From 0caed984e39c07849a13662894f4cbdbe0a98091 Mon Sep 17 00:00:00 2001 From: flysmart <66983043+flysmart@users.noreply.github.com> Date: Thu, 12 May 2022 12:27:17 +0800 Subject: [PATCH] The new vocoder Fre-GAN is now supported (#546) * The new vocoder Fre-GAN is now supported * Improved some fregan details --- README-CN.md | 4 +- toolbox/__init__.py | 10 ++ vocoder/fregan/.gitignore | 129 ++++++++++++++ vocoder/fregan/LICENSE | 21 +++ vocoder/fregan/README.md | 25 +++ vocoder/fregan/config.json | 41 +++++ vocoder/fregan/discriminator.py | 303 ++++++++++++++++++++++++++++++++ vocoder/fregan/dwt.py | 76 ++++++++ vocoder/fregan/generator.py | 210 ++++++++++++++++++++++ vocoder/fregan/inference.py | 74 ++++++++ vocoder/fregan/loss.py | 35 ++++ vocoder/fregan/meldataset.py | 176 +++++++++++++++++++ vocoder/fregan/modules.py | 201 +++++++++++++++++++++ vocoder/fregan/requirements.txt | 1 + vocoder/fregan/train.py | 246 ++++++++++++++++++++++++++ vocoder/fregan/utils.py | 65 +++++++ vocoder_train.py | 8 + 17 files changed, 1624 insertions(+), 1 deletion(-) create mode 100644 vocoder/fregan/.gitignore create mode 100644 vocoder/fregan/LICENSE create mode 100644 vocoder/fregan/README.md create mode 100644 vocoder/fregan/config.json create mode 100644 vocoder/fregan/discriminator.py create mode 100644 vocoder/fregan/dwt.py create mode 100644 vocoder/fregan/generator.py create mode 100644 vocoder/fregan/inference.py create mode 100644 vocoder/fregan/loss.py create mode 100644 vocoder/fregan/meldataset.py create mode 100644 vocoder/fregan/modules.py create mode 100644 vocoder/fregan/requirements.txt create mode 100644 vocoder/fregan/train.py create mode 100644 vocoder/fregan/utils.py diff --git a/README-CN.md b/README-CN.md index 038deb5..50f4763 100644 --- a/README-CN.md +++ b/README-CN.md @@ -87,7 +87,9 @@ * 训练hifigan声码器: `python vocoder_train.py hifigan` > ``替换为你想要的标识，同一标识再次训练时会延续原模型 - +* 训练fregan声码器: +`python vocoder_train.py --config config.json fregan` +> ``替换为你想要的标识，同一标识再次训练时会延续原模型 ### 3. 启动程序或工具箱 您可以尝试使用以下命令： diff --git a/toolbox/__init__.py b/toolbox/__init__.py index 76cd36a..b51164f 100644 --- a/toolbox/__init__.py +++ b/toolbox/__init__.py @@ -3,6 +3,7 @@ from encoder import inference as encoder from synthesizer.inference import Synthesizer from vocoder.wavernn import inference as rnn_vocoder from vocoder.hifigan import inference as gan_vocoder +from vocoder.fregan import inference as fgan_vocoder from pathlib import Path from time import perf_counter as timer from toolbox.utterance import Utterance @@ -451,6 +452,15 @@ class Toolbox: return if len(model_config_fpaths) > 0: model_config_fpath = model_config_fpaths[0] + elif model_fpath.name is not None and model_fpath.name.find("fregan") > -1: + vocoder = fgan_vocoder + self.ui.log("set fregan as vocoder") + # search a config file + model_config_fpaths = list(model_fpath.parent.rglob("*.json")) + if self.vc_mode and self.ui.current_extractor_fpath is None: + return + if len(model_config_fpaths) > 0: + model_config_fpath = model_config_fpaths[0] else: vocoder = rnn_vocoder self.ui.log("set wavernn as vocoder") diff --git a/vocoder/fregan/.gitignore b/vocoder/fregan/.gitignore new file mode 100644 index 0000000..b6e4761 --- /dev/null +++ b/vocoder/fregan/.gitignore @@ -0,0 +1,129 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ diff --git a/vocoder/fregan/LICENSE b/vocoder/fregan/LICENSE new file mode 100644 index 0000000..eb916f2 --- /dev/null +++ b/vocoder/fregan/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2021 Rishikesh (ऋषिकेश) + +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. diff --git a/vocoder/fregan/README.md b/vocoder/fregan/README.md new file mode 100644 index 0000000..964b67c --- /dev/null +++ b/vocoder/fregan/README.md @@ -0,0 +1,25 @@ +# Fre-GAN Vocoder +[Fre-GAN: Adversarial Frequency-consistent Audio Synthesis](https://arxiv.org/abs/2106.02297) + +## Training: +``` +python train.py --config config.json +``` + +## Citation: +``` +@misc{kim2021fregan, + title={Fre-GAN: Adversarial Frequency-consistent Audio Synthesis}, + author={Ji-Hoon Kim and Sang-Hoon Lee and Ji-Hyun Lee and Seong-Whan Lee}, + year={2021}, + eprint={2106.02297}, + archivePrefix={arXiv}, + primaryClass={eess.AS} +} +``` +## Note +* For more complete and end to end Voice cloning or Text to Speech (TTS) toolbox please visit [Deepsync Technologies](https://deepsync.co/). + +## References: +* [Hi-Fi-GAN repo](https://github.com/jik876/hifi-gan) +* [WaveSNet repo](https://github.com/LiQiufu/WaveSNet) diff --git a/vocoder/fregan/config.json b/vocoder/fregan/config.json new file mode 100644 index 0000000..f227785 --- /dev/null +++ b/vocoder/fregan/config.json @@ -0,0 +1,41 @@ +{ + "resblock": "1", + "num_gpus": 0, + "batch_size": 16, + "learning_rate": 0.0002, + "adam_b1": 0.8, + "adam_b2": 0.99, + "lr_decay": 0.999, + "seed": 1234, + + + "upsample_rates": [5,5,2,2,2], + "upsample_kernel_sizes": [10,10,4,4,4], + "upsample_initial_channel": 512, + "resblock_kernel_sizes": [3,7,11], + "resblock_dilation_sizes": [[1, 3, 5, 7], [1,3,5,7], [1,3,5,7]], + + "segment_size": 6400, + "num_mels": 80, + "num_freq": 1025, + "n_fft": 1024, + "hop_size": 200, + "win_size": 800, + + "sampling_rate": 16000, + + "fmin": 0, + "fmax": 7600, + "fmax_for_loss": null, + + "num_workers": 4, + + "dist_config": { + "dist_backend": "nccl", + "dist_url": "tcp://localhost:54321", + "world_size": 1 + } + + + +} \ No newline at end of file diff --git a/vocoder/fregan/discriminator.py b/vocoder/fregan/discriminator.py new file mode 100644 index 0000000..5f94092 --- /dev/null +++ b/vocoder/fregan/discriminator.py @@ -0,0 +1,303 @@ +import torch +import torch.nn.functional as F +import torch.nn as nn +from torch.nn import Conv1d, AvgPool1d, Conv2d +from torch.nn.utils import weight_norm, spectral_norm +from vocoder.fregan.utils import get_padding +from vocoder.fregan.stft_loss import stft +from vocoder.fregan.dwt import DWT_1D +LRELU_SLOPE = 0.1 + + + +class SpecDiscriminator(nn.Module): + """docstring for Discriminator.""" + + def __init__(self, fft_size=1024, shift_size=120, win_length=600, window="hann_window", use_spectral_norm=False): + super(SpecDiscriminator, self).