336 lines
13 KiB
Python
336 lines
13 KiB
Python
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from models.diffusion.fp16_util import convert_module_to_f32, convert_module_to_f16
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from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
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from models.diffusion.unet_diffusion import AttentionPool2d, AttentionBlock, ResBlock, TimestepEmbedSequential, \
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Downsample, Upsample
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import torch
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import torch.nn as nn
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from models.gpt_voice.mini_encoder import AudioMiniEncoder, EmbeddingCombiner
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from trainer.networks import register_model
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from utils.util import get_mask_from_lengths
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class DiscreteSpectrogramConditioningBlock(nn.Module):
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def __init__(self, discrete_codes, channels):
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super().__init__()
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self.emb = nn.Embedding(discrete_codes, channels)
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"""
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Embeds the given codes and concatenates them onto x. Return shape: bx2cxS
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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, codes):
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_, c, S = x.shape
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b, N = codes.shape
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assert N <= S
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emb = self.emb(codes).permute(0,2,1)
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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 DiffusionVocoderWithRef(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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num_res_blocks,
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in_channels=1,
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out_channels=2, # mean and variance
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discrete_codes=8192,
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dropout=0,
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# 38400 -> 19200 -> 9600 -> 4800 -> 2400 -> 1200 -> 600