├── .gitignore ├── LICENSE ├── README.md ├── corpus └── VCC2018 │ ├── conf │ └── pow_f0_dict.yml │ ├── scp │ ├── vcc18eval.scp │ ├── vcc18eval_VCC2SF1.scp │ ├── vcc18eval_VCC2SF2.scp │ ├── vcc18eval_VCC2SF3.scp │ ├── vcc18eval_VCC2SF4.scp │ ├── vcc18eval_VCC2SM1.scp │ ├── vcc18eval_VCC2SM2.scp │ ├── vcc18eval_VCC2SM3.scp │ ├── vcc18eval_VCC2SM4.scp │ ├── vcc18ref.scp │ ├── vcc18ref_VCC2TF1.scp │ ├── vcc18ref_VCC2TF2.scp │ ├── vcc18ref_VCC2TM1.scp │ ├── vcc18ref_VCC2TM2.scp │ ├── vcc18temp_VCC2SF3.scp │ ├── vcc18tr.scp │ ├── vcc18tr_VCC2SF1.scp │ ├── vcc18tr_VCC2SF2.scp │ ├── vcc18tr_VCC2SF3.scp │ ├── vcc18tr_VCC2SF4.scp │ ├── vcc18tr_VCC2SM1.scp │ ├── vcc18tr_VCC2SM2.scp │ ├── vcc18tr_VCC2SM3.scp │ ├── vcc18tr_VCC2SM4.scp │ ├── vcc18tr_VCC2TF1.scp │ ├── vcc18tr_VCC2TF2.scp │ ├── vcc18tr_VCC2TM1.scp │ ├── vcc18tr_VCC2TM2.scp │ ├── vcc18up_VCC2SF3.scp │ ├── vcc18up_VCC2SF4.scp │ ├── vcc18up_VCC2SM3.scp │ ├── vcc18up_VCC2SM4.scp │ ├── vcc18up_VCC2TF1.scp │ ├── vcc18up_VCC2TF2.scp │ ├── vcc18up_VCC2TM1.scp │ ├── vcc18up_VCC2TM2.scp │ ├── vcc18va_VCC2SF3.scp │ ├── vcc18va_VCC2SF4.scp │ ├── vcc18va_VCC2SM3.scp │ ├── vcc18va_VCC2SM4.scp │ ├── vcc18va_VCC2TF1.scp │ ├── vcc18va_VCC2TF2.scp │ ├── vcc18va_VCC2TM1.scp │ └── vcc18va_VCC2TM2.scp │ └── wav │ └── .gitignore ├── qpnet_models └── .gitignore └── src ├── bin ├── calc_stats.py ├── feature_extract.py ├── initialize_speaker.py ├── noise_restored.py ├── noise_shaping.py ├── qpnet_decode.py ├── qpnet_train.py ├── qpnet_update.py └── qpnet_validate.py ├── nets └── qpnet.py ├── parse_options.sh ├── runFE.py ├── runQP.py ├── run_FE.sh ├── run_QP.sh └── utils ├── __init__.py ├── multi_process.py ├── param_feat.py ├── param_model.py ├── param_path.py ├── utils.py └── utils_pathlist.py /.gitignore: -------------------------------------------------------------------------------- 1 | # general 2 | *~ 3 | *.pyc 4 | \#*\# 5 | .\#* 6 | *DS_Store 7 | out.txt 8 | parallel_wavegan.egg-info/ 9 | doc/_build 10 | slurm-*.out 11 | tmp* 12 | .eggs/ 13 | .hypothesis/ 14 | .idea 15 | .backup/ 16 | .pytest_cache/ 17 | __pycache__/ 18 | .coverage* 19 | coverage.xml* 20 | .vscode* 21 | .nfs* 22 | 23 | # corpus related 24 | corpus/*/wav_h5_ns 25 | corpus/*/h5 26 | corpus/*/h5_restored 27 | corpus/*/hist 28 | corpus/*/stats 29 | 30 | # qpnet related 31 | qpnet_output/* 32 | temp/ 33 | 34 | # src related 35 | tempsrc/ 36 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [2019] [Yi-Chiao Wu] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | [![Python Version](https://img.shields.io/badge/Python-3.5%2C%203.6-green.svg)](https://img.shields.io/badge/Python-3.5%2C%203.6-green.svg) 2 | 3 | # Quasi-Periodic WaveNet (QPNet) 4 | 5 | ## Introduction 6 | The repository is the official QPNet [[1](https://arxiv.org/abs/1907.00797), [2](https://ieeexplore.ieee.org/document/9361096)] implementation with Pytorch. 7 | 8 | The generated samples can be found on our [Demo](https://bigpon.github.io/QuasiPeriodicWaveNet_demo) page. 9 | 10 | The repository includes two parts: 11 | 1. **Acoustic feature extraction** 12 | to extract spectral and prosodic features by WORLD 13 | 2. **QPNet vocoder** (*SI: speaker-independent; SD: speaker-dependent*) 14 | to generate speech based on the input acoustic features 15 | 16 | ## Requirements 17 | This repository is tested on 18 | - Python 3.6 19 | - Cuda 10.0 20 | - Pytorch 1.3 21 | - torchvision 0.4.1 22 | 23 | ## Setup 24 | The code works with both anaconda and virtualenv. 25 | The following example uses anaconda. 26 | ```bash 27 | $ conda create -n venvQPNet python=3.6 28 | $ source activate venvQPNet 29 | $ pip install sprocket-vc 30 | $ pip install torch torchvision 31 | $ git clone https://github.com/bigpon/QPNet.git 32 | ``` 33 | 34 | ## Folder architecture 35 | - **corpus** 36 | the folder to put corpora 37 | -- each corpus subfolder includes a ***scp*** subfolder for file lists and a ***wav*** subfolder for speech files 38 | - **qpnet_models** 39 | the folder for trained models 40 | - **qpnet_output** 41 | the folder for decoding output files 42 | - **src** 43 | the folder for source code 44 | 45 | ## Example 46 | 47 | ### Corpus download: 48 | - Dowdlod the [Voice Conversion Challenge 2018](https://datashare.is.ed.ac.uk/handle/10283/3061) (VCC2018) corpus to run the QPNet example 49 | ```bash 50 | $ cd QPNet/corpus/VCC2018/wav/ 51 | 52 | $ wget -o train.log -O train.zip https://datashare.is.ed.ac.uk/bitstream/handle/10283/3061/vcc2018_database_training.zip 53 | 54 | $ wget -o eval.log -O eval.zip https://datashare.is.ed.ac.uk/bitstream/handle/10283/3061/vcc2018_database_evaluation.zip 55 | 56 | $ wget -o ref.log -O ref.zip https://datashare.is.ed.ac.uk/bitstream/handle/10283/3061/vcc2018_database_reference.zip 57 | 58 | $ unzip train.zip 59 | $ unzip eval.zip 60 | $ unzip ref.zip 61 | ``` 62 | - **SI-QPNet training set**: `corpus/VCC2018/scp/vcc18tr.scp` 63 | - **SD-QPNet updating set**: `corpus/VCC2018/scp/vcc18up_VCC2SPK.scp` 64 | - **SD-QPNet validation set**: `corpus/VCC2018/scp/vcc18va_VCC2SPK.scp` 65 | - **Testing set**: `corpus/VCC2018/scp/vcc18eval.scp` 66 | 67 | ### Path setup: 68 | - Modify the corresponding CUDA and project root paths in `src/utils/param_path.py` 69 | ``` bash 70 | # move to the source code folder to run the following scripts 71 | $ cd QPNet/src/ 72 | ``` 73 | 74 | ### Feature extraction: 75 | 1. Output the F0 and power distributions histogram figures to `corpus/VCC2018/hist/` 76 | ``` bash 77 | $ bash run_FE.sh --stage 0 78 | ``` 79 | 80 | 2. Modify the **f0_min** (*lower bound of F0 range*), **f0_max** (*upper bound of F0 range*), and **pow_th** (*power threshold for VAD*) values of the speakers in `corpus/VCC2018/conf/pow_f0_dict.yml` 81 | *The F0 ranges setting details can be found [here](https://github.com/k2kobayashi/sprocket/blob/master/docs/vc_example.md). 82 | 83 | 3. Extract and save acoustic features of the training, evaluation, and reference sets in `corpus/VCC2018/h5/` 84 | *The analysis-synthesis speech files of the training set are also saved in `corpus/VCC2018/h5_restored/`. 85 | ``` bash 86 | $ bash run_FE.sh --stage 123 87 | ``` 88 | 89 | 4. Process waveform files by noise shaping for QPNet training and save the shaped files in `corpus/VCC2018/wav_h5_ns/` 90 | ``` bash 91 | $ bash run_FE.sh -stage 4 92 | ``` 93 | 94 | ### QPNet vocoder: 95 | 1. Train and test SI-QPNet 96 | ``` bash 97 | # the gpu ID can be set by --gpu GPU_ID (default: 0) 98 | $ bash run_QP.sh --gpu 0 --stage 03 99 | ``` 100 | 101 | 2. Update SD-QPNet for each speaker with the corresponding partial training data 102 | ``` bash 103 | $ bash run_QP.sh --gpu 0 --stage 1 104 | ``` 105 | 106 | 3. Validate SD-QPNet for each speaker with the corresponding partial training data 107 | ``` bash 108 | # the validation results are in `qpnet_models/modelname/validation_result.yml` 109 | $ bash run_QP.sh --gpu 0 --stage 2 110 | ``` 111 | 112 | 4. Test SD-QPNet with the updating iteration number according to the validation results 113 | ``` bash 114 | # the iter number can be set by --miter NUM (default: 1000) 115 | $ bash run_QP.sh --gpu 0 --miter 1000 --stage 4 116 | ``` 117 | 118 | 5. Test SI-QPNet with scaled F0 (0.5 * F0 and 1.5 * F0) 119 | ``` bash 120 | # default F0 scaled factors=("0.50" "1.50") 121 | # the scaled factors can be changed in run_QP.sh 122 | $ bash run_QP.sh --gpu 0 --stage 5 123 | ``` 124 | 125 | 6. Test SD-QPNet with scaled F0 (0.5 * F0 and 1.5 * F0) 126 | ``` bash 127 | $ bash run_QP.sh --gpu 0 --miter 1000 --stage 6 128 | ``` 129 | 130 | ## Hints 131 | 132 | - The program only support WORLD acoustic features now, but you can modify the feature extraction script and change the '**feature_type**' in `src/runFE.py` and `src/runQP.py` for new features. 133 | 134 | - You can extract acoustic feature with different settings (ex: frame length ...) and set different '**feature_format**' (default: h5) in `src/runFE.py` and `src/runQP.py` for each setting, and the program will create the corresponding folders. 135 | 136 | - You can easily change the generation model by setting different '**network**' (default: qpnet) in `src/runQP.py` when you create new generation models. 137 | 138 | - When working with new corpus, You only need to create the file lists of wav files because the program will create feature list based on the wav file list. 139 | 140 | - When you create the wav file lists, please follow the form as the example 141 | (ex: rootpath/wav/xxx/xxx.wav). 142 | 143 | ## Models and results 144 | 145 | - The pre-trained models and generated utterances are released. 146 | - You can download all pre-trained models via the [link](https://drive.google.com/drive/folders/1HghyuYG4V0_KTBwUB1KxwZtZuzzGVgCM?usp=sharing). 147 | - Please put the downloaded models in the `qpnet_models` folder. 148 | - The SD (speaker-dependent) models are adapted from the SI (speaker-independent) model. 149 | - You can download all generated utterances via the [link](https://drive.google.com/drive/folders/1VcoKBPk5kjvueE7oUsmbZUdalqxoCKeM?usp=sharing). 150 | - The released models are only trained with the vcc18 corpus (~ 1 hr). 151 | - To achieve higher speech qualities, more training data is required. (In our papers, the training data was ~ 3 hrs) 152 | 153 | 154 | 155 | 156 | 157 | 158 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 169 | 170 | 171 | 172 | 175 | 178 | 179 | 180 | 183 | 186 | 187 | 188 | 191 | 194 | 195 | 196 | 199 | 202 | 203 | 204 | 207 | 210 | 211 | 212 |
CorpusLanguageFs [Hz]FeatureModelResult
168 | vcc18EN22050world
(uv + f0 + mcep + ap)
(shiftms: 5)		 173 | 174 | SI 176 | 177 | link
181 | 182 | SD_VCC2SF3 184 | 185 | link
189 | 190 | SD_VCC2SF4 192 | 193 | link
197 | 198 | SD_VCC2SM3 200 | 201 | link
205 | 206 | SD_VCC2SM4 208 | 209 | link