__init__() + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + self.fft_size = fft_size + self.shift_size = shift_size + self.win_length = win_length + self.window = getattr(torch, window)(win_length) + self.discriminators = nn.ModuleList([ + norm_f(nn.Conv2d(1, 32, kernel_size=(3, 9), padding=(1, 4))), + norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1,2), padding=(1, 4))), + norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1,2), padding=(1, 4))), + norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1,2), padding=(1, 4))), + norm_f(nn.Conv2d(32, 32, kernel_size=(3, 3), stride=(1,1), padding=(1, 1))), + ]) + + self.out = norm_f(nn.Conv2d(32, 1, 3, 1, 1)) + + def forward(self, y): + + fmap = [] + with torch.no_grad(): + y = y.squeeze(1) + y = stft(y, self.fft_size, self.shift_size, self.win_length, self.window.to(y.get_device())) + y = y.unsqueeze(1) + for i, d in enumerate(self.discriminators): + y = d(y) + y = F.leaky_relu(y, LRELU_SLOPE) + fmap.append(y) + + y = self.out(y) + fmap.append(y) + + return torch.flatten(y, 1, -1), fmap + +class MultiResSpecDiscriminator(torch.nn.Module): + + def __init__(self, + fft_sizes=[1024, 2048, 512], + hop_sizes=[120, 240, 50], + win_lengths=[600, 1200, 240], + window="hann_window"): + + super(MultiResSpecDiscriminator, self).__init__() + self.discriminators = nn.ModuleList([ + SpecDiscriminator(fft_sizes[0], hop_sizes[0], win_lengths[0], window), + SpecDiscriminator(fft_sizes[1], hop_sizes[1], win_lengths[1], window), + SpecDiscriminator(fft_sizes[2], hop_sizes[2], win_lengths[2], window) + ]) + + def forward(self, y, y_hat): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + for i, d in enumerate(self.discriminators): + y_d_r, fmap_r = d(y) + y_d_g, fmap_g = d(y_hat) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + + +class DiscriminatorP(torch.nn.Module): + def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): + super(DiscriminatorP, self).__init__() + self.period = period + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + self.dwt1d = DWT_1D() + self.dwt_conv1 = norm_f(Conv1d(2, 1, 1)) + self.dwt_proj1 = norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))) + self.dwt_conv2 = norm_f(Conv1d(4, 1, 1)) + self.dwt_proj2 = norm_f(Conv2d(1, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))) + self.dwt_conv3 = norm_f(Conv1d(8, 1, 1)) + self.dwt_proj3 = norm_f(Conv2d(1, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))) + self.convs = nn.ModuleList([ + norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))), + ]) + self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) + + def forward(self, x): + fmap = [] + + # DWT 1 + x_d1_high1, x_d1_low1 = self.dwt1d(x) + x_d1 = self.dwt_conv1(torch.cat([x_d1_high1, x_d1_low1], dim=1)) + # 1d to 2d + b, c, t = x_d1.shape + if t % self.period != 0: # pad first + n_pad = self.period - (t % self.period) + x_d1 = F.pad(x_d1, (0, n_pad), "reflect") + t = t + n_pad + x_d1 = x_d1.view(b, c, t // self.period, self.period) + + x_d1 = self.dwt_proj1(x_d1) + + # DWT 2 + x_d2_high1, x_d2_low1 = self.dwt1d(x_d1_high1) + x_d2_high2, x_d2_low2 = self.dwt1d(x_d1_low1) + x_d2 = self.dwt_conv2(torch.cat([x_d2_high1, x_d2_low1, x_d2_high2, x_d2_low2], dim=1)) + # 1d to 2d + b, c, t = x_d2.shape + if t % self.period != 0: # pad first + n_pad = self.period - (t % self.period) + x_d2 = F.pad(x_d2, (0, n_pad), "reflect") + t = t + n_pad + x_d2 = x_d2.view(b, c, t // self.period, self.period) + + x_d2 = self.dwt_proj2(x_d2) + + # DWT 3 + + x_d3_high1, x_d3_low1 = self.dwt1d(x_d2_high1) + x_d3_high2, x_d3_low2 = self.dwt1d(x_d2_low1) + x_d3_high3, x_d3_low3 = self.dwt1d(x_d2_high2) + x_d3_high4, x_d3_low4 = self.dwt1d(x_d2_low2) + x_d3 = self.dwt_conv3( + torch.cat([x_d3_high1, x_d3_low1, x_d3_high2, x_d3_low2, x_d3_high3, x_d3_low3, x_d3_high4, x_d3_low4], + dim=1)) + # 1d to 2d + b, c, t = x_d3.shape + if t % self.period != 0: # pad first + n_pad = self.period - (t % self.period) + x_d3 = F.pad(x_d3, (0, n_pad), "reflect") + t = t + n_pad + x_d3 = x_d3.view(b, c, t // self.period, self.period) + + x_d3 = self.dwt_proj3(x_d3) + + # 1d to 2d + b, c, t = x.shape + if t % self.period != 0: # pad first + n_pad = self.period - (t % self.period) + x = F.pad(x, (0, n_pad), "reflect") + t = t + n_pad + x = x.view(b, c, t // self.period, self.period) + i = 0 + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, LRELU_SLOPE) + + fmap.append(x) + if i == 0: + x = torch.cat([x, x_d1], dim=2) + elif i == 1: + x = torch.cat([x, x_d2], dim=2) + elif i == 2: + x = torch.cat([x, x_d3], dim=2) + else: + x = x + i = i + 1 + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class ResWiseMultiPeriodDiscriminator(torch.nn.Module): + def __init__(self): + super(ResWiseMultiPeriodDiscriminator, self).__init__() + self.discriminators = nn.ModuleList([ + DiscriminatorP(2), + DiscriminatorP(3), + DiscriminatorP(5), + DiscriminatorP(7), + DiscriminatorP(11), + ]) + + def forward(self, y, y_hat): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + for i, d in enumerate(self.discriminators): + y_d_r, fmap_r = d(y) + y_d_g, fmap_g = d(y_hat) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + + +class DiscriminatorS(torch.nn.Module): + def __init__(self, use_spectral_norm=False): + super(DiscriminatorS, self).__init__() + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + self.dwt1d = DWT_1D() + self.dwt_conv1 = norm_f(Conv1d(2, 128, 15, 1, padding=7)) + self.dwt_conv2 = norm_f(Conv1d(4, 128, 41, 2, padding=20)) + self.convs = nn.ModuleList([ + norm_f(Conv1d(1, 128, 15, 1, padding=7)), + norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)), + norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)), + norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)), + norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)), + norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)), + norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), + ]) + self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) + + def forward(self, x): + fmap = [] + + # DWT 1 + x_d1_high1, x_d1_low1 = self.dwt1d(x) + x_d1 = self.dwt_conv1(torch.cat([x_d1_high1, x_d1_low1], dim=1)) + + # DWT 2 + x_d2_high1, x_d2_low1 = self.dwt1d(x_d1_high1) + x_d2_high2, x_d2_low2 = self.dwt1d(x_d1_low1) + x_d2 = self.dwt_conv2(torch.cat([x_d2_high1, x_d2_low1, x_d2_high2, x_d2_low2], dim=1)) + + i = 0 + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, LRELU_SLOPE) + fmap.append(x) + if i == 0: + x = torch.cat([x, x_d1], dim=2) + if i == 1: + x = torch.cat([x, x_d2], dim=2) + i = i + 1 + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class ResWiseMultiScaleDiscriminator(torch.nn.Module): + def __init__(self, use_spectral_norm=False): + super(ResWiseMultiScaleDiscriminator, self).