April 16, 2019

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PyTorch implementation of convolutional neural networks-based text-to-speech synthesis models

repo name r9y9/deepvoice3_pytorch
repo link https://github.com/r9y9/deepvoice3_pytorch
homepage https://r9y9.github.io/deepvoice3_pytorch/
language Python
size (curr.) 7110 kB
stars (curr.) 1249
created 2017-10-31
license Other

alt text


PyPI Build Status Build status DOI

PyTorch implementation of convolutional networks-based text-to-speech synthesis models:

  1. arXiv:1710.07654: Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning.
  2. arXiv:1710.08969: Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention.

Audio samples are available at https://r9y9.github.io/deepvoice3_pytorch/.


Online TTS demo

Notebooks supposed to be executed on https://colab.research.google.com are available:


  • Convolutional sequence-to-sequence model with attention for text-to-speech synthesis
  • Multi-speaker and single speaker versions of DeepVoice3
  • Audio samples and pre-trained models
  • Preprocessor for LJSpeech (en), JSUT (jp) and VCTK datasets, as well as carpedm20/multi-speaker-tacotron-tensorflow compatible custom dataset (in JSON format)
  • Language-dependent frontend text processor for English and Japanese


Pretrained models

NOTE: pretrained models are not compatible to master. To be updated soon.

URL Model Data Hyper paramters Git commit Steps
link DeepVoice3 LJSpeech link abf0a21 640k
link Nyanko LJSpeech builder=nyanko,preset=nyanko_ljspeech ba59dc7 585k
link Multi-speaker DeepVoice3 VCTK builder=deepvoice3_multispeaker,preset=deepvoice3_vctk 0421749 300k + 300k

To use pre-trained models, it’s highly recommended that you are on the specific git commit noted above. i.e.,

git checkout ${commit_hash}

Then follow the “Synthesize from a checkpoint” section in the README of the specific git commit. Please notice that the latest development version of the repository may not work.

You could try for example:

# pretrained model (20180505_deepvoice3_checkpoint_step000640000.pth)
# hparams (20180505_deepvoice3_ljspeech.json)
git checkout 4357976
python synthesis.py --preset=20180505_deepvoice3_ljspeech.json \
  20180505_deepvoice3_checkpoint_step000640000.pth \
  sentences.txt \

Notes on hyper parameters

  • Default hyper parameters, used during preprocessing/training/synthesis stages, are turned for English TTS using LJSpeech dataset. You will have to change some of parameters if you want to try other datasets. See hparams.py for details.
  • builder specifies which model you want to use. deepvoice3, deepvoice3_multispeaker [1] and nyanko [2] are surpprted.
  • Hyper parameters described in DeepVoice3 paper for single speaker didn’t work for LJSpeech dataset, so I changed a few things. Add dilated convolution, more channels, more layers and add guided attention loss, etc. See code for details. The changes are also applied for multi-speaker model.
  • Multiple attention layers are hard to learn. Empirically, one or two (first and last) attention layers seems enough.
  • With guided attention (see https://arxiv.org/abs/1710.08969), alignments get monotonic more quickly and reliably if we use multiple attention layers. With guided attention, I can confirm five attention layers get monotonic, though I cannot get speech quality improvements.
  • Binary divergence (described in https://arxiv.org/abs/1710.08969) seems stabilizes training particularly for deep (> 10 layers) networks.
  • Adam with step lr decay works. However, for deeper networks, I find Adam + noam’s lr scheduler is more stable.


  • Python >= 3.5
  • CUDA >= 8.0
  • PyTorch >= v1.0.0
  • nnmnkwii >= v0.0.11
  • MeCab (Japanese only)


Please install packages listed above first, and then

git clone https://github.com/r9y9/deepvoice3_pytorch && cd deepvoice3_pytorch
pip install -e ".[bin]"

Getting started

Preset parameters

There are many hyper parameters to be turned depends on what model and data you are working on. For typical datasets and models, parameters that known to work good (preset) are provided in the repository. See presets directory for details. Notice that