213 | 214 | 215 | ## References 216 | The QPNet repository is developed based on 217 | - [Pytorch WaveNet](https://github.com/kan-bayashi/PytorchWaveNetVocoder) implementation by [@kan-bayashi](https://github.com/kan-bayashi) 218 | - [Voice conversion](https://github.com/k2kobayashi/sprocket) implementation by [@k2kobayashi](https://github.com/k2kobayashi) 219 | 220 | ## Citation 221 | 222 | If you find the code is helpful, please cite the following papers. 223 | 224 | ``` 225 | @InProceedings{qpnet_2019, 226 | author="Y.-C. Wu and T. Hayashi and P. L. Tobing and K. Kobayashi and T. Toda", 227 | title="{Q}uasi-{P}eriodic {W}ave{N}et vocoder: a pitch dependent dilated convolution model for parametric speech generation", 228 | booktitle="Proc. Interspeech", 229 | year="2019", 230 | month="Sept.", 231 | pages="196-200" 232 | } 233 | 234 | @ARTICLE{qpnet_2021, 235 | author="Y.-C. Wu and T. Hayashi and P. L. Tobing and K. Kobayashi and T. Toda", 236 | journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing}, 237 | title={Quasi-Periodic WaveNet: An Autoregressive Raw Waveform Generative Model With Pitch-Dependent Dilated Convolution Neural Network}, 238 | year={2021}, 239 | volume={29}, 240 | pages={1134-1148}, 241 | doi={10.1109/TASLP.2021.3061245}} 242 | ``` 243 | 244 | ## Authors 245 | 246 | Development: 247 | Yi-Chiao Wu @ Nagoya University ([@bigpon](https://github.com/bigpon)) 248 | E-mail: `yichiao.wu@g.sp.m.is.nagoya-u.ac.jp` 249 | 250 | Advisor: 251 | Tomoki Toda @ Nagoya University 252 | E-mail: `tomoki@icts.nagoya-u.ac.jp` 253 | 254 | 255 | 256 | -------------------------------------------------------------------------------- /corpus/VCC2018/conf/pow_f0_dict.yml: -------------------------------------------------------------------------------- 1 | VCC2SF1: 2 | f0_max: 450 3 | f0_min: 100 4 | pow_th: -31 5 | VCC2SF2: 6 | f0_max: 350 7 | f0_min: 110 8 | pow_th: -31 9 | VCC2SF3: 10 | f0_max: 340 11 | f0_min: 110 12 | pow_th: -38 13 | VCC2SF4: 14 | f0_max: 330 15 | f0_min: 120 16 | pow_th: -34 17 | VCC2SM1: 18 | f0_max: 200 19 | f0_min: 50 20 | pow_th: -31 21 | VCC2SM2: 22 | f0_max: 300 23 | f0_min: 70 24 | pow_th: -40 25 | VCC2SM3: 26 | f0_max: 220 27 | f0_min: 45 28 | pow_th: -35 29 | VCC2SM4: 30 | f0_max: 260 31 | f0_min: 45 32 | pow_th: -32 33 | VCC2TF1: 34 | f0_max: 350 35 | f0_min: 140 36 | pow_th: -45 37 | VCC2TF2: 38 | f0_max: 400 39 | f0_min: 100 40 | pow_th: -30 41 | VCC2TM1: 42 | f0_max: 200 43 | f0_min: 60 44 | pow_th: -23 45 | VCC2TM2: 46 | f0_max: 280 47 | f0_min: 50 48 | pow_th: -31 -------------------------------------------------------------------------------- /corpus/VCC2018/scp/vcc18eval.scp: -------------------------------------------------------------------------------- 1 | rootpath/wav/vcc2018_evaluation/VCC2SF1/30001.wav 2 | rootpath/wav/vcc2018_evaluation/VCC2SF1/30002.wav 3 | rootpath/wav/vcc2018_evaluation/VCC2SF1/30003.wav 4 | 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rootpath/wav/vcc2018_training/VCC2TM2/10002.wav 3 | rootpath/wav/vcc2018_training/VCC2TM2/10003.wav 4 | rootpath/wav/vcc2018_training/VCC2TM2/10004.wav 5 | rootpath/wav/vcc2018_training/VCC2TM2/10005.wav 6 | rootpath/wav/vcc2018_training/VCC2TM2/10006.wav 7 | rootpath/wav/vcc2018_training/VCC2TM2/10007.wav 8 | rootpath/wav/vcc2018_training/VCC2TM2/10008.wav 9 | rootpath/wav/vcc2018_training/VCC2TM2/10009.wav 10 | rootpath/wav/vcc2018_training/VCC2TM2/10010.wav -------------------------------------------------------------------------------- /corpus/VCC2018/wav/.gitignore: -------------------------------------------------------------------------------- 1 | # Ignore everything in this directory 2 | * 3 | # Except this file 4 | !.gitignore -------------------------------------------------------------------------------- /qpnet_models/.gitignore: -------------------------------------------------------------------------------- 1 | # Ignore everything in this directory 2 | * 3 | # Except this file 4 | !.gitignore -------------------------------------------------------------------------------- /src/bin/calc_stats.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | 4 | # Copyright 2017 Tomoki Hayashi (Nagoya University) 5 | # Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) 6 | 7 | import argparse 8 | import logging 9 | 10 | import numpy as np 11 | 12 | from sklearn.preprocessing import StandardScaler 13 | 14 | from utils import read_hdf5 15 | from utils import read_txt 16 | from utils import write_hdf5 17 | 18 | 19 | def calc_stats(file_list, args): 20 | """CALCULATE STATISTICS""" 21 | scaler = StandardScaler() 22 | 23 | # process over all of data 24 | for i, filename in enumerate(file_list): 25 | logging.info("now processing %s (%d/%d)" % (filename, i + 1, len(file_list))) 26 | feat = read_hdf5(filename, "/%s" % args.feature_type) 27 | scaler.partial_fit(feat[:, 1:]) 28 | 29 | # add uv term 30 | mean = np.zeros((feat.shape[1])) 31 | scale = np.ones((feat.shape[1])) 32 | mean[1:] = scaler.mean_ 33 | scale[1:] = scaler.scale_ 34 | 35 | # write to hdf5 36 | write_hdf5(args.stats, "/%s/mean" % args.feature_type, mean) 37 | write_hdf5(args.stats, "/%s/scale" % args.feature_type, scale) 38 | 39 | 40 | def main(): 41 | parser = argparse.ArgumentParser() 42 | 43 | parser.add_argument("--features", default=None, required=True, 44 | help="name of the list of hdf5 files") 45 | parser.add_argument("--feature_type", default="world", choices=["world"], 46 | type=str, help="feature type") 47 | parser.add_argument("--stats", default=None, required=True, 48 | help="filename of hdf5 format") 49 | parser.add_argument("--verbose", default=1, 50 | type=int, help="log message level") 51 | 52 | args = parser.parse_args() 53 | 54 | # set log level 55 | if args.verbose == 1: 56 | logging.basicConfig(level=logging.INFO, 57 | format='%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s', 58 | datefmt='%m/%d/%Y %I:%M:%S') 59 | elif args.verbose > 1: 60 | logging.basicConfig(level=logging.DEBUG, 61 | format='%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s', 62 | datefmt='%m/%d/%Y %I:%M:%S') 63 | else: 64 | logging.basicConfig(level=logging.WARN, 65 | format='%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s', 66 | datefmt='%m/%d/%Y %I:%M:%S') 67 | logging.warn("logging is disabled.") 68 | 69 | # show argmument 70 | for key, value in vars(args).items(): 71 | logging.info("%s = %s" % (key, str(value))) 72 | 73 | # read file list 74 | file_list = read_txt(args.features) 75 | logging.info("number of utterances = %d" % len(file_list)) 76 | 77 | # calculate statistics 78 | calc_stats(file_list, args) 79 | 80 | 81 | if __name__ == "__main__": 82 | main() 83 | -------------------------------------------------------------------------------- /src/bin/initialize_speaker.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | 4 | # Copyright 2019 Wu Yi-Chiao (Nagoya University) 5 | # based on sprocket-vc script by Kazuhiro Kobayashi (Nagoya University) 6 | # (https://github.com/k2kobayashi/sprocket) 7 | # Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) 8 | 9 | """ 10 | Generate histograms to decide speaker-dependent parameters 11 | 12 | """ 13 | 14 | import argparse 15 | import os 16 | import sys 17 | import logging 18 | import multiprocessing as mp 19 | 20 | import matplotlib 21 | import numpy as np 22 | from scipy.io import wavfile 23 | 24 | from sprocket.speech.feature_extractor import FeatureExtractor 25 | from utils import find_files, read_txt, write_hdf5, check_hdf5 26 | 27 | matplotlib.use('Agg') # noqa #isort:skip 28 | import matplotlib.pyplot as plt # isort:skip 29 | 30 | 31 | def create_histogram(data, figure_path, range_min=-70, range_max=20, 32 | step=10, xlabel='Power [dB]'): 33 | """Create histogram 34 | Parameters 35 | ---------- 36 | data : list, 37 | List of several data sequences 38 | figure_path : str, 39 | Filepath to be output figure 40 | range_min : int, optional, 41 | Minimum range for histogram 42 | Default set to -70 43 | range_max : int, optional, 44 | Maximum range for histogram 45 | Default set to -20 46 | step : int, optional 47 | Stap size of label in horizontal axis 48 | Default set to 10 49 | xlabel : str, optional 50 | Label of the horizontal axis 51 | Default set to 'Power [dB]' 52 | 53 | """ 54 | 55 | # plot histgram 56 | plt.hist(data, bins=200, range=(range_min, range_max), 57 | density=True, histtype="stepfilled") 58 | plt.xlabel(xlabel) 59 | plt.ylabel("Probability") 60 | plt.xticks(np.arange(range_min, range_max, step)) 61 | 62 | figure_dir = os.path.dirname(figure_path) 63 | if not os.path.exists(figure_dir): 64 | os.makedirs(figure_dir) 65 | 66 | plt.savefig(figure_path) 67 | plt.close() 68 | 69 | def world_feature_extract(wav_list, idx, f0_dict, npow_dict): 70 | f0s = [] 71 | npows = [] 72 | for f in wav_list: 73 | # open waveform 74 | wavf = f.rstrip() 75 | fs, x = wavfile.read(wavf) 76 | x = np.array(x, dtype=np.float) 77 | logging.info("Extract: " + wavf) 78 | 79 | # constract FeatureExtractor class 80 | feat = FeatureExtractor(analyzer='world', fs=fs) 81 | 82 | # f0 and npow extraction 83 | f0, _, _ = feat.analyze(x) 84 | npow = feat.npow() 85 | 86 | f0s.append(f0) 87 | npows.append(npow) 88 | f0_dict[idx] = f0s 89 | npow_dict[idx] = npows 90 | 91 | def main(): 92 | parser = argparse.ArgumentParser( 93 | description='Create histogram for speaker-dependent configure') 94 | parser.add_argument('--speaker', default=None, 95 | type=str, help='Input speaker label') 96 | parser.add_argument("--waveforms", default=None, 97 | type=str, help="directory or list of filename of input wavfile") 98 | parser.add_argument('--figure_dir', default=None, 99 | type=str, help='Directory for figure output') 100 | parser.add_argument("--n_jobs", default=10, 101 | type=int, help="number of parallel jobs") 102 | args = parser.parse_args() 103 | 104 | logging.basicConfig(level=logging.INFO, 105 | format='%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s', 106 | datefmt='%m/%d/%Y %I:%M:%S') 107 | 108 | # show argmument 109 | for key, value in vars(args).items(): 110 | logging.info("%s = %s" % (key, str(value))) 111 | 112 | # read list 113 | if os.path.isdir(args.waveforms): 114 | file_list = sorted(find_files(args.waveforms, "*.wav")) 115 | else: 116 | file_list = read_txt(args.waveforms) 117 | logging.info("number of utterances = %d" % len(file_list)) 118 | 119 | # divie list 120 | file_lists = np.array_split(file_list, args.n_jobs) 121 | file_lists = [f_list.tolist() for f_list in file_lists] 122 | 123 | f0s = [[]] * args.n_jobs 124 | npows = [[]] * args.n_jobs 125 | processes = [] 126 | target_fn = world_feature_extract 127 | manager = mp.Manager() 128 | f0_dict = manager.dict() 129 | npow_dict = manager.dict() 130 | for idx, f in enumerate(file_lists): 131 | p = mp.Process(target=target_fn, args=(f, idx, f0_dict, npow_dict)) 132 | p.start() 133 | processes.append(p) 134 | 135 | for p in processes: 136 | p.join() 137 | 138 | logging.info("extraction finished!") 