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Repository: chomeyama/SiFiGAN
Branch: main
Commit: cdb20daeb4da
Files: 56
Total size: 20.8 MB

Directory structure:
gitextract_jyc_yf0t/

├── .gitignore
├── LICENSE
├── README.md
├── egs/
│   ├── libritts-r-clean+nus-48e_all/
│   │   └── data/
│   │       └── scp/
│   │           ├── libritts_r_clean+nus-48e_dev.list
│   │           ├── libritts_r_clean+nus-48e_dev.scp
│   │           ├── libritts_r_clean+nus-48e_eval.list
│   │           ├── libritts_r_clean+nus-48e_eval.scp
│   │           ├── libritts_r_clean+nus-48e_train_no_dev.list
│   │           └── libritts_r_clean+nus-48e_train_no_dev.scp
│   └── namine_ritsu/
│       └── data/
│           └── scp/
│               ├── namine_ritsu_all.scp
│               ├── namine_ritsu_dev.scp
│               ├── namine_ritsu_eval.scp
│               └── namine_ritsu_train_no_dev.scp
├── setup.py
└── sifigan/
    ├── __init__.py
    ├── bin/
    │   ├── __init__.py
    │   ├── anasyn.py
    │   ├── compute_statistics.py
    │   ├── config/
    │   │   ├── __init__.py
    │   │   ├── anasyn.yaml
    │   │   ├── compute_statistics.yaml
    │   │   ├── data/
    │   │   │   ├── libritts-r-clean+nus-48e.yaml
    │   │   │   └── namine_ritsu.yaml
    │   │   ├── decode.yaml
    │   │   ├── discriminator/
    │   │   │   ├── hifigan.yaml
    │   │   │   └── univnet.yaml
    │   │   ├── extract_features.yaml
    │   │   ├── generator/
    │   │   │   ├── hifigan.yaml
    │   │   │   ├── sifigan.direct.yaml
    │   │   │   └── sifigan.yaml
    │   │   ├── param_count.yaml
    │   │   ├── train/
    │   │   │   ├── hifigan.yaml
    │   │   │   ├── sifigan.yaml
    │   │   │   └── sifigan_1000k.yaml
    │   │   └── train.yaml
    │   ├── decode.py
    │   ├── extract_features.py
    │   ├── param_count.py
    │   └── train.py
    ├── datasets/
    │   ├── __init__.py
    │   └── audio_feat_dataset.py
    ├── layers/
    │   ├── __init__.py
    │   ├── cheaptrick.py
    │   ├── residual_block.py
    │   └── snake.py
    ├── losses/
    │   ├── __init__.py
    │   ├── adv.py
    │   ├── mel.py
    │   └── reg.py
    ├── models/
    │   ├── __init__.py
    │   ├── discriminator.py
    │   └── generator.py
    └── utils/
        ├── __init__.py
        ├── features.py
        ├── index.py
        └── utils.py

================================================
FILE CONTENTS
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================================================
FILE: .gitignore
================================================
__pycache__
.eggs
*.egg-info
*.log
*.out
*.wav
*.h5
.venv

egs/*/exp

================================================
FILE: LICENSE
================================================
MIT License

Copyright (c) 2022 Reo YONEYAMA

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
================================================
# Source-Filter HiFi-GAN (SiFi-GAN)

This repo provides official PyTorch implementation of [SiFi-GAN](https://arxiv.org/abs/2210.15533), a fast and pitch controllable high-fidelity neural vocoder.<br>
For more information, please see our [DEMO](https://chomeyama.github.io/SiFiGAN-Demo/).

## Environment setup

```bash
$ cd SiFiGAN
$ pip install -e .
```

Please refer to the [Parallel WaveGAN](https://github.com/kan-bayashi/ParallelWaveGAN) repo for more details.

## Folder architecture
- **egs**:
The folder for projects.
- **egs/namine_ritsu**:
The folder of the [Namine Ritsu](https://www.youtube.com/watch?v=pKeo9IE_L1I) project example.
- **sifigan**:
The folder of the source codes.

The dataset preparation of Namine Ritsu database is based on [NNSVS](https://github.com/nnsvs/nnsvs/).
Please refer to it for the procedure and details.

## Run

In this repo, hyperparameters are managed using [Hydra](https://hydra.cc/docs/intro/).<br>
Hydra provides an easy way to dynamically create a hierarchical configuration by composition and override it through config files and the command line.

### Dataset preparation

Make dataset and scp files denoting paths to each audio files according to your own dataset (e.g., `egs/namine_ritsu/data/scp/namine_ritsu.scp`).<br>
List files denoting paths to the extracted features are automatically created in the next step (e.g., `egs/namine_ritsu/data/scp/namine_ritsu.list`).<br>
Note that scp/list files for training/validation/evaluation are needed.

