Full Code of r9y9/gantts for AI

Repository: r9y9/gantts
Branch: master
Commit: fb1e75fa9cab
Files: 28
Total size: 12.3 MB

Directory structure:
gitextract_31fi3jc2/

├── .github/
│   └── stale.yml
├── .gitignore
├── .gitmodules
├── .travis.yml
├── LICENSE.md
├── README.md
├── data/
│   └── .gitignore
├── evaluation_tts.py
├── evaluation_vc.py
├── gantts/
│   ├── __init__.py
│   ├── models.py
│   ├── multistream.py
│   └── seqloss.py
├── generated/
│   └── .gitignore
├── hparams.py
├── notebooks/
│   ├── Test RNN VC.ipynb
│   ├── Test TTS.ipynb
│   └── Test VC.ipynb
├── prepare_features_tts.py
├── prepare_features_vc.py
├── release.sh
├── setup.py
├── tests/
│   └── test_gantts.py
├── tox.ini
├── train.py
├── train_gan.sh
├── tts_demo.sh
└── vc_demo.sh

================================================
FILE CONTENTS
================================================

================================================
FILE: .github/stale.yml
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# Number of days of inactivity before an Issue or Pull Request becomes stale
daysUntilStale: 60

# Number of days of inactivity before an Issue or Pull Request with the stale label is closed.
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markComment: >
  This issue has been automatically marked as stale because it has not had
  recent activity. It will be closed if no further activity occurs. Thank you
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limitPerRun: 30

# Limit to only `issues` or `pulls`
only: issues


================================================
FILE: .gitignore
================================================
checkpoints*
log
gantts/version.py

# Created by https://www.gitignore.io

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================================================
FILE: .gitmodules
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[submodule "nnmnkwii_gallery"]
	path = nnmnkwii_gallery
	url = https://github.com/r9y9/nnmnkwii_gallery


================================================
FILE: .travis.yml
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language: python

python:
  - "2.7"
  - "3.6"

notifications:
  email: false

before_install:
  - sudo apt-get update
  - if [["$TRAVIS_PYTHON_VERSION" == "2.7"]]; then
      wget http://repo.continuum.io/miniconda/Miniconda-3.8.3-Linux-x86_64.sh -O miniconda.sh;
    else
      wget http://repo.continuum.io/miniconda/Miniconda3-3.8.3-Linux-x86_64.sh -O miniconda.sh;
    fi
  - bash miniconda.sh -b -p $HOME/miniconda
  - export PATH="$HOME/miniconda/bin:$PATH"
  - hash -r
  - conda config --set always_yes yes --set changeps1 no
  - conda update -q conda
  # Useful for debugging any issues with conda
  - conda config --add channels pypi
  - conda info -a
  - deps='pip numpy scipy cython nose'
  - conda create -q -n test-environment "python=$TRAVIS_PYTHON_VERSION" $deps
  - source activate test-environment
  - conda install pytorch -c soumith
  - pip install codecov flake8

install:
  - pip install -e ".[test]"

script:
  - nosetests -v -w tests/


================================================
FILE: LICENSE.md
================================================
The tacotron_pytorch package is licensed under the MIT "Expat" License:

> Copyright (c) 2017: Ryuichi Yamamoto.
>
> Permission is hereby granted, free of charge, to any person obtaining
> a copy of this software and associated documentation files (the
> "Software"), to deal in the Software without restriction, including
> without limitation the rights to use, copy, modify, merge, publish,
> distribute, sublicense, and/or sell copies of the Software, and to
> permit persons to whom the Software is furnished to do so, subject to
> the following conditions:
>
> The above copyright notice and this permission notice shall be
> included in all copies or substantial portions of the Software.
>
> THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
> EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
> MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
> IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
> CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
> TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
> SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.


================================================
FILE: README.md
================================================
# GAN TTS

[![Build Status](https://travis-ci.org/r9y9/gantts.svg?branch=master)](https://travis-ci.org/r9y9/gantts)
[![PyPI](https://img.shields.io/pypi/v/gantts.svg)](https://pypi.python.org/pypi/gantts)
[![DOI](https://zenodo.org/badge/105146494.svg)](https://zenodo.org/badge/latestdoi/105146494)

PyTorch implementation of Generative adversarial Networks (GAN) based text-to-speech (TTS) and voice conversion (VC).

1. [Saito, Yuki, Shinnosuke Takamichi, and Hiroshi Saruwatari. "Statistical Parametric Speech Synthesis Incorporating Generative Adversarial Networks." IEEE/ACM Transactions on Audio, Speech, and Language Processing (2017).](http://ieeexplore.ieee.org/abstract/document/8063435/)
2. [Shan Yang, Lei Xie, Xiao Chen, Xiaoyan Lou, Xuan Zhu, Dongyan Huang, Haizhou Li, "
Statistical Parametric Speech Synthesis Using Generative Adversarial Networks Under A Multi-task Learning Framework", 	arXiv:1707.01670, Jul 2017.](https://arxiv.org/abs/1707.01670)

## Generated audio samples

Audio samples are available in the Jupyter notebooks at the link below:

- [Voice conversion (en, MLP)](http://nbviewer.jupyter.org/github/r9y9/gantts/blob/master/notebooks/Test%20VC.ipynb)
- [Voice conversion (en, RNN)](http://nbviewer.jupyter.org/github/r9y9/gantts/blob/master/notebooks/Test%20RNN%20VC.ipynb)
- [Text-to-speech synthesis (en, MLP)](http://nbviewer.jupyter.org/github/r9y9/gantts/blob/master/notebooks/Test%20TTS.ipynb)
- [Text-to-speech synthesis (ja, MLP)](http://nbviewer.jupyter.org/gist/r9y9/185a56417cee27d9f785b8caf1c9f5ec)


## Notes on hyper parameters

- `adversarial_streams`, which represents streams (mgc, lf0, vuv, bap) to be used to compute adversarial loss, is a very speech quality sensitive parameter. Computing adversarial loss on mgc features (except for first few dimensions) seems to be working good.
- If `mask_nth_mgc_for_adv_loss` > 0, first `mask_nth_mgc_for_adv_loss` dimension for mgc will be ignored for computing adversarial loss. As described in [saito2017asja](http://sython.org/papers/ASJ/saito2017asja.pdf), I confirmed that using 0-th (and 1-th) mgc for computing adversarial loss affects speech quality. From my experience, `mask_nth_mgc_for_adv_loss` = 1 for mgc order 25, `mask_nth_mgc_for_adv_loss` = 2 for mgc order 59 are working to me.
- F0 extracted by WORLD will be spline interpolated. Set `f0_interpolation_kind` to "slinear" if you want frist-order spline interpolation, which is same as Merlin's default.
- Set `use_harvest` to True if you want to use Harvest F0 estimation algorithm. If False, Dio and StoneMask are used to estimate/refine F0.
- If you see `cuda runtime error (2) : out of memory`, try smaller batch size. https://github.com/r9y9/gantts/issues/3

### Notes on [2]

Though I haven't got improvements over Saito's approach [1] yet, but the GAN-based models described in [2] should be achieved by the following configurations:

- Set `generator_add_noise` to True. This will enable generator to use Gaussian noise as input. Linguistic features are concatenated with the noise vector.
- Set `discriminator_linguistic_condition` to True. The discriminator uses linguistic features as condition.

