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update readme for v0.1.0 setup and models #130

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123 changes: 93 additions & 30 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -12,15 +12,6 @@ PyTorch implementation of convolutional networks-based text-to-speech synthesis
1. [arXiv:1710.07654](https://arxiv.org/abs/1710.07654): Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning.
2. [arXiv:1710.08969](https://arxiv.org/abs/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:

- [DeepVoice3: Multi-speaker text-to-speech demo](https://colab.research.google.com/github/r9y9/Colaboratory/blob/master/DeepVoice3_multi_speaker_TTS_en_demo.ipynb)
- [DeepVoice3: Single-speaker text-to-speech demo](https://colab.research.google.com/github/r9y9/Colaboratory/blob/master/DeepVoice3_single_speaker_TTS_en_demo.ipynb)

## Highlights

- Convolutional sequence-to-sequence model with attention for text-to-speech synthesis
Expand All @@ -29,7 +20,13 @@ Notebooks supposed to be executed on https://colab.research.google.com are avail
- Preprocessor for [LJSpeech (en)](https://keithito.com/LJ-Speech-Dataset/), [JSUT (jp)](https://sites.google.com/site/shinnosuketakamichi/publication/jsut) and [VCTK](http://homepages.inf.ed.ac.uk/jyamagis/page3/page58/page58.html) datasets, as well as [carpedm20/multi-speaker-tacotron-tensorflow](https://github.com/carpedm20/multi-Speaker-tacotron-tensorflow) compatible custom dataset (in JSON format)
- Language-dependent frontend text processor for English and Japanese

### Samples
## Samples and demo

### English samples

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

### Japanese and Korean Samples

- [Ja Step000380000 Predicted](https://soundcloud.com/user-623907374/ja-step000380000-predicted)
- [Ja Step000370000 Predicted](https://soundcloud.com/user-623907374/ja-step000370000-predicted)
Expand All @@ -38,13 +35,82 @@ Notebooks supposed to be executed on https://colab.research.google.com are avail
- [Ko_multi Step001680000 Predicted](https://soundcloud.com/user-623907374/step001680000-predicted)
- [Ko_multi Step001700000 Predicted](https://soundcloud.com/user-623907374/step001700000-predicted)

### Online TTS demo

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

- [DeepVoice3: Multi-speaker text-to-speech demo](https://colab.research.google.com/github/r9y9/Colaboratory/blob/master/DeepVoice3_multi_speaker_TTS_en_demo.ipynb)
- [DeepVoice3: Single-speaker text-to-speech demo](https://colab.research.google.com/github/r9y9/Colaboratory/blob/master/DeepVoice3_single_speaker_TTS_en_demo.ipynb)

## Installation and test of version v0.1.0
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I don't think the script is worth putting it to the README. Setup procedure depends on envinronments where people work.

What about making your own gist for the script and put the link to the trouble shooting or a new section (e.g., Examples)?

Also, if you like self-contained shell script that runs everything to prepare end-to-end TTS models, it might be useful to create Docerfile instead.

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I think that at least the part:

pip install numpy # Required because nnmnkwii setup has it as dependency and else pip install nnmnkwii (within next command) fails
pip install -e ".[bin]"
pip install matplotlib # This is not installed by previous command. Maybe it should be added to the bin target
pip install tensorflow # No special nn routines are used. A proposed patch gets rid of this requirement

python -c "import nltk; nltk.download('cmudict')"
python -c "import nltk; nltk.download('punkt')"

should be included.
To me at least it is confusing when I follow the requirements described in a readme, start executing the code and then get errors over and over again, having to "decipher" each one, researching what is missing, and having to install it.
So I appreciate and exhaustive requirements or install list.

The rest was information on how the models were trained and how they worked. I actually trained them with those commands.

Also, I will consider your gist suggestion, but go ahead for now merging only the additions you feel appropriate.