### Preprocessing

```bash
# Move to the project directory
$ cd egs/namine_ritsu

# Extract acoustic features (F0, mel-cepstrum, and etc.)
# You can customize parameters according to sifigan/bin/config/extract_features.yaml
$ sifigan-extract-features audio=data/scp/namine_ritsu_all.scp

# Compute statistics of training data
$ sifigan-compute-statistics feats=data/scp/namine_ritsu_train.list stats=data/stats/namine_ritsu_train.joblib
```

### Training

```bash
# Train a model customizing the hyperparameters as you like
$ sifigan-train generator=sifigan discriminator=univnet train=sifigan data=namine_ritsu out_dir=exp/sifigan
```

### Inference

```bash
# Decode with several F0 scaling factors
$ sifigan-decode generator=sifigan data=namine_ritsu out_dir=exp/sifigan checkpoint_steps=400000 f0_factors=[0.5,1.0,2.0]
```

### Analysis-Synthesis

```bash
# WORLD analysis + Neural vocoder synthesis
$ sifigan-anasyn generator=sifigan in_dir=your_own_input_wav_dir out_dir=your_own_output_wav_dir stats=pretrained_sifigan/namine_ritsu_train_no_dev.joblib checkpoint_path=pretrained_sifigan/checkpoint-400000steps.pkl f0_factors=[1.0]
```

### Pretrained model

~~I provide a pretrained SiFiGAN model [HERE](https://www.dropbox.com/s/akofngycxxz1dg5/pretrained_sifigan.tar.gz?dl=0) which is trained on the Namine Ritsu corpus in the same training manner described in the paper.
You can download and place it in your own directory. Then set the appropriate path to the pretrained model and the command should work.~~


~~However, since the Namine Ritsu corpus includes a single female Japanese singer, there is a possibility that the model would not work well especially for male singers.
I am planning to publish another pretrained model trained on larger dataset including many speakers.~~

Due to being trained on the code before bug fixes, I have decided to cancel the release of the model trained on the Namine Ritsu database. Instead, a model trained on the following large-scale dataset is available.

A pretrained model on 24 kHz speech + singing datasets is available [HERE](https://drive.google.com/file/d/1uzqTeumvkPQpfdK_D4U41MDL5-s-hs0l/view?usp=sharing). We used train-clean-100 and train-clean-360 in [LibriTTS-R](https://google.github.io/df-conformer/librittsr/), and [NUS-48E](https://www.smcnus.org/wp-content/uploads/2013/09/05-Pub-NUS-48E.pdf) for training.
Two speakers, ADIZ and JLEE in NUS-48E, were excluded from the training data for evaluation. Also, the wav data of NUS-48E were divided into clips of approximately one second each before the feature extraction step.

The feature preprocessing and training commands are as follows:
```bash
sifigan-extract-features audio=data/scp/libritts_r_clean+nus-48e_train_no_dev.scp minf0=60 maxf0=1000
sifigan-extract-features audio=data/scp/libritts_r_clean+nus-48e_dev.scp minf0=60 maxf0=1000
sifigan-extract-features audio=data/scp/libritts_r_clean+nus-48e_eval.scp minf0=60 maxf0=1000

sifigan-compute-statistics feats=data/scp/libritts_r_clean+nus-48e_train_no_dev.list stats=data/stats/libritts_r_clean+nus-48e_train_no_dev.joblib

sifigan-train out_dir=test/sifigan generator=sifigan data=libritts_r_clean+nus-48e train=sifigan_1000k
```

### Monitor training progress

```bash
$ tensorboard --logdir exp
```

## Citation
If you find the code is helpful, please cite the following article.

```
@INPROCEEDINGS{10095298,
  author={Yoneyama, Reo and Wu, Yi-Chiao and Toda, Tomoki},
  booktitle={ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  title={{Source-Filter HiFi-GAN: Fast and Pitch Controllable High-Fidelity Neural Vocoder}},
  year={2023},
  volume={},
  number={},
  pages={1-5},
  doi={10.1109/ICASSP49357.2023.10095298}
}
```

## Authors

Development:
[Reo Yoneyama](https://chomeyama.github.io/Profile/) @ Nagoya University, Japan<br>
E-mail: `yoneyama.reo@g.sp.m.is.nagoya-u.ac.jp`

Advisors:<br>
[Yi-Chiao Wu](https://bigpon.github.io/) @ Meta Reality Labs Research, USA<br>
E-mail: `yichiaowu@fb.com`<br>
[Tomoki Toda](https://sites.google.com/site/tomokitoda/) @ Nagoya University, Japan<br>
E-mail: `tomoki@icts.nagoya-u.ac.jp`


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FILE: egs/libritts-r-clean+nus-48e_all/data/scp/libritts_r_clean+nus-48e_dev.list
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LibriTTS_R/dev-cle