## Requirements

- [PyTorch](http://pytorch.org/) >= v0.2.0
- [TensorFlow](https://www.tensorflow.org/) (just for `tf.contrib.training.HParams`)
- [nnmnkwii](https://github.com/r9y9/nnmnkwii)
- [PyWorld](https://github.com/JeremyCCHsu/Python-Wrapper-for-World-Vocoder)
- https://github.com/taolei87/sru (if you want to try SRU-based models)
- Python

## Installation

Please install PyTorch, TensorFlow and SRU (if needed) first. Once you have those, then

```
git clone --recursive https://github.com/r9y9/gantts && cd gantts
pip install -e ".[train]"
```

should install all other dependencies.

## Repository structure

- **gantts/**: Network definitions, utilities for working on sequence-loss optimization.
- **prepare_features_vc.py**: Acoustic feature extraction script for voice conversion.
- **prepare_features_tts.py**: Linguistic/duration/acoustic feature extraction script for TTS.
- **train.py**: GAN-based training script. This is written to be generic so that can be used for training voice conversion models as well as text-to-speech models (duration/acoustic).
- **train_gan.sh**: Adversarial training wrapper script for `train.py`.
- **hparams.py**: Hyper parameters for VC and TTS experiments.
- **evaluation_vc.py**: Evaluation script for VC.
- **evaluation_tts.py**: Evaluation script for TTS.

Feature extraction scripts are written for CMU ARCTIC dataset, but can be easily adapted for other datasets.

## Run demos

### Voice conversion (en)

`vc_demo.sh` is a `clb` to `clt` voice conversion demo script. Before running the script, please download wav files for `clb` and `slt` from [CMU ARCTIC](http://festvox.org/cmu_arctic/) and check that you have all data in a directory as follows:

```
> tree ~/data/cmu_arctic/ -d -L 1
/home/ryuichi/data/cmu_arctic/
├── cmu_us_awb_arctic
├── cmu_us_bdl_arctic
├── cmu_us_clb_arctic
├── cmu_us_jmk_arctic
├── cmu_us_ksp_arctic
├── cmu_us_rms_arctic
└── cmu_us_slt_arctic
```

Once you have downloaded datasets, then:

```
./vc_demo.sh ${experimental_id} ${your_cmu_arctic_data_root}
```

e.g.,

```
 ./vc_demo.sh vc_gan_test ~/data/cmu_arctic/
```

Model checkpoints will be saved at `./checkpoints/${experimental_id}` and audio samples
are saved at `./generated/${experimental_id}`.

### Text-to-speech synthesis (en)

`tts_demo.sh` is a self-contained TTS demo script. The usage is:

```
./tts_demo.sh ${experimental_id}
```

This will download `slt_arctic_full_data` used in Merlin's demo, perform feature extraction, train models and synthesize audio samples for eval/test set. `${experimenta_id}` can be arbitrary string, for example,

```
./tts_demo.sh tts_test
```


Model checkpoints will be saved at `./checkpoints/${experimental_id}` and audio samples
are saved at `./generated/${experimental_id}`.

## Hyper paramters

See ``hparams.py``.

## Monitoring training progress

```
tensorboard --logdir=log
```

## References

- [Yuki Saito, Shinnosuke Takamichi, Hiroshi Saruwatari, "Statistical Parametric Speech Synthesis Incorporating Generative Adversarial Networks", arXiv:1709.08041 [cs.SD], Sep. 2017](https://arxiv.org/abs/1709.08041)
- [Yuki Saito, Shinnosuke Takamichi, and Hiroshi Saruwatari, "Training algorithm to deceive anti-spoofing verification for DNN-based text-to-speech synthesis," IPSJ SIG Technical Report, 2017-SLP-115, no. 1, pp. 1-6, Feb., 2017. (in Japanese)](http://sython.org/papers/SIG-SLP/saito201702slp.pdf)
- [Yuki Saito, Shinnosuke Takamichi, and Hiroshi Saruwatari, "Voice conversion using input-to-output highway networks," IEICE Transactions on Information and Systems, Vol.E100-D, No.8, pp.1925--1928, Aug. 2017](https://www.jstage.jst.go.jp/article/transinf/E100.D/8/E100.D_2017EDL8034/_article)
- https://www.slideshare.net/ShinnosukeTakamichi/dnnantispoofing
- https://www.slideshare.net/YukiSaito8/Saito2017icassp

## Notice

The repository doesn't try to reproduce same results reported in their papers because 1) data is not publically available and 2). hyper parameters are highly depends on data. Instead, I tried same ideas on different data with different hyper parameters.


================================================
FILE: data/.gitignore
================================================
*
!.gitignore


================================================
FILE: evaluation_tts.py
================================================
# coding: utf-8
"""Evaluation script for GAN-based text-to-speech synthesis.

usage: evaluation_vc.py [options] <acoustic_checkpoint> <duration_checkpoint> \
    <data_dir> <labels_dir> <outputs_dir>

options:
    --fs=<fs>                   Sampling frequency [default: 16000].
    --disable-duraton-gen       Disable duration generation.
    --post-filter               Apply Merlin's post filter to spectral features.
    -h, --help                  Show this help message and exit.
"""
from docopt import docopt
import numpy as np

import torch
from torch import nn
from torch.autograd import Variable

from scipy.io import wavfile
import pyworld
import pysptk

import sys
import os
from os.path import splitext, join, abspath, basename, exists

from nnmnkwii import preprocessing as P
from nnmnkwii import paramgen
from nnmnkwii.datasets import FileSourceDataset, FileDataSource
from nnmnkwii.io import hts
from nnmnkwii.frontend import merlin as fe
from nnmnkwii.postfilters import merlin_post_filter

import gantts
from gantts.multistream import multi_stream_mlpg, get_static_features
from gantts.multistream import get_static_stream_sizes, select_streams
from gantts.seqloss import MaskedMSELoss, sequence_mask

from hparams import tts_acoustic as hp_acoustic
from hparams import tts_duration as hp_duration

from train import NPYDataSource


use_cuda = torch.cuda.is_available()

binary_dict, continuous_dict = hts.load_question_set(hp_acoustic.question_path)


def gen_parameters(y_predicted, Y_mean, Y_std, mge_training=True):
    mgc_dim, lf0_dim, vuv_dim, bap_dim = hp_acoustic.stream_sizes

    mgc_start_idx = 0
    lf0_start_idx = mgc_dim
    vuv_start_idx = lf0_start_idx + lf0_dim
    bap_start_idx = vuv_start_idx + vuv_dim

    windows = hp_acoustic.windows

    ty = "acoustic"

    # MGE training
    if mge_training:
        # Split acoustic features
        mgc = y_predicted[:, :lf0_start_idx]
        lf0 = y_predicted[:, lf0_start_idx:vuv_start_idx]
        vuv = y_predicted[:, vuv_start_idx]
        bap = y_predicted[:, bap_start_idx:]

        # Perform MLPG on normalized features
        mgc = paramgen.mlpg(mgc, np.ones(mgc.shape[-1]), windows)
        lf0 = paramgen.mlpg(lf0, np.ones(lf0.shape[-1]), windows)
        bap = paramgen.mlpg(bap, np.ones(bap.shape[-1]), windows)