  1. preprocess.py
  2. train.py
  3. synthesis.py

accepts --preset=<json> optional parameter, which specifies where to load preset parameters. If you are going to use preset parameters, then you must use same --preset=<json> throughout preprocessing, training and evaluation. e.g.,

python preprocess.py --preset=presets/deepvoice3_ljspeech.json ljspeech ~/data/LJSpeech-1.0
python train.py --preset=presets/deepvoice3_ljspeech.json --data-root=./data/ljspeech

instead of

python preprocess.py ljspeech ~/data/LJSpeech-1.0
# warning! this may use different hyper parameters used at preprocessing stage
python train.py --preset=presets/deepvoice3_ljspeech.json --data-root=./data/ljspeech

0. Download dataset

1. Preprocessing


python preprocess.py ${dataset_name} ${dataset_path} ${out_dir} --preset=<json>

Supported ${dataset_name}s are:

  • ljspeech (en, single speaker)
  • vctk (en, multi-speaker)
  • jsut (jp, single speaker)
  • nikl_m (ko, multi-speaker)
  • nikl_s (ko, single speaker)

Assuming you use preset parameters known to work good for LJSpeech dataset / DeepVoice3 and have data in ~/data/LJSpeech-1.0, then you can preprocess data by:

python preprocess.py --preset=presets/deepvoice3_ljspeech.json ljspeech ~/data/LJSpeech-1.0/ ./data/ljspeech

When this is done, you will see extracted features (mel-spectrograms and linear spectrograms) in ./data/ljspeech.

1-1. Building custom dataset. (using json_meta)

Building your own dataset, with metadata in JSON format (compatible with carpedm20/multi-speaker-tacotron-tensorflow) is currently supported. Usage:

python preprocess.py json_meta ${list-of-JSON-metadata-paths} ${out_dir} --preset=<json>

You may need to modify pre-existing preset JSON file, especially n_speakers. For english multispeaker, start with presets/deepvoice3_vctk.json.

Assuming you have dataset A (Speaker A) and dataset B (Speaker B), each described in the JSON metadata file ./datasets/datasetA/alignment.json and ./datasets/datasetB/alignment.json, then you can preprocess data by:

python preprocess.py json_meta "./datasets/datasetA/alignment.json,./datasets/datasetB/alignment.json" "./datasets/processed_A+B" --preset=(path to preset json file)

1-2. Preprocessing custom english datasets with long silence. (Based on vctk_preprocess)

Some dataset, especially automatically generated dataset may include long silence and undesirable leading/trailing noises, undermining the char-level seq2seq model. (e.g. VCTK, although this is covered in vctk_preprocess)

To deal with the problem, gentle_web_align.py will

  • Prepare phoneme alignments for all utterances
  • Cut silences during preprocessing

gentle_web_align.py uses Gentle, a kaldi based speech-text alignment tool. This accesses web-served Gentle application, aligns given sound segments with transcripts and converts the result to HTK-style label files, to be processed in preprocess.py. Gentle can be run in Linux/Mac/Windows(via Docker).

Preliminary results show that while HTK/festival/merlin-based method in vctk_preprocess/prepare_vctk_labels.py works better on VCTK, Gentle is more stable with audio clips with ambient noise. (e.g. movie excerpts)

Usage: (Assuming Gentle is running at localhost:8567 (Default when not specified))

  1. When sound file and transcript files are saved in separate folders. (e.g. sound files are at datasetA/wavs and transcripts are at datasetA/txts)
python gentle_web_align.py -w "datasetA/wavs/*.wav" -t "datasetA/txts/*.txt" --server_addr=localhost --port=8567
  1. When sound file and transcript files are saved in nested structure. (e.g. datasetB/speakerN/blahblah.wav and datasetB/speakerN/blahblah.txt)
python gentle_web_align.py --nested-directories="datasetB" --server_addr=localhost --port=8567

Once you have phoneme alignment for each utterance, you can extract features by running preprocess.py

2. Training


python train.py --data-root=${data-root} --preset=<json> --hparams="parameters you may want to override"

Suppose you build a DeepVoice3-style model using LJSpeech dataset, then you can train your model by:

python train.py --preset=presets/deepvoice3_ljspeech.json --data-root=./data/ljspeech/

Model checkpoints (.pth) and alignments (.png) are saved in ./checkpoints directory per 10000 steps by default.