Download .txt

gitextract_jyc_yf0t/

├── .gitignore
├── LICENSE
├── README.md
├── egs/
│   ├── libritts-r-clean+nus-48e_all/
│   │   └── data/
│   │       └── scp/
│   │           ├── libritts_r_clean+nus-48e_dev.list
│   │           ├── libritts_r_clean+nus-48e_dev.scp
│   │           ├── libritts_r_clean+nus-48e_eval.list
│   │           ├── libritts_r_clean+nus-48e_eval.scp
│   │           ├── libritts_r_clean+nus-48e_train_no_dev.list
│   │           └── libritts_r_clean+nus-48e_train_no_dev.scp
│   └── namine_ritsu/
│       └── data/
│           └── scp/
│               ├── namine_ritsu_all.scp
│               ├── namine_ritsu_dev.scp
│               ├── namine_ritsu_eval.scp
│               └── namine_ritsu_train_no_dev.scp
├── setup.py
└── sifigan/
    ├── __init__.py
    ├── bin/
    │   ├── __init__.py
    │   ├── anasyn.py
    │   ├── compute_statistics.py
    │   ├── config/
    │   │   ├── __init__.py
    │   │   ├── anasyn.yaml
    │   │   ├── compute_statistics.yaml
    │   │   ├── data/
    │   │   │   ├── libritts-r-clean+nus-48e.yaml
    │   │   │   └── namine_ritsu.yaml
    │   │   ├── decode.yaml
    │   │   ├── discriminator/
    │   │   │   ├── hifigan.yaml
    │   │   │   └── univnet.yaml
    │   │   ├── extract_features.yaml
    │   │   ├── generator/
    │   │   │   ├── hifigan.yaml
    │   │   │   ├── sifigan.direct.yaml
    │   │   │   └── sifigan.yaml
    │   │   ├── param_count.yaml
    │   │   ├── train/
    │   │   │   ├── hifigan.yaml
    │   │   │   ├── sifigan.yaml
    │   │   │   └── sifigan_1000k.yaml
    │   │   └── train.yaml
    │   ├── decode.py
    │   ├── extract_features.py
    │   ├── param_count.py
    │   └── train.py
    ├── datasets/
    │   ├── __init__.py
    │   └── audio_feat_dataset.py
    ├── layers/
    │   ├── __init__.py
    │   ├── cheaptrick.py
    │   ├── residual_block.py
    │   └── snake.py
    ├── losses/
    │   ├── __init__.py
    │   ├── adv.py
    │   ├── mel.py
    │   └── reg.py
    ├── models/
    │   ├── __init__.py
    │   ├── discriminator.py
    │   └── generator.py
    └── utils/
        ├── __init__.py
        ├── features.py
        ├── index.py
        └── utils.py

Download .txt

SYMBOL INDEX (157 symbols across 18 files)

FILE: sifigan/bin/anasyn.py
  function convert_continuos_f0 (line 46) | def convert_continuos_f0(f0):
  function main (line 72) | def main(config: DictConfig) -> None:

FILE: sifigan/bin/compute_statistics.py
  function calc_stats (line 29) | def calc_stats(file_list, config):
  function main (line 66) | def main(config: DictConfig):

FILE: sifigan/bin/decode.py
  function main (line 33) | def main(config: DictConfig) -> None:

FILE: sifigan/bin/extract_features.py
  function path_create (line 51) | def path_create(wav_list, in_dir, out_dir, extname):
  function path_replace (line 56) | def path_replace(filepath, inputpath, outputpath, extname=None):
  function spk_division (line 64) | def spk_division(file_list, config, spkinfo, split="/"):
  function aux_list_create (line 107) | def aux_list_create(wav_list_file, config):
  function low_cut_filter (line 128) | def low_cut_filter(x, fs, cutoff=70):
  function low_pass_filter (line 148) | def low_pass_filter(x, fs, cutoff=70, padding=True):
  function convert_continuos_f0 (line 171) | def convert_continuos_f0(f0):
  function melfilterbank (line 204) | def melfilterbank(
  function world_feature_extract (line 257) | def world_feature_extract(queue, wav_list, config):
  function main (line 351) | def main(config: DictConfig):

FILE: sifigan/bin/param_count.py
  function main (line 18) | def main(config: DictConfig) -> None:

FILE: sifigan/bin/train.py
  class Trainer (line 43) | class Trainer(object):
    method __init__ (line 46) | def __init__(
    method run (line 86) | def run(self):
    method save_checkpoint (line 102) | def save_checkpoint(self, checkpoint_path):
    method load_checkpoint (line 130) | def load_checkpoint(self, checkpoint_path, load_only_params=False):
    method _train_step (line 159) | def _train_step(self, batch):
    method _train_epoch (line 252) | def _train_epoch(self):
    method _eval_step (line 276) | def _eval_step(self, batch):
    method _eval_epoch (line 338) | def _eval_epoch(self):
    method _genearete_and_save_intermediate_result (line 381) | def _genearete_and_save_intermediate_result(self, batch):
    method _write_to_tensorboard (line 449) | def _write_to_tensorboard(self, loss):
    method _check_save_interval (line 454) | def _check_save_interval(self):
    method _check_eval_interval (line 465) | def _check_eval_interval(self):
    method _check_log_interval (line 469) | def _check_log_interval(self):
    method _check_train_finish (line 481) | def _check_train_finish(self):
  class Collater (line 486) | class Collater(object):
    method __init__ (line 489) | def __init__(
    method __call__ (line 535) | def __call__(self, batch):
    method _check_length (line 600) | def _check_length(self, x, c, f0, cf0, dfs):
  function main (line 610) | def main(config: DictConfig) -> None:

FILE: sifigan/datasets/audio_feat_dataset.py
  class AudioFeatDataset (line 29) | class AudioFeatDataset(Dataset):
    method __init__ (line 32) | def __init__(
    method __getitem__ (line 121) | def __getitem__(self, idx):
    method __len__ (line 177) | def __len__(self):
  class FeatDataset (line 187) | class FeatDataset(Dataset):
    method __init__ (line 190) | def __init__(
    method __getitem__ (line 257) | def __getitem__(self, idx):
    method __len__ (line 306) | def __len__(self):

FILE: sifigan/layers/cheaptrick.py
  class AdaptiveWindowing (line 21) | class AdaptiveWindowing(nn.Module):
    method __init__ (line 24) | def __init__(
    method forward (line 64) | def forward(self, x, f, power=False):
  class AdaptiveLiftering (line 88) | class AdaptiveLiftering(nn.Module):
    method __init__ (line 91) | def __init__(
    method forward (line 138) | def forward(self, x, f, elim_0th=False):
  class CheapTrick (line 171) | class CheapTrick(nn.Module):
    method __init__ (line 174) | def __init__(
    method forward (line 219) | def forward(self, x, f, power=False, elim_0th=False):

FILE: sifigan/layers/residual_block.py
  class Conv1d (line 26) | class Conv1d(nn.Conv1d):
    method __init__ (line 29) | def __init__(self, *args, **kwargs):
    method reset_parameters (line 33) | def reset_parameters(self):
  class Conv1d1x1 (line 40) | class Conv1d1x1(Conv1d):
    method __init__ (line 43) | def __init__(self, in_channels, out_channels, bias=True):
  class Conv2d (line 50) | class Conv2d(nn.Conv2d):
    method __init__ (line 53) | def __init__(self, *args, **kwargs):
    method reset_parameters (line 57) | def reset_parameters(self):
  class Conv2d1x1 (line 64) | class Conv2d1x1(Conv2d):
    method __init__ (line 67) | def __init__(self, in_channels, out_channels, bias=True):
  class ResidualBlock (line 74) | class ResidualBlock(nn.Module):
    method __init__ (line 77) | def __init__(
    method forward (line 142) | def forward(self, x):
  class AdaptiveResidualBlock (line 160) | class AdaptiveResidualBlock(nn.Module):
    method __init__ (line 163) | def __init__(
    method forward (line 239) | def forward(self, x, d):

FILE: sifigan/layers/snake.py
  class Snake (line 20) | class Snake(nn.Module):
    method __init__ (line 23) | def __init__(self, channels, init=50):
    method forward (line 37) | def forward(self, x):

FILE: sifigan/losses/adv.py
  class AdversarialLoss (line 18) | class AdversarialLoss(nn.Module):
    method __init__ (line 21) | def __init__(
    method forward (line 40) | def forward(self, p_fakes, p_reals=None):
    method _mse_adv_loss (line 80) | def _mse_adv_loss(self, x):
    method _mse_real_loss (line 83) | def _mse_real_loss(self, x):
    method _mse_fake_loss (line 86) | def _mse_fake_loss(self, x):
    method _hinge_adv_loss (line 89) | def _hinge_adv_loss(self, x):
    method _hinge_real_loss (line 92) | def _hinge_real_loss(self, x):
    method _hinge_fake_loss (line 95) | def _hinge_fake_loss(self, x):
  class FeatureMatchLoss (line 99) | class FeatureMatchLoss(nn.Module):
    method __init__ (line 102) | def __init__(
    method forward (line 110) | def forward(self, fmaps_fake, fmaps_real):

FILE: sifigan/losses/mel.py
  function stft (line 19) | def stft(
  class MelSpectralLoss (line 54) | class MelSpectralLoss(nn.Module):
    method __init__ (line 57) | def __init__(
    method forward (line 95) | def forward(self, x, y):

FILE: sifigan/losses/reg.py
  class FlattenLoss (line 16) | class FlattenLoss(nn.Module):
    method __init__ (line 19) | def __init__(
    method forward (line 60) | def forward(self, s, f):
  class ResidualLoss (line 77) | class ResidualLoss(nn.Module):
    method __init__ (line 80) | def __init__(
    method forward (line 133) | def forward(self, s, y, f):