Tested on a Linux mint 18.3 live-DVD.

```
cd ~

# # Required to be able to pip install: lws, nnmnkwii, bandmat (from nnmnkwii), pysptk (from nnmnkwii)
# sudo apt update
# sudo apt install build-essential
# sudo apt install python3-dev

git clone -b v0.1.0 https://github.com/r9y9/deepvoice3_pytorch.git ~/r9y9_deepvoice3_pytorch_v0.1.0
cd ~/r9y9_deepvoice3_pytorch_v0.1.0

virtualenv -p python3 ~/r9y9_deepvoice3_pytorch_v0.1.0_env/
source ~/r9y9_deepvoice3_pytorch_v0.1.0_env/bin/activate

pip install numpy # Required because nnmnkwii setup has it as dependency and else pip install nnmnkwii (within next command) fails
pip install -e ".[bin]"
pip install matplotlib # This is not installed by previous command. Maybe it should be added to the bin target
pip install tensorflow # No special nn routines are used. A proposed patch gets rid of this requirement
# pip install "tensorboardX<=1.2" # Necessary fix for training in this release

python -c "import nltk; nltk.download('cmudict')"
python -c "import nltk; nltk.download('punkt')"

# # Preprocess dataset
# # (omitted)
#
# # Train the models
#
# MPLBACKEND=Agg python train.py --preset=presets/deepvoice3_ljspeech.json --data-root=$HOME/PREPROCESSED/ljspeech_dv3/ --checkpoint-dir=checkpoints_deepvoice3 --log-event-path=log_deepvoice3
# MPLBACKEND=Agg python train.py --preset=presets/nyanko_ljspeech.json --data-root=$HOME/PREPROCESSED/ljspeech_nyanko/ --checkpoint-dir=checkpoints_nyanko --log-event-path=log_nyanko
#
# cp checkpoints_deepvoice3/checkpoint_step001000000.pth ./deepvoice3_pytorch_v0.1.0_ljspeech_deepvoice3_checkpoint_step001000000.pth
# cp checkpoints_nyanko/checkpoint_step001000000.pth ./deepvoice3_pytorch_v0.1.0_ljspeech_nyanko_checkpoint_step001000000.pth

# # Download the models
#
# wget http://hostingservice/deepvoice3_pytorch_v0.1.0_ljspeech_deepvoice3_checkpoint_step001000000.pth
# wget http://hostingservice/deepvoice3_pytorch_v0.1.0_ljspeech_nyanko_checkpoint_step001000000.pth

echo -e "Scientists at the CERN laboratory say they have discovered a new particle.\nThere's a way to measure the acute emotional intelligence that has never gone out of style.\nPresident Trump met with other leaders at the Group of 20 conference.\nGenerative adversarial network or variational auto-encoder.\nPlease call Stella.\nSome have accepted this as a miracle without any physical explanation." > sentences.txt
mkdir out_deepvoice3
MPLBACKEND=Agg python synthesis.py deepvoice3_pytorch_v0.1.0_ljspeech_deepvoice3_checkpoint_step001000000.pth sentences.txt out_deepvoice3 --preset=presets/deepvoice3_ljspeech.json
mkdir out_nyanko
MPLBACKEND=Agg python synthesis.py deepvoice3_pytorch_v0.1.0_ljspeech_nyanko_checkpoint_step001000000.pth sentences.txt out_nyanko --preset=presets/nyanko_ljspeech.json

deactivate
cd ~
```

## Pretrained models

| URL | Model | Data | Hyper paramters | Git commit | Steps |
|-----|------------|----------|--------------------------------------------------|----------------------|--------|
| [link](https://drive.google.com/open?id=1FauMlx2OPpVC5cSgdfT86_eWJksCfd2C) | DeepVoice3 | LJSpeech 1.1 | presets/deepvoice3_ljspeech.json | [v0.1.0](https://github.com/r9y9/deepvoice3_pytorch/tree/v0.1.0) | 1000k |
| [link](https://drive.google.com/open?id=1eZglEroWcU5-HpRwjebNGRDp_CEfQxUe) | Nyanko | LJSpeech 1.1 | presets/nyanko_ljspeech.json | [v0.1.0](https://github.com/r9y9/deepvoice3_pytorch/tree/v0.1.0) | 1000k |

These models were trained by @Martin-Laclaustra at the Supercomputing Centre of Aragon / Centro de Supercomputación de Aragón [(CESAR)](http://cesar.unizar.es/)
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👍


