow passage and an elicitation paragraph used for the speech accent archive.
- Any of both single-speaker TTS dataset (e.g., Blizzard Challenge 2013) and multi-speaker TTS dataset (e.g., LibriTTS) can be added following LJSpeech and VCTK, respectively.
Preprocessing
-
For a multi-speaker TTS with external speaker embedder, download ResCNN Softmax+Triplet pretrained model of philipperemy's DeepSpeaker for the speaker embedding and locate it in
./deepspeaker/pretrained_models/. -
Run the preprocessing script
python3 preprocess.py --dataset DATASET
Training
Train your model with
python3 train.py --dataset DATASET
TensorBoard
Use
tensorboard --logdir output/log
to serve TensorBoard on your localhost.
Implementation Issues
- Support
n_frames_per_step>1mode (which is not supported by NVIDIA's tacotron2). This is the key factor to get the robustness of the decoder alignment as described in the paper. Also, it reduces the training & inference time by the factor time. - The current implementation provides pre-trained model of
n_frames_per_step==2, but it should also work for any number greater than 2. - Add espnet's implementation of diagonal guided attention loss to force diagonal alignment in the decoder attention module. You can toggle it by setting config.
- Two options for embedding for the multi-speaker TTS setting: training speaker embedder from scratch or using a pre-trained philipperemy's DeepSpeaker model (as STYLER did). You can toggle it by setting the config (between
'none'and'DeepSpeaker'). - DeepSpeaker on VCTK dataset shows clear identification among speakers. The following figure shows the T-SNE plot of extracted speaker embedding.
- For the vocoder, the current implementation supports HiFi-GAN and MelGAN, which are much better than WaveNet.
- Currently,
fp16_runmode is not supported.
Citation
@misc{lee2021comprehensive-tacotron2,
author = {Lee, Keon},
title = {Comprehensive-Tacotron2},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/keonlee9420/Comprehensive-Tacotron2}}
}