Mods to support contrastive learning on audio files

This commit is contained in:
James Betker 2021-08-05 05:57:04 -06:00
parent 341f28dd82
commit 5037220ac7
6 changed files with 484 additions and 9 deletions

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@ -74,6 +74,8 @@ def create_dataset(dataset_opt, return_collate=False):
from data.audio.gpt_tts_dataset import GptTtsDataset as D
from data.audio.gpt_tts_dataset import GptTtsCollater as C
collate = C(dataset_opt)
elif mode == 'wavfile_clips':
from data.audio.wavfile_dataset import WavfileDataset as D
else:
raise NotImplementedError('Dataset [{:s}] is not recognized.'.format(mode))
dataset = D(dataset_opt)

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@ -0,0 +1,81 @@
import os
import random
import torch
import torch.utils.data
from tqdm import tqdm
from data.util import get_image_paths, is_wav_file
from models.tacotron2.taco_utils import load_wav_to_torch
class WavfileDataset(torch.utils.data.Dataset):
def __init__(self, opt):
self.path = os.path.dirname(opt['path'])
cache_path = os.path.join(self.path, 'cache.pth')
if os.path.exists(cache_path):
self.audiopaths = torch.load(cache_path)
else:
print("Building cache..")
self.audiopaths = get_image_paths('img', opt['path'], qualifier=is_wav_file)[0]
torch.save(self.audiopaths, cache_path)
self.max_wav_value = 32768.0
self.sampling_rate = 24000
self.window = 2 * self.sampling_rate
def get_audio_for_index(self, index):
audiopath = self.audiopaths[index]
filename = os.path.join(self.path, audiopath)
audio, sampling_rate = load_wav_to_torch(filename)
if sampling_rate != self.sampling_rate:
raise ValueError(f"Input sampling rate does not match specified rate {self.sampling_rate}")
audio_norm = audio / self.max_wav_value
audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
return audio_norm, audiopath
def __getitem__(self, index):
clip1, clip2 = None, None
while clip1 is None and clip2 is None:
# Split audio_norm into two tensors of equal size.
audio_norm, filename = self.get_audio_for_index(index)
if audio_norm.shape[0] < self.window * 2:
# Try next index. This adds a bit of bias and ideally we'd filter the dataset rather than do this.
index = (index + 1) % len(self)
continue
j = random.randint(0, audio_norm.shape[0] - self.window)
clip1 = audio_norm[j:j+self.window]
j = random.randint(0, audio_norm.shape[0]-self.window)
clip2 = audio_norm[j:j+self.window]
return {
'clip1': clip1.unsqueeze(0),
'clip2': clip2.unsqueeze(0),
'path': filename,
}
def __len__(self):
return len(self.audiopaths)
if __name__ == '__main__':
params = {
'mode': 'wavfile_clips',
'path': 'E:\\audio\\LibriTTS\\train-other-500',
'phase': 'train',
'n_workers': 0,
'batch_size': 16,
}
from data import create_dataset, create_dataloader, util
ds, c = create_dataset(params, return_collate=True)
dl = create_dataloader(ds, params, collate_fn=c)
i = 0
m = []
max_text = 0
max_mel = 0
for b in tqdm(dl):
pass
m=torch.stack(m)
print(m.mean(), m.std())

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@ -39,14 +39,17 @@ def cv2torch(cv, batchify=True):
def is_image_file(filename):
return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
def is_wav_file(filename):
return filename.endswith('.wav')
def _get_paths_from_images(path):
def _get_paths_from_images(path, qualifier=is_image_file):
"""get image path list from image folder"""
assert os.path.isdir(path), '{:s} is not a valid directory'.format(path)
images = []
for dirpath, _, fnames in sorted(os.walk(path)):
for fname in sorted(fnames):
if is_image_file(fname) and 'ref.jpg' not in fname:
if qualifier(fname) and 'ref.jpg' not in fname:
img_path = os.path.join(dirpath, fname)
images.append(img_path)
if not images:
@ -64,7 +67,7 @@ def _get_paths_from_lmdb(dataroot):
return paths, sizes
def get_image_paths(data_type, dataroot, weights=[]):
def get_image_paths(data_type, dataroot, weights=[], qualifier=is_image_file):
"""get image path list
support lmdb or image files"""
paths, sizes = None, None
@ -82,11 +85,11 @@ def get_image_paths(data_type, dataroot, weights=[]):
if weights:
extends = weights[i]
for j in range(extends):
paths.extend(_get_paths_from_images(r))
paths.extend(_get_paths_from_images(r, qualifier))
paths = sorted(paths)
sizes = len(paths)
else:
paths = sorted(_get_paths_from_images(dataroot))
paths = sorted(_get_paths_from_images(dataroot, qualifier))
sizes = len(paths)
else:
raise NotImplementedError('data_type [{:s}] is not recognized.'.format(data_type))
@ -117,9 +120,9 @@ def read_img(env, path, size=None, rgb=False):
stream = open(path, "rb")
bytes = bytearray(stream.read())
img = cv2.imdecode(np.asarray(bytes, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
elif env is 'lmdb':
elif env == 'lmdb':
img = _read_img_lmdb(env, path, size)
elif env is 'buffer':
elif env == 'buffer':
img = cv2.imdecode(path, cv2.IMREAD_UNCHANGED)
else:
raise NotImplementedError("Unsupported env: %s" % (env,))

