from models.diffusion.fp16_util import convert_module_to_f32, convert_module_to_f16 from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear from models.diffusion.unet_diffusion import AttentionPool2d, AttentionBlock, ResBlock, TimestepEmbedSequential, \ Downsample, Upsample import torch import torch.nn as nn from models.gpt_voice.mini_encoder import AudioMiniEncoder, EmbeddingCombiner from trainer.networks import register_model from utils.util import get_mask_from_lengths class DiscreteSpectrogramConditioningBlock(nn.Module): def __init__(self, discrete_codes, channels): super().__init__() self.emb = nn.Embedding(discrete_codes, channels) self.norm = normalization(channels) self.act = nn.SiLU() self.intg = nn.Sequential(nn.Conv1d(channels*2, channels*2, kernel_size=1), normalization(channels*2), nn.SiLU(), nn.Conv1d(channels*2, channels, kernel_size=3, padding=1), normalization(channels), nn.SiLU(), zero_module(nn.Conv1d(channels, channels, kernel_size=1))) """ 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, codes): _, c, S = x.shape b, N = codes.shape assert N <= S emb = self.emb(codes).permute(0,2,1) emb = nn.functional.interpolate(emb, size=(S,), mode='nearest') together = torch.cat([self.act(self.norm(x)), emb], dim=1) together = self.intg(together) return together + x class DiffusionVocoderWithRef(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 discrete_codes=8192, 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, 1, 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=(1,8,64,512), 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, use_new_attention_order=False, kernel_size=3, scale_factor=2, conditioning_inputs_provided=True, conditioning_input_dim=80, ): 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 * 4 self.time_embed = nn.Sequential( linear(model_channels, time_embed_dim), nn.SiLU(), linear(time_embed_dim, time_embed_dim), ) self.conditioning_enabled = conditioning_inputs_provided if conditioning_inputs_provided: self.contextual_embedder = AudioMiniEncoder(conditioning_input_dim, time_embed_dim) self.query_gen = 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) self.embedding_combiner = EmbeddingCombiner(time_embed_dim) self.input_blocks = nn.ModuleList( [ TimestepEmbedSequential( conv_nd(dims, in_channels, model_channels, kernel_size, padding=padding) ) ] ) 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: self.input_blocks.append(DiscreteSpectrogramConditioningBlock(discrete_codes, ch)) for _ in range(num_blocks): layers = [ ResBlock( ch, time_embed_dim, dropout, out_channels=int(mult * model_channels), dims=dims, 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, use_new_attention_order=use_new_attention_order, ) ) self.input_blocks.append(TimestepEmbedSequential(*layers)) self._feature_size += ch input_block_chans.append(ch) if level != len(channel_mult) - 1: out_ch = ch self.input_blocks.append( TimestepEmbedSequential( ResBlock( ch, time_embed_dim, dropout, out_channels=out_ch, dims=dims, use_scale_shift_norm=use_scale_shift_norm, down=True, kernel_size=kernel_size, ) if resblock_updown else Downsample( ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor ) ) ) ch = out_ch input_block_chans.append(ch) ds *= 2 self._feature_size += ch self.middle_block = TimestepEmbedSequential( ResBlock( ch, time_embed_dim, dropout, dims=dims, use_scale_shift_norm=use_scale_shift_norm, kernel_size=kernel_size, ), AttentionBlock( ch, num_heads=num_heads, num_head_channels=num_head_channels, use_new_attention_order=use_new_attention_order, ), ResBlock( ch, time_embed_dim, dropout, dims=dims, 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 = [ ResBlock( ch + ich, time_embed_dim, dropout, out_channels=int(model_channels * mult), dims=dims, 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, use_new_attention_order=use_new_attention_order, ) ) if level and i == num_blocks: out_ch = ch layers.append( ResBlock( ch, time_embed_dim, dropout, out_channels=out_ch, dims=dims, use_scale_shift_norm=use_scale_shift_norm, up=True, kernel_size=kernel_size, ) if resblock_updown else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor) ) ds //= 2 self.output_blocks.append(TimestepEmbedSequential(*layers)) self._feature_size += ch self.out = nn.Sequential( normalization(ch), nn.SiLU(), zero_module(conv_nd(dims, model_channels, out_channels, kernel_size, padding=padding)), ) def convert_to_fp16(self): """ Convert the torso of the model to float16. """ self.input_blocks.apply(convert_module_to_f16) self.middle_block.apply(convert_module_to_f16) self.output_blocks.apply(convert_module_to_f16) def convert_to_fp32(self): """ Convert the torso of the model to float32. """ self.input_blocks.apply(convert_module_to_f32) self.middle_block.apply(convert_module_to_f32) self.output_blocks.apply(convert_module_to_f32) def forward(self, x, timesteps, discrete_spectrogram, conditioning_inputs=None, num_conditioning_signals=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] % 4096 == 0 # This model operates at base//4096 at it's bottom levels, thus this requirement. if self.conditioning_enabled: assert conditioning_inputs is not None assert num_conditioning_signals is not None hs = [] emb1 = self.time_embed(timestep_embedding(timesteps, self.model_channels)) if self.conditioning_enabled: emb2 = torch.stack([self.contextual_embedder(ci.squeeze(1)) for ci in list(torch.chunk(conditioning_inputs, conditioning_inputs.shape[1], dim=1))], dim=1) emb = torch.cat([emb1.unsqueeze(1), emb2], dim=1) emb = self.embedding_combiner(emb, None, self.query_gen(x)) else: emb = emb1 h = x.type(self.dtype) for k, module in enumerate(self.input_blocks): if isinstance(module, DiscreteSpectrogramConditioningBlock): h = module(h, discrete_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) @register_model def register_unet_diffusion_vocoder_with_ref(opt_net, opt): return DiffusionVocoderWithRef(**opt_net['kwargs']) # Test for ~4 second audio clip at 22050Hz if __name__ == '__main__': clip = torch.randn(2, 1, 40960) spec = torch.randint(8192, (2, 40,)) cond = torch.randn(2, 3, 80, 173) ts = torch.LongTensor([555, 556]) model = DiffusionVocoderWithRef(32, conditioning_inputs_provided=False) print(model(clip, ts, spec, cond, 4).shape)