__init__() + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + self.dwt1d = DWT_1D() + self.dwt_conv1 = norm_f(Conv1d(2, 1, 1)) + self.dwt_conv2 = norm_f(Conv1d(4, 1, 1)) + self.discriminators = nn.ModuleList([ + DiscriminatorS(use_spectral_norm=True), + DiscriminatorS(), + DiscriminatorS(), + ]) + + def forward(self, y, y_hat): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + # DWT 1 + y_hi, y_lo = self.dwt1d(y) + y_1 = self.dwt_conv1(torch.cat([y_hi, y_lo], dim=1)) + x_d1_high1, x_d1_low1 = self.dwt1d(y_hat) + y_hat_1 = self.dwt_conv1(torch.cat([x_d1_high1, x_d1_low1], dim=1)) + + # DWT 2 + x_d2_high1, x_d2_low1 = self.dwt1d(y_hi) + x_d2_high2, x_d2_low2 = self.dwt1d(y_lo) + y_2 = self.dwt_conv2(torch.cat([x_d2_high1, x_d2_low1, x_d2_high2, x_d2_low2], dim=1)) + + x_d2_high1, x_d2_low1 = self.dwt1d(x_d1_high1) + x_d2_high2, x_d2_low2 = self.dwt1d(x_d1_low1) + y_hat_2 = self.dwt_conv2(torch.cat([x_d2_high1, x_d2_low1, x_d2_high2, x_d2_low2], dim=1)) + + for i, d in enumerate(self.discriminators): + + if i == 1: + y = y_1 + y_hat = y_hat_1 + if i == 2: + y = y_2 + y_hat = y_hat_2 + + y_d_r, fmap_r = d(y) + y_d_g, fmap_g = d(y_hat) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs \ No newline at end of file diff --git a/vocoder/fregan/dwt.py b/vocoder/fregan/dwt.py new file mode 100644 index 0000000..1c5d995 --- /dev/null +++ b/vocoder/fregan/dwt.py @@ -0,0 +1,76 @@ +# Copyright (c) 2019, Adobe Inc. All rights reserved. +# +# This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike +# 4.0 International Public License. To view a copy of this license, visit +# https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode. + +# DWT code borrow from https://github.com/LiQiufu/WaveSNet/blob/12cb9d24208c3d26917bf953618c30f0c6b0f03d/DWT_IDWT/DWT_IDWT_layer.py + + +import pywt +import torch +import torch.nn as nn +import torch.nn.functional as F + +__all__ = ['DWT_1D'] +Pad_Mode = ['constant', 'reflect', 'replicate', 'circular'] + + +class DWT_1D(nn.Module): + def __init__(self, pad_type='reflect', wavename='haar', + stride=2, in_channels=1, out_channels=None, groups=None, + kernel_size=None, trainable=False): + + super(DWT_1D, self).__init__() + self.trainable = trainable + self.kernel_size = kernel_size + if not self.trainable: + assert self.kernel_size == None + self.in_channels = in_channels + self.out_channels = self.in_channels if out_channels == None else out_channels + self.groups = self.in_channels if groups == None else groups + assert isinstance(self.groups, int) and self.in_channels % self.groups == 0 + self.stride = stride + assert self.stride == 2 + self.wavename = wavename + self.pad_type = pad_type + assert self.pad_type in Pad_Mode + self.get_filters() + self.initialization() + + def get_filters(self): + wavelet = pywt.Wavelet(self.wavename) + band_low = torch.tensor(wavelet.rec_lo) + band_high = torch.tensor(wavelet.rec_hi) + length_band = band_low.size()[0] + self.kernel_size = length_band if self.kernel_size == None else self.kernel_size + assert self.kernel_size >= length_band + a = (self.kernel_size - length_band) // 2 + b = - (self.kernel_size - length_band - a) + b = None if b == 0 else b + self.filt_low = torch.zeros(self.kernel_size) + self.filt_high = torch.zeros(self.kernel_size) + self.filt_low[a:b] = band_low + self.filt_high[a:b] = band_high + + def initialization(self): + self.filter_low = self.filt_low[None, None, :].repeat((self.out_channels, self.in_channels // self.groups, 1)) + self.filter_high = self.filt_high[None, None, :].repeat((self.out_channels, self.in_channels // self.groups, 1)) + if torch.cuda.is_available(): + self.filter_low = self.filter_low.cuda() + self.filter_high = self.filter_high.cuda() + if self.trainable: + self.filter_low = nn.Parameter(self.filter_low) + self.filter_high = nn.Parameter(self.filter_high) + if self.kernel_size % 2 == 0: + self.pad_sizes = [self.kernel_size // 2 - 1, self.kernel_size // 2 - 1] + else: + self.pad_sizes = [self.kernel_size // 2, self.kernel_size // 2] + + def forward(self, input): + assert isinstance(input, torch.Tensor) + assert len(input.size()) == 3 + assert input.size()[1] == self.in_channels + input = F.pad(input, pad=self.pad_sizes, mode=self.pad_type) + return F.conv1d(input, self.filter_low.to(input.device), stride=self.stride, groups=self.groups), \ + F.conv1d(input, self.filter_high.to(input.device), stride=self.stride, groups=self.groups) diff --git a/vocoder/fregan/generator.py b/vocoder/fregan/generator.py new file mode 100644 index 0000000..c0dd3a8 --- /dev/null +++ b/vocoder/fregan/generator.py @@ -0,0 +1,210 @@ +import torch +import torch.nn.functional as F +import torch.nn as nn +from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d +from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm +from vocoder.fregan.utils import init_weights, get_padding + +LRELU_SLOPE = 0.1 + + +class ResBlock1(torch.nn.Module): + def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5, 7)): + super(ResBlock1, self).__init__() + self.h = h + self.convs1 = nn.ModuleList([ + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], + padding=get_padding(kernel_size, dilation[2]))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[3], + padding=get_padding(kernel_size, dilation[3]))) + ]) + self.convs1.apply(init_weights) + + self.convs2 = nn.ModuleList([ + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, + padding=get_padding(kernel_size, 1))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, + padding=get_padding(kernel_size, 1))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, + padding=get_padding(kernel_size, 1))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, + padding=get_padding(kernel_size, 1))) + ]) + self.convs2.apply(init_weights) + + def forward(self, x): + for c1, c2 in zip(self.convs1, self.convs2): + xt = F.leaky_relu(x, LRELU_SLOPE) + xt = c1(xt) + xt = F.leaky_relu(xt, LRELU_SLOPE) + xt = c2(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for l in self.convs1: + remove_weight_norm(l) + for l in self.convs2: + remove_weight_norm(l) + + +class ResBlock2(torch.nn.Module): + def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)): + super(ResBlock2, self).