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