        # When we use MGE training, denormalization should be done after MLPG.
        mgc = P.inv_scale(mgc, Y_mean[ty][:mgc_dim // len(windows)],
                          Y_std[ty][:mgc_dim // len(windows)])
        lf0 = P.inv_scale(lf0, Y_mean[ty][lf0_start_idx:lf0_start_idx + lf0_dim // len(windows)],
                          Y_std[ty][lf0_start_idx:lf0_start_idx + lf0_dim // len(windows)])
        bap = P.inv_scale(bap, Y_mean[ty][bap_start_idx:bap_start_idx + bap_dim // len(windows)],
                          Y_std[ty][bap_start_idx:bap_start_idx + bap_dim // len(windows)])
        vuv = P.inv_scale(vuv, Y_mean[ty][vuv_start_idx], Y_std[ty][vuv_start_idx])
    else:
        # Denormalization first
        y_predicted = P.inv_scale(y_predicted, Y_mean, Y_std)

        # Split acoustic features
        mgc = y_predicted[:, :lf0_start_idx]
        lf0 = y_predicted[:, lf0_start_idx:vuv_start_idx]
        vuv = y_predicted[:, vuv_start_idx]
        bap = y_predicted[:, bap_start_idx:]

        # Perform MLPG
        Y_var = Y_std[ty] * Y_std[ty]
        mgc = paramgen.mlpg(mgc, Y_var[:lf0_start_idx], windows)
        lf0 = paramgen.mlpg(lf0, Y_var[lf0_start_idx:vuv_start_idx], windows)
        bap = paramgen.mlpg(bap, Y_var[bap_start_idx:], windows)

    return mgc, lf0, vuv, bap


def gen_waveform(y_predicted, Y_mean, Y_std, post_filter=False, coef=1.4,
                 fs=16000, mge_training=True):
    alpha = pysptk.util.mcepalpha(fs)
    fftlen = fftlen = pyworld.get_cheaptrick_fft_size(fs)
    frame_period = hp_acoustic.frame_period

    # Generate parameters and split streams
    mgc, lf0, vuv, bap = gen_parameters(y_predicted, Y_mean, Y_std, mge_training)

    if post_filter:
        mgc = merlin_post_filter(mgc, alpha, coef=coef)

    spectrogram = pysptk.mc2sp(mgc, fftlen=fftlen, alpha=alpha)
    aperiodicity = pyworld.decode_aperiodicity(bap.astype(np.float64), fs, fftlen)
    f0 = lf0.copy()
    f0[vuv < 0.5] = 0
    f0[np.nonzero(f0)] = np.exp(f0[np.nonzero(f0)])

    generated_waveform = pyworld.synthesize(f0.flatten().astype(np.float64),
                                            spectrogram.astype(np.float64),
                                            aperiodicity.astype(np.float64),
                                            fs, frame_period)
    # Convert range to int16
    generated_waveform = generated_waveform / \
        np.max(np.abs(generated_waveform)) * 32767

    # return features as well to compare natural/genearted later
    return generated_waveform, mgc, lf0, vuv, bap


def _generator_input(hp, x, seed=None):
    if seed is not None:
        torch.manual_seed(seed)
    if hp.generator_add_noise:
        z = torch.rand(x.size(0), x.size(1), hp.generator_noise_dim)
        z = Variable(z)
        return torch.cat((x, z), -1)
    return x


def gen_duration(label_path, duration_model, X_min, X_max, Y_mean, Y_std):
    # Linguistic features for duration
    hts_labels = hts.load(label_path)
    duration_linguistic_features = fe.linguistic_features(
        hts_labels,
        binary_dict, continuous_dict,
        add_frame_features=hp_duration.add_frame_features,
        subphone_features=hp_duration.subphone_features).astype(np.float32)

    # Apply normali--post-filterzation
    ty = "duration"
    duration_linguistic_features = P.minmax_scale(
        duration_linguistic_features,
        X_min[ty], X_max[ty], feature_range=(0.01, 0.99))

    # Apply models
    duration_model.eval()

    #  Apply model
    x = Variable(torch.from_numpy(duration_linguistic_features)).float()
    xl = len(x)
    x = x.view(1, -1, x.size(-1))
    x = _generator_input(hp_duration, x)
    x = x.cuda() if use_cuda else x
    duration_predicted = duration_model(x, [xl]).data.cpu().numpy()
    duration_predicted = duration_predicted.reshape(-1, duration_predicted.shape[-1])

    # Apply denormalization
    duration_predicted = P.inv_scale(duration_predicted, Y_mean[ty], Y_std[ty])
    duration_predicted = np.round(duration_predicted)

    # Set minimum state duration to 1
    #  print(duration_predicted)
    duration_predicted[duration_predicted <= 0] = 1
    hts_labels.set_durations(duration_predicted)

    return hts_labels


def tts_from_label(models, label_path, X_min, X_max, Y_mean, Y_std,
                   post_filter=False,
                   apply_duration_model=True, coef=1.4, fs=16000,
                   mge_training=True):
    duration_model, acoustic_model = models["duration"], models["acoustic"]

    if use_cuda:
        duration_model = duration_model.cuda()
        acoustic_model = acoustic_model.cuda()

    # Predict durations
    if apply_duration_model:
        duration_modified_hts_labels = gen_duration(
            label_path, duration_model, X_min, X_max, Y_mean, Y_std)
    else:
        duration_modified_hts_labels = hts.load(label_path)

    # Linguistic features
    linguistic_features = fe.linguistic_features(
        duration_modified_hts_labels,
        binary_dict, continuous_dict,
        add_frame_features=hp_acoustic.add_frame_features,
        subphone_features=hp_acoustic.subphone_features)
    # Trim silences
    indices = duration_modified_hts_labels.silence_frame_indices()
    linguistic_features = np.delete(linguistic_features, indices, axis=0)

    # Apply normalization
    ty = "acoustic"
    linguistic_features = P.minmax_scale(
        linguistic_features, X_min[ty], X_max[ty], feature_range=(0.01, 0.99))

    # Predict acoustic features
    acoustic_model.eval()
    x = Variable(torch.from_numpy(linguistic_features)).float()
    xl = len(x)
    x = x.view(1, -1, x.size(-1))
    x = _generator_input(hp_duration, x)
    x = x.cuda() if use_cuda else x
    acoustic_predicted = acoustic_model(x, [xl]).data.cpu().numpy()
    acoustic_predicted = acoustic_predicted.reshape(-1, acoustic_predicted.shape[-1])

    return gen_waveform(acoustic_predicted, Y_mean, Y_std, post_filter,
                        coef=coef, fs=fs, mge_training=mge_training)


def load_checkpoint(model, optimizer, checkpoint_path):
    print("Load checkpoint from: {}".format(checkpoint_path))
    checkpoint = torch.load(checkpoint_path)
    model.load_state_dict(checkpoint["state_dict"])
    if optimizer is not None:
        optimizer.load_state_dict(checkpoint["optimizer"])


def get_lab_files(data_dir, label_dir, test=False):
    if test:
        files = NPYDataSource(join(data_dir, "X_acoustic"), test=True).collect_files()
    else:
        files = NPYDataSource(join(data_dir, "X_acoustic"), train=False).collect_files()

    lab_files = list(map(
        lambda f: join(label_dir, splitext(basename(f))[0] + ".lab"), files))
    return lab_files


def get_wav_files(data_dir, wav_dir, test=False):
    if test:
        files = NPYDataSource(join(data_dir, "X_acoustic"), test=True).collect_files()
    else:
        files = NPYDataSource(join(data_dir, "X_acoustic"), train=False).collect_files()

    wav_files = list(map(
        lambda f: join(wav_dir, splitext(basename(f))[0] + ".wav"), files))
    return wav_files


if __name__ == "__main__":
    args = docopt(__doc__)
    print("Command line args:\n", args)
    acoustic_checkpoint = args["<acoustic_checkpoint>"]
    duration_checkpoint = args["<duration_checkpoint>"]
    data_dir = args["<data_dir>"]
    labels_dir = args["<labels_dir>"]
    outputs_dir = args["<outputs_dir>"]
    post_filter = args["--post-filter"]
    disable_duration_gen = args["--disable-duraton-gen"]
    fs = int(args["--fs"])