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@ -0,0 +1,387 @@
import torch
from torch import Tensor
import torch.nn as nn
from trainer.networks import register_model
from utils.util import opt_get
from typing import Type, Any, Callable, Union, List, Optional
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
'wide_resnet50_2', 'wide_resnet101_2']
def conv3x3(in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1) -> nn.Conv1d:
"""3x3 convolution with padding"""
return nn.Conv1d(in_planes, out_planes, kernel_size=5, stride=stride,
padding=2, groups=groups, bias=False, dilation=dilation)
def conv1x1(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv1d:
"""1x1 convolution"""
return nn.Conv1d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
class BasicBlock(nn.Module):
expansion: int = 1
def __init__(
self,
inplanes: int,
planes: int,
stride: int = 1,
downsample: Optional[nn.Module] = None,
groups: int = 1,
base_width: int = 64,
dilation: int = 1,
norm_layer: Optional[Callable[..., nn.Module]] = None
) -> None:
super(BasicBlock, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm1d
if groups != 1 or base_width != 64:
raise ValueError('BasicBlock only supports groups=1 and base_width=64')
if dilation > 1:
raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
# Both self.conv1 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = norm_layer(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = norm_layer(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x: Tensor) -> Tensor:
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class Bottleneck(nn.Module):
# Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
# while original implementation places the stride at the first 1x1 convolution(self.conv1)
# according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
# This variant is also known as ResNet V1.5 and improves accuracy according to
# https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.
expansion: int = 4
def __init__(
self,
inplanes: int,
planes: int,
stride: int = 1,
downsample: Optional[nn.Module] = None,
groups: int = 1,
base_width: int = 64,
dilation: int = 1,
norm_layer: Optional[Callable[..., nn.Module]] = None
) -> None:
super(Bottleneck, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm1d
width = int(planes * (base_width / 64.)) * groups
# Both self.conv2 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv1x1(inplanes, width)
self.bn1 = norm_layer(width)
self.conv2 = conv3x3(width, width, stride, groups, dilation)
self.bn2 = norm_layer(width)
self.conv3 = conv1x1(width, planes * self.expansion)
self.bn3 = norm_layer(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x: Tensor) -> Tensor:
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(
self,
block: Type[Union[BasicBlock, Bottleneck]],
layers: List[int],
num_classes: int = 1000,
zero_init_residual: bool = False,
groups: int = 1,
width_per_group: int = 64,
replace_stride_with_dilation: Optional[List[bool]] = None,
norm_layer: Optional[Callable[..., nn.Module]] = None
) -> None:
super(ResNet, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm1d
self._norm_layer = norm_layer
self.inplanes = 64
self.dilation = 1
if replace_stride_with_dilation is None:
# each element in the tuple indicates if we should replace
# the 2x2 stride with a dilated convolution instead
replace_stride_with_dilation = [False, False, False]
if len(replace_stride_with_dilation) != 3:
raise ValueError("replace_stride_with_dilation should be None "
"or a 3-element tuple, got {}".format(replace_stride_with_dilation))
self.groups = groups
self.base_width = width_per_group
self.conv1 = nn.Conv1d(1, self.inplanes, kernel_size=7, stride=4, padding=3,
bias=False)
self.bn1 = norm_layer(self.inplanes)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool1d(kernel_size=5, stride=4, padding=2)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=4,
dilate=replace_stride_with_dilation[0])
self.layer3 = self._make_layer(block, 256, layers[2], stride=4,
dilate=replace_stride_with_dilation[1])
self.layer4 = self._make_layer(block, 512, layers[3], stride=4,
dilate=replace_stride_with_dilation[2])
self.avgpool = nn.AdaptiveAvgPool1d(1)
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv1d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, (nn.BatchNorm1d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# Zero-initialize the last BN in each residual branch,
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
if zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
nn.init.constant_(m.bn3.weight, 0) # type: ignore[arg-type]
elif isinstance(m, BasicBlock):
nn.init.constant_(m.bn2.weight, 0) # type: ignore[arg-type]
def _make_layer(self, block: Type[Union[BasicBlock, Bottleneck]], planes: int, blocks: int,
stride: int = 1, dilate: bool = False) -> nn.Sequential:
norm_layer = self._norm_layer
downsample = None