FILE: sifigan/models/discriminator.py
  class HiFiGANPeriodDiscriminator (line 28) | class HiFiGANPeriodDiscriminator(nn.Module):
    method __init__ (line 31) | def __init__(
    method forward (line 111) | def forward(self, x, return_fmaps=False):
    method apply_weight_norm (line 144) | def apply_weight_norm(self):
    method apply_spectral_norm (line 154) | def apply_spectral_norm(self):
  class HiFiGANMultiPeriodDiscriminator (line 165) | class HiFiGANMultiPeriodDiscriminator(nn.Module):
    method __init__ (line 168) | def __init__(
    method forward (line 200) | def forward(self, x, return_fmaps=False):
  class HiFiGANScaleDiscriminator (line 226) | class HiFiGANScaleDiscriminator(nn.Module):
    method __init__ (line 229) | def __init__(
    method forward (line 347) | def forward(self, x, return_fmaps=False):
    method apply_weight_norm (line 370) | def apply_weight_norm(self):
    method apply_spectral_norm (line 380) | def apply_spectral_norm(self):
  class HiFiGANMultiScaleDiscriminator (line 391) | class HiFiGANMultiScaleDiscriminator(nn.Module):
    method __init__ (line 394) | def __init__(
    method forward (line 446) | def forward(self, x, return_fmaps=False):
  class HiFiGANMultiScaleMultiPeriodDiscriminator (line 473) | class HiFiGANMultiScaleMultiPeriodDiscriminator(nn.Module):
    method __init__ (line 476) | def __init__(
    method forward (line 543) | def forward(self, x, return_fmaps=False):
  class UnivNetSpectralDiscriminator (line 569) | class UnivNetSpectralDiscriminator(nn.Module):
    method __init__ (line 572) | def __init__(
    method forward (line 670) | def forward(self, x, return_fmaps=False):
    method apply_weight_norm (line 703) | def apply_weight_norm(self):
  class UnivNetMultiResolutionSpectralDiscriminator (line 714) | class UnivNetMultiResolutionSpectralDiscriminator(nn.Module):
    method __init__ (line 717) | def __init__(
    method forward (line 759) | def forward(self, x, return_fmaps=False):
  class UnivNetMultiResolutionMultiPeriodDiscriminator (line 785) | class UnivNetMultiResolutionMultiPeriodDiscriminator(nn.Module):
    method __init__ (line 788) | def __init__(
    method forward (line 845) | def forward(self, x, return_fmaps=False):

FILE: sifigan/models/generator.py
  class HiFiGANGenerator (line 25) | class HiFiGANGenerator(nn.Module):
    method __init__ (line 28) | def __init__(
    method forward (line 180) | def forward(self, x, c, d=None):
    method reset_parameters (line 215) | def reset_parameters(self):
    method remove_weight_norm (line 230) | def remove_weight_norm(self):
    method apply_weight_norm (line 242) | def apply_weight_norm(self):
  class SiFiGANGenerator (line 253) | class SiFiGANGenerator(nn.Module):
    method __init__ (line 256) | def __init__(
    method forward (line 449) | def forward(self, x, c, d):
    method reset_parameters (line 500) | def reset_parameters(self):
    method remove_weight_norm (line 515) | def remove_weight_norm(self):
    method apply_weight_norm (line 527) | def apply_weight_norm(self):
  class SiFiGANDirectGenerator (line 538) | class SiFiGANDirectGenerator(nn.Module):
    method __init__ (line 541) | def __init__(
    method forward (line 716) | def forward(self, x, c, d):
    method reset_parameters (line 762) | def reset_parameters(self):
    method remove_weight_norm (line 777) | def remove_weight_norm(self):
    method apply_weight_norm (line 789) | def apply_weight_norm(self):

FILE: sifigan/utils/features.py
  function validate_length (line 25) | def validate_length(xs, ys=None, hop_size=None):
  function dilated_factor (line 50) | def dilated_factor(batch_f0, fs, dense_factor):
  class SignalGenerator (line 70) | class SignalGenerator:
    method __init__ (line 73) | def __init__(
    method __call__ (line 104) | def __call__(self, f0):
    method random_noise (line 123) | def random_noise(self, f0):
    method sinusoid (line 139) | def sinusoid(self, f0):
    method sinusoids (line 161) | def sinusoids(self, f0):
    method vuv_binary (line 188) | def vuv_binary(self, f0):

FILE: sifigan/utils/index.py
  function pd_indexing (line 11) | def pd_indexing(x, d, dilation, batch_index, ch_index):
  function index_initial (line 45) | def index_initial(n_batch, n_ch, tensor=True):

FILE: sifigan/utils/utils.py
  function read_hdf5 (line 21) | def read_hdf5(hdf5_name, hdf5_path):
  function write_hdf5 (line 48) | def write_hdf5(hdf5_name, hdf5_path, write_data, is_overwrite=True):
  function check_hdf5 (line 91) | def check_hdf5(hdf5_name, hdf5_path):
  function read_txt (line 112) | def read_txt(file_list):
  function check_filename (line 127) | def check_filename(list1, list2):

Copy disabled (too large) Download .json

Condensed preview — 56 files, each showing path, character count, and a content snippet. Download the .json file for the full structured content (22,210K chars).