__init__() + self.h = h + self.convs = nn.ModuleList([ + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]))) + ]) + self.convs.apply(init_weights) + + def forward(self, x): + for c in self.convs: + xt = F.leaky_relu(x, LRELU_SLOPE) + xt = c(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for l in self.convs: + remove_weight_norm(l) + + +class FreGAN(torch.nn.Module): + def __init__(self, h, top_k=4): + super(FreGAN, self).__init__() + self.h = h + + self.num_kernels = len(h.resblock_kernel_sizes) + self.num_upsamples = len(h.upsample_rates) + self.upsample_rates = h.upsample_rates + self.up_kernels = h.upsample_kernel_sizes + self.cond_level = self.num_upsamples - top_k + self.conv_pre = weight_norm(Conv1d(80, h.upsample_initial_channel, 7, 1, padding=3)) + resblock = ResBlock1 if h.resblock == '1' else ResBlock2 + + self.ups = nn.ModuleList() + self.cond_up = nn.ModuleList() + self.res_output = nn.ModuleList() + upsample_ = 1 + kr = 80 + + for i, (u, k) in enumerate(zip(self.upsample_rates, self.up_kernels)): +# self.ups.append(weight_norm( + # ConvTranspose1d(h.upsample_initial_channel // (2 ** i), h.upsample_initial_channel // (2 ** (i + 1)), + # k, u, padding=(k - u) // 2))) + self.ups.append(weight_norm(ConvTranspose1d(h.upsample_initial_channel//(2**i), + h.upsample_initial_channel//(2**(i+1)), + k, u, padding=(u//2 + u%2), output_padding=u%2))) + + if i > (self.num_upsamples - top_k): + self.res_output.append( + nn.Sequential( + nn.Upsample(scale_factor=u, mode='nearest'), + weight_norm(nn.Conv1d(h.upsample_initial_channel // (2 ** i), + h.upsample_initial_channel // (2 ** (i + 1)), 1)) + ) + ) + if i >= (self.num_upsamples - top_k): + self.cond_up.append( + weight_norm( + ConvTranspose1d(kr, h.upsample_initial_channel // (2 ** i), + self.up_kernels[i - 1], self.upsample_rates[i - 1], + padding=(self.upsample_rates[i-1]//2+self.upsample_rates[i-1]%2), output_padding=self.upsample_rates[i-1]%2)) + ) + kr = h.upsample_initial_channel // (2 ** i) + + upsample_ *= u + + self.resblocks = nn.ModuleList() + for i in range(len(self.ups)): + ch = h.upsample_initial_channel // (2 ** (i + 1)) + for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)): + self.resblocks.append(resblock(h, ch, k, d)) + + self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) + self.ups.apply(init_weights) + self.conv_post.apply(init_weights) + self.cond_up.apply(init_weights) + self.res_output.apply(init_weights) + + def forward(self, x): + mel = x + x = self.conv_pre(x) + output = None + for i in range(self.num_upsamples): + if i >= self.cond_level: + mel = self.cond_up[i - self.cond_level](mel) + x += mel + if i > self.cond_level: + if output is None: + output = self.res_output[i - self.cond_level - 1](x) + else: + output = self.res_output[i - self.cond_level - 1](output) + x = F.leaky_relu(x, LRELU_SLOPE) + x = self.ups[i](x) + xs = None + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i * self.num_kernels + j](x) + else: + xs += self.resblocks[i * self.num_kernels + j](x) + x = xs / self.num_kernels + if output is not None: + output = output + x + + x = F.leaky_relu(output) + x = self.conv_post(x) + x = torch.tanh(x) + + return x + + def remove_weight_norm(self): + print('Removing weight norm...') + for l in self.ups: + remove_weight_norm(l) + for l in self.resblocks: + l.remove_weight_norm() + for l in self.cond_up: + remove_weight_norm(l) + for l in self.res_output: + remove_weight_norm(l[1]) + remove_weight_norm(self.conv_pre) + remove_weight_norm(self.conv_post) + + +''' + to run this, fix + from . import ResStack + into + from res_stack import ResStack +''' +if __name__ == '__main__': + ''' + torch.Size([3, 80, 10]) + torch.Size([3, 1, 2000]) + 4527362 + ''' + with open('config.json') as f: + data = f.read() + from utils import AttrDict + import json + json_config = json.loads(data) + h = AttrDict(json_config) + model = FreGAN(h) + + c = torch.randn(3, 80, 10) # (B, channels, T). + print(c.shape) + + y = model(c) # (B, 1, T ** prod(upsample_scales) + print(y.shape) + assert y.shape == torch.Size([3, 1, 2560]) # For normal melgan torch.Size([3, 1, 2560]) + + pytorch_total_params = sum(p.numel() for p in model.parameters() if p.requires_grad) + print(pytorch_total_params) \ No newline at end of file diff --git a/vocoder/fregan/inference.py b/vocoder/fregan/inference.py new file mode 100644 index 0000000..780a613 --- /dev/null +++ b/vocoder/fregan/inference.py @@ -0,0 +1,74 @@ +from __future__ import absolute_import, division, print_function, unicode_literals + +import os +import json +import torch +from utils.util import AttrDict +from vocoder.fregan.generator import FreGAN + +generator = None # type: FreGAN +output_sample_rate = None +_device = None + + +def load_checkpoint(filepath, device): + assert os.path.isfile(filepath) + print("Loading '{}'".format(filepath)) + checkpoint_dict = torch.load(filepath, map_location=device) + print("Complete.") + return checkpoint_dict + + +def load_model(weights_fpath, config_fpath=None, verbose=True): + global generator, _device, output_sample_rate + + if verbose: + print("Building fregan") + + if config_fpath == None: + model_config_fpaths = list(weights_fpath.parent.rglob("*.json")) + if len(model_config_fpaths) > 0: + config_fpath = model_config_fpaths[0] + else: + config_fpath = "./vocoder/fregan/config.json" + with open(config_fpath) as f: + data = f.read() + json_config = json.loads(data) + h = AttrDict(json_config) + output_sample_rate = h.sampling_rate + torch.manual_seed(h.seed) + + if torch.cuda.is_available(): + # _model = _model.cuda() + _device = torch.device('cuda') + else: + _device = torch.device('cpu') + + generator = FreGAN(h).to(_device) + state_dict_g = load_checkpoint( + weights_fpath, _device + ) + generator.load_state_dict(state_dict_g['generator']) + generator.eval() + generator.remove_weight_norm() + + +def is_loaded(): + return generator is not None + + +def infer_waveform(mel, progress_callback=None): + + if generator is None: + raise Exception("Please load fre-gan in memory before using it") + + mel = torch.FloatTensor(mel).to(_device) + mel = mel.unsqueeze(0) + + with torch.no_grad(): + y_g_hat = generator(mel) + audio = y_g_hat.squeeze() + audio = audio.cpu().numpy() + + return audio, output_sample_rate + diff --git a/vocoder/fregan/loss.py b/vocoder/fregan/loss.py new file mode 100644 index 0000000..e37dc64 --- /dev/null +++ b/vocoder/fregan/loss.py @@ -0,0 +1,35 @@ +import torch + + +def feature_loss(fmap_r, fmap_g): + loss = 0 + for dr, dg in zip(fmap_r, fmap_g): + for