139 | f0 =[] 140 | npow = [] 141 | for i in range(args.n_jobs): 142 | f0 += f0_dict[i] 143 | npow += npow_dict[i] 144 | 145 | 146 | f0s = np.hstack(f0).flatten() 147 | npows = np.hstack(npow).flatten() 148 | 149 | # create a histogram to visualize F0 range of the speaker 150 | f0histogrampath = os.path.join( 151 | args.figure_dir, args.speaker + '_f0histogram.png') 152 | create_histogram(f0s, f0histogrampath, range_min=40, range_max=700, 153 | step=50, xlabel='Fundamental frequency [Hz]') 154 | logging.info("save %s"%f0histogrampath) 155 | # create a histogram to visualize npow range of the speaker 156 | npowhistogrampath = os.path.join( 157 | args.figure_dir, args.speaker + '_npowhistogram.png') 158 | create_histogram(npows, npowhistogrampath, range_min=-70, range_max=20, 159 | step=10, xlabel="Frame power [dB]") 160 | logging.info("save %s"%npowhistogrampath) 161 | 162 | 163 | 164 | if __name__ == '__main__': 165 | main() 166 | -------------------------------------------------------------------------------- /src/bin/noise_restored.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | 4 | # Copyright 2019 Wu Yi-Chiao (Nagoya University) 5 | # based on a WaveNet script by Tomoki Hayashi (Nagoya University) 6 | # (https://github.com/kan-bayashi/PytorchWaveNetVocoder) 7 | # Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) 8 | 9 | from __future__ import division 10 | 11 | import argparse 12 | import logging 13 | import multiprocessing as mp 14 | import os 15 | import sys 16 | 17 | from distutils.util import strtobool 18 | 19 | import numpy as np 20 | 21 | from scipy.io import wavfile 22 | from sprocket.speech.feature_extractor import FeatureExtractor 23 | from sprocket.speech.synthesizer import Synthesizer 24 | 25 | from feature_extract import low_cut_filter 26 | from utils import find_files, read_hdf5, read_txt, check_hdf5 27 | 28 | def _get_arguments(): 29 | parser = argparse.ArgumentParser() 30 | # path setting 31 | parser.add_argument("--feats", required=True, 32 | type=str, help="list or directory of aux feat files") 33 | parser.add_argument("--stats", required=True, 34 | type=str, help="hdf5 file including statistics") 35 | parser.add_argument("--outdir", required=True, 36 | type=str, help="directory of noise shaped wav files") 37 | parser.add_argument("--writedir", required=True, 38 | type=str, help="directory to save restored wav file") 39 | # feature setting 40 | parser.add_argument("--feature_type", default="world", 41 | type=str, help="feature type") 42 | parser.add_argument("--feature_format", default="h5", 43 | type=str, help="feature format") 44 | parser.add_argument("--pow_adjust", default="1.0", 45 | type=float, help="power adjust factor") 46 | parser.add_argument("--fs", default=16000, 47 | type=int, help="Sampling frequency") 48 | parser.add_argument("--shiftms", default=5, 49 | type=float, help="Frame shift in msec") 50 | parser.add_argument("--fftl", default=1024, 51 | type=int, help="FFT length") 52 | parser.add_argument("--mcep_dim_start", default=2, 53 | type=int, help="Start index of mel cepstrum") 54 | parser.add_argument("--mcep_dim_end", default=27, 55 | type=int, help="End index of mel cepstrum") 56 | parser.add_argument("--mcep_alpha", default=0.41, 57 | type=float, help="Alpha of mel cepstrum") 58 | parser.add_argument("--mag", default=0.5, 59 | type=float, help="magnification of noise shaping") 60 | # other setting 61 | parser.add_argument("--verbose", default=1, 62 | type=int, help="log message level") 63 | parser.add_argument('--n_jobs', default=40, 64 | type=int, help="number of parallel jobs") 65 | parser.add_argument('--inv', default=False, 66 | type=strtobool, help="if True, inverse filtering will be performed") 67 | 68 | return parser.parse_args() 69 | 70 | def noise_shaping(wav_list, args): 71 | """APPLY NOISE SHAPING""" 72 | # define feature extractor 73 | feature_extractor = FeatureExtractor( 74 | analyzer="world", 75 | fs=args.fs, 76 | shiftms=args.shiftms, 77 | fftl=args.fftl) 78 | 79 | # define synthesizer 80 | synthesizer = Synthesizer( 81 | fs=args.fs, 82 | shiftms=args.shiftms, 83 | fftl=args.fftl) 84 | 85 | for i, feat_id in enumerate(wav_list): 86 | logging.info("now processing %s (%d/%d)" % (feat_id, i + 1, len(wav_list))) 87 | # load wavfile and apply low cut filter 88 | wav_filename = args.outdir.replace("feat_id", feat_id) 89 | fs, x = wavfile.read(wav_filename) 90 | wav_type = x.dtype 91 | x = np.array(x, dtype=np.float64) 92 | 93 | # check sampling frequency 94 | if not fs == args.fs: 95 | logging.error("sampling frequency is not matched.") 96 | sys.exit(1) 97 | 98 | ## extract features (only for get the number of frames) 99 | f0, _, _ = feature_extractor.analyze(x) 100 | num_frames = f0.shape[0] 101 | 102 | # load average mcep 103 | mlsa_coef = read_hdf5(args.stats, "/%s/mean" % args.feature_type) 104 | mlsa_coef = mlsa_coef[args.mcep_dim_start:args.mcep_dim_end] * args.mag 105 | mlsa_coef[0] = 0.0 106 | if args.inv: 107 | mlsa_coef[1:] = -1.0 * mlsa_coef[1:] 108 | mlsa_coef = np.tile(mlsa_coef, [num_frames, 1]) 109 | 110 | # synthesis and write 111 | x_ns = synthesizer.synthesis_diff(x, mlsa_coef, alpha=args.mcep_alpha) 112 | x_ns = low_cut_filter(x_ns, args.fs, cutoff=70) 113 | write_name = args.writedir.replace("feat_id", feat_id) 114 | # check directory existence 115 | wav = np.clip(x_ns, -32768, 32767) 116 | if wav_type == np.int16: 117 | wavfile.write(write_name, args.fs, np.int16(wav)) 118 | else: 119 | wavfile.write(write_name, args.fs, wav) 120 | 121 | 122 | def main(): 123 | # parser arguments 124 | args = _get_arguments() 125 | # set log level 126 | if args.verbose == 1: 127 | logging.basicConfig(level=logging.INFO, 128 | format='%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s', 129 | datefmt='%m/%d/%Y %I:%M:%S') 130 | elif args.verbose > 1: 131 | logging.basicConfig(level=logging.DEBUG, 132 | format='%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s', 133 | datefmt='%m/%d/%Y %I:%M:%S') 134 | else: 135 | logging.basicConfig(level=logging.WARN, 136 | format='%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s', 137 | datefmt='%m/%d/%Y %I:%M:%S') 138 | logging.warn("logging is disabled.") 139 | 140 | # show argmument 141 | for key, value in vars(args).items(): 142 | logging.info("%s = %s" % (key, str(value))) 143 | 144 | # check directory existence 145 | if not os.path.exists(os.path.dirname(args.writedir)): 146 | os.makedirs(os.path.dirname(args.writedir)) 147 | 148 | # get file list 149 | if os.path.isdir(args.feats): 150 | feat_list = sorted(find_files(args.feats, "*.%s" % args.feature_format)) 151 | elif os.path.isfile(args.feats): 152 | feat_list = read_txt(args.feats) 153 | else: 154 | logging.error("--feats should be directory or list.") 155 | sys.exit(1) 156 | feat_ids = [os.path.basename(f).replace(".%s" % args.feature_format, "") for f in feat_list] 157 | logging.info("number of utterances = %d" % len(feat_ids)) 158 | 159 | # divie list 160 | feat_ids = np.array_split(feat_ids, args.n_jobs) 161 | feat_ids = [f_ids.tolist() for f_ids in feat_ids] 162 | 163 | # multi processing 164 | processes = [] 165 | # for f in file_lists: 166 | for f in feat_ids: 167 | p = mp.Process(target=noise_shaping, args=(f, args,)) 168 | p.start() 169 | processes.append(p) 170 | 171 | # wait for all process 172 | for p in processes: 173 | p.join() 174 | 175 | 176 | if __name__ == "__main__": 177 | main() 178 | -------------------------------------------------------------------------------- /src/bin/noise_shaping.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | 4 | # Copyright 2019 Wu Yi-Chiao (Nagoya University) 5 | # based on a WaveNet script by Tomoki Hayashi (Nagoya University) 6 | # (https://github.com/kan-bayashi/PytorchWaveNetVocoder) 7 | # Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) 8 | 9 | from __future__ import division 10 | 11 | import argparse 12 | import logging 13 | import multiprocessing as mp 14 | import os 15 | import sys 16 | 17 | from distutils.util import strtobool 18 | 19 | import numpy as np 20 | 21 | from scipy.io import wavfile 22 | from scipy.signal import firwin 23 | from scipy.signal import lfilter 24 | from sprocket.speech.feature_extractor import FeatureExtractor 25 | from sprocket.speech.synthesizer import Synthesizer 26 | from utils import find_files, read_hdf5, read_txt 27 | 28 | 29 | def _get_arguments(): 30 | parser = argparse.ArgumentParser() 31 | # path setting 32 | parser.add_argument("--waveforms", default=None, 33 | type=str, help="directory or list of filename of input wavfile") 34 | parser.add_argument("--stats", default=None, 35 | type=str, help="filename of hdf5 format") 36 | # acoustic feature setting 37 | parser.add_argument("--feature_type", default="world", 38 | type=str, help="feature type") 39 | parser.add_argument("--feature_format", default="h5", 40 | type=str, help="feature format") 41 | parser.add_argument("--wavtype", default='ns', 42 | type=str, help="filtered wav type") 43 | parser.add_argument("--fs", default=22050, 44 | type=int, help="sampling frequency") 45 | parser.add_argument("--shiftms", default=5.0, 46 | type=float, help="frame shift in msec") 47 | parser.add_argument("--fftl", default=1024, 48 | type=int, help="FFT length") 49 | parser.add_argument("--mcep_dim_start", default=2, 50 | type=int, help="start index of mel cepstrum") 51 | parser.add_argument("--mcep_dim_end", default=37, 52 | type=int, help="end index of mel cepstrum") 53 | parser.add_argument("--mcep_alpha", default=0.455, 54 | type=float, help="Alpha of mel cepstrum") 55 | parser.add_argument("--mag", default=0.5, 56 | type=float, help="magnification of noise shaping") 57 | # other setting 58 | parser.add_argument("--verbose", default=1, 59 | type=int, help="log message level") 60 | parser.add_argument('--n_jobs', default=10, 61 | type=int, help="number of parallel jobs") 62 | parser.add_argument('--inv', default=True, 63 | type=strtobool, help="if True, inverse filtering will be performed") 64 | 65 | return parser.parse_args() 66 | 67 | def low_cut_filter(x, fs, cutoff=70): 68 | """APPLY LOW CUT FILTER 69 | Args: 70 | x (ndarray): Waveform sequence 71 | fs (int): Sampling frequency 72 | cutoff (float): Cutoff frequency of low cut filter 73 | 74 | Return: 75 | (ndarray): Low cut filtered waveform sequence 76 | """ 77 | 78 | nyquist = fs // 2 79 | norm_cutoff = cutoff / nyquist 80 | 81 | # low cut filter 82 | fil = firwin(255, norm_cutoff, pass_zero=False) 83 | lcf_x = lfilter(fil, 1, x) 84 | 85 | return lcf_x 86 | 87 | def filepath_create(wav_list, wav_set): 88 | for wav_name in wav_list: 89 | write_name = wav_name.replace("wav", wav_set).replace(".