diff --git a/codes/models/gpt_voice/unet_diffusion_vocoder_with_ref_trunc_top.py b/codes/models/gpt_voice/unet_diffusion_vocoder_with_ref_trunc_top.py new file mode 100644 index 00000000..8b1997e5 --- /dev/null +++ b/codes/models/gpt_voice/unet_diffusion_vocoder_with_ref_trunc_top.py @@ -0,0 +1,394 @@ +import random + +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 +import torch.nn.functional as F + +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, dvae_channels, channels): + 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)) + + """ + 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 DiffusionVocoderWithRefTruncatedTop(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=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, + use_new_attention_order=False, + kernel_size=3, + scale_factor=2, + conditioning_inputs_provided=True, + conditioning_input_dim=80, + time_embed_dim_multiplier=4, + only_train_dvae_connection_layers=False, + ): + 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( + 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(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.cheater_input_block = TimestepEmbedSequential(conv_nd(dims, in_channels, model_channels//2, kernel_size, padding=padding, stride=2)) + self.input_blocks = nn.ModuleList( + [ + TimestepEmbedSequential( + conv_nd(dims, model_channels//2, model_channels, kernel_size, padding=padding) + ) + ] + ) + 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(discrete_codes, ch) + self.input_blocks.append(spec_cond_block) + spectrogram_blocks.append(spec_cond_block) + ch *= 2 + + 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 + + # These are the special input and output blocks that are pseudo-disconnected from the rest of the graph, + # allowing them to be trained on a smaller subset of input. + self.top_inp_raw = TimestepEmbedSequential( + conv_nd(dims, in_channels, model_channels, kernel_size, padding=padding) + ) + self.top_inp_blocks = nn.ModuleList([TimestepEmbedSequential(ResBlock( + model_channels, + time_embed_dim, + dropout, + out_channels=model_channels, + dims=dims, + use_scale_shift_norm=use_scale_shift_norm, + kernel_size=kernel_size, + )) for _ in range(num_blocks)]) + self.top_out_upsample = TimestepEmbedSequential(ResBlock( + model_channels, + time_embed_dim, + dropout, + out_channels=model_channels, + 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=model_channels, factor=scale_factor)) + self.top_out_blocks = nn.ModuleList([TimestepEmbedSequential(ResBlock( + 2 * model_channels, + time_embed_dim, + dropout, + out_channels=model_channels, + dims=dims, + use_scale_shift_norm=use_scale_shift_norm, + kernel_size=kernel_size, + )) for _ in range(num_blocks) + ]) + self.top_out_final = nn.Sequential( + normalization(model_channels), + nn.SiLU(), + zero_module(conv_nd(dims, model_channels, out_channels, kernel_size, padding=padding)), + ) + + if only_train_dvae_connection_layers: + for p in self.parameters(): + p.DO_NOT_TRAIN = True + p.requires_grad = False + for sb in spectrogram_blocks: + for p in sb.parameters(): + del p.DO_NOT_TRAIN + p.requires_grad = True + + 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, halved in size and the bounds of the original input that was halved. + """ + 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_input is not None + + 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 + + # Handle the top blocks first, independently of the rest of the unet. These only process half of x. + if self.training: + rand_start = (random.randint(0, x.shape[-1] // 2) // 2) * 2 # Must be a multiple of 2, to align with the next lower layer. + rand_stop = rand_start + x.shape[-1] // 2 + else: + rand_start = 0 # When in eval, rand_start:rand_stop spans the entire input. + rand_stop = x.shape[-1] + top_blocks = [] + ht = self.top_inp_raw(x.type(self.dtype)[:, :, rand_start:rand_stop], emb) + for block in self.top_inp_blocks: + ht = block(ht, emb) + top_blocks.append(ht) + + # Now the standard unet (notice how it doesn't use ht at all, and uses a bare x fed through a strided conv. + h = self.cheater_input_block(x.type(self.dtype), emb) + hs = [] + 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) + + # And finally the top output blocks, which do consume the unet's outputs as well as the cross-input blocks. First we'll need to only take a subset of the unets output. + hb = h[:, :, rand_start//2:rand_stop//2] + hb = self.top_out_upsample(hb, emb) + for block in self.top_out_blocks: + hb = torch.cat([hb, top_blocks.pop()], dim=1) + hb = block(hb, emb) + + hb = hb.type(x.dtype) + return self.top_out_final(hb), rand_start, rand_stop + + +@register_model +def register_unet_diffusion_vocoder_with_ref_trunc_top(opt_net, opt): + return DiffusionVocoderWithRefTruncatedTop(**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,)) + spec = torch.randn(2, 512, 160) + cond = torch.randn(2, 1, 40960) + ts = torch.LongTensor([555, 556]) + model = DiffusionVocoderWithRefTruncatedTop(32, conditioning_inputs_provided=True, time_embed_dim_multiplier=8) + print(model(clip, ts, spec, cond))