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# Copyright (c) 2021-2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# MIT License
#
# Copyright (c) 2020 Jungil Kong
#
# 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.
# The following functions/classes were based on code from https://github.com/jik876/hifi-gan:
# mel_spectrogram, MelDataset
import math
import os
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
from librosa.util import normalize
from numpy import random
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
from common.audio_processing import dynamic_range_compression
from common.utils import load_filepaths_and_text, load_wav
MAX_WAV_VALUE = 32768.0
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
fmax_key = f'{fmax}_{y.device}'
if fmax_key not in mel_basis:
mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
mel_basis[fmax_key] = torch.from_numpy(mel).float().to(y.device)
hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device)
pad = int((n_fft-hop_size)/2)
y = F.pad(y.unsqueeze(1), (pad, pad), 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,
return_complex=True)
spec = torch.view_as_real(spec)
spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9))
spec = torch.matmul(mel_basis[str(fmax)+'_'+str(y.device)], spec)
spec = dynamic_range_compression(spec) # spectral normalize
return spec
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,
device=None, fmax_loss=None, fine_tuning=False,
base_mels_path=None, repeat=1, deterministic=False,
max_wav_value=MAX_WAV_VALUE):
self.audio_files = training_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.max_wav_value = max_wav_value
self.fine_tuning = fine_tuning
self.base_mels_path = base_mels_path
self.repeat = repeat
self.deterministic = deterministic
self.rng = random.default_rng()
def __getitem__(self, index):
if index >= len(self):
raise IndexError('Dataset index out of range')
rng = random.default_rng(index) if self.deterministic else self.rng
index = index % len(self.audio_files) # collapse **after** setting seed
filename = self.audio_files[index]
audio, sampling_rate = load_wav(filename)
audio = audio / self.max_wav_value
if not self.fine_tuning:
audio = normalize(audio) * 0.95
if sampling_rate != self.sampling_rate:
raise ValueError("{} SR doesn't match target {} SR".format(
sampling_rate, self.sampling_rate))
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 = rng.integers(0, max_audio_start)
audio = audio[:, audio_start:audio_start+self.segment_size]
else:
audio = F.pad(audio, (0, self.segment_size - audio.size(1)))
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 = np.load(
os.path.join(self.base_mels_path,
os.path.splitext(os.path.split(filename)[-1])[0] + '.npy'))
mel = torch.from_numpy(mel).float()
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 = rng.integers(0, mel.size(2) - frames_per_seg - 1)
mel = mel[:, :, mel_start:mel_start + frames_per_seg]
a = mel_start * self.hop_size
b = (mel_start + frames_per_seg) * self.hop_size
audio = audio[:, a:b]
else:
mel = F.pad(mel, (0, frames_per_seg - mel.size(2)))
audio = F.pad(audio, (0, self.segment_size - audio.size(1)))
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) * self.repeat
def get_data_loader(args, distributed_run, train=True, batch_size=None,
val_kwargs=None):
filelists = args.training_files if train else args.validation_files
files = load_filepaths_and_text(args.dataset_path, filelists)
files = list(zip(*files))[0]
dataset_kw = {
'segment_size': args.segment_size,
'n_fft': args.filter_length,
'num_mels': args.num_mels,
'hop_size': args.hop_length,
'win_size': args.win_length,
'sampling_rate': args.sampling_rate,
'fmin': args.mel_fmin,
'fmax': args.mel_fmax,
'fmax_loss': args.mel_fmax_loss,
'max_wav_value': args.max_wav_value,
'fine_tuning': args.fine_tuning,
'base_mels_path': args.input_mels_dir,
'deterministic': not train
}
if train:
dataset = MelDataset(files, **dataset_kw)
sampler = DistributedSampler(dataset) if distributed_run else None
else:
dataset_kw.update(val_kwargs or {})
dataset = MelDataset(files, **dataset_kw)
sampler = (DistributedSampler(dataset, shuffle=False)
if distributed_run else None)
loader = DataLoader(dataset,
# NOTE On DGX-1 and DGX A100 =1 is optimal
num_workers=args.num_workers if train else 1,
shuffle=(train and not distributed_run),
sampler=sampler,
batch_size=batch_size or args.batch_size,
pin_memory=True,
persistent_workers=True,
drop_last=train)
return loader
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