rl, gl in zip(dr, dg): + loss += torch.mean(torch.abs(rl - gl)) + + return loss*2 + + +def discriminator_loss(disc_real_outputs, disc_generated_outputs): + loss = 0 + r_losses = [] + g_losses = [] + for dr, dg in zip(disc_real_outputs, disc_generated_outputs): + r_loss = torch.mean((1-dr)**2) + g_loss = torch.mean(dg**2) + loss += (r_loss + g_loss) + r_losses.append(r_loss.item()) + g_losses.append(g_loss.item()) + + return loss, r_losses, g_losses + + +def generator_loss(disc_outputs): + loss = 0 + gen_losses = [] + for dg in disc_outputs: + l = torch.mean((1-dg)**2) + gen_losses.append(l) + loss += l + + return loss, gen_losses \ No newline at end of file diff --git a/vocoder/fregan/meldataset.py b/vocoder/fregan/meldataset.py new file mode 100644 index 0000000..53b2c94 --- /dev/null +++ b/vocoder/fregan/meldataset.py @@ -0,0 +1,176 @@ +import math +import os +import random +import torch +import torch.utils.data +import numpy as np +from librosa.util import normalize +from scipy.io.wavfile import read +from librosa.filters import mel as librosa_mel_fn + +MAX_WAV_VALUE = 32768.0 + + +def load_wav(full_path): + sampling_rate, data = read(full_path) + return data, sampling_rate + + +def dynamic_range_compression(x, C=1, clip_val=1e-5): + return np.log(np.clip(x, a_min=clip_val, a_max=None) * C) + + +def dynamic_range_decompression(x, C=1): + return np.exp(x) / C + + +def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): + return torch.log(torch.clamp(x, min=clip_val) * C) + + +def dynamic_range_decompression_torch(x, C=1): + return torch.exp(x) / C + + +def spectral_normalize_torch(magnitudes): + output = dynamic_range_compression_torch(magnitudes) + return output + + +def spectral_de_normalize_torch(magnitudes): + output = dynamic_range_decompression_torch(magnitudes) + return output + + +mel_basis = {} +hann_window = {} + + +def mel_spectrogram(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False): + if torch.min(y) < -1.: + print('min value is ', torch.min(y)) + if torch.max(y) > 1.: + print('max value is ', torch.max(y)) + + global mel_basis, hann_window + if fmax not in mel_basis: + mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax) + mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device) + hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device) + + y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect') + y = y.squeeze(1) + + spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[str(y.device)], + center=center, pad_mode='reflect', normalized=False, onesided=True) + + spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9)) + + spec = torch.matmul(mel_basis[str(fmax)+'_'+str(y.device)], spec) + spec = spectral_normalize_torch(spec) + + return spec + + +def get_dataset_filelist(a): + #with open(a.input_training_file, 'r', encoding='utf-8') as fi: + # training_files = [os.path.join(a.input_wavs_dir, x.split('|')[0] + '.wav') + # for x in fi.read().split('\n') if len(x) > 0] + + #with open(a.input_validation_file, 'r', encoding='utf-8') as fi: + # validation_files = [os.path.join(a.input_wavs_dir, x.split('|')[0] + '.wav') + # for x in fi.read().split('\n') if len(x) > 0] + files = os.listdir(a.input_wavs_dir) + random.shuffle(files) + files = [os.path.join(a.input_wavs_dir, f) for f in files] + training_files = files[: -int(len(files) * 0.05)] + validation_files = files[-int(len(files) * 0.05):] + return training_files, validation_files + + +class MelDataset(torch.utils.data.Dataset): + def __init__(self, training_files, segment_size, n_fft, num_mels, + hop_size, win_size, sampling_rate, fmin, fmax, split=True, shuffle=True, n_cache_reuse=1, + device=None, fmax_loss=None, fine_tuning=False, base_mels_path=None): + self.audio_files = training_files + random.seed(1234) + if shuffle: + random.shuffle(self.audio_files) + self.segment_size = segment_size + self.sampling_rate = sampling_rate + self.split = split + self.n_fft = n_fft + self.num_mels = num_mels + self.hop_size = hop_size + self.win_size = win_size + self.fmin = fmin + self.fmax = fmax + self.fmax_loss = fmax_loss + self.cached_wav = None + self.n_cache_reuse = n_cache_reuse + self._cache_ref_count = 0 + self.device = device + self.fine_tuning = fine_tuning + self.base_mels_path = base_mels_path + + def __getitem__(self, index): + filename = self.audio_files[index] + if self._cache_ref_count == 0: + #audio, sampling_rate = load_wav(filename) + #audio = audio / MAX_WAV_VALUE + audio = np.load(filename) + if not self.fine_tuning: + audio = normalize(audio) * 0.95 + self.cached_wav = audio + #if sampling_rate != self.sampling_rate: + # raise ValueError("{} SR doesn't match target {} SR".format( + # sampling_rate, self.sampling_rate)) + self._cache_ref_count = self.n_cache_reuse + else: + audio = self.cached_wav + self._cache_ref_count -= 1 + + audio = torch.FloatTensor(audio) + audio = audio.unsqueeze(0) + + if not self.fine_tuning: + if self.split: + if audio.size(1) >= self.segment_size: + max_audio_start = audio.size(1) - self.segment_size + audio_start = random.randint(0, max_audio_start) + audio = audio[:, audio_start:audio_start+self.segment_size] + else: + audio = torch.nn.functional.pad(audio, (0, self.segment_size - audio.size(1)), 'constant') + + mel = mel_spectrogram(audio, self.n_fft, self.num_mels, + self.sampling_rate, self.hop_size, self.win_size, self.fmin, self.fmax, + center=False) + else: + mel_path = os.path.join(self.base_mels_path, "mel" + "-" + filename.split("/")[-1].split("-")[-1]) + mel = np.load(mel_path).T + #mel = np.load( + # os.path.join(self.base_mels_path, os.path.splitext(os.path.split(filename)[-1])[0] + '.npy')) + mel = torch.from_numpy(mel) + + if len(mel.shape) < 3: + mel = mel.unsqueeze(0) + + if self.split: + frames_per_seg = math.ceil(self.segment_size / self.hop_size) + + if audio.size(1) >= self.segment_size: + mel_start = random.randint(0, mel.size(2) - frames_per_seg - 1) + mel = mel[:, :, mel_start:mel_start + frames_per_seg] + audio = audio[:, mel_start * self.hop_size:(mel_start + frames_per_seg) * self.hop_size] + else: + mel = torch.nn.functional.pad(mel, (0, frames_per_seg - mel.size(2)), 'constant') + audio = torch.nn.functional.pad(audio, (0, self.segment_size - audio.size(1)), 'constant') + + mel_loss = mel_spectrogram(audio, self.n_fft, self.num_mels, + self.sampling_rate, self.hop_size, self.win_size, self.fmin, self.fmax_loss, + center=False) + + return (mel.squeeze(), audio.squeeze(0), filename, mel_loss.squeeze()) + + def __len__(self): + return len(self.audio_files) \ No newline at end of file diff --git a/vocoder/fregan/modules.py b/vocoder/fregan/modules.py new file mode 100644 index 0000000..9b8160e --- /dev/null +++ b/vocoder/fregan/modules.py @@ -0,0 +1,201 @@ +import torch +import torch.nn.functional as F + +class KernelPredictor(torch.nn.Module): + ''' Kernel predictor for the location-variable convolutions + ''' + + def __init__(self, + cond_channels, + conv_in_channels, + conv_out_channels, + conv_layers, + conv_kernel_size=3, + kpnet_hidden_channels=64, + kpnet_conv_size=3, + kpnet_dropout=0.0, + kpnet_nonlinear_activation="LeakyReLU", + kpnet_nonlinear_activation_params={"negative_slope": 0.1} + ): + ''' + Args: + cond_channels (int): number of channel for the conditioning sequence, + conv_in_channels (int): number of channel for the input sequence, + conv_out_channels (int): number of channel for the output sequence, + conv_layers (int): + kpnet_ + ''' + super().__init__() + + self.conv_in_channels = conv_in_channels + self.conv_out_channels = conv_out_channels + self.conv_kernel_size = conv_kernel_size + self.conv_layers = conv_layers + + l_w = conv_in_channels * conv_out_channels * conv_kernel_size * conv_layers + l_b = conv_out_channels * conv_layers + + padding = (kpnet_conv_size - 1) // 2 + self.input_conv = torch.nn.Sequential( + torch.nn.Conv1d(cond_channels, kpnet_hidden_channels, 5, padding=(5 - 1) // 2, bias=True), + getattr(torch.nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params), + ) + + self.residual_conv = torch.nn.Sequential( + torch.nn.Dropout(kpnet_dropout), + torch.nn.Conv1d(kpnet_hidden_channels, kpnet_hidden_channels, kpnet_conv_size, padding=padding, bias=True), + getattr(torch.nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params), + torch.nn.Conv1d(kpnet_hidden_channels, kpnet_hidden_channels, kpnet_conv_size, padding=padding, bias=True), + getattr(torch.nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params), + torch.nn.Dropout(kpnet_dropout), + torch.nn.Conv1d(kpnet_hidden_channels, kpnet_hidden_channels, kpnet_conv_size, padding=padding, bias=True), + getattr(torch.nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params), + torch.nn.Conv1d(kpnet_hidden_channels, kpnet_hidden_channels, kpnet_conv_size, padding=padding, bias=True), + getattr(torch.nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params), + torch.nn.Dropout(kpnet_dropout), + torch.nn.Conv1d(kpnet_hidden_channels, kpnet_hidden_channels, kpnet_conv_size, padding=padding, bias=True), + getattr(torch.nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params), + torch.nn.Conv1d(kpnet_hidden_channels, kpnet_hidden_channels, kpnet_conv_size, padding=padding, bias=True), + getattr(torch.nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params), + ) + + self.kernel_conv = torch.nn.Conv1d(kpnet_hidden_channels, l_w, kpnet_conv_size, + padding=padding, bias=True) + self.bias_conv = torch.nn.Conv1d(kpnet_hidden_channels, l_b, kpnet_conv_size, padding=padding, + bias=True) + + def forward(self, c): + ''' + Args: + c (Tensor): the conditioning sequence (batch, cond_channels, cond_length) + Returns: + ''' + batch, cond_channels, cond_length = c.shape + + c = self.input_conv(c) + c = c + self.residual_conv(c) + k = self.kernel_conv(c) + b = self.bias_conv(c) + + kernels = k.contiguous().view(batch, + self.conv_layers, + self.conv_in_channels, + self.conv_out_channels, + self.conv_kernel_size, + cond_length) + bias = b.contiguous().view(batch, + self.conv_layers, + self.conv_out_channels, + cond_length) + return kernels, bias + + +class LVCBlock(torch.nn.Module): + ''' the location-variable convolutions + ''' + + def __init__(self, + in_channels, + cond_channels, + upsample_ratio, + conv_layers=4, + conv_kernel_size=3, + cond_hop_length=256, + kpnet_hidden_channels=64, + kpnet_conv_size=3, + kpnet_dropout=0.0 + ): + super().__init__() + + self.cond_hop_length = cond_hop_length + self.conv_layers = conv_layers + self.conv_kernel_size = conv_kernel_size + self.convs = torch.nn.ModuleList() + + self.upsample = torch.nn.ConvTranspose1d(in_channels, in_channels, + kernel_size=upsample_ratio*2, stride=upsample_ratio, + padding=upsample_ratio // 2 + upsample_ratio % 2, + output_padding=upsample_ratio % 2) + + + self.kernel_predictor = KernelPredictor( + cond_channels=cond_channels, + conv_in_channels=in_channels, + conv_out_channels=2 * in_channels, + conv_layers=conv_layers, + conv_kernel_size=conv_kernel_size, + kpnet_hidden_channels=kpnet_hidden_channels, + kpnet_conv_size=kpnet_conv_size, + kpnet_dropout=kpnet_dropout + ) + + + for i in range(conv_layers): + padding = (3 ** i) * int((conv_kernel_size - 1) / 2) + conv = torch.nn.Conv1d(in_channels, in_channels, kernel_size=conv_kernel_size, padding=padding, dilation=3 ** i) + + self.convs.append(conv) + + + def forward(self, x, c): + ''' forward propagation of the location-variable convolutions. + Args: + x (Tensor): the input sequence (batch, in_channels, in_length) + c (Tensor): the conditioning sequence (batch, cond_channels, cond_length) + + Returns: + Tensor: the output sequence (batch, in_channels, in_length) + ''' + batch, in_channels, in_length = x.shape + + + kernels, bias = self.kernel_predictor(c) + + x = F.leaky_relu(x, 0.2) + x = self.upsample(x) + + for i in range(self.conv_layers): + y = F.leaky_relu(x, 0.2) + y = self.convs[i](y) + y = F.leaky_relu(y, 0.2) + + k = kernels[:, i, :, :, :, :] + b = bias[:, i, :, :] + y = self.location_variable_convolution(y, k, b, 1, self.cond_hop_length) + x = x + torch.sigmoid(y[:, :in_channels, :]) * torch.tanh(y[:, in_channels:, :]) + return x + + def location_variable_convolution(self, x, kernel, bias, dilation, hop_size): + ''' perform location-variable convolution operation on the input sequence (x) using the local convolution kernl. + Time: 414 μs ± 309 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each), test on NVIDIA V100. + Args: + x (Tensor): the input sequence (batch, in_channels, in_length). + kernel (Tensor): the local convolution kernel (batch, in_channel, out_channels, kernel_size, kernel_length) + bias (Tensor): the bias for the local convolution (batch, out_channels, kernel_length) + dilation (int): the dilation of convolution. + hop_size (int): the hop_size of the conditioning sequence. + Returns: + (Tensor): the output sequence