Pleae check this in advance and follow the commands below.

python preprocess.py nikl_s ${your_nikl_root_path} data/nikl_s --preset=presets/deepvoice3_nikls.json

python train.py --data-root=./data/nikl_s --checkpoint-dir checkpoint_nikl_s --preset=presets/deepvoice3_nikls.json

4. Monitor with Tensorboard

Logs are dumped in ./log directory by default. You can monitor logs by tensorboard:

tensorboard --logdir=log

5. Synthesize from a checkpoint

Given a list of text, synthesis.py synthesize audio signals from trained model. Usage is:

python synthesis.py ${checkpoint_path} ${text_list.txt} ${output_dir} --preset=<json>

Example test_list.txt:

Generative adversarial network or variational auto-encoder.
Once upon a time there was a dear little girl who was loved by every one who looked at her, but most of all by her grandmother, and there was nothing that she would not have given to the child.
A text-to-speech synthesis system typically consists of multiple stages, such as a text analysis frontend, an acoustic model and an audio synthesis module.

Advanced usage

Multi-speaker model

VCTK and NIKL are supported dataset for building a multi-speaker model.


Since some audio samples in VCTK have long silences that affect performance, it’s recommended to do phoneme alignment and remove silences according to vctk_preprocess.

Once you have phoneme alignment for each utterance, you can extract features by:

python preprocess.py vctk ${your_vctk_root_path} ./data/vctk

Now that you have data prepared, then you can train a multi-speaker version of DeepVoice3 by:

python train.py --data-root=./data/vctk --checkpoint-dir=checkpoints_vctk \
   --preset=presets/deepvoice3_vctk.json \

If you want to reuse learned embedding from other dataset, then you can do this instead by:

python train.py --data-root=./data/vctk --checkpoint-dir=checkpoints_vctk \
   --preset=presets/deepvoice3_vctk.json \
   --log-event-path=log/deepvoice3_multispeaker_vctk_preset \

This may improve training speed a bit.


You will be able to obtain cleaned-up audio samples in ../nikl_preprocoess. Details are found in here.

Once NIKL corpus is ready to use from the preprocessing, you can extract features by:

python preprocess.py nikl_m ${your_nikl_root_path} data/nikl_m

Now that you have data prepared, then you can train a multi-speaker version of DeepVoice3 by:

python train.py --data-root=./data/nikl_m  --checkpoint-dir checkpoint_nikl_m \

Speaker adaptation

If you have very limited data, then you can consider to try fine-turn pre-trained model. For example, using pre-trained model on LJSpeech, you can adapt it to data from VCTK speaker p225 (30 mins) by the following command:

python train.py --data-root=./data/vctk --checkpoint-dir=checkpoints_vctk_adaptation \
    --preset=presets/deepvoice3_ljspeech.json \
    --log-event-path=log/deepvoice3_vctk_adaptation \

From my experience, it can get reasonable speech quality very quickly rather than training the model from scratch.

There are two important options used above:

  • --restore-parts=<N>: It specifies where to load model parameters. The differences from the option --checkpoint=<N> are 1) --restore-parts=<N> ignores all invalid parameters, while --checkpoint=<N> doesn’t. 2) --restore-parts=<N> tell trainer to start from 0-step, while --checkpoint=<N> tell trainer to continue from last step. --checkpoint=<N> should be ok if you are using exactly same model and continue to train, but it would be useful if you want to customize your model architecture and take advantages of pre-trained model.
  • --speaker-id=<N>: It specifies what speaker of data is used for training. This should only be specified if you are using multi-speaker dataset. As for VCTK, speaker id is automatically assigned incrementally (0, 1, …, 107) according to the speaker_info.txt in the dataset.

If you are training multi-speaker model, speaker adaptation will only work when n_speakers is identical.

Trouble shooting

#5 RuntimeError: main thread is not in main loop

This may happen depending on backends you have for matplotlib. Try changing backend for matplotlib and see if it works as follows:

MPLBACKEND=Qt5Agg python train.py ${args...}

In #78, engiecat reported that changing the backend of matplotlib from Tkinter(TkAgg) to PyQt5(Qt5Agg) fixed the problem.


Part of code was adapted from the following projects:

Banner and logo created by @jraulhernandezi (#76)

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