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"""
This model is based on OpenAI's UNet from improved diffusion, with modifications to support a MEL conditioning signal
and an audio conditioning input. It has also been simplified somewhat.
Credit: https://github.com/openai/improved-diffusion
"""
import math
from abc import abstractmethod
import torch
import torch.nn as nn
from models.arch_util import normalization, zero_module, Downsample, Upsample, AudioMiniEncoder, AttentionBlock
def timestep_embedding(timesteps, dim, max_period=10000):
"""
Create sinusoidal timestep embeddings.
:param timesteps: a 1-D Tensor of N indices, one per batch element.
These may be fractional.
:param dim: the dimension of the output.
:param max_period: controls the minimum frequency of the embeddings.
:return: an [N x dim] Tensor of positional embeddings.
"""
half = dim // 2
freqs = torch.exp(
-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
).to(device=timesteps.device)
args = timesteps[:, None].float() * freqs[None]
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
if dim % 2:
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
return embedding
class TimestepBlock(nn.Module):
"""
Any module where forward() takes timestep embeddings as a second argument.
"""
@abstractmethod
def forward(self, x, emb):
"""
Apply the module to `x` given `emb` timestep embeddings.
"""
class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
"""
A sequential module that passes timestep embeddings to the children that
support it as an extra input.
"""
def forward(self, x, emb):
for layer in self:
if isinstance(layer, TimestepBlock):
x = layer(x, emb)
else:
x = layer(x)
return x
class TimestepResBlock(TimestepBlock):
"""
A residual block that can optionally change the number of channels.
:param channels: the number of input channels.
:param emb_channels: the number of timestep embedding channels.
:param dropout: the rate of dropout.
:param out_channels: if specified, the number of out channels.
:param use_conv: if True and out_channels is specified, use a spatial
convolution instead of a smaller 1x1 convolution to change the
channels in the skip connection.
:param dims: determines if the signal is 1D, 2D, or 3D.
:param up: if True, use this block for upsampling.
:param down: if True, use this block for downsampling.
"""
def __init__(
self,
channels,
emb_channels,
dropout,
out_channels=None,
use_conv=False,
use_scale_shift_norm=False,
up=False,
down=False,
kernel_size=3,
):
super().__init__()
self.channels = channels
self.emb_channels = emb_channels
self.dropout = dropout
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.use_scale_shift_norm = use_scale_shift_norm
padding = 1 if kernel_size == 3 else (2 if kernel_size == 5 else 0)
self.in_layers = nn.Sequential(
normalization(channels),
nn.SiLU(),
nn.Conv1d(channels, self.out_channels, kernel_size, padding=padding),
)
self.updown = up or down
if up:
self.h_upd = Upsample(channels, False, dims)
self.x_upd = Upsample(channels, False, dims)
elif down:
self.h_upd = Downsample(channels, False, dims)
self.x_upd = Downsample(channels, False, dims)
else:
self.h_upd = self.x_upd = nn.Identity()
self.emb_layers = nn.Sequential(
nn.SiLU(),
nn.Linear(
emb_channels,
2 * self.out_channels if use_scale_shift_norm else self.out_channels,
),
)
self.out_layers = nn.Sequential(
normalization(self.out_channels),
nn.SiLU(),
nn.Dropout(p=dropout),
zero_module(
nn.Conv1d(self.out_channels, self.out_channels, kernel_size, padding=padding)
),
)
if self.out_channels == channels:
self.skip_connection = nn.Identity()
elif use_conv:
self.skip_connection = nn.Conv1d(
channels, self.out_channels, kernel_size, padding=padding
)
else:
self.skip_connection = nn.Conv1d(channels, self.out_channels, 1)
def forward(self, x, emb):
if self.updown:
in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
h = in_rest(x)
h = self.h_upd(h)
x = self.x_upd(x)
h = in_conv(h)
else:
h = self.in_layers(x)
emb_out = self.emb_layers(emb).type(h.dtype)
while len(emb_out.shape) < len(h.shape):
emb_out = emb_out[..., None]
if self.use_scale_shift_norm:
out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
scale, shift = torch.chunk(emb_out, 2, dim=1)
h = out_norm(h) * (1 + scale) + shift
h = out_rest(h)
else:
h = h + emb_out
h = self.out_layers(h)
return self.skip_connection(x) + h
class DiscreteSpectrogramConditioningBlock(nn.Module):
def __init__(self, dvae_channels, channels, level):
super().__init__()
self.intg = nn.Sequential(nn.Conv1d(dvae_channels, channels, kernel_size=1),
normalization(channels),
nn.SiLU(),
nn.Conv1d(channels, channels, kernel_size=3))
self.level = level
"""
Embeds the given codes and concatenates them onto x. Return shape is the same as x.shape.