after performing local convolution. (batch, out_channels, in_length). + ''' + batch, in_channels, in_length = x.shape + batch, in_channels, out_channels, kernel_size, kernel_length = kernel.shape + + + assert in_length == (kernel_length * hop_size), "length of (x, kernel) is not matched" + + padding = dilation * int((kernel_size - 1) / 2) + x = F.pad(x, (padding, padding), 'constant', 0) # (batch, in_channels, in_length + 2*padding) + x = x.unfold(2, hop_size + 2 * padding, hop_size) # (batch, in_channels, kernel_length, hop_size + 2*padding) + + if hop_size < dilation: + x = F.pad(x, (0, dilation), 'constant', 0) + x = x.unfold(3, dilation, + dilation) # (batch, in_channels, kernel_length, (hop_size + 2*padding)/dilation, dilation) + x = x[:, :, :, :, :hop_size] + x = x.transpose(3, 4) # (batch, in_channels, kernel_length, dilation, (hop_size + 2*padding)/dilation) + x = x.unfold(4, kernel_size, 1) # (batch, in_channels, kernel_length, dilation, _, kernel_size) + + o = torch.einsum('bildsk,biokl->bolsd', x, kernel) + o = o + bias.unsqueeze(-1).unsqueeze(-1) + o = o.contiguous().view(batch, out_channels, -1) + return o diff --git a/vocoder/fregan/requirements.txt b/vocoder/fregan/requirements.txt new file mode 100644 index 0000000..2438901 --- /dev/null +++ b/vocoder/fregan/requirements.txt @@ -0,0 +1 @@ +PyWavelets \ No newline at end of file diff --git a/vocoder/fregan/train.py b/vocoder/fregan/train.py new file mode 100644 index 0000000..2f31d8b --- /dev/null +++ b/vocoder/fregan/train.py @@ -0,0 +1,246 @@ +import warnings + +warnings.simplefilter(action='ignore', category=FutureWarning) +import itertools +import os +import time +import torch +import torch.nn.functional as F +from torch.utils.tensorboard import SummaryWriter +from torch.utils.data import DistributedSampler, DataLoader +from torch.distributed import init_process_group +from torch.nn.parallel import DistributedDataParallel +from vocoder.fregan.meldataset import MelDataset, mel_spectrogram, get_dataset_filelist +from vocoder.fregan.generator import FreGAN +from vocoder.fregan.discriminator import ResWiseMultiPeriodDiscriminator, ResWiseMultiScaleDiscriminator +from vocoder.fregan.loss import feature_loss, generator_loss, discriminator_loss +from vocoder.fregan.utils import plot_spectrogram, scan_checkpoint, load_checkpoint, save_checkpoint + + +torch.backends.cudnn.benchmark = True + + +def train(rank, a, h): + + a.checkpoint_path = a.models_dir.joinpath(a.run_id+'_fregan') + a.checkpoint_path.mkdir(exist_ok=True) + a.training_epochs = 3100 + a.stdout_interval = 5 + a.checkpoint_interval = a.backup_every + a.summary_interval = 5000 + a.validation_interval = 1000 + a.fine_tuning = True + + a.input_wavs_dir = a.syn_dir.joinpath("audio") + a.input_mels_dir = a.syn_dir.joinpath("mels") + + if h.num_gpus > 1: + init_process_group(backend=h.dist_config['dist_backend'], init_method=h.dist_config['dist_url'], + world_size=h.dist_config['world_size'] * h.num_gpus, rank=rank) + + torch.cuda.manual_seed(h.seed) + device = torch.device('cuda:{:d}'.format(rank)) + + generator = FreGAN(h).to(device) + mpd = ResWiseMultiPeriodDiscriminator().to(device) + msd = ResWiseMultiScaleDiscriminator().to(device) + + if rank == 0: + print(generator) + os.makedirs(a.checkpoint_path, exist_ok=True) + print("checkpoints directory : ", a.checkpoint_path) + + if os.path.isdir(a.checkpoint_path): + cp_g = scan_checkpoint(a.checkpoint_path, 'g_') + cp_do = scan_checkpoint(a.checkpoint_path, 'do_') + + steps = 0 + if cp_g is None or cp_do is None: + state_dict_do = None + last_epoch = -1 + else: + state_dict_g = load_checkpoint(cp_g, device) + state_dict_do = load_checkpoint(cp_do, device) + generator.load_state_dict(state_dict_g['generator']) + mpd.load_state_dict(state_dict_do['mpd']) + msd.load_state_dict(state_dict_do['msd']) + steps = state_dict_do['steps'] + 1 + last_epoch = state_dict_do['epoch'] + + if h.num_gpus > 1: + generator = DistributedDataParallel(generator, device_ids=[rank]).to(device) + mpd = DistributedDataParallel(mpd, device_ids=[rank]).to(device) + msd = DistributedDataParallel(msd, device_ids=[rank]).to(device) + + optim_g = torch.optim.AdamW(generator.parameters(), h.learning_rate, betas=[h.adam_b1, h.adam_b2]) + optim_d = torch.optim.AdamW(itertools.chain(msd.parameters(), mpd.parameters()), + h.learning_rate, betas=[h.adam_b1, h.adam_b2]) + + if state_dict_do is not None: + optim_g.load_state_dict(state_dict_do['optim_g']) + optim_d.load_state_dict(state_dict_do['optim_d']) + + scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=h.lr_decay, last_epoch=last_epoch) + scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=h.lr_decay, last_epoch=last_epoch) + + training_filelist, validation_filelist = get_dataset_filelist(a) + + trainset = MelDataset(training_filelist, h.segment_size, h.n_fft, h.num_mels, + h.hop_size, h.win_size, h.sampling_rate, h.fmin, h.fmax, n_cache_reuse=0, + shuffle=False if h.num_gpus > 1 else True, fmax_loss=h.fmax_for_loss, device=device, + fine_tuning=a.fine_tuning, base_mels_path=a.input_mels_dir) + + train_sampler = DistributedSampler(trainset) if h.num_gpus > 1 else None + + train_loader = DataLoader(trainset, num_workers=h.num_workers, shuffle=False, + sampler=train_sampler, + batch_size=h.batch_size, + pin_memory=True, + drop_last=True) + + if rank == 0: + validset = MelDataset(validation_filelist, h.segment_size, h.n_fft, h.num_mels, + h.hop_size, h.win_size, h.sampling_rate, h.fmin, h.fmax, False, False, n_cache_reuse=0, + fmax_loss=h.fmax_for_loss, device=device, fine_tuning=a.fine_tuning, + base_mels_path=a.input_mels_dir) + validation_loader = DataLoader(validset, num_workers=1, shuffle=False, + sampler=None, + batch_size=1, + pin_memory=True, + drop_last=True) + + sw = SummaryWriter(os.path.join(a.checkpoint_path, 'logs')) + + generator.train() + mpd.train() + msd.train() + for epoch in range(max(0, last_epoch), a.training_epochs): + if rank == 0: + start = time.time() + print("Epoch: {}".format(epoch + 1)) + + if h.num_gpus > 1: + train_sampler.set_epoch(epoch) + + for i, batch in enumerate(train_loader): + if rank == 0: + start_b = time.time() + x, y, _, y_mel = batch + x = torch.autograd.Variable(x.to(device, non_blocking=True)) + y = torch.autograd.Variable(y.to(device, non_blocking=True)) + y_mel = torch.autograd.Variable(y_mel.to(device, non_blocking=True)) + y = y.unsqueeze(1) + y_g_hat = generator(x) + y_g_hat_mel = mel_spectrogram(y_g_hat.squeeze(1), h.n_fft, h.num_mels, h.sampling_rate, h.hop_size, + h.win_size, + h.fmin, h.fmax_for_loss) + + + + optim_d.zero_grad() + + # MPD + y_df_hat_r, y_df_hat_g, _, _ = mpd(y, y_g_hat.detach()) + loss_disc_f, losses_disc_f_r, losses_disc_f_g = discriminator_loss(y_df_hat_r, y_df_hat_g) + + # MSD + y_ds_hat_r, y_ds_hat_g, _, _ = msd(y, y_g_hat.detach()) + loss_disc_s, losses_disc_s_r, losses_disc_s_g = discriminator_loss(y_ds_hat_r, y_ds_hat_g) + + loss_disc_all = loss_disc_s + loss_disc_f + + loss_disc_all.backward() + optim_d.step() + + # Generator + optim_g.zero_grad() + + + # L1 Mel-Spectrogram Loss + loss_mel = F.l1_loss(y_mel, y_g_hat_mel) * 45 + + # sc_loss, mag_loss = stft_loss(y_g_hat[:, :, :y.size(2)].squeeze(1), y.squeeze(1)) + # loss_mel = h.lambda_aux * (sc_loss + mag_loss) # STFT Loss + + + y_df_hat_r, y_df_hat_g, fmap_f_r, fmap_f_g = mpd(y, y_g_hat) + y_ds_hat_r, y_ds_hat_g, fmap_s_r, fmap_s_g = msd(y, y_g_hat) + loss_fm_f = feature_loss(fmap_f_r, fmap_f_g) + loss_fm_s = feature_loss(fmap_s_r, fmap_s_g) + loss_gen_f, losses_gen_f = generator_loss(y_df_hat_g) + loss_gen_s, losses_gen_s = generator_loss(y_ds_hat_g) + loss_gen_all = loss_gen_s + loss_gen_f + (2 * (loss_fm_s + loss_fm_f)) + loss_mel + + + loss_gen_all.backward() + optim_g.step() + + if rank == 0: + # STDOUT logging + if steps % a.stdout_interval == 0: + with torch.no_grad(): + mel_error = F.l1_loss(y_mel, y_g_hat_mel).item() + + print('Steps : {:d}, Gen Loss Total : {:4.3f}, Mel-Spec. Error : {:4.3f}, s/b : {:4.3f}'. + format(steps, loss_gen_all, mel_error, time.time() - start_b)) + + # checkpointing + if steps % a.checkpoint_interval == 0 and steps != 0: + checkpoint_path = "{}/g_fregan_{:08d}.pt".format(a.checkpoint_path, steps) + save_checkpoint(checkpoint_path, + {'generator': (generator.module if h.num_gpus > 1 else generator).state_dict()}) + checkpoint_path = "{}/do_fregan_{:08d}.pt".format(a.checkpoint_path, steps) + save_checkpoint(checkpoint_path, + {'mpd': (mpd.module if h.num_gpus > 1 + else mpd).state_dict(), + 'msd': (msd.module if h.num_gpus > 1 + else msd).state_dict(), + 'optim_g': optim_g.state_dict(), 'optim_d': optim_d.state_dict(), 'steps': steps, + 'epoch': epoch}) + + # Tensorboard summary logging + if steps % a.summary_interval == 0: + sw.add_scalar("training/gen_loss_total", loss_gen_all, steps) + sw.add_scalar("training/mel_spec_error", mel_error, steps) + + # Validation + if steps % a.validation_interval == 0: # and steps != 0: + generator.eval() + torch.cuda.empty_cache() + val_err_tot = 0 + with torch.no_grad(): + for j, batch in enumerate(validation_loader): + x, y, _, y_mel = batch + y_g_hat = generator(x.to(device)) + y_mel = torch.autograd.Variable(y_mel.to(device, non_blocking=True)) + y_g_hat_mel = mel_spectrogram(y_g_hat.squeeze(1), h.n_fft, h.num_mels, h.sampling_rate, + h.hop_size, h.win_size, + h.fmin, h.fmax_for_loss) + #val_err_tot += F.l1_loss(y_mel, y_g_hat_mel).item() + + if j <= 4: + if steps == 0: + sw.add_audio('gt/y_{}'.format(j), y[0], steps, h.sampling_rate) + sw.add_figure('gt/y_spec_{}'.format(j), plot_spectrogram(x[0]), steps) + + sw.add_audio('generated/y_hat_{}'.format(j), y_g_hat[0], steps, h.sampling_rate) + y_hat_spec = mel_spectrogram(y_g_hat.squeeze(1), h.n_fft, h.num_mels, + h.sampling_rate, h.hop_size, h.win_size, + h.fmin, h.fmax) + sw.add_figure('generated/y_hat_spec_{}'.format(j), + plot_spectrogram(y_hat_spec.squeeze(0).cpu().numpy()), steps) + + val_err = val_err_tot / (j + 1) + sw.add_scalar("validation/mel_spec_error", val_err, steps) + + generator.train() + + steps += 1 + + scheduler_g.step() + scheduler_d.step() + + if rank == 0: + print('Time taken for epoch {} is {} sec\n'.format(epoch + 1, int(time.time() - start))) + + diff --git a/vocoder/fregan/utils.py b/vocoder/fregan/utils.py new file mode 100644 index 0000000..e5d9de7 --- /dev/null +++ b/vocoder/fregan/utils.py @@ -0,0 +1,65 @@ +import glob +import os +import matplotlib +import torch +from torch.nn.utils import weight_norm +matplotlib.use("Agg") +import matplotlib.pylab as plt +import shutil + + +def build_env(config, config_name, path): + t_path = os.path.join(path, config_name) + if config != t_path: + os.makedirs(path, exist_ok=True) + shutil.copyfile(config, os.path.join(path, config_name)) + + +def plot_spectrogram(spectrogram): + fig, ax = plt.subplots(figsize=(10, 2)) + im = ax.imshow(spectrogram, aspect="auto", origin="lower", + interpolation='none') + plt.colorbar(im, ax=ax) + + fig.canvas.draw() + plt.close() + + return fig + + +def init_weights(m, mean=0.0, std=0.01): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + m.weight.data.normal_(mean, std) + + +def apply_weight_norm(m): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + weight_norm(m) + + +def get_padding(kernel_size, dilation=1): + return int((kernel_size*dilation - dilation)/2) + + +def load_checkpoint(filepath, device): + assert os.path.isfile(filepath) + print("Loading '{}'".format(filepath)) + checkpoint_dict = torch.load(filepath, map_location=device) + print("Complete.") + return checkpoint_dict + + +def save_checkpoint(filepath, obj): + print("Saving checkpoint to {}".format(filepath)) + torch.save(obj, filepath) + print("Complete.") + + +def scan_checkpoint(cp_dir, prefix): + pattern = os.path.join(cp_dir, prefix + '????????') + cp_list = glob.glob(pattern) + if len(cp_list) == 0: + return None + return sorted(cp_list)[-1] \ No newline at end of file diff --git a/vocoder_train.py b/vocoder_train.py index 1ef0e30..de30d59 100644 --- a/vocoder_train.py +++ b/vocoder_train.py @@ -1,6 +1,7 @@ from utils.argutils import print_args from vocoder.wavernn.train import train from vocoder.hifigan.train import train as train_hifigan +from vocoder.fregan.train import train as train_fregan from utils.util import AttrDict from pathlib import Path import argparse @@ -61,11 +62,18 @@ if __name__ == "__main__": # Process the arguments if args.vocoder_type == "wavernn": # Run the training wavernn + delattr(args, 'vocoder_type') + delattr(args, 'config') train(**vars(args)) elif args.vocoder_type == "hifigan": with open(args.config) as f: json_config = json.load(f) h = AttrDict(json_config) train_hifigan(0, args, h) + elif args.vocoder_type == "fregan": + with open('vocoder/fregan/config.json') as f: + json_config = json.load(f) + h = AttrDict(json_config) + train_fregan(0, args, h) \ No newline at end of file