    # Collect stats and create models
    X_min = {}
    X_max = {}
    Y_mean = {}
    Y_var = {}
    Y_std = {}
    models = {"acoustic": {}, "duration": {}}

    for typ in ["acoustic", "duration"]:
        X_min[typ] = np.load(join(data_dir, "X_{}_data_min.npy".format(typ)))
        X_max[typ] = np.load(join(data_dir, "X_{}_data_max.npy".format(typ)))
        Y_mean[typ] = np.load(join(data_dir, "Y_{}_data_mean.npy".format(typ)))
        Y_var[typ] = np.load(join(data_dir, "Y_{}_data_var.npy".format(typ)))
        Y_std[typ] = np.sqrt(Y_var[typ])

        hp = hp_acoustic if typ == "acoustic" else hp_duration
        if hp.generator_params["in_dim"] is None:
            D = X_min[typ].shape[-1]
            if hp.generator_add_noise:
                D = D + hp.generator_noise_dim
            hp.generator_params["in_dim"] = D
        if hp.generator_params["out_dim"] is None:
            hp.generator_params["out_dim"] = Y_mean[typ].shape[-1]

        models[typ] = getattr(gantts.models, hp.generator)(**hp.generator_params)
        checkpoint_path = duration_checkpoint if hp == hp_duration \
            else acoustic_checkpoint
        load_checkpoint(models[typ], None, checkpoint_path)
    print(models)

    # Generate samples for
    # 1. Evaluation set
    # 2. Test set
    eval_dir = join(outputs_dir, "eval")
    test_dir = join(outputs_dir, "test")
    if not exists(eval_dir):
        os.makedirs(eval_dir)
    if not exists(test_dir):
        os.makedirs(test_dir)
    eval_lab_files = get_lab_files(data_dir, labels_dir, test=False)
    test_lab_files = get_lab_files(data_dir, labels_dir, test=True)
    for dst_dir, files in [(eval_dir, eval_lab_files), (test_dir, test_lab_files)]:
        for label_path in files:
            print(dst_dir, label_path)
            name = splitext(basename(label_path))[0]
            dst_path = join(dst_dir, name + ".wav")
            waveform, mgc, lf0, vuv, bap = tts_from_label(
                models, label_path, X_min, X_max, Y_mean, Y_std,
                apply_duration_model=not disable_duration_gen,
                post_filter=post_filter, fs=fs)
            wavfile.write(dst_path, fs, waveform.astype(np.int16))

    sys.exit(0)


================================================
FILE: evaluation_vc.py
================================================
# coding: utf-8
"""Evaluation script for GAN-based VC models.

usage: evaluation_vc.py [options] <checkpoint> <data_dir> <wav_dir> <outputs_dir>

options:
    -h, --help                  Show this help message and exit
    --diffvc                    Enable DIFF VC.
"""
from docopt import docopt
import numpy as np

import torch
from torch import nn
from torch.autograd import Variable

from scipy.io import wavfile
import pysptk
from pysptk.synthesis import Synthesizer, MLSADF
import pyworld

import sys
import os
from os.path import splitext, join, abspath, basename, exists

from nnmnkwii import preprocessing as P
from nnmnkwii.paramgen import unit_variance_mlpg_matrix
from nnmnkwii.datasets import FileSourceDataset, FileDataSource

import gantts
from gantts.multistream import multi_stream_mlpg, get_static_features
from gantts.multistream import get_static_stream_sizes, select_streams
from gantts.seqloss import MaskedMSELoss, sequence_mask

from hparams import vc as hp

from train import NPYDataSource


def test_vc_from_path(model, x, fs, data_mean, data_std, diffvc=True):
    model.eval()

    hop_length = int(fs * (hp.frame_period * 0.001))
    x = x.astype(np.float64)
    f0, timeaxis = pyworld.dio(x, fs, frame_period=hp.frame_period)
    f0 = pyworld.stonemask(x, f0, timeaxis, fs)
    spectrogram = pyworld.cheaptrick(x, f0, timeaxis, fs)
    aperiodicity = pyworld.d4c(x, f0, timeaxis, fs)
    alpha = pysptk.util.mcepalpha(fs)
    mc = pysptk.sp2mc(spectrogram, order=hp.order, alpha=alpha)
    c0, mc = mc[:, 0], mc[:, 1:]
    static_dim = mc.shape[-1]
    mc = P.modspec_smoothing(mc, fs / hop_length, cutoff=50)
    mc = P.delta_features(mc, hp.windows).astype(np.float32)

    T = mc.shape[0]

    inputs = mc[:, :static_dim].copy()

    # Normalization
    mc_scaled = P.scale(mc, data_mean, data_std)

    mc_scaled = Variable(torch.from_numpy(mc_scaled))
    lengths = [len(mc_scaled)]

    # Add batch axis
    mc_scaled = mc_scaled.view(1, -1, mc_scaled.size(-1))

    # For MLPG
    R = unit_variance_mlpg_matrix(hp.windows, T)
    R = torch.from_numpy(R)

    # Apply model
    if model.include_parameter_generation():
        # Case: models include parameter generation in itself
        # Mulistream features cannot be used in this case
        y_hat, y_hat_static = model(mc_scaled, R, lengths=lengths)
    else:
        # Case: generic models (can be sequence model)
        assert hp.has_dynamic_features is not None
        y_hat = model(mc_scaled, lengths=lengths)
        y_hat_static = multi_stream_mlpg(
            y_hat, R, hp.stream_sizes, hp.has_dynamic_features)

    mc_static_pred = y_hat_static.data.cpu().numpy().reshape(-1, static_dim)

    # Denormalize
    mc_static_pred = P.inv_scale(
        mc_static_pred, data_mean[:static_dim], data_std[:static_dim])

    outputs = mc_static_pred.copy()

    if diffvc:
        mc_static_pred = mc_static_pred - mc[:, :static_dim]

    mc = np.hstack((c0[:, None], mc_static_pred))
    if diffvc:
        mc[:, 0] = 0  # remove power coefficients
        engine = Synthesizer(MLSADF(order=hp.order, alpha=alpha),
                             hopsize=hop_length)
        b = pysptk.mc2b(mc.astype(np.float64), alpha=alpha)
        waveform = engine.synthesis(x, b)
    else:
        fftlen = pyworld.get_cheaptrick_fft_size(fs)
        spectrogram = pysptk.mc2sp(
            mc.astype(np.float64), alpha=alpha, fftlen=fftlen)
        waveform = pyworld.synthesize(
            f0, spectrogram, aperiodicity, fs, hp.frame_period)

    return waveform, inputs, outputs


def load_checkpoint(model, optimizer, checkpoint_path):
    print("Load checkpoint from: {}".format(checkpoint_path))
    checkpoint = torch.load(checkpoint_path)
    model.load_state_dict(checkpoint["state_dict"])
    if optimizer is not None:
        optimizer.load_state_dict(checkpoint["optimizer"])


def get_wav_files(data_dir, wav_dir, test=False):
    if test:
        files = NPYDataSource(join(data_dir, "X"), test=True).collect_files()
    else:
        files = NPYDataSource(join(data_dir, "X"), train=False).collect_files()

    wav_files = list(map(
        lambda f: join(wav_dir, splitext(basename(f))[0] + ".wav"), files))
    return wav_files


if __name__ == "__main__":
    args = docopt(__doc__)
    print("Command line args:\n", args)
    checkpoint_path = args["<checkpoint>"]
    data_dir = args["<data_dir>"]
    wav_dir = args["<wav_dir>"]
    outputs_dir = args["<outputs_dir>"]
    diffvc = args["--diffvc"]