[
  {
    "path": ".gitignore",
    "chars": 68,
    "preview": "__pycache__\n.eggs\n*.egg-info\n*.log\n*.out\n*.wav\n*.h5\n.venv\n\negs/*/exp"
  },
  {
    "path": "LICENSE",
    "chars": 1069,
    "preview": "MIT License\n\nCopyright (c) 2022 Reo YONEYAMA\n\nPermission is hereby granted, free of charge, to any person obtaining a co"
  },
  {
    "path": "README.md",
    "chars": 5680,
    "preview": "# Source-Filter HiFi-GAN (SiFi-GAN)\n\nThis repo provides official PyTorch implementation of [SiFi-GAN](https://arxiv.org/"
  },
  {
    "path": "egs/libritts-r-clean+nus-48e_all/data/scp/libritts_r_clean+nus-48e_dev.list",
    "chars": 339556,
    "preview": "LibriTTS_R/dev-clean/1272/hdf5/1272_128104_000001_000000.h5\nLibriTTS_R/dev-clean/1272/hdf5/1272_128104_000003_000000.h5\n"
  },
  {
    "path": "egs/libritts-r-clean+nus-48e_all/data/scp/libritts_r_clean+nus-48e_dev.scp",
    "chars": 339556,
    "preview": "LibriTTS_R/dev-clean/1272/wav/1272_128104_000001_000000.wav\nLibriTTS_R/dev-clean/1272/wav/1272_128104_000003_000000.wav\n"
  },
  {
    "path": "egs/libritts-r-clean+nus-48e_all/data/scp/libritts_r_clean+nus-48e_eval.list",
    "chars": 350238,
    "preview": "LibriTTS_R/test-clean/1089/hdf5/1089_134686_000001_000001.h5\nLibriTTS_R/test-clean/1089/hdf5/1089_134686_000002_000000.h"
  },
  {
    "path": "egs/libritts-r-clean+nus-48e_all/data/scp/libritts_r_clean+nus-48e_eval.scp",
    "chars": 350238,
    "preview": "LibriTTS_R/test-clean/1089/wav/1089_134686_000001_000001.wav\nLibriTTS_R/test-clean/1089/wav/1089_134686_000002_000000.wa"
  },
  {
    "path": "egs/libritts-r-clean+nus-48e_all/data/scp/libritts_r_clean+nus-48e_train_no_dev.list",
    "chars": 10039266,
    "preview": "LibriTTS_R/train-clean-100/1034/hdf5/1034_121119_000001_000001.h5\nLibriTTS_R/train-clean-100/1034/hdf5/1034_121119_00000"
  },
  {
    "path": "egs/libritts-r-clean+nus-48e_all/data/scp/libritts_r_clean+nus-48e_train_no_dev.scp",
    "chars": 10039266,
    "preview": "LibriTTS_R/train-clean-100/1034/wav/1034_121119_000001_000001.wav\nLibriTTS_R/train-clean-100/1034/wav/1034_121119_000002"
  },
  {
    "path": "egs/namine_ritsu/data/scp/namine_ritsu_all.scp",
    "chars": 93903,
    "preview": "data/wav/namine_ritsu_1st_color_seg0.wav\ndata/wav/namine_ritsu_1st_color_seg10.wav\ndata/wav/namine_ritsu_1st_color_seg11"
  },
  {
    "path": "egs/namine_ritsu/data/scp/namine_ritsu_dev.scp",
    "chars": 3594,
    "preview": "data/wav/namine_ritsu_BRD_seg0.wav\ndata/wav/namine_ritsu_BRD_seg1.wav\ndata/wav/namine_ritsu_BRD_seg10.wav\ndata/wav/namin"
  },
  {
    "path": "egs/namine_ritsu/data/scp/namine_ritsu_eval.scp",
    "chars": 4204,
    "preview": "data/wav/namine_ritsu_1st_color_seg0.wav\ndata/wav/namine_ritsu_1st_color_seg1.wav\ndata/wav/namine_ritsu_1st_color_seg10."
  },
  {
    "path": "egs/namine_ritsu/data/scp/namine_ritsu_train_no_dev.scp",
    "chars": 86105,
    "preview": "data/wav/namine_ritsu_2018_seg0.wav\ndata/wav/namine_ritsu_2018_seg1.wav\ndata/wav/namine_ritsu_2018_seg10.wav\ndata/wav/na"
  },
  {
    "path": "setup.py",
    "chars": 2586,
    "preview": "# -*- coding: utf-8 -*-\n\n\"\"\"Setup Source-Filter HiFiGAN Library.\"\"\"\n\nimport os\nimport sys\nfrom distutils.version import "
  },
  {
    "path": "sifigan/__init__.py",
    "chars": 47,
    "preview": "# -*- coding: utf-8 -*-\n\n__version__ = \"0.0.1\"\n"
  },
  {
    "path": "sifigan/bin/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "sifigan/bin/anasyn.py",
    "chars": 6534,
    "preview": "# -*- coding: utf-8 -*-\n\n# Copyright 2022 Reo Yoneyama (Nagoya University)\n#  MIT License (https://opensource.org/licens"
  },
  {
    "path": "sifigan/bin/compute_statistics.py",
    "chars": 2388,
    "preview": "# -*- coding: utf-8 -*-\n\n# Copyright 2022 Reo Yoneyama (Nagoya University)\n#  MIT License (https://opensource.org/licens"
  },
  {
    "path": "sifigan/bin/config/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "sifigan/bin/config/anasyn.yaml",
    "chars": 1506,
    "preview": "# @package _global_\n\ndefaults:\n  - _self_\n  - generator: sifigan\n\nhydra:\n  run:\n    dir: ./\n  output_subdir: null\n  job_"