%s"%wav_set, ".wav") 90 | # check directory existence 91 | if not os.path.exists(os.path.dirname(write_name)): 92 | os.makedirs(os.path.dirname(write_name)) 93 | 94 | def noise_shaping(wav_list, wav_set, args): 95 | """APPLY NOISE SHAPING""" 96 | # define feature extractor 97 | feature_extractor = FeatureExtractor( 98 | analyzer="world", 99 | fs=args.fs, 100 | shiftms=args.shiftms, 101 | fftl=args.fftl) 102 | 103 | # define synthesizer 104 | synthesizer = Synthesizer( 105 | fs=args.fs, 106 | shiftms=args.shiftms, 107 | fftl=args.fftl) 108 | 109 | for i, wav_name in enumerate(wav_list): 110 | logging.info("now processing %s (%d/%d)" % (wav_name, i + 1, len(wav_list))) 111 | # load wavfile and apply low cut filter 112 | fs, x = wavfile.read(wav_name) 113 | wav_type = x.dtype 114 | x = np.array(x, dtype=np.float64) 115 | 116 | # check sampling frequency 117 | if not fs == args.fs: 118 | logging.error("sampling frequency is not matched.") 119 | sys.exit(1) 120 | 121 | # extract features (only for get the number of frames) 122 | f0, _, _ = feature_extractor.analyze(x) 123 | num_frames = f0.shape[0] 124 | 125 | # load average mcep 126 | mlsa_coef = read_hdf5(args.stats, "/%s/mean" % args.feature_type) 127 | mlsa_coef = mlsa_coef[args.mcep_dim_start:args.mcep_dim_end] * args.mag 128 | mlsa_coef[0] = 0.0 129 | if args.inv: 130 | mlsa_coef[1:] = -1.0 * mlsa_coef[1:] 131 | mlsa_coef = np.tile(mlsa_coef, [num_frames, 1]) 132 | 133 | # synthesis and write 134 | x_ns = synthesizer.synthesis_diff( 135 | x, mlsa_coef, alpha=args.mcep_alpha) 136 | x_ns = low_cut_filter(x_ns, args.fs, cutoff=70) 137 | write_name = wav_name.replace("wav", wav_set).replace(".%s"%wav_set, ".wav") 138 | x_ns = np.clip(x_ns, -32768, 32767) 139 | if wav_type == np.int16: 140 | wavfile.write(write_name, args.fs, np.int16(x_ns)) 141 | else: 142 | wavfile.write(write_name, args.fs, x_ns) 143 | 144 | 145 | def main(): 146 | # parser arguments 147 | args = _get_arguments() 148 | # set log level 149 | if args.verbose == 1: 150 | logging.basicConfig(level=logging.INFO, 151 | format='%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s', 152 | datefmt='%m/%d/%Y %I:%M:%S') 153 | elif args.verbose > 1: 154 | logging.basicConfig(level=logging.DEBUG, 155 | format='%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s', 156 | datefmt='%m/%d/%Y %I:%M:%S') 157 | else: 158 | logging.basicConfig(level=logging.WARN, 159 | format='%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s', 160 | datefmt='%m/%d/%Y %I:%M:%S') 161 | logging.warn("logging is disabled.") 162 | 163 | # show argmument 164 | for key, value in vars(args).items(): 165 | logging.info("%s = %s" % (key, str(value))) 166 | # read list 167 | if os.path.isdir(args.waveforms): 168 | file_list = sorted(find_files(args.waveforms, "*.wav")) 169 | else: 170 | file_list = read_txt(args.waveforms) 171 | logging.info("number of utterances = %d" % len(file_list)) 172 | wav_set = 'wav_%s_%s' % (args.feature_format, args.wavtype) 173 | # create file folders 174 | filepath_create(file_list, wav_set) 175 | # divide list 176 | file_lists = np.array_split(file_list, args.n_jobs) 177 | file_lists = [f_list.tolist() for f_list in file_lists] 178 | # multi processing 179 | processes = [] 180 | # for f in file_lists: 181 | for f in file_lists: 182 | p = mp.Process(target=noise_shaping, args=(f, wav_set, args,)) 183 | p.start() 184 | processes.append(p) 185 | 186 | # wait for all process 187 | for p in processes: 188 | p.join() 189 | 190 | 191 | if __name__ == "__main__": 192 | main() 193 | -------------------------------------------------------------------------------- /src/bin/qpnet_decode.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | 4 | # Copyright 2019 Wu Yi-Chiao (Nagoya University) 5 | # based on a WaveNet script by Tomoki Hayashi (Nagoya University) 6 | # (https://github.com/kan-bayashi/PytorchWaveNetVocoder) 7 | # Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) 8 | 9 | from __future__ import division 10 | 11 | import argparse 12 | import logging 13 | import math 14 | import os 15 | import sys 16 | import copy 17 | import numpy as np 18 | import torch 19 | import torch.multiprocessing as mp 20 | 21 | from sklearn.preprocessing import StandardScaler 22 | from torchvision import transforms 23 | from distutils.util import strtobool 24 | from scipy.io import wavfile 25 | 26 | from utils import extend_time 27 | from utils import find_files 28 | from utils import read_hdf5 29 | from utils import read_txt 30 | from utils import shape_hdf5 31 | 32 | from qpnet import encode_mu_law 33 | from qpnet import decode_mu_law 34 | from qpnet import QPNet 35 | 36 | 37 | def _get_arguments(): 38 | parser = argparse.ArgumentParser() 39 | # decode setting 40 | parser.add_argument("--feats", required=True, 41 | type=str, help="list or directory of testing aux feat files") 42 | parser.add_argument("--stats", required=True, 43 | type=str, help="hdf5 file including statistics") 44 | parser.add_argument("--config", required=True, 45 | type=str, help="configure file") 46 | parser.add_argument("--outdir", required=True, 47 | type=str, help="directory to save generated samples") 48 | parser.add_argument("--checkpoint", required=True, 49 | type=str, help="model file") 50 | parser.add_argument("--fs", default=22050, 51 | type=int, help="sampling rate") 52 | parser.add_argument("--batch_size", default=1, 53 | type=int, help="number of batch size in decoding") 54 | # other setting 55 | parser.add_argument("--extra_memory", default=False, 56 | type=strtobool, 57 | help=" set True will accelerate the decoding but consume more memory") 58 | parser.add_argument("--intervals", default=1000, 59 | type=int, help="log interval") 60 | parser.add_argument("--seed", default=100, 61 | type=int, help="seed number") 62 | parser.add_argument("--n_gpus", default=1, 63 | type=int, help="number of gpus") 64 | parser.add_argument("--verbose", default=1, 65 | type=int, help="log level") 66 | # f0 scaled setting 67 | parser.add_argument("--f0_factor", default=1.0, 68 | type=float, help="f0 scaled factor") 69 | parser.add_argument("--f0_dim_index", default=1, 70 | type=int, help="f0 dimension index") 71 | return parser.parse_args() 72 | 73 | def pad_list(batch_list, pad_value=0.0): 74 | """PAD VALUE 75 | Args: 76 | batch_list (list): list of batch, where the shape of i-th batch (T_i, C) 77 | pad_value (float): value to pad 78 | Return: 79 | (ndarray): padded batch with the shape (B, T_max, C) 80 | """ 81 | batch_size = len(batch_list) 82 | maxlen = max([batch.shape[0] for batch in batch_list]) 83 | n_feats = batch_list[0].shape[-1] 84 | batch_pad = np.zeros((batch_size, maxlen, n_feats)) 85 | for idx, batch in enumerate(batch_list): 86 | batch_pad[idx, :batch.shape[0]] = batch 87 | 88 | return batch_pad 89 | 90 | def _dilated_factor(batch_f0, fs, dense_factor): 91 | """PITCH-DEPENDENT DILATED FACTOR 92 | Args: 93 | batch_f0 (numpy array): the f0 sequence (T) 94 | fs (int): sampling rate 95 | dense_factor (int): the number of taps in one cycle 96 | Return: 97 | dilated_factors(np array): 98 | float array of the pitch-dependent dilated factors (T) 99 | """ 100 | f0s = copy.deepcopy(batch_f0) 101 | f0s[f0s == 0] = fs/dense_factor 102 | dilated_factors = np.ones(f0s.shape)*fs 103 | dilated_factors /= f0s 104 | dilated_factors /= dense_factor 105 | assert np.all(dilated_factors > 0) 106 | return dilated_factors 107 | 108 | def _batch_f0(h): 109 | """LOAD F0 SEQUENCE 110 | Args: 111 | h (numpy array): the auxiliary acoustic features (T x D) 112 | Return: 113 | cont_f0_lpf(numpy array): 114 | float array of the continuous pitch sequence (T) 115 | """ 116 | #uv = h[:, 0].copy(order='C') # voive/unvoice feature 117 | cont_f0_lpf = h[:, 1].copy(order='C') # continuous f0 118 | #mcep = h[:, 2:feat_param['mcep_dim_end']].copy(order='C') # mcc 119 | #codeap = h[:, feat_param['codeap_index']:].copy(order='C') # coded ap 120 | return cont_f0_lpf 121 | 122 | def decode_generator(feat_list, 123 | fs, 124 | wav_transform=None, 125 | feat_transform=None, 126 | feature_type="world", 127 | feat_ext=".h5", 128 | dense_factor=8, 129 | batch_size=32, 130 | upsampling_factor=80, 131 | f0_factor=1.0, 132 | f0_dim_index=1, 133 | extra_memory=False): 134 | """DECODE BATCH GENERATOR 135 | Args: 136 | feat_list (str): list of feat files 137 | fs (int): sampling rate 138 | wav_transform (func): preprocessing function for waveform 139 | feat_transform (func): preprocessing function for aux feats 140 | feature_type (str): feature type 141 | feat_ext (str): feature filename extension 142 | dense_factor (int): the number of taps in one cycle 143 | batch_size (int): batch size in decoding 144 | upsampling_factor (int): upsampling factor 145 | f0_factor (float): the ratio of scaled f0 146 | f0_dim_index (int): the dimension index of the f0 feature 147 | extra_memory(bool): processing dilated factor in tensor format or not 148 | * tensor mode will accelerate the decoding but consume more memory 149 | Return: 150 | (object): generator instance 151 | """ 152 | # sort with the feature length 153 | shape_list = [shape_hdf5(f, "/" + feature_type)[0] for f in feat_list] 154 | idx = np.argsort(shape_list) 155 | feat_list = [feat_list[i] for i in idx] 156 | 157 | # divide into batch list 158 | n_batch = math.ceil(len(feat_list) / batch_size) 159 | batch_lists = np.array_split(feat_list, n_batch) 160 | batch_lists = [f.tolist() for f in batch_lists] 161 | 162 | for batch_list in batch_lists: 163 | batch_x = [] 164 | batch_h = [] 165 | batch_d = [] 166 | feat_ids = [] 167 | n_samples_list = [] 168 | for featfile in batch_list: 169 | # make seed waveform and load aux feature 170 | x = np.zeros((1)) 171 | h = read_hdf5(featfile, "/" + feature_type) 172 | if f0_factor is not 1.0: 173 | h[:, f0_dim_index] = h[:, f0_dim_index] * f0_factor 174 | d = _dilated_factor(_batch_f0(h), fs, dense_factor) 175 | d = extend_time(np.expand_dims(d, -1), upsampling_factor) # T x 1 176 | 177 | # perform pre-processing 178 | if wav_transform is not None: 179 | x = wav_transform(x) 180 | if feat_transform is not None: 181 | h = feat_transform(h) 182 | 183 | # append to list 184 | batch_x += [x] 185 | batch_h += [h] 186 | batch_d += [d] 187 | feat_ids += [os.path.basename(featfile).replace(feat_ext, "")] 188 | n_samples_list += [h.shape[0] * upsampling_factor - 1] 189 | 190 | # convert list to ndarray 191 | batch_x = np.stack(batch_x, axis=0) 192 | batch_h = pad_list(batch_h) 193 | batch_d = pad_list(batch_d) 194 | # convert to torch variable 195 | batch_x = torch.from_numpy(batch_x).long() 196 | batch_h = torch.from_numpy(batch_h).float().transpose(1, 2) 197 | if extra_memory: 198 | batch_d = torch.from_numpy(batch_d).float().squeeze(-1) 199 | else: 200 | batch_d = batch_d.squeeze(-1) 201 | 202 | # send to cuda 203 | if torch.cuda.is_available(): 204 | batch_x = batch_x.cuda() 205 | batch_h = batch_h.cuda() 206 | if extra_memory: 207 | batch_d = batch_d.cuda() 208 | 209 | yield feat_ids, batch_x, batch_h, n_samples_list, batch_d 210 | 211 | 212 | def main(): 213 | # parser arguments 214 | args = _get_arguments() 215 | # set log level 216 | if args.verbose > 0: 217 | logging.basicConfig(level=logging.INFO, 218 | format='%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s', 219 | datefmt='%m/%d/%Y %I:%M:%S') 220 | elif args.verbose > 1: 221 | logging.basicConfig(level=logging.DEBUG, 222 | format='%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s', 223 | datefmt='%m/%d/%Y %I:%M:%S') 224 | else: 225 | logging.basicConfig(level=logging.WARN, 226 | format='%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s', 227 | datefmt='%m/%d/%Y %I:%M:%S') 228 | logging.warn("logging is disabled.") 229 | 230 | # show argmument 231 | for key, value in vars(args).items(): 232 | logging.info("%s = %s" % (key, str(value))) 233 | 234 | # check directory existence 235 | if not os.path.isdir(os.path.dirname(args.outdir)): 236 | os.makedirs(os.path.dirname(args.outdir)) 237 | 238 | # fix seed 239 | np.random.seed(args.seed) 240 | torch.manual_seed(args.seed) 241 | torch.cuda.manual_seed(args.seed) 242 | os.environ['PYTHONHASHSEED'] = str(args.seed) 243 | 244 | # load config 245 | config = torch.load(args.config) 246 | 247 | # get file list 248 | feat_ext = ".%s" % config.feature_format # feature file extension 249 | if os.path.isdir(args.feats): 250 | feat_list = sorted(find_files(args.feats, "*%s" % feat_ext)) 251 | elif os.path.isfile(args.feats): 252 | feat_list = read_txt(args.feats) 253 | else: 254 | logging.error("--feats should be directory or list.") 255 | sys.exit(1) 256 | 257 | # prepare the file list for parallel decoding 258 | feat_lists = np.array_split(feat_list, args.n_gpus) 259 | feat_lists = [f_list.tolist() for f_list in feat_lists] 260 | 261 | # define transform 262 | scaler = StandardScaler() 263 | scaler.mean_ = read_hdf5(args.stats, "/%s/mean" % config.feature_type) 264 | scaler.scale_ = read_hdf5(args.stats, "/%s/scale" % config.feature_type) 265 | scaler.n_features_in_ = scaler.mean_.shape[0] 266 | wav_transform = transforms.Compose([ 267 | lambda x: encode_mu_law(x, config.n_quantize)]) 268 | feat_transform = transforms.Compose([ 269 | lambda x: scaler.transform(x)]) 270 | # define gpu decode function 271 | def _decode(feat_list, gpu): 272 | # define model and load parameters 273 | torch.set_grad_enabled(False) 274 | upsampling_factor = config.upsampling_factor 275 | model = QPNet( 276 | n_quantize=config.n_quantize, 277 | n_aux=config.n_aux, 278 | n_resch=config.n_resch, 279 | n_skipch=config.n_skipch, 280 | dilationF_depth=config.dilationF_depth, 281 | dilationF_repeat=config.dilationF_repeat, 282 | dilationA_depth=config.dilationA_depth, 283 | dilationA_repeat=config.dilationA_repeat, 284 | kernel_size=config.kernel_size, 285 | upsampling_factor=upsampling_factor) 286 | model.load_state_dict(torch.load( 287 | args.checkpoint, 288 | map_location=lambda storage, 289 | loc: storage)["model"]) 290 | model.eval() 291 | if torch.cuda.is_available(): 292 | torch.cuda.set_device(gpu) 293 | model.cuda() 294 | # define generator 295 | generator = decode_generator( 296 | feat_list, 297 | fs=args.fs, 298 | wav_transform=wav_transform, 299 | feat_transform=feat_transform, 300 | feature_type=config.feature_type, 301 | feat_ext=feat_ext, 302 | dense_factor=config.dense_factor, 303 | batch_size=args.batch_size, 304 | upsampling_factor=upsampling_factor, 305 | f0_factor=args.f0_factor, 306 | f0_dim_index=args.f0_dim_index, 307 | extra_memory=args.extra_memory) 308 | 309 | # decode 310 | for feat_ids, batch_x, batch_h, n_samples_list, batch_d in generator: 311 | logging.info("decoding start!") 312 | samples_list = model.batch_fast_generate( 313 | batch_x, batch_h, n_samples_list, batch_d, 314 | intervals=args.intervals, extra_memory=args.extra_memory) 315 | for feat_id, samples in zip(feat_ids, samples_list): 316 | wav = decode_mu_law(samples, config.n_quantize) 317 | wav_filename = args.outdir.replace("feat_id", feat_id) 318 | wav = np.clip(wav*32768, -32768, 32767) 319 | wavfile.write(wav_filename, args.fs, wav.astype(np.int16)) 320 | logging.info("wrote %s." % (wav_filename)) 321 | 322 | # parallel decode 323 | processes = [] 324 | for gpu, feat_list in enumerate(feat_lists): 325 | p = mp.Process(target=_decode, args=(feat_list, gpu,)) 326 | p.start() 327 | processes.append(p) 328 | 329 | # wait for all process 330 | for p in processes: 331 | p.join() 332 | 333 | 334 | if __name__ == "__main__": 335 | main() 336 | -------------------------------------------------------------------------------- /src/parse_options.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | # Copyright 2012 Johns Hopkins University (Author: Daniel Povey); 4 | # Arnab Ghoshal, Karel Vesely 5 | 6 | # Licensed under the Apache License, Version 2.0 (the "License"); 7 | # you may not use this file except in compliance with the License. 8 | # You may obtain a copy of the License at 9 | # 10 | # http://www.apache.org/licenses/LICENSE-2.0 11 | # 12 | # THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY 13 | # KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED 14 | # WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE, 15 | # MERCHANTABLITY OR NON-INFRINGEMENT. 16 | # See the Apache 2 License for the specific language governing permissions and 17 | # limitations under the License. 18 | 19 | 20 | # Parse command-line options. 21 | # To be sourced by another script (as in ". parse_options.sh"). 22 | # Option format is: --option-name arg 23 | # and shell variable "option_name" gets set to value "arg." 24 | # The exception is --help, which takes no arguments, but prints the 25 | # $help_message variable (if defined). 26 | 27 | 28 | ### 29 | ### The --config file options have lower priority to command line 30 | ### options, so we need to import them first... 31 | ### 32 | 33 | # Now import all the configs specified by command-line, in left-to-right order 34 | for ((argpos=1; argpos<$#; argpos++)); do 35 | if [ "${!argpos}" == "--config" ]; then 36 | argpos_plus1=$((argpos+1)) 37 | config=${!argpos_plus1} 38 | [ ! -r $config ] && echo "$0: missing config '$config'" && exit 1 39 | . $config # source the config file. 40 | fi 41 | done 42 | 43 | 44 | ### 45 | ### Now we process the command line options 46 | ### 47 | while true; do 48 | [ -z "${1:-}" ] && break; # break if there are no arguments 49 | case "$1" in 50 | # If the enclosing script is called with --help option, print the help 51 | # message and exit. Scripts should put help messages in $help_message 52 | --help|-h) if [ -z "$help_message" ]; then echo "No help found." 1>&2; 53 | else printf "$help_message\n" 1>&2 ; fi; 54 | exit 0 ;; 55 | --*=*) echo "$0: options to scripts must be of the form --name value, got '$1'" 56 | exit 1 ;; 57 | # If the first command-line argument begins with "--" (e.g. --foo-bar), 58 | # then work out the variable name as $name, which will equal "foo_bar". 59 | --*) name=`echo "$1" | sed s/^--// | sed s/-/_/g`; 60 | # Next we test whether the variable in question is undefned-- if so it's 61 | # an invalid option and we die. Note: $0 evaluates to the name of the 62 | # enclosing script. 63 | # The test [ -z ${foo_bar+xxx} ] will return true if the variable foo_bar 64 | # is undefined. We then have to wrap this test inside "eval" because 65 | # foo_bar is itself inside a variable ($name). 66 | eval '[ -z "${'$name'+xxx}" ]' && echo "$0: invalid option $1" 1>&2 && exit 1; 67 | 68 | oldval="`eval echo \\$$name`"; 69 | # Work out whether we seem to be expecting a Boolean argument. 70 | if [ "$oldval" == "true" ] || [ "$oldval" == "false" ]; then 71 | was_bool=true; 72 | else 73 | was_bool=false; 74 | fi 75 | 76 | # Set the variable to the right value-- the escaped quotes make it work if 77 | # the option had spaces, like --cmd "queue.pl -sync y" 78 | eval $name=\"$2\"; 79 | 80 | # Check that Boolean-valued arguments are really Boolean. 