    # Collect stats
    data_mean = np.load(join(data_dir, "data_mean.npy"))
    data_var = np.load(join(data_dir, "data_var.npy"))
    data_std = np.sqrt(data_var)

    if hp.generator_params["in_dim"] is None:
        hp.generator_params["in_dim"] = data_mean.shape[-1]
    if hp.generator_params["out_dim"] is None:
        hp.generator_params["out_dim"] = data_mean.shape[-1]

    # Model
    model = getattr(gantts.models, hp.generator)(**hp.generator_params)
    load_checkpoint(model, None, checkpoint_path)
    print(model)

    # Generate samples for
    # 1. Evaluation set
    # 2. Test set
    eval_dir = join(outputs_dir, "eval")
    test_dir = join(outputs_dir, "test")
    if not exists(eval_dir):
        os.makedirs(eval_dir)
    if not exists(test_dir):
        os.makedirs(test_dir)
    eval_files = get_wav_files(data_dir, wav_dir, test=False)
    test_files = get_wav_files(data_dir, wav_dir, test=True)
    for dst_dir, files in [(eval_dir, eval_files), (test_dir, test_files)]:
        for path in files:
            print(dst_dir, path)
            name = splitext(basename(path))[0]
            dst_path = join(dst_dir, name + ".wav")
            fs, x = wavfile.read(path)
            waveform, _, _ = test_vc_from_path(
                model, x, fs, data_mean, data_std, diffvc=diffvc)
            wavfile.write(dst_path, fs, waveform.astype(np.int16))

    sys.exit(0)


================================================
FILE: gantts/__init__.py
================================================
# coding: utf-8
from __future__ import with_statement, print_function, absolute_import

from .version import __version__

from gantts import models


================================================
FILE: gantts/models.py
================================================
# coding: utf-8
import torch
from torch import nn
from torch.autograd import Variable

import numpy as np

from nnmnkwii.autograd import unit_variance_mlpg


class AbstractModel(object):
    """Interface for VC and TTS models
    """

    def include_parameter_generation(self):
        """Whether model includes parameter generation or not.
        """
        return False


class In2OutHighwayNet(AbstractModel, nn.Module):
    """Input-to-Output Highway Networks for voice conversion.

    Trying to replicate the model described in the following paper:
    https://www.jstage.jst.go.jp/article/transinf/E100.D/8/E100.D_2017EDL8034/

    .. note::
        Since model architecture itself includes parameter generation, we cannot
        simply use the model for multi-stream features (e.g., in TTS, acoustic
        features often consist multiple features; mgc, f0, vuv and bap.)
    """

    def __init__(self, in_dim=118, out_dim=118, static_dim=118 // 2,
                 num_hidden=3, hidden_dim=512, dropout=0.5):
        super(In2OutHighwayNet, self).__init__()
        self.static_dim = static_dim
        self.relu = nn.LeakyReLU(inplace=True)
        self.sigmoid = nn.Sigmoid()
        # Transform gate (can be deep?)
        self.T = nn.Linear(static_dim, static_dim)

        # Hidden layers
        in_sizes = [in_dim] + [hidden_dim] * (num_hidden - 1)
        out_sizes = [hidden_dim] * num_hidden
        self.H = nn.ModuleList(
            [nn.Linear(in_size, out_size) for (in_size, out_size)
             in zip(in_sizes, out_sizes)])
        self.last_linear = nn.Linear(hidden_dim, out_dim)
        self.dropout = nn.Dropout(dropout)

    def include_parameter_generation(self):
        return True

    def forward(self, x, R, lengths=None):
        # Add batch axis
        x = x.unsqueeze(0) if x.dim() == 2 else x
        x_static = x[:, :, :self.static_dim]

        # T(x)
        Tx = self.sigmoid(self.T(x_static))

        # G(x)
        for layer in self.H:
            x = self.dropout(self.relu(layer(x)))
        x = self.last_linear(x)
        Gx = unit_variance_mlpg(R, x)

        # y^ = x + T(x) * G(x)
        return x, x_static + Tx * Gx


class In2OutRNNHighwayNet(AbstractModel, nn.Module):
    def __init__(self, in_dim=118, out_dim=118, static_dim=118 // 2,
                 num_hidden=3, hidden_dim=512, bidirectional=False, dropout=0.5):
        super(In2OutRNNHighwayNet, self).__init__()
        self.static_dim = static_dim
        self.num_direction = 2 if bidirectional else 1
        self.relu = nn.LeakyReLU(inplace=True)
        self.sigmoid = nn.Sigmoid()
        # Transform gate (can be deep?)
        self.T = nn.Linear(static_dim, static_dim)

        # Recurrent hidden layers
        self.lstm = nn.LSTM(in_dim, hidden_dim, num_hidden, batch_first=True,
                            bidirectional=bidirectional, dropout=dropout)
        self.hidden2out = nn.Linear(hidden_dim * self.num_direction, out_dim)
        self.dropout = nn.Dropout(dropout)

    def include_parameter_generation(self):
        return True

    def forward(self, x, R, lengths=None):
        # Add batch axis
        x = x.unsqueeze(0) if x.dim() == 2 else x
        x_static = x[:, :, :self.static_dim]

        # T(x)
        Tx = self.sigmoid(self.T(x_static))

        # Pack padded sequence for CuDNN
        if isinstance(lengths, Variable):
            lengths = lengths.data.cpu().long().numpy()
        if lengths is not None:
            inputs = nn.utils.rnn.pack_padded_sequence(
                x, lengths, batch_first=True)
        else:
            inputs = x

        # G(x)
        output, _ = self.lstm(inputs)
        if lengths is not None:
            output, _ = nn.utils.rnn.pad_packed_sequence(output, batch_first=True)
        output = self.hidden2out(output)

        Gx = unit_variance_mlpg(R, output)

        # y^ = x + T(x) * G(x)
        return x, x_static + Tx * Gx


class MLP(AbstractModel, nn.Module):
    def __init__(self, in_dim=118, out_dim=1, num_hidden=2, hidden_dim=256,
                 dropout=0.5, last_sigmoid=True, bidirectional=None):
        # bidirectional is dummy
        super(MLP, self).__init__()
        in_sizes = [in_dim] + [hidden_dim] * (num_hidden - 1)
        out_sizes = [hidden_dim] * num_hidden
        self.layers = nn.ModuleList(
            [nn.Linear(in_size, out_size) for (in_size, out_size)
             in zip(in_sizes, out_sizes)])
        self.last_linear = nn.Linear(hidden_dim, out_dim)
        self.relu = nn.LeakyReLU(inplace=True)
        self.sigmoid = nn.Sigmoid()
        self.dropout = nn.Dropout(dropout)
        self.last_sigmoid = last_sigmoid

    def forward(self, x, lengths=None):
        for layer in self.layers:
            x = self.dropout(self.relu(layer(x)))
        x = self.last_linear(x)
        return self.sigmoid(x) if self.last_sigmoid else x