previous_dilation = self.dilation
if dilate:
self.dilation *= stride
stride = 1
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes * block.expansion, stride),
norm_layer(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
self.base_width, previous_dilation, norm_layer))
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(block(self.inplanes, planes, groups=self.groups,
base_width=self.base_width, dilation=self.dilation,
norm_layer=norm_layer))
return nn.Sequential(*layers)
def _forward_impl(self, x: Tensor) -> Tensor:
# See note [TorchScript super()]
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.fc(x)
return x
def forward(self, x: Tensor) -> Tensor:
return self._forward_impl(x)
def _resnet(
arch: str,
block: Type[Union[BasicBlock, Bottleneck]],
layers: List[int],
pretrained: bool,
progress: bool,
**kwargs: Any
) -> ResNet:
model = ResNet(block, layers, **kwargs)
return model
def resnet18(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
r"""ResNet-18 model from
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress,
**kwargs)
def resnet34(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
r"""ResNet-34 model from
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress,
**kwargs)
def resnet50(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
r"""ResNet-50 model from
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress,
**kwargs)
def resnet101(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
r"""ResNet-101 model from
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress,
**kwargs)
def resnet152(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
r"""ResNet-152 model from
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress,
**kwargs)
def resnext50_32x4d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
r"""ResNeXt-50 32x4d model from
`"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
kwargs['groups'] = 32
kwargs['width_per_group'] = 4
return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3],
pretrained, progress, **kwargs)
def resnext101_32x8d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
r"""ResNeXt-101 32x8d model from
`"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
kwargs['groups'] = 32
kwargs['width_per_group'] = 8
return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3],
pretrained, progress, **kwargs)
def wide_resnet50_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
r"""Wide ResNet-50-2 model from
`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
The model is the same as ResNet except for the bottleneck number of channels
which is twice larger in every block. The number of channels in outer 1x1
convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
channels, and in Wide ResNet-50-2 has 2048-1024-2048.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
kwargs['width_per_group'] = 64 * 2
return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3],
pretrained, progress, **kwargs)
def wide_resnet101_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
r"""Wide ResNet-101-2 model from
`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
The model is the same as ResNet except for the bottleneck number of channels
which is twice larger in every block. The number of channels in outer 1x1
convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
channels, and in Wide ResNet-50-2 has 2048-1024-2048.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
kwargs['width_per_group'] = 64 * 2
return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3],
pretrained, progress, **kwargs)
@register_model
def register_audio_resnet(opt_net, opt):
type = opt_net['type']
fn = globals()[type]
return fn(**opt_get(opt_net, ['kwargs'], {}))
if __name__ == '__main__':
m = resnet34()
o = m(torch.randn((1,1,48000)))
print(o.shape)

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@ -300,7 +300,7 @@ class Trainer:
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_gpt_tts_lj.yml')
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_byol_audio_clips.yml')
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
parser.add_argument('--local_rank', type=int, default=0)
args = parser.parse_args()

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@ -4,6 +4,8 @@ from collections import defaultdict
import torch
from torch.optim.lr_scheduler import _LRScheduler
from utils.util import opt_get
def get_scheduler_for_name(name, optimizers, scheduler_opt):
schedulers = []
@ -19,7 +21,7 @@ def get_scheduler_for_name(name, optimizers, scheduler_opt):
gamma=scheduler_opt['lr_gamma'],
clear_state=scheduler_opt['clear_state'],
force_lr=scheduler_opt['force_lr'],
warmup_steps=scheduler_opt['warmup_steps'])
warmup_steps=opt_get(scheduler_opt, ['warmup_steps'], 0))
elif name == 'ProgressiveMultiStepLR':
sched = ProgressiveMultiStepLR(o, scheduler_opt['gen_lr_steps'],
scheduler_opt['progressive_starts'],