81 | if $was_bool && [[ "$2" != "true" && "$2" != "false" ]]; then 82 | echo "$0: expected \"true\" or \"false\": $1 $2" 1>&2 83 | exit 1; 84 | fi 85 | shift 2; 86 | ;; 87 | *) break; 88 | esac 89 | done 90 | 91 | 92 | # Check for an empty argument to the --cmd option, which can easily occur as a 93 | # result of scripting errors. 94 | [ ! -z "${cmd+xxx}" ] && [ -z "$cmd" ] && echo "$0: empty argument to --cmd option" 1>&2 && exit 1; 95 | 96 | 97 | true; # so this script returns exit code 0. 98 | -------------------------------------------------------------------------------- /src/runFE.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | 4 | # Copyright 2019 Wu Yi-Chiao (Nagoya University) 5 | # based on a WaveNet script by Tomoki Hayashi (Nagoya University) 6 | # (https://github.com/kan-bayashi/PytorchWaveNetVocoder) 7 | # based on a voice conversion script by Kazuhiro Kobayashi (Nagoya University) 8 | # (https://github.com/k2kobayashi/sprocket) 9 | # Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) 10 | 11 | """Feature extraction script 12 | 13 | Usage: runFE.py -e EVALLIST 14 | [-hri] [-f FS] 15 | [-1] [-2] [-3] [-4] SPK 16 | 17 | Options: 18 | -h, --help Show the help 19 | -r, --replace Over write the exist data 20 | -i, --inverse Inverse flag of filter 21 | -f FS The sampling rate 22 | -e EVALLIST The name of the execute list file 23 | -1, --step1 Execute step1 (f0 & power statistic) 24 | -2, --step2 Execute step2 (feature extraction / synthesis) 25 | -3, --step3 Execute step3 (feature statistic) 26 | -4, --step4 Execute step4 (waveform noise shaping) 27 | SPK The name of speaker 28 | 29 | """ 30 | import os 31 | import sys 32 | import h5py 33 | import math 34 | import yaml 35 | import copy 36 | import numpy as np 37 | from docopt import docopt 38 | from sprocket.model.f0statistics import F0statistics 39 | from utils.utils import write_hdf5, check_hdf5, read_hdf5, read_txt 40 | from utils.param_feat import acoustic_parameter 41 | from utils.multi_process import multi_processing 42 | from utils.utils_pathlist import _path_initial, _path_check, _templist, _remove_temp_file 43 | from utils.param_path import ROOT_DIR, PRJ_DIR, COP_DIR, SRC_DIR 44 | # FEATURE FLAG 45 | SAVE_F0 = True 46 | SAVE_AP = False 47 | SAVE_SPC = False 48 | SAVE_NPOW = True 49 | SAVE_EXTEND = False 50 | SAVE_VAD = True 51 | N_JOBS = 20 52 | 53 | # MAIN 54 | if __name__ == "__main__": 55 | args = docopt(__doc__) # pylint: disable=invalid-name 56 | os.environ['PYTHONPATH'] = (SRC_DIR + "utils") 57 | #print(args) 58 | # STEP CONTRAL 59 | execute_steps = [False] + [args["--step{}".format(step_index)] for step_index in range(1, 5)] 60 | if not any(execute_steps): 61 | raise("Please specify steps with options") 62 | # ACOUSTIC FEATURE & WAVEFORM SETTING 63 | feat_format = "h5" 64 | shiftms = 5 65 | fs = "22050" 66 | if args['-f'] is not None: 67 | fs = args['-f'] 68 | feat_param = acoustic_parameter(fs, shiftms=shiftms) 69 | synonym_root = "rootpath" 70 | # PATH INITIALIZATION 71 | spk = args['SPK'] 72 | tempdir = "%stemp/" % PRJ_DIR 73 | corpus_dir = COP_DIR 74 | stats_dir = "%sstats/" % (corpus_dir) 75 | figure_dir = "%shist/" % (corpus_dir) 76 | wavs = "%sscp/%s" % (corpus_dir, args["-e"]) 77 | spkinfof = "%sconf/pow_f0_dict.yml" % (corpus_dir) 78 | _path_check([corpus_dir]) 79 | _path_initial([tempdir, figure_dir, stats_dir, os.path.dirname(spkinfof)]) 80 | running_set = os.path.basename(wavs).split('.')[0].split("-")[-1] 81 | stats = "%s%s_stats.%s" % (stats_dir, running_set, feat_format) 82 | waveforms = "%swavs_%s.tmp" % (tempdir, spk) 83 | _templist(wavs, waveforms, "", [synonym_root], [corpus_dir]) 84 | feats = "%sfeat_%s.tmp" % (tempdir, running_set) 85 | _templist(waveforms, feats, "", ["wav"], [feat_format]) 86 | 87 | # F0 & POW STATISTIC 88 | if execute_steps[1]: 89 | cmd = "python ./bin/initialize_speaker.py" + \ 90 | " --speaker " + spk + \ 91 | " --waveforms " + waveforms + \ 92 | " --figure_dir " + figure_dir + \ 93 | " --n_jobs " + str(N_JOBS) 94 | #print(cmd) 95 | os.system(cmd) 96 | print('f0 & power statistics are created, please modify the %s file for the speaker %s.' % (spkinfof, spk)) 97 | if os.path.exists(spkinfof): 98 | with open(spkinfof,"r") as f: 99 | spk_dict = yaml.safe_load(f) 100 | if not (spk in spk_dict): 101 | spk_dict.update({spk:{'f0_min':40, 'f0_max':800, 'pow_th':-30}}) 102 | else: 103 | spk_dict = {spk:{'f0_min':40, 'f0_max':800, 'pow_th':-30}} 104 | with open(spkinfof,"w") as f: 105 | yaml.safe_dump(spk_dict, f) 106 | sys.exit(0) 107 | 108 | # FEATURE EXTRACTION / SYNTHESIS 109 | if execute_steps[2]: 110 | with open(spkinfof,"r") as f: 111 | spk_dict = yaml.safe_load(f) 112 | minf0 = spk_dict[spk]['f0_min'] 113 | maxf0 = spk_dict[spk]['f0_max'] 114 | pow_th = spk_dict[spk]['pow_th'] 115 | cmd = "python ./bin/feature_extract.py" + \ 116 | " --waveforms " + waveforms + \ 117 | " --feature_type " + str(feat_param.feature_type)+ \ 118 | " --feature_format " + str(feat_format) + \ 119 | " --fs " + str(fs) + \ 120 | " --shiftms " + str(feat_param.shiftms) + \ 121 | " --fftl " + str(feat_param.fftl) + \ 122 | " --minf0 " + str(minf0) + \ 123 | " --maxf0 " + str(maxf0) + \ 124 | " --pow_th " + str(pow_th) + \ 125 | " --mcep_dim " + str(feat_param.mcep_dim) + \ 126 | " --mcep_dim_start " + str(feat_param.mcep_dim_start) + \ 127 | " --mcep_dim_end " + str(feat_param.mcep_dim_end) + \ 128 | " --mcep_alpha " + str(feat_param.mcep_alpha) + \ 129 | " --highpass_cutoff " + str(feat_param.highpass_cutoff) + \ 130 | " --f0_dim_idx " + str(feat_param.f0_dim_idx) + \ 131 | " --ap_dim_idx " + str(feat_param.ap_dim_idx) + \ 132 | " --save_f0 " + str(SAVE_F0) + \ 133 | " --save_ap " + str(SAVE_AP) + \ 134 | " --save_spc " + str(SAVE_SPC) + \ 135 | " --save_npow " + str(SAVE_NPOW) + \ 136 | " --save_extended " + str(SAVE_EXTEND) + \ 137 | " --save_vad " + str(SAVE_VAD) + \ 138 | " --overwrite " + str(args['--replace']) + \ 139 | " --inv " + str(args['--inverse']) + \ 140 | " --n_jobs " + str(N_JOBS) 141 | #print(cmd) 142 | os.system(cmd) 143 | 144 | # FEATURE STATISTIC 145 | if execute_steps[3]: 146 | cmd = "python ./bin/calc_stats.py" + \ 147 | " --features " + feats + \ 148 | " --feature_type " + str(feat_param.feature_type)+ \ 149 | " --stats " + stats 150 | # print(cmd) 151 | os.system(cmd) 152 | 153 | # NOISE SHAPING 154 | if execute_steps[4]: 155 | wavtype = 'ns' 156 | cmd = "python ./bin/noise_shaping.py" + \ 157 | " --waveforms " + waveforms + \ 158 | " --feature_type " + str(feat_param.feature_type)+ \ 159 | " --feature_format " + str(feat_format) + \ 160 | " --wavtype " + wavtype + \ 161 | " --stats " + stats + \ 162 | " --fs " + str(fs) + \ 163 | " --shiftms " + str(feat_param.shiftms) + \ 164 | " --fftl " + str(feat_param.fftl) + \ 165 | " --mcep_dim_start " + str(feat_param.mcep_dim_start) + \ 166 | " --mcep_dim_end " + str(feat_param.mcep_dim_end) + \ 167 | " --mcep_alpha " + str(feat_param.mcep_alpha) + \ 168 | " --mag " + str(feat_param.mag) + \ 169 | " --n_jobs " + str(N_JOBS) + \ 170 | " --inv true " 171 | # print(cmd) 172 | os.system(cmd) 173 | 174 | _remove_temp_file([waveforms, feats]) 175 | 176 | 177 | 178 | -------------------------------------------------------------------------------- /src/run_FE.sh: -------------------------------------------------------------------------------- 1 | #! /bin/bash 2 | 3 | stage= 4 | # stage 0: feature distribution extraction 5 | # stage 1: feature extraction of training set 6 | # stage 2: feature extraction of evaluation set 7 | # stage 3: feature extraction of reference set 8 | # stage 4: noise shaping of training waveforms 9 | . parse_options.sh || exit 1; 10 | hubspks=("VCC2SF1" "VCC2SF2" "VCC2SM1" "VCC2SM2") 11 | spospks=("VCC2SF3" "VCC2SF4" "VCC2SM3" "VCC2SM4") 12 | srcspks=("${hubspks[@]}" "${spospks[@]}") 13 | tarspks=("VCC2TM1" "VCC2TM2" "VCC2TF1" "VCC2TF2") 14 | allspks=("${srcspks[@]}" "${tarspks[@]}") 15 | 16 | # Feature distribution extraction 17 | if echo ${stage} | grep -q 0; then 18 | for spk in ${allspks[*]}; 19 | do 20 | python runFE.py -f 22050 -e vcc18tr_$spk.scp -1 $spk 21 | done 22 | fi 23 | 24 | # Feature extraction: training set 25 | if echo ${stage} | grep -q 1; then 26 | for spk in ${allspks[*]}; 27 | do 28 | # feature extraction 29 | python runFE.py -r -i -f 22050 -e vcc18tr_$spk.scp -2 $spk 30 | # feature restored 31 | python runFE.py -r -f 22050 -e vcc18tr_$spk.scp -2 $spk 32 | done 33 | fi 34 | 35 | # Feature extraction: evaluation set 36 | if echo ${stage} | grep -q 2; then 37 | for spk in ${srcspks[*]}; 38 | do 39 | # feature extraction 40 | python runFE.py -r -i -f 22050 -e vcc18eval_$spk.scp -2 $spk 41 | # feature restored 42 | #python runFE.py -f 22050 -e vcc18eval_$spk.scp -2 $spk 43 | done 44 | fi 45 | 46 | # Feature extraction: reference set 47 | if echo ${stage} | grep -q 3; then 48 | for spk in ${tarspks[*]}; 49 | do 50 | # feature extraction 51 | python runFE.py -r -i -f 22050 -e vcc18ref_$spk.scp -2 $spk 52 | # feature restored 53 | #python runFE.py -f 22050 -e vcc18ref_$spk.scp -2 $spk 54 | done 55 | fi 56 | 57 | # Waveform noise shaping 58 | if echo ${stage} | grep -q 4; then 59 | # feature statistic 60 | python runFE.py -r -f 22050 -e vcc18tr.scp -3 allspk 61 | # noise shaping 62 | python runFE.py -r -f 22050 -e vcc18tr.scp -4 allspk 63 | fi -------------------------------------------------------------------------------- /src/run_QP.sh: -------------------------------------------------------------------------------- 1 | #! /bin/bash 2 | gpu=0 3 | miter=1000 4 | stage= 5 | # stage 0: training SI-QPNet 6 | # stage 1: updating SD-QPNet 7 | # stage 2: validation of SD-QPNet 8 | # stage 3: testing w/ SI-QPNet 9 | # stage 4: testing w/ SD-QPNet 10 | # stage 5: testing w/ SI-QPNet and scaled F0 11 | # stage 6: testing w/ SD-QPNet and scaled F0 12 | . parse_options.sh || exit 1; 13 | hubspks=("VCC2SF1" "VCC2SF2" "VCC2SM1" "VCC2SM2") 14 | spospks=("VCC2SF3" "VCC2SF4" "VCC2SM3" "VCC2SM4") 15 | srcspks=("${hubspks[@]}" "${spospks[@]}") 16 | tarspks=("VCC2TM1" "VCC2TM2" "VCC2TF1" "VCC2TF2") 17 | allspks=("${srcspks[@]}" "${tarspks[@]}") 18 | factors=("0.50" "1.50") 19 | 20 | # Speaker independent QPNet (SI-QPNet) training 21 | if echo ${stage} | grep -q 0; then 22 | echo "SI-QPNet training." 