# needs https://github.com/taolei87/sru
class SRURNN(AbstractModel, nn.Module):
    def __init__(self, in_dim=118, out_dim=118, num_hidden=2, hidden_dim=256,
                 bidirectional=False, dropout=0, last_sigmoid=False,
                 use_relu=0, rnn_dropout=0.0):
        super(SRURNN, self).__init__()
        from cuda_functional import SRU
        self.num_direction = 2 if bidirectional else 1
        self.gru = SRU(in_dim, hidden_dim, num_hidden,
                       bidirectional=bidirectional, dropout=dropout,
                       use_relu=use_relu, rnn_dropout=rnn_dropout)
        self.hidden2out = nn.Linear(hidden_dim * self.num_direction, out_dim)
        self.sigmoid = nn.Sigmoid()
        self.last_sigmoid = last_sigmoid

    def forward(self, sequence, lengths):
        # Batch first -> Time first
        sequence = sequence.transpose(0, 1)
        output, _ = self.gru(sequence)
        # Time first -> Batch first
        output = output.transpose(0, 1)
        output = self.hidden2out(output)

        return self.sigmoid(output) if self.last_sigmoid else output


class GRURNN(AbstractModel, nn.Module):
    def __init__(self, in_dim=118, out_dim=118, num_hidden=2, hidden_dim=256,
                 bidirectional=False, dropout=0, last_sigmoid=False):
        super(GRURNN, self).__init__()
        self.num_direction = 2 if bidirectional else 1
        self.gru = nn.LSTM(in_dim, hidden_dim, num_hidden, batch_first=True,
                           bidirectional=bidirectional, dropout=dropout)
        self.hidden2out = nn.Linear(hidden_dim * self.num_direction, out_dim)
        self.sigmoid = nn.Sigmoid()
        self.last_sigmoid = last_sigmoid

    def forward(self, sequence, lengths):
        if isinstance(lengths, Variable):
            lengths = lengths.data.cpu().long().numpy()
        sequence = nn.utils.rnn.pack_padded_sequence(
            sequence, lengths, batch_first=True)
        output, _ = self.gru(sequence)
        output, _ = nn.utils.rnn.pad_packed_sequence(output, batch_first=True)
        output = self.hidden2out(output)

        return self.sigmoid(output) if self.last_sigmoid else output


class LSTMRNN(AbstractModel, nn.Module):
    def __init__(self, in_dim=118, out_dim=118, num_hidden=2, hidden_dim=256,
                 bidirectional=False, dropout=0, last_sigmoid=False):
        super(LSTMRNN, self).__init__()
        self.num_direction = 2 if bidirectional else 1
        self.lstm = nn.LSTM(in_dim, hidden_dim, num_hidden, batch_first=True,
                            bidirectional=bidirectional, dropout=dropout)
        self.hidden2out = nn.Linear(hidden_dim * self.num_direction, out_dim)
        self.sigmoid = nn.Sigmoid()
        self.last_sigmoid = last_sigmoid

    def forward(self, sequence, lengths):
        if isinstance(lengths, Variable):
            lengths = lengths.data.cpu().long().numpy()
        sequence = nn.utils.rnn.pack_padded_sequence(
            sequence, lengths, batch_first=True)
        output, _ = self.lstm(sequence)
        output, _ = nn.utils.rnn.pad_packed_sequence(output, batch_first=True)
        output = self.hidden2out(output)

        return self.sigmoid(output) if self.last_sigmoid else output


================================================
FILE: gantts/multistream.py
================================================
# coding: utf-8

# Utils for multi-stream features

import torch
from torch import nn
from torch.autograd import Variable

import numpy as np

from nnmnkwii.autograd import unit_variance_mlpg
from nnmnkwii import preprocessing as P


def recompute_delta_features(Y, Y_data_mean, Y_data_std,
                             windows,
                             stream_sizes=[180, 3, 1, 3],
                             has_dynamic_features=[True, True, False, True]):
    start_indices = np.hstack(([0], np.cumsum(stream_sizes)[:-1]))
    end_indices = np.cumsum(stream_sizes)
    static_stream_sizes = get_static_stream_sizes(
        stream_sizes, has_dynamic_features, len(windows))

    for start_idx, end_idx, static_size, has_dynamic in zip(
            start_indices, end_indices, static_stream_sizes, has_dynamic_features):
        if has_dynamic:
            y_static = Y[:, start_idx:start_idx + static_size]
            Y[:, start_idx:end_idx] = P.delta_features(y_static, windows)

    return Y


def select_streams(inputs, stream_sizes=[60, 1, 1, 1],
                   streams=[True, True, True, True]):
    ret = []
    start_indices = np.hstack(([0], np.cumsum(stream_sizes)[:-1]))
    for start_idx, size, enabled in zip(
            start_indices, stream_sizes, streams):
        if not enabled:
            continue
        ret.append(inputs[:, :, start_idx:start_idx + size])

    return torch.cat(ret, dim=-1)


def get_static_stream_sizes(stream_sizes, has_dynamic_features, num_windows):
    """Get static dimention for each feature stream.
    """
    static_stream_sizes = np.array(stream_sizes)
    static_stream_sizes[has_dynamic_features] = \
        static_stream_sizes[has_dynamic_features] / num_windows

    return static_stream_sizes


def get_static_features(inputs, num_windows, stream_sizes=[180, 3, 1, 3],
                        has_dynamic_features=[True, True, False, True],
                        streams=[True, True, True, True]):
    """Get static features from static+dynamic features.
    """
    _, _, D = inputs.size()
    if stream_sizes is None or (len(stream_sizes) == 1 and has_dynamic_features[0]):
        return inputs[:, :, :D // num_windows]
    if len(stream_sizes) == 1 and not has_dynamic_features[0]:
        return inputs

    # Multi stream case
    ret = []
    start_indices = np.hstack(([0], np.cumsum(stream_sizes)[:-1]))
    for start_idx, size, v, enabled in zip(
            start_indices, stream_sizes, has_dynamic_features, streams):
        if not enabled:
            continue
        if v:
            static_features = inputs[:, :, start_idx:start_idx + size // num_windows]
        else:
            static_features = inputs[:, :, start_idx:start_idx + size]
        ret.append(static_features)
    return torch.cat(ret, dim=-1)


def multi_stream_mlpg(inputs, R,
                      stream_sizes=[180, 3, 1, 3],
                      has_dynamic_features=[True, True, False, True],
                      streams=[True, True, True, True]):
    """Split streams and do apply MLPG if stream has dynamic features.
    """
    if R is None:
        num_windows = 1
    else:
        num_windows = R.size(1) / R.size(0)
    B, T, D = inputs.size()
    if D != sum(stream_sizes):
        raise RuntimeError("You probably have specified wrong dimention params.")