  },
  {
    "path": "sifigan/bin/config/compute_statistics.yaml",
    "chars": 476,
    "preview": "# @package _global_\n\nhydra:\n  run:\n    dir: ./\n  output_subdir: null\n  job_logging:\n    formatters:\n      simple:\n      "
  },
  {
    "path": "sifigan/bin/config/data/libritts-r-clean+nus-48e.yaml",
    "chars": 2126,
    "preview": "# Dataset settings\ntrain_audio: data/scp/libritts-r-clean+nus-48e_train_no_dev.scp  # List file of training audio files."
  },
  {
    "path": "sifigan/bin/config/data/namine_ritsu.yaml",
    "chars": 2054,
    "preview": "# Dataset settings\ntrain_audio: data/scp/namine_ritsu_train_no_dev.scp  # List file of training audio files.\ntrain_feat:"
  },
  {
    "path": "sifigan/bin/config/decode.yaml",
    "chars": 678,
    "preview": "# @package _global_\n\ndefaults:\n  - _self_\n  - generator: sifigan\n  - data: namine_ritsu\n\nhydra:\n  run:\n    dir: ./\n  out"
  },
  {
    "path": "sifigan/bin/config/discriminator/hifigan.yaml",
    "chars": 2398,
    "preview": "_target_: sifigan.models.HiFiGANMultiScaleMultiPeriodDiscriminator\nscales: 3                              # Number of mu"
  },
  {
    "path": "sifigan/bin/config/discriminator/univnet.yaml",
    "chars": 2141,
    "preview": "_target_: sifigan.models.UnivNetMultiResolutionMultiPeriodDiscriminator\nfft_sizes: [1024, 2048, 512]           # FFT siz"
  },
  {
    "path": "sifigan/bin/config/extract_features.yaml",
    "chars": 1748,
    "preview": "# @package _global_\n\nhydra:\n  run:\n    dir: ./\n  output_subdir: null\n  job_logging:\n    formatters:\n      simple:\n      "
  },
  {
    "path": "sifigan/bin/config/generator/hifigan.yaml",
    "chars": 1543,
    "preview": "_target_: sifigan.models.HiFiGANGenerator\nin_channels: 45                       # Number of input channels.\nout_channels"
  },
  {
    "path": "sifigan/bin/config/generator/sifigan.direct.yaml",
    "chars": 1616,
    "preview": "_target_: sifigan.models.SiFiGANDirectGenerator\nin_channels: 43                       # Number of input channels.\nout_ch"
  },
  {
    "path": "sifigan/bin/config/generator/sifigan.yaml",
    "chars": 1687,
    "preview": "_target_: sifigan.models.SiFiGANGenerator\nin_channels: 43                       # Number of input channels.\nout_channels"
  },
  {
    "path": "sifigan/bin/config/param_count.yaml",
    "chars": 278,
    "preview": "# @package _global_\n\ndefaults:\n  - _self_\n  - generator: sifigan\n\nhydra:\n  run:\n    dir: ./\n  output_subdir: null\n  job_"
  },
  {
    "path": "sifigan/bin/config/train/hifigan.yaml",
    "chars": 2133,
    "preview": "# Interval setting\ndiscriminator_train_start_steps: 0    # Number of steps to start to train discriminator.\ntrain_max_st"
  },
  {
    "path": "sifigan/bin/config/train/sifigan.yaml",
    "chars": 2905,
    "preview": "# Interval setting\ndiscriminator_train_start_steps: 0    # Number of steps to start to train discriminator.\ntrain_max_st"
  },
  {
    "path": "sifigan/bin/config/train/sifigan_1000k.yaml",
    "chars": 2907,
    "preview": "# Interval setting\ndiscriminator_train_start_steps: 0    # Number of steps to start to train discriminator.\ntrain_max_st"
  },
  {
    "path": "sifigan/bin/config/train.yaml",
    "chars": 449,
    "preview": "# @package _global_\n\ndefaults:\n  - _self_\n  - generator: sifigan\n  - discriminator: univnet\n  - train: sifigan\n  - data:"
  },
  {
    "path": "sifigan/bin/decode.py",
    "chars": 5054,
    "preview": "# -*- coding: utf-8 -*-\n\n# Copyright 2022 Reo Yoneyama (Nagoya University)\n#  MIT License (https://opensource.org/licens"
  },
  {
    "path": "sifigan/bin/extract_features.py",
    "chars": 11566,
    "preview": "# -*- coding: utf-8 -*-\n\n# Copyright 2022 Reo Yoneyama (Nagoya University)\n#  MIT License (https://opensource.org/licens"
  },
  {
    "path": "sifigan/bin/param_count.py",
    "chars": 790,
    "preview": "# -*- coding: utf-8 -*-\n\n# Copyright 2022 Reo Yoneyama (Nagoya University)\n#  MIT License (https://opensource.org/licens"