23 | python runQP.py -g ${gpu} -f 22050 \ 24 | -w vcc18tr.scp -a vcc18tr.scp -d 8 -1 25 | fi 26 | 27 | # Speaker dependent QPNet (SD-QPNet) updating 28 | if echo ${stage} | grep -q 1; then 29 | for spk in ${spospks[*]}; 30 | do 31 | echo "${spk} SD-QPNet updating." 32 | python runQP.py -g ${gpu} -f 22050 \ 33 | -w vcc18tr.scp -a vcc18tr.scp \ 34 | -x vcc18up_${spk}.scp -u vcc18up_${spk}.scp -d 8 -2 35 | done 36 | fi 37 | 38 | # Speaker dependent QPNet (SD-QPNet) validation 39 | if echo ${stage} | grep -q 2; then 40 | for spk in ${spospks[*]}; 41 | do 42 | echo "${spk} SD-QPNet validation." 43 | python runQP.py -g ${gpu} -f 22050 \ 44 | -w vcc18tr.scp -a vcc18tr.scp \ 45 | -x vcc18up_${spk}.scp -u vcc18up_${spk}.scp \ 46 | -y vcc18va_${spk}.scp -v vcc18va_${spk}.scp -d 8 -5 47 | done 48 | fi 49 | 50 | # Decoding with SI-QPNet 51 | if echo ${stage} | grep -q 3; then 52 | for spk in ${spospks[*]}; 53 | do 54 | echo "${spk} SI-QPNet decoding." 55 | python runQP.py -m -g ${gpu} -f 22050 \ 56 | -w vcc18tr.scp -a vcc18tr.scp \ 57 | -e vcc18eval_${spk}.scp -d 8 -3 -4 ${spk} 58 | done 59 | fi 60 | 61 | # Decoding with SD-QPNet 62 | if echo ${stage} | grep -q 4; then 63 | for spk in ${spospks[*]}; 64 | do 65 | echo "${spk} SD-QPNet decoding." 66 | python runQP.py -g ${gpu} -f 22050 -M ${miter}\ 67 | -w vcc18tr.scp -a vcc18tr.scp \ 68 | -x vcc18up_${spk}.scp -u vcc18up_${spk}.scp \ 69 | -e vcc18eval_${spk}.scp -d 8 -3 -4 ${spk} 70 | done 71 | fi 72 | 73 | # Decoding with SI-QPNet 74 | if echo ${stage} | grep -q 5; then 75 | for spk in ${spospks[*]}; 76 | do 77 | for factor in ${factors[*]}; 78 | do 79 | echo "${spk} SI-QPNet decoding w/ f0 ${factor}." 80 | python runQP.py -m -g ${gpu} -f 22050 -F ${factor} \ 81 | -w vcc18tr.scp -a vcc18tr.scp \ 82 | -e vcc18eval_${spk}.scp -d 8 -3 -4 ${spk} 83 | done 84 | done 85 | fi 86 | 87 | # Decoding with SD-QPNet 88 | if echo ${stage} | grep -q 6; then 89 | for spk in ${spospks[*]}; 90 | do 91 | for factor in ${factors[*]}; 92 | do 93 | echo "${spk} SD-QPNet decoding w/ f0 ${factor}." 94 | python runQP.py -g ${gpu} -f 22050 -M ${miter} -F ${factor} \ 95 | -w vcc18tr.scp -a vcc18tr.scp \ 96 | -x vcc18up_${spk}.scp -u vcc18up_${spk}.scp \ 97 | -e vcc18eval_${spk}.scp -d 8 -3 -4 ${spk} 98 | done 99 | done 100 | fi -------------------------------------------------------------------------------- /src/utils/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bigpon/QPNet/657fcb01b23e9e3371b5a4b2ebeec5757ad33e2d/src/utils/__init__.py -------------------------------------------------------------------------------- /src/utils/multi_process.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | 4 | # Copyright 2019 Wu Yi-Chiao (Nagoya University) 5 | # based on a WaveNet script by Tomoki Hayashi (Nagoya University) 6 | # (https://github.com/kan-bayashi/PytorchWaveNetVocoder) 7 | # Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) 8 | 9 | import os 10 | import numpy as np 11 | import multiprocessing as mp 12 | 13 | def multi_processing(file_list, target_fn, n_jobs): 14 | # divie list 15 | file_lists = np.array_split(file_list, n_jobs) 16 | file_lists = [f_list.tolist() for f_list in file_lists] 17 | #print(file_lists[0]) 18 | # multi processing 19 | processes = [] 20 | for f in file_lists: 21 | p = mp.Process(target=target_fn, args=(f)) 22 | p.start() 23 | processes.append(p) 24 | # wait for all process 25 | for p in processes: 26 | p.join() -------------------------------------------------------------------------------- /src/utils/param_feat.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | 4 | # Copyright 2019 Wu Yi-Chiao (Nagoya University) 5 | # Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) 6 | 7 | import math 8 | 9 | # ACOUSTIC PARAMETER 10 | class acoustic_parameter(object): 11 | def __init__(self, fs, feature_type="world", 12 | shiftms=5, fftl=1024, mag=0.5, 13 | mcep_dim_start=2, f0_dim_idx=1, 14 | highpass_cutoff = 70, 15 | minf0=40, maxf0=800): 16 | self.feature_type = feature_type 17 | self.shiftms = shiftms 18 | self.fftl = fftl 19 | self.mag = mag 20 | self.mcep_dim_start = mcep_dim_start 21 | self.f0_dim_idx = f0_dim_idx 22 | self.highpass_cutoff = highpass_cutoff 23 | self._update_fs(fs) 24 | self._update_f0range(minf0, maxf0) 25 | 26 | def _update_f0range(self, minf0, maxf0): 27 | self.minf0 = minf0 28 | self.maxf0 = maxf0 29 | 30 | def _update_fs(self, fs): 31 | self.fs = str(fs) 32 | if self.fs == "16000": 33 | #mcep_dim_end = 27 #2+24+1 34 | #aux_dim = 28 #(24+1)mcep + 1uv + 1f0 + 1ap 35 | self._update_mcep_param(mcep_alpha=0.410, aux_dim=28, 36 | mcep_dim=24, mcep_dim_end=27, 37 | ap_dim_idx=-1) 38 | elif self.fs == "22050": 39 | #mcep_dim_end = 37 #2+34+1 40 | #aux_dim = 39 #(34+1)mcep + 1uv + 1f0 + 2ap 41 | self._update_mcep_param(mcep_alpha=0.455, aux_dim=39, 42 | mcep_dim=34, mcep_dim_end=37, 43 | ap_dim_idx=-2) 44 | elif self.fs == "24000": 45 | #mcep_dim_end = 42 #2+39+1 46 | #aux_dim = 45 #(39+1)mcep + 1uv + 1f0 + 3ap 47 | self._update_mcep_param(mcep_alpha=0.466, aux_dim=45, 48 | mcep_dim=39, mcep_dim_end=42, 49 | ap_dim_idx=-3) 50 | else: 51 | raise ValueError("%s is not supported!" % self.fs) 52 | self._get_upsampling_factor(fs) 53 | 54 | def _update_mcep_param(self, 55 | mcep_alpha, aux_dim, 56 | mcep_dim, mcep_dim_end, 57 | ap_dim_idx): 58 | self.mcep_alpha = mcep_alpha 59 | self.aux_dim = aux_dim 60 | self.mcep_dim = mcep_dim 61 | self.mcep_dim_end = mcep_dim_end 62 | self.ap_dim_idx = ap_dim_idx 63 | 64 | def _get_upsampling_factor(self, fs): 65 | self.upsampling_factor = math.floor(self.shiftms * float(fs) / 1000) -------------------------------------------------------------------------------- /src/utils/param_model.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | 4 | # Copyright 2019 Wu Yi-Chiao (Nagoya University) 5 | # Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) 6 | 7 | # NETWORK & TRAINING PARAMETERS INITIALIZATION 8 | class network_parameter(object): 9 | def __init__(self, 10 | upsampling_flag=True, spk_code_flag=False, 11 | quantize=256, aux=39, resch=512, skipch=256, 12 | learningrate=1e-4, weight_decay=0.0, 13 | iters=200000, update_iters=50000, 14 | checkpoint_interval=10000, 15 | update_interval=5000): 16 | self.upsampling_flag = upsampling_flag 17 | self.spk_code_flag = spk_code_flag 18 | self.quantize = quantize 19 | self.aux = aux 20 | self.resch = resch 21 | self.skipch = skipch 22 | self.lr = learningrate 23 | self.weight_decay = weight_decay 24 | self.iters = iters 25 | self.update_iters = update_iters 26 | self.checkpoint_interval = checkpoint_interval 27 | self.update_interval = update_interval 28 | 29 | def set_batch_param(self, batch_length=20000, batch_size=1): 30 | self.batch_length = batch_length 31 | self.batch_size = batch_size 32 | 33 | def set_network_ch(self, quantize=256, aux=39, resch=512, skipch=256): 34 | self.quantize = quantize 35 | self.aux = aux 36 | self.resch = resch 37 | self.skipch = skipch 38 | 39 | class qpwn_parameter(network_parameter): 40 | def __init__(self, network, 41 | upsampling_flag=True, spk_code_flag=False, 42 | quantize=256, aux=39, resch=512, skipch=256, 43 | learningrate=1e-4, weight_decay=0.0, 44 | iters=200000, update_iters=3000, 45 | checkpoint_interval=10000, 46 | update_interval=100, 47 | decode_batch_size=12): 48 | super().__init__(upsampling_flag, spk_code_flag, 49 | quantize, aux, resch, skipch, 50 | learningrate, weight_decay, 51 | iters, update_iters, 52 | checkpoint_interval, 53 | update_interval) 54 | self._update_network(network, decode_batch_size) 55 | 56 | def _update_network(self, network, decode_batch_size): 57 | self.network = network 58 | if network == 'default': 59 | self._update_network_param( 60 | dilationF_depth=4, dilationF_repeat=3, 61 | dilationA_depth=4, dilationA_repeat=1, 62 | kernel_size=2, max_length=30000, 63 | batch_length=20000, batch_size=1, 64 | f0_threshold=0, decode_batch_size=decode_batch_size) 65 | elif network == 'Rd10Rr3Ed4Er1': 66 | self._update_network_param( 67 | dilationF_depth=10, dilationF_repeat=3, 68 | dilationA_depth=4, dilationA_repeat=1, 69 | kernel_size=2, max_length=22500, 70 | batch_length=20000, batch_size=1, 71 | f0_threshold=0, decode_batch_size=7) 72 | else: 73 | raise ValueError("%s is not supported!" % network) 74 | 75 | def _update_network_param(self, 76 | dilationF_depth, dilationF_repeat, 77 | dilationA_depth, dilationA_repeat, 78 | kernel_size, max_length, 79 | batch_length, batch_size, 80 | f0_threshold, decode_batch_size): 81 | self.dilationF_depth = dilationF_depth 82 | self.dilationF_repeat = dilationF_repeat 83 | self.dilationA_depth = dilationA_depth 84 | self.dilationA_repeat = dilationA_repeat 85 | self.kernel_size = kernel_size 86 | self.max_length = max_length 87 | self.batch_length = batch_length 88 | self.batch_size = batch_size 89 | self.f0_threshold = f0_threshold 90 | self.decode_batch_size = decode_batch_size 91 | 92 | -------------------------------------------------------------------------------- /src/utils/param_path.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | 4 | # Copyright 2019 Wu Yi-Chiao (Nagoya University) 5 | # Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) 6 | 7 | # PATH 8 | LIBRARY_DIR = "/usr/local/cuda-10.0/lib64" 9 | CUDA_DIR = "/usr/local/cuda-10.0" 10 | #ROOT_DIR = "/home/yourname/projects/" 11 | ROOT_DIR = "/home/yichiao17/VoiceConversion_nas01/" 12 | PRJ_DIR = ROOT_DIR + "QPNet/" 13 | COP = "VCC2018" 14 | COP_DIR = "%scorpus/%s/" % (PRJ_DIR, COP) 15 | SCP_DIR = COP_DIR + "scp/" 16 | SRC_DIR = PRJ_DIR + "src/" 17 | -------------------------------------------------------------------------------- /src/utils/utils.