    # Straem indices for static+delta features
    # [0,   180, 183, 184]
    start_indices = np.hstack(([0], np.cumsum(stream_sizes)[:-1]))
    # [180, 183, 184, 187]
    end_indices = np.cumsum(stream_sizes)

    # Stream sizes for static features
    # [60, 1, 1, 1]
    static_stream_sizes = get_static_stream_sizes(
        stream_sizes, has_dynamic_features, num_windows)

    # [0,  60, 61, 62]
    static_stream_start_indices = np.hstack(
        ([0], np.cumsum(static_stream_sizes)[:-1]))
    # [60, 61, 62, 63]
    static_stream_end_indices = np.cumsum(static_stream_sizes)

    ret = []
    for in_start_idx, in_end_idx, out_start_idx, out_end_idx, v, enabled in zip(
            start_indices, end_indices, static_stream_start_indices,
            static_stream_end_indices, has_dynamic_features, streams):
        if not enabled:
            continue
        x = inputs[:, :, in_start_idx:in_end_idx]
        y = unit_variance_mlpg(R, x) if v else x
        ret.append(y)

    return torch.cat(ret, dim=-1)


================================================
FILE: gantts/seqloss.py
================================================
# coding: utf-8
import torch
from torch import nn
from torch.autograd import Variable

import numpy as np


def sequence_mask(sequence_length, max_len=None):
    if max_len is None:
        max_len = sequence_length.data.max()
    batch_size = sequence_length.size(0)
    seq_range = torch.arange(0, max_len).long()
    seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len)
    seq_range_expand = Variable(seq_range_expand)
    if sequence_length.is_cuda:
        seq_range_expand = seq_range_expand.cuda()
    seq_length_expand = sequence_length.unsqueeze(1) \
        .expand_as(seq_range_expand)
    return (seq_range_expand < seq_length_expand).float()


# Adapted from:
# https://github.com/facebookresearch/loop/blob/master/model.py


class MaskedMSELoss(nn.Module):
    def __init__(self):
        super(MaskedMSELoss, self).__init__()
        self.criterion = nn.MSELoss(reduction="sum")

    def forward(self, input, target, lengths=None, mask=None, max_len=None):
        if lengths is None and mask is None:
            raise RuntimeError("Should provide either lengths or mask")

        # (B, T, 1)
        if mask is None:
            mask = sequence_mask(lengths, max_len).unsqueeze(-1)

        # (B, T, D)
        mask_ = mask.expand_as(input)
        loss = self.criterion(input * mask_, target * mask_)
        return loss / mask.sum()


================================================
FILE: generated/.gitignore
================================================
*
!.gitignore


================================================
FILE: hparams.py
================================================
# coding: utf-8

import tensorflow as tf
import numpy as np

from os.path import join, dirname


def hparams_debug_string(params):
    values = params.values()
    hp = ['  %s: %s' % (name, values[name]) for name in sorted(values)]
    return 'Hyperparameters:\n' + '\n'.join(hp)


# Hyper parameters for voice conversion
vc = tf.contrib.training.HParams(
    name="vc",

    # Acoustic features
    order=59,
    frame_period=5,
    windows=[
        (0, 0, np.array([1.0])),
        (1, 1, np.array([-0.5, 0.0, 0.5])),
        (1, 1, np.array([1.0, -2.0, 1.0])),
    ],
    stream_sizes=[59 * 3],
    has_dynamic_features=[True],

    adversarial_streams=[True],
    # In vc, 0-th coefficients are masked in feature extraction stage.
    mask_nth_mgc_for_adv_loss=0,

    # Generator
    # For RNN VC, use In2OutRNNHighwayNet
    generator_add_noise=False,
    generator_noise_dim=200,
    generator="In2OutHighwayNet",
    generator_params={
        "in_dim": None,
        "out_dim": None,
        "num_hidden": 3,
        "hidden_dim": 512,
        # "bidirectional": True,
        "static_dim": 59,
        "dropout": 0.5,
    },
    optimizer_g="Adagrad",
    optimizer_g_params={
        "lr": 0.01,
        "weight_decay": 0,
    },

    # Discriminator
    discriminator_linguistic_condition=False,
    discriminator="MLP",
    discriminator_params={
        "in_dim": 59,
        "out_dim": 1,
        "num_hidden": 2,
        "hidden_dim": 256,
        "dropout": 0.5,
        "last_sigmoid": True,
    },
    optimizer_d="Adagrad",
    optimizer_d_params={
        "lr": 0.01,
        "weight_decay": 0,
    },

    # This should be overrided
    nepoch=200,

    # LR schedule
    lr_decay_schedule=False,
    lr_decay_epoch=10,

    # Datasets and data loader
    batch_size=20,
    num_workers=1,
    pin_memory=True,
    cache_size=1200,
)


# Hyper paramters for TTS duration model
tts_duration = tf.contrib.training.HParams(
    name="duration",

    # Linguistic features
    use_phone_alignment=False,
    subphone_features=None,
    add_frame_features=False,
    question_path=join(dirname(__file__), "nnmnkwii_gallery", "data",
                       "questions-radio_dnn_416.hed"),

    # Duration features
    windows=[
        (0, 0, np.array([1.0])),
    ],
    stream_sizes=[5],
    has_dynamic_features=[False],

    recompute_delta_features=False,

    # Streams used for computing adversarial loss
    adversarial_streams=[True],
    mask_nth_mgc_for_adv_loss=0,

    # Generator
    generator="SRURNN",
    generator_add_noise=False,
    generator_noise_dim=200,
    generator_params={
        "in_dim": None,  # None wil be set automatically
        "out_dim": None,
        "num_hidden": 6,
        "hidden_dim": 512,
        "bidirectional": True,
        "dropout": 0.0,
        "use_relu": 1,
        "rnn_dropout": 0.2,
        "last_sigmoid": False,
    },
    optimizer_g="Adam",
    optimizer_g_params={
        "lr": 0.001,
        "betas": (0.5, 0.9),
        "weight_decay": 0,
    },


    # Discriminator
    discriminator_linguistic_condition=True,
    discriminator="MLP",
    discriminator_params={
        "in_dim": None,  # None wil be set automatically
        "out_dim": 1,
        "num_hidden": 3,
        "hidden_dim": 256,
        # "bidirectional": True,
        "dropout": 0.0,
        "last_sigmoid": True,
    },
    optimizer_d="Adam",
    optimizer_d_params={
        "lr": 0.001,
        "betas": (0.5, 0.9),
        "weight_decay": 0,
    },

    # This should be overrided
    nepoch=200,

    # LR schedule
    lr_decay_schedule=False,
    lr_decay_epoch=25,

    # Datasets and data loader
    batch_size=32,
    num_workers=1,
    pin_memory=True,
    cache_size=1200,
)

# Hyper paramters for TTS acoustic model
tts_acoustic = tf.contrib.training.HParams(
    name="acoustic",

    # Linguistic
    use_phone_alignment=False,
    subphone_features="full",
    add_frame_features=True,
    question_path=join(dirname(__file__), "nnmnkwii_gallery", "data",
                       "questions-radio_dnn_416.hed"),

    # Acoustic features
    order=59,
    frame_period=5,
    f0_floor=71.0,
    f0_ceil=700,
    use_harvest=True,  # If False, use dio and stonemask
    windows=[
        (0, 0, np.array([1.0])),
        (1, 1, np.array([-0.5, 0.0, 0.5])),
        (1, 1, np.array([1.0, -2.0, 1.0])),
    ],
    f0_interpolation_kind="quadratic",
    mod_spec_smoothing=True,
    mod_spec_smoothing_cutoff=50,  # Hz

    recompute_delta_features=False,

    # Stream info
    # (mgc, lf0, vuv, bap)
    stream_sizes=[180, 3, 1, 3],
    has_dynamic_features=[True, True, False, True],