  },
  {
    "path": "sifigan/bin/train.py",
    "chars": 29352,
    "preview": "# -*- coding: utf-8 -*-\n\n# Copyright 2022 Reo Yoneyama (Nagoya University)\n#  MIT License (https://opensource.org/licens"
  },
  {
    "path": "sifigan/datasets/__init__.py",
    "chars": 58,
    "preview": "from sifigan.datasets.audio_feat_dataset import *  # NOQA\n"
  },
  {
    "path": "sifigan/datasets/audio_feat_dataset.py",
    "chars": 10740,
    "preview": "# -*- coding: utf-8 -*-\n\n# Copyright 2022 Reo Yoneyama (Nagoya University)\n#  MIT License (https://opensource.org/licens"
  },
  {
    "path": "sifigan/layers/__init__.py",
    "chars": 143,
    "preview": "from sifigan.layers.cheaptrick import *  # NOQA\nfrom sifigan.layers.snake import *  # NOQA\nfrom sifigan.layers.residual_"
  },
  {
    "path": "sifigan/layers/cheaptrick.py",
    "chars": 7797,
    "preview": "# -*- coding: utf-8 -*-\n\n# Copyright 2022 Reo Yoneyama (Nagoya University)\n#  MIT License (https://opensource.org/licens"
  },
  {
    "path": "sifigan/layers/residual_block.py",
    "chars": 8717,
    "preview": "# -*- coding: utf-8 -*-\n\n# Copyright 2022 Reo Yoneyama (Nagoya University)\n#  MIT License (https://opensource.org/licens"
  },
  {
    "path": "sifigan/layers/snake.py",
    "chars": 1322,
    "preview": "# -*- coding: utf-8 -*-\n\n# Copyright 2022 Reo Yoneyama (Nagoya University)\n#  MIT License (https://opensource.org/licens"
  },
  {
    "path": "sifigan/losses/__init__.py",
    "chars": 123,
    "preview": "from sifigan.losses.adv import *  # NOQA\nfrom sifigan.losses.mel import *  # NOQA\nfrom sifigan.losses.reg import *  # NO"
  },
  {
    "path": "sifigan/losses/adv.py",
    "chars": 3840,
    "preview": "# -*- coding: utf-8 -*-\n\n# Copyright 2022 Reo Yoneyama (Nagoya University)\n#  MIT License (https://opensource.org/licens"
  },
  {
    "path": "sifigan/losses/mel.py",
    "chars": 3380,
    "preview": "# -*- coding: utf-8 -*-\n\n# Copyright 2022 Reo Yoneyama (Nagoya University)\n#  MIT License (https://opensource.org/licens"
  },
  {
    "path": "sifigan/losses/reg.py",
    "chars": 6069,
    "preview": "# -*- coding: utf-8 -*-\n\n# Copyright 2022 Reo Yoneyama (Nagoya University)\n#  MIT License (https://opensource.org/licens"
  },
  {
    "path": "sifigan/models/__init__.py",
    "chars": 98,
    "preview": "from sifigan.models.discriminator import *  # NOQA\nfrom sifigan.models.generator import *  # NOQA\n"
  },
  {
    "path": "sifigan/models/discriminator.py",
    "chars": 29829,
    "preview": "# -*- coding: utf-8 -*-\n\n# Copyright 2022 Reo Yoneyama (Nagoya University)\n#  MIT License (https://opensource.org/licens"
  },
  {
    "path": "sifigan/models/generator.py",
    "chars": 30982,
    "preview": "# -*- coding: utf-8 -*-\n\n# Copyright 2022 Reo Yoneyama (Nagoya University)\n#  MIT License (https://opensource.org/licens"
  },
  {
    "path": "sifigan/utils/__init__.py",
    "chars": 129,
    "preview": "from sifigan.utils.features import *  # NOQA\nfrom sifigan.utils.index import *  # NOQA\nfrom sifigan.utils.utils import *"
  },
  {
    "path": "sifigan/utils/features.py",
    "chars": 6020,
    "preview": "# -*- coding: utf-8 -*-\n\n# Copyright 2022 Reo Yoneyama (Nagoya University)\n#  MIT License (https://opensource.org/licens"
  },
  {
    "path": "sifigan/utils/index.py",
    "chars": 2165,
    "preview": "# -*- coding: utf-8 -*-\n\n# Copyright 2020 Yi-Chiao Wu (Nagoya University)\n#  MIT License (https://opensource.org/license"
  },
  {
    "path": "sifigan/utils/utils.py",
    "chars": 3577,
    "preview": "# -*- coding: utf-8 -*-\n\n# Copyright 2020 Yi-Chiao Wu (Nagoya University)\n# based on a Parallel WaveGAN script by Tomoki"
  }
]

About this extraction

This page contains the full source code of the chomeyama/SiFiGAN GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 56 files (20.8 MB), approximately 5.5M tokens, and a symbol index with 157 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.

Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.

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