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | 4 | # Copyright 2019 Wu Yi-Chiao (Nagoya University) 5 | # based on a WaveNet script by Tomoki Hayashi (Nagoya University) 6 | # (https://github.com/kan-bayashi/PytorchWaveNetVocoder) 7 | # Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) 8 | 9 | from __future__ import division 10 | from __future__ import print_function 11 | 12 | import fnmatch 13 | import os 14 | import sys 15 | import threading 16 | 17 | import h5py 18 | import numpy as np 19 | from numpy.matlib import repmat 20 | 21 | 22 | 23 | def check_hdf5(hdf5_name, hdf5_path): 24 | """FUNCTION TO CHECK HDF5 EXISTENCE 25 | 26 | Args: 27 | hdf5_name (str): filename of hdf5 file 28 | hdf5_path (str): dataset name in hdf5 file 29 | 30 | Return: 31 | (bool): dataset exists then return true 32 | """ 33 | if not os.path.exists(hdf5_name): 34 | return False 35 | else: 36 | with h5py.File(hdf5_name, "r") as f: 37 | if hdf5_path in f: 38 | return True 39 | else: 40 | return False 41 | 42 | 43 | def read_hdf5(hdf5_name, hdf5_path): 44 | """FUNCTION TO READ HDF5 DATASET 45 | 46 | Args: 47 | hdf5_name (str): filename of hdf5 file 48 | hdf5_path (str): dataset name in hdf5 file 49 | 50 | Return: 51 | dataset values 52 | """ 53 | if not os.path.exists(hdf5_name): 54 | print("ERROR: There is no such a hdf5 file. (%s)" % hdf5_name) 55 | print("Please check the hdf5 file path.") 56 | sys.exit(-1) 57 | 58 | hdf5_file = h5py.File(hdf5_name, "r") 59 | 60 | if hdf5_path not in hdf5_file: 61 | print("ERROR: There is no such a data in hdf5 file. (%s)" % hdf5_path) 62 | print("Please check the data path in hdf5 file('%s')." % hdf5_name) 63 | sys.exit(-1) 64 | 65 | hdf5_data = hdf5_file[hdf5_path][()] 66 | hdf5_file.close() 67 | 68 | return hdf5_data 69 | 70 | 71 | def shape_hdf5(hdf5_name, hdf5_path): 72 | """FUNCTION TO GET HDF5 DATASET SHAPE 73 | 74 | Args: 75 | hdf5_name (str): filename of hdf5 file 76 | hdf5_path (str): dataset name in hdf5 file 77 | 78 | Return: 79 | (tuple): shape of dataset 80 | """ 81 | if check_hdf5(hdf5_name, hdf5_path): 82 | with h5py.File(hdf5_name, "r") as f: 83 | hdf5_shape = f[hdf5_path].shape 84 | return hdf5_shape 85 | else: 86 | print("There is no such a file or dataset. (%s, %s)" % (hdf5_name, hdf5_path)) 87 | sys.exit(-1) 88 | 89 | 90 | def write_hdf5(hdf5_name, hdf5_path, write_data, is_overwrite=True): 91 | """FUNCTION TO WRITE DATASET TO HDF5 92 | 93 | Args : 94 | hdf5_name (str): hdf5 dataset filename 95 | hdf5_path (str): dataset path in hdf5 96 | write_data (ndarray): data to write 97 | is_overwrite (bool): flag to decide whether to overwrite dataset 98 | """ 99 | # convert to numpy array 100 | write_data = np.array(write_data) 101 | 102 | # check folder existence 103 | folder_name, _ = os.path.split(hdf5_name) 104 | if not os.path.exists(folder_name) and len(folder_name) != 0: 105 | os.makedirs(folder_name) 106 | 107 | # check hdf5 existence 108 | if os.path.exists(hdf5_name): 109 | # if already exists, open with r+ mode 110 | hdf5_file = h5py.File(hdf5_name, "r+") 111 | # check dataset existence 112 | if hdf5_path in hdf5_file: 113 | if is_overwrite: 114 | print("Warning: data in hdf5 file already exists. recreate dataset in hdf5.") 115 | hdf5_file.__delitem__(hdf5_path) 116 | else: 117 | print("ERROR: there is already dataset.") 118 | print("if you want to overwrite, please set is_overwrite = True.") 119 | hdf5_file.close() 120 | sys.exit(1) 121 | else: 122 | # if not exists, open with w mode 123 | hdf5_file = h5py.File(hdf5_name, "w") 124 | 125 | # write data to hdf5 126 | hdf5_file.create_dataset(hdf5_path, data=write_data) 127 | hdf5_file.flush() 128 | hdf5_file.close() 129 | 130 | 131 | def find_files(directory, pattern="*.wav", use_dir_name=True): 132 | """FUNCTION TO FIND FILES RECURSIVELY 133 | 134 | Args: 135 | directory (str): root directory to find 136 | pattern (str): query to find 137 | use_dir_name (bool): if False, directory name is not included 138 | 139 | Return: 140 | (list): list of found filenames 141 | """ 142 | files = [] 143 | for root, dirnames, filenames in os.walk(directory, followlinks=True): 144 | for filename in fnmatch.filter(filenames, pattern): 145 | files.append(os.path.join(root, filename)) 146 | if not use_dir_name: 147 | files = [file_.replace(directory + "/", "") for file_ in files] 148 | return files 149 | 150 | 151 | def read_txt(file_list): 152 | """FUNCTION TO READ TXT FILE 153 | 154 | Arg: 155 | file_list (str): txt file filename 156 | 157 | Return: 158 | (list): list of read lines 159 | """ 160 | with open(file_list, "r") as f: 161 | filenames = f.readlines() 162 | return [filename.replace("\n", "") for filename in filenames] 163 | 164 | 165 | class BackgroundGenerator(threading.Thread): 166 | """BACKGROUND GENERATOR 167 | 168 | reference: 169 | https://stackoverflow.com/questions/7323664/python-generator-pre-fetch 170 | 171 | Args: 172 | generator (object): generator instance 173 | max_prefetch (int): max number of prefetch 174 | """ 175 | 176 | def __init__(self, generator, max_prefetch=1): 177 | threading.Thread.__init__(self) 178 | if sys.version_info.major == 2: 179 | from Queue import Queue 180 | else: 181 | from queue import Queue 182 | self.queue = Queue(max_prefetch) 183 | self.generator = generator 184 | self.daemon = True 185 | self.start() 186 | 187 | def run(self): 188 | for item in self.generator: 189 | self.queue.put(item) 190 | self.queue.put(None) 191 | 192 | def next(self): 193 | next_item = self.queue.get() 194 | if next_item is None: 195 | raise StopIteration 196 | return next_item 197 | 198 | def __next__(self): 199 | return self.next() 200 | 201 | def __iter__(self): 202 | return self 203 | 204 | 205 | class background(object): 206 | """BACKGROUND GENERATOR DECORATOR""" 207 | 208 | def __init__(self, max_prefetch=1): 209 | self.max_prefetch = max_prefetch 210 | 211 | def __call__(self, gen): 212 | def bg_generator(*args, **kwargs): 213 | return BackgroundGenerator(gen(*args, **kwargs)) 214 | return bg_generator 215 | 216 | def extend_time(feats, upsampling_factor): 217 | """FUNCTION TO EXTEND TIME RESOLUTION 218 | Args: 219 | feats (ndarray): feature vector with the shape (T x D) 220 | upsampling_factor (int): upsampling_factor 221 | Return: 222 | (ndarray): extend feats with the shape (upsampling_factor*T x D) 223 | """ 224 | # get number 225 | n_frames = feats.shape[0] 226 | n_dims = feats.shape[1] 227 | 228 | # extend time 229 | feats_extended = np.zeros((n_frames * upsampling_factor, n_dims)) 230 | for j in range(n_frames): 231 | start_idx = j * upsampling_factor 232 | end_idx = (j + 1) * upsampling_factor 233 | feats_extended[start_idx: end_idx] = repmat(feats[j, :], upsampling_factor, 1) 234 | 235 | return feats_extended 236 | 237 | def check_filenames(filename_list): 238 | filename_list = [os.path.basename(x).split('.')[0] for x in filename_list] 239 | return len(set(filename_list))==1 240 | -------------------------------------------------------------------------------- /src/utils/utils_pathlist.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | 4 | # Copyright 2019 Wu Yi-Chiao (Nagoya University) 5 | # Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) 6 | 7 | import os 8 | 9 | # PATH INITIALIZATION 10 | def _path_initial(pathlist): 11 | for pathdir in pathlist: 12 | if not os.path.exists(pathdir): 13 | os.makedirs(pathdir) 14 | 15 | # PATH CHECK 16 | def _path_check(pathlist): 17 | for pathdir in pathlist: 18 | if not os.path.exists(pathdir): 19 | raise FileNotFoundError("%s doesn't exist!!" % pathdir) 20 | 21 | # LIST INITIALIZATION 22 | def _list_initial(replace_falg, feat_format, wavs, feats, outdir, keyword, subword): 23 | if replace_falg: 24 | _templist_eval(wavs, feats, outdir, overwrite=True, 25 | rootdir="", keyword=keyword, subword=subword) 26 | else: 27 | num_files = _templist_eval(wavs, feats, outdir, overwrite=False, 28 | rootdir="", keyword=keyword, 29 | subword=subword, feat_format=feat_format) 30 | if num_files is 0: 31 | return False 32 | return True 33 | 34 | # TRAINING LIST PROCESSING 35 | def _templist(inputlist, outputlist, 36 | rootdir="", keyword="", subword=""): 37 | num_files = 0 38 | if not os.path.exists(inputlist): 39 | message = "Cannot find the list file {}.".format(inputlist) 40 | raise FileNotFoundError(message) 41 | if os.path.exists(outputlist) is True: 42 | os.remove(outputlist) 43 | file = open(inputlist, "r") 44 | tempfile = open(outputlist, "w") 45 | for line in file: 46 | line = line.rstrip() 47 | if (len(keyword) and len(subword)) is not 0: 48 | for (kword, sword) in zip(keyword, subword): 49 | #print(line) 50 | #print(kword, sword) 51 | line = line.replace(kword, sword) 52 | phy_path = rootdir + line 53 | tempfile.write("%s\n" % (phy_path)) 54 | file.close() 55 | tempfile.close() 56 | return num_files 57 | 58 | # TESTING LIST PROCESSING 59 | def _templist_eval(inputlist, outputlist, outdir, overwrite=False, 60 | rootdir="", keyword="", subword="", feat_format="h5"): 61 | num_files = 0 62 | if not os.path.exists(inputlist): 63 | message = "Cannot find the list file {}.".format(inputlist) 64 | raise FileNotFoundError(message) 65 | if os.path.exists(outputlist) is True: 66 | os.remove(outputlist) 67 | file = open(inputlist, "r") 68 | tempfile = open(outputlist, "w") 69 | for line in file: 70 | line = line.rstrip() 71 | if (len(keyword) and len(subword)) is not 0: 72 | for (kword, sword) in zip(keyword, subword): 73 | line = line.replace(kword, sword) 74 | phy_path = rootdir + line 75 | feat_id = os.path.basename(phy_path).replace(".%s" % feat_format, "") 76 | outputfile = outdir.replace("feat_id", feat_id) 77 | if os.path.exists(outputfile): 78 | if overwrite: 79 | print("over write %s" % (outputfile)) 80 | else: 81 | print("%s skipped" % (outputfile)) 82 | continue 83 | tempfile.write("%s\n" % (phy_path)) 84 | num_files += 1 85 | file.close() 86 | tempfile.close() 87 | return num_files 88 | 89 | # REMOVE TEMP FILE 90 | def _remove_temp_file(file_list): 91 | for file in file_list: 92 | if os.path.exists(file): 93 | os.remove(file) 94 | --------------------------------------------------------------------------------