:param x: bxcxS waveform latent
:param codes: bxN discrete codes, N <= S
"""
def forward(self, x, dvae_in):
b, c, S = x.shape
_, q, N = dvae_in.shape
emb = self.intg(dvae_in)
emb = nn.functional.interpolate(emb, size=(S,), mode='nearest')
return torch.cat([x, emb], dim=1)
class DiscreteDiffusionVocoder(nn.Module):
"""
The full UNet model with attention and timestep embedding.
Customized to be conditioned on a spectrogram prior.
:param in_channels: channels in the input Tensor.
:param spectrogram_channels: channels in the conditioning spectrogram.
:param model_channels: base channel count for the model.
:param out_channels: channels in the output Tensor.
:param num_res_blocks: number of residual blocks per downsample.
:param attention_resolutions: a collection of downsample rates at which
attention will take place. May be a set, list, or tuple.
For example, if this contains 4, then at 4x downsampling, attention
will be used.
:param dropout: the dropout probability.
:param channel_mult: channel multiplier for each level of the UNet.
:param conv_resample: if True, use learned convolutions for upsampling and
downsampling.
:param dims: determines if the signal is 1D, 2D, or 3D.
:param num_heads: the number of attention heads in each attention layer.
:param num_heads_channels: if specified, ignore num_heads and instead use
a fixed channel width per attention head.
:param num_heads_upsample: works with num_heads to set a different number
of heads for upsampling. Deprecated.
:param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
:param resblock_updown: use residual blocks for up/downsampling.
:param use_new_attention_order: use a different attention pattern for potentially
increased efficiency.
"""
def __init__(
self,
model_channels,
in_channels=1,
out_channels=2, # mean and variance
dvae_dim=512,
dropout=0,
# res 1, 2, 4, 8,16,32,64,128,256,512, 1K, 2K
channel_mult= (1,1.5,2, 3, 4, 6, 8, 12, 16, 24, 32, 48),
num_res_blocks=(1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2),
# spec_cond: 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0)
# attn: 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1
spectrogram_conditioning_resolutions=(512,),
attention_resolutions=(512,1024,2048),
conv_resample=True,
dims=1,
use_fp16=False,
num_heads=1,
num_head_channels=-1,
num_heads_upsample=-1,
use_scale_shift_norm=False,
resblock_updown=False,
kernel_size=3,
scale_factor=2,
conditioning_inputs_provided=True,
time_embed_dim_multiplier=4,
):
super().__init__()
if num_heads_upsample == -1:
num_heads_upsample = num_heads
self.in_channels = in_channels
self.model_channels = model_channels
self.out_channels = out_channels
self.attention_resolutions = attention_resolutions
self.dropout = dropout
self.channel_mult = channel_mult
self.conv_resample = conv_resample
self.dtype = torch.float16 if use_fp16 else torch.float32
self.num_heads = num_heads
self.num_head_channels = num_head_channels
self.num_heads_upsample = num_heads_upsample
self.dims = dims
padding = 1 if kernel_size == 3 else 2
time_embed_dim = model_channels * time_embed_dim_multiplier
self.time_embed = nn.Sequential(
nn.Linear(model_channels, time_embed_dim),
nn.SiLU(),
nn.Linear(time_embed_dim, time_embed_dim),
)
self.conditioning_enabled = conditioning_inputs_provided
if conditioning_inputs_provided:
self.contextual_embedder = AudioMiniEncoder(in_channels, time_embed_dim, base_channels=32, depth=6, resnet_blocks=1,
attn_blocks=2, num_attn_heads=2, dropout=dropout, downsample_factor=4, kernel_size=5)
seqlyr = TimestepEmbedSequential(
nn.Conv1d(in_channels, model_channels, kernel_size, padding=padding)
)
seqlyr.level = 0
self.input_blocks = nn.ModuleList([seqlyr])
spectrogram_blocks = []
self._feature_size = model_channels
input_block_chans = [model_channels]
ch = model_channels
ds = 1
for level, (mult, num_blocks) in enumerate(zip(channel_mult, num_res_blocks)):
if ds in spectrogram_conditioning_resolutions:
spec_cond_block = DiscreteSpectrogramConditioningBlock(dvae_dim, ch, 2 ** level)
self.input_blocks.append(spec_cond_block)
spectrogram_blocks.append(spec_cond_block)
ch *= 2
for _ in range(num_blocks):
layers = [
TimestepResBlock(
ch,
time_embed_dim,
dropout,
out_channels=int(mult * model_channels),
use_scale_shift_norm=use_scale_shift_norm,
kernel_size=kernel_size,
)
]
ch = int(mult * model_channels)
if ds in attention_resolutions:
layers.append(
AttentionBlock(
ch,
num_heads=num_heads,
num_head_channels=num_head_channels,
)
)
layer = TimestepEmbedSequential(*layers)
layer.level = 2 ** level
self.input_blocks.append(layer)
self._feature_size += ch
input_block_chans.append(ch)
if level != len(channel_mult) - 1:
out_ch = ch
upblk = TimestepEmbedSequential(
TimestepResBlock(
ch,
time_embed_dim,
dropout,
out_channels=out_ch,
use_scale_shift_norm=use_scale_shift_norm,
down=True,
kernel_size=kernel_size,
)
if resblock_updown
else Downsample(
ch, conv_resample, out_channels=out_ch, factor=scale_factor
)
)
upblk.level = 2 ** level
self.input_blocks.append(upblk)
ch = out_ch
input_block_chans.append(ch)
ds *= 2
self._feature_size += ch
self.middle_block = TimestepEmbedSequential(
TimestepResBlock(
ch,
time_embed_dim,
dropout,
use_scale_shift_norm=use_scale_shift_norm,
kernel_size=kernel_size,
),
AttentionBlock(
ch,
num_heads=num_heads,
num_head_channels=num_head_channels,
),
TimestepResBlock(
ch,
time_embed_dim,
dropout,
use_scale_shift_norm=use_scale_shift_norm,
kernel_size=kernel_size,
),
)
self._feature_size += ch
self.output_blocks = nn.ModuleList([])
for level, (mult, num_blocks) in list(enumerate(zip(channel_mult, num_res_blocks)))[::-1]:
for i in range(num_blocks + 1):
ich = input_block_chans.pop()
layers = [
TimestepResBlock(
ch + ich,
time_embed_dim,
dropout,
out_channels=int(model_channels * mult),
use_scale_shift_norm=use_scale_shift_norm,
kernel_size=kernel_size,
)
]
ch = int(model_channels * mult)
if ds in attention_resolutions:
layers.append(
AttentionBlock(
ch,
num_heads=num_heads_upsample,
num_head_channels=num_head_channels,
)
)
if level and i == num_blocks:
out_ch = ch
layers.append(
TimestepResBlock(
ch,
time_embed_dim,
dropout,
out_channels=out_ch,
use_scale_shift_norm=use_scale_shift_norm,
up=True,
kernel_size=kernel_size,
)
if resblock_updown
else Upsample(ch, conv_resample, out_channels=out_ch, factor=scale_factor)
)
ds //= 2
layer = TimestepEmbedSequential(*layers)
layer.level = 2 ** level
self.output_blocks.append(layer)
self._feature_size += ch
self.out = nn.Sequential(
normalization(ch),
nn.SiLU(),
zero_module(nn.Conv1d(model_channels, out_channels, kernel_size, padding=padding)),
)
def forward(self, x, timesteps, spectrogram, conditioning_input=None):
"""
Apply the model to an input batch.
:param x: an [N x C x ...] Tensor of inputs.
:param timesteps: a 1-D batch of timesteps.
:param y: an [N] Tensor of labels, if class-conditional.
:return: an [N x C x ...] Tensor of outputs.
"""
assert x.shape[-1] % 2048 == 0 # This model operates at base//2048 at it's bottom levels, thus this requirement.
if self.conditioning_enabled:
assert conditioning_input is not None
hs = []
emb1 = self.time_embed(timestep_embedding(timesteps, self.model_channels))
if self.conditioning_enabled:
emb2 = self.contextual_embedder(conditioning_input)
emb = emb1 + emb2
else:
emb = emb1
h = x.type(self.dtype)
for k, module in enumerate(self.input_blocks):
if isinstance(module, DiscreteSpectrogramConditioningBlock):
h = module(h, spectrogram)
else:
h = module(h, emb)
hs.append(h)
h = self.middle_block(h, emb)
for module in self.output_blocks:
h = torch.cat([h, hs.pop()], dim=1)
h = module(h, emb)
h = h.type(x.dtype)
return self.out(h)
# Test for ~4 second audio clip at 22050Hz
if __name__ == '__main__':
clip = torch.randn(2, 1, 40960)
spec = torch.randn(2,80,160)
cond = torch.randn(2, 1, 40960)
ts = torch.LongTensor([555, 556])
model = DiscreteDiffusionVocoder(model_channels=128, channel_mult=[1, 1, 1.5, 2, 3, 4, 6, 8, 8, 8, 8],
num_res_blocks=[1,2, 2, 2, 2, 2, 2, 2, 2, 1, 1 ], spectrogram_conditioning_resolutions=[2,512],
dropout=.05, attention_resolutions=[512,1024], num_heads=4, kernel_size=3, scale_factor=2,
conditioning_inputs_provided=True, conditioning_input_dim=80, time_embed_dim_multiplier=4,
dvae_dim=80)
print(model(clip, ts, spec, cond).shape)
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