    # Streams used for computing adversarial loss
    # NOTE: you should probably change discriminator's `in_dim`
    # if you change the adv_streams
    adversarial_streams=[True, False, False, False],
    # Don't set the value > 0 unless you are sure what you are doing
    # mask 0 to n-th mgc for adversarial loss
    # e.g, for n=2, 0-th and 1-th mgc coefficients will be masked
    mask_nth_mgc_for_adv_loss=2,

    # Generator
    generator_add_noise=False,
    generator_noise_dim=200,
    generator="SRURNN",
    generator_params={
        "in_dim": None,  # None wil be set automatically
        "out_dim": None,
        "num_hidden": 6,
        "hidden_dim": 512,
        "bidirectional": True,
        "dropout": 0.2,
        "use_relu": 1,
        "rnn_dropout": 0.2,
        "last_sigmoid": False,
    },
    optimizer_g="Adagrad",
    optimizer_g_params={
        "lr": 0.01,
        "weight_decay": 1e-7,
    },

    # Discriminator
    discriminator_linguistic_condition=True,
    discriminator="MLP",
    discriminator_params={
        "in_dim": None,  # None wil be set automatically
        "out_dim": 1,
        "num_hidden": 3,
        "hidden_dim": 256,
        "dropout": 0.5,
        "last_sigmoid": True,
    },
    optimizer_d="Adagrad",
    optimizer_d_params={
        "lr": 0.01,
        "weight_decay": 1e-7,
    },

    # This should be overrided
    nepoch=200,

    # LR schedule
    lr_decay_schedule=False,
    lr_decay_epoch=25,

    # Datasets and data loader
    batch_size=20,
    num_workers=1,
    pin_memory=True,
    cache_size=1200,
)


================================================
FILE: notebooks/Test RNN VC.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The effects of adversarial training in voice conversion\n",
    "\n",
    "Minimum generation error (MGE) training vs Adversarial training. Generated audio samples are available at the middle of the notebook.\n",
    "\n",
    "Code: https://github.com/r9y9/gantts\n",
    "\n",
    "### Refereneces\n",
    "\n",
    "- [Yuki Saito, Shinnosuke Takamichi, Hiroshi Saruwatari, \"Statistical Parametric Speech Synthesis Incorporating Generative Adversarial Networks\", arXiv:1709.08041 [cs.SD], Sep. 2017](https://arxiv.org/abs/1709.08041)\n",
    "- [Yuki Saito, Shinnosuke Takamichi, and Hiroshi Saruwatari, \"Training algorithm to deceive anti-spoofing verification for DNN-based text-to-speech synthesis,\" IPSJ SIG Technical Report, 2017-SLP-115, no. 1, pp. 1-6, Feb., 2017. (in Japanese)](http://sython.org/papers/SIG-SLP/saito201702slp.pdf)\n",
    "- [Yuki Saito, Shinnosuke Takamichi, and Hiroshi Saruwatari, \"Voice conversion using input-to-output highway networks,\" IEICE Transactions on Information and Systems, Vol.E100-D, No.8, pp.1925--1928, Aug. 2017](https://www.jstage.jst.go.jp/article/transinf/E100.D/8/E100.D_2017EDL8034/_article)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "rcParams[\"figure.figsize\"] = (16,5)\n",
    "\n",
    "import sys\n",
    "sys.path.insert(0, \"..\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "from scipy.io import wavfile\n",
    "import pysptk\n",
    "from pysptk.synthesis import Synthesizer, MLSADF\n",
    "import pyworld\n",
    "from os.path import join, basename\n",
    "\n",
    "from nnmnkwii import preprocessing as P\n",
    "from nnmnkwii.paramgen import unit_variance_mlpg_matrix\n",
    "\n",
    "import gantts\n",
    "from hparams import vc as hp\n",
    "\n",
    "import librosa\n",
    "import librosa.display\n",
    "import IPython\n",
    "from IPython.display import Audio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_dir = \"../data/cmu_arctic_vc/\"\n",
    "clb_wav_dir = \"/home/ryuichi/data/cmu_arctic/cmu_us_clb_arctic/wav/\"\n",
    "slt_wav_dir = \"/home/ryuichi/data/cmu_arctic/cmu_us_slt_arctic/wav/\"\n",
    "\n",
    "data_mean = np.load(join(data_dir, \"data_mean.npy\"))\n",
    "data_var = np.load(join(data_dir, \"data_var.npy\"))\n",
    "data_std = np.sqrt(data_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if hp.generator_params[\"in_dim\"] is None:\n",
    "    hp.generator_params[\"in_dim\"] = data_mean.shape[-1]\n",
    "if hp.generator_params[\"out_dim\"] is None:\n",
    "    hp.generator_params[\"out_dim\"] = data_mean.shape[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fs = 16000\n",
    "hop_length = int(fs * (hp.frame_period * 0.001))\n",
    "fftlen = pyworld.get_cheaptrick_fft_size(fs)\n",
    "static_dim = hp.order\n",
    "checkpoints_dir = \"../checkpoints/vc_test_in2out_bilstm_again/\"\n",
    "epoch = 200"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Baseline: In2out highway networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In2OutRNNHighwayNet (\n",
      "  (relu): LeakyReLU (0.01, inplace)\n",
      "  (sigmoid): Sigmoid ()\n",
      "  (T): Linear (59 -> 59)\n",
      "  (lstm): LSTM(177, 512, num_layers=3, batch_first=True, dropout=0.5, bidirectional=True)\n",
      "  (hidden2out): Linear (1024 -> 177)\n",
      "  (dropout): Dropout (p = 0.5)\n",
      ")\n",
      "Load checkpoint from: ../checkpoints/vc_test_in2out_bilstm_again/baseline/checkpoint_epoch200_Generator.pth\n"
     ]
    }
   ],
   "source": [
    "model_in2out = getattr(gantts.models, hp.generator)(**hp.generator_params)\n",
    "print(model_in2out)\n",
    "\n",
    "checkpoint_path = join(checkpoints_dir, \"baseline/checkpoint_epoch{}_Generator.pth\".format(epoch))\n",
    "print(\"Load checkpoint from: {}\".format(checkpoint_path))\n",
    "checkpoint = torch.load(checkpoint_path)\n",
    "model_in2out.load_state_dict(checkpoint[\"state_dict\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In2OutRNNHighwayNet (\n",
      "  (relu): LeakyReLU (0.01, inplace)\n",
      "  (sigmoid): Sigmoid ()\n",
      "  (T): Linear (59 -> 59)\n",
      "  (lstm): LSTM(177, 512, num_layers=3, batch_first=True, dropout=0.5, bidirectional=True)\n",
      "  (hidden2out): Linear (1024 -> 177)\n",
      "  (dropout): Dropout (p = 0.5)\n",
      ")\n",
      "Load checkpoint from: ../checkpoints/vc_test_in2out_bilstm_again/gan/checkpoint_epoch200_Generator.pth\n"
     ]
    }
   ],
   "source": [
    "model_gan = getattr(gantts.models, hp.generator)(**hp.generator_params)\n",
    "print(model_gan)\n",
    "\n",
    "checkpoint_path = join(checkpoints_dir, \"gan/checkpoint_epoch{}_Generator.pth\".format(epoch))\n",
    "print(\"Load checkpoint from: {}\".format(checkpoint_path))\n",
    "checkpoint = torch.load(checkpoint_path)\n",
    "model_gan.load_state_dict(checkpoint[\"state_dict\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare generated audio samples\n",
    "\n",
    "Baseline vs GAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arctic_a0496.wav : source, target, baseline, GAN\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio controls=\"controls\" >\n",
       "                    <source 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