""" 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)