### Models for older versions

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

| URL | Model | Data | Hyper paramters | Git commit | Steps |
|-----|------------|----------|--------------------------------------------------|----------------------|--------|
| [link](https://www.dropbox.com/s/5ucl9remrwy5oeg/20180505_deepvoice3_checkpoint_step000640000.pth?dl=0) | DeepVoice3 | LJSpeech | [link](https://www.dropbox.com/s/0ck82unm0bo0rxd/20180505_deepvoice3_ljspeech.json?dl=0) | [abf0a21](https://github.com/r9y9/deepvoice3_pytorch/tree/abf0a21f83aeb451b918f867bc23378f1e2e608b)| 640k |
| [link](https://www.dropbox.com/s/5ucl9remrwy5oeg/20180505_deepvoice3_checkpoint_step000640000.pth?dl=0) | DeepVoice3 | LJSpeech | [link](https://www.dropbox.com/s/0ck82unm0bo0rxd/20180505_deepvoice3_ljspeech.json?dl=0) | [abf0a21](https://github.com/r9y9/deepvoice3_pytorch/tree/abf0a21f83aeb451b918f867bc23378f1e2e608b) | 640k |
| [link](https://www.dropbox.com/s/1y8bt6bnggbzzlp/20171129_nyanko_checkpoint_step000585000.pth?dl=0) | Nyanko | LJSpeech | `builder=nyanko,preset=nyanko_ljspeech` | [ba59dc7](https://github.com/r9y9/deepvoice3_pytorch/tree/ba59dc75374ca3189281f6028201c15066830116) | 585k |
| [link](https://www.dropbox.com/s/uzmtzgcedyu531k/20171222_deepvoice3_vctk108_checkpoint_step000300000.pth?dl=0) | Multi-speaker DeepVoice3 | VCTK | `builder=deepvoice3_multispeaker,preset=deepvoice3_vctk` | [0421749](https://github.com/r9y9/deepvoice3_pytorch/tree/0421749af908905d181f089f06956fddd0982d47) | 300k + 300k |

Expand All @@ -71,7 +137,7 @@ python synthesis.py --preset=20180505_deepvoice3_ljspeech.json \
## 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.
- `builder` specifies which model you want to use. `deepvoice3`, `deepvoice3_multispeaker` [1] and `nyanko` [2] are surpported.
- 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.
Expand All @@ -81,44 +147,35 @@ python synthesis.py --preset=20180505_deepvoice3_ljspeech.json \
## Requirements

- Python 3
- CUDA >= 8.0
- CUDA >= 8.0 (optional)
- PyTorch >= v0.4.0
- TensorFlow >= v1.3
- TensorFlow >= v1.3 (this requirement may not be needed in future versions)
- [nnmnkwii](https://github.com/r9y9/nnmnkwii) >= v0.0.11
- [MeCab](http://taku910.github.io/mecab/) (Japanese only)

## Installation

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
There are many hyper parameters to be tuned which depend on what model and data you are working on. For typical datasets and models, parameters that are 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.,
accepts `--preset=<json>` optional parameter, which specifies from 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
python preprocess.py --preset=presets/deepvoice3_ljspeech.json ljspeech ~/CORPUS/LJSpeech-1.1 ~/PREPROCESSED/ljspeech_dv3
MPLBACKEND=Agg python train.py --preset=presets/deepvoice3_ljspeech.json --data-root=$HOME/PREPROCESSED/ljspeech_dv3/
```

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
python preprocess.py ljspeech ~/CORPUS/LJSpeech-1.1 ~/PREPROCESSED/ljspeech_dv3
# warning! this may use different hyper parameters at preprocessing stage
MPLBACKEND=Agg python train.py --preset=presets/deepvoice3_ljspeech.json --data-root=$HOME/PREPROCESSED/ljspeech_dv3/
```

### 0. Download dataset
Expand Down Expand Up @@ -327,6 +384,12 @@ This may happen depending on backends you have for matplotlib. Try changing back
MPLBACKEND=Qt5Agg python train.py ${args...}
```

Or even better:

```
MPLBACKEND=Agg python train.py ${args...}
```

In [#78](https://github.com/r9y9/deepvoice3_pytorch/pull/78#issuecomment-385327057), engiecat reported that changing the backend of matplotlib from Tkinter(TkAgg) to PyQt5(Qt5Agg) fixed the problem.

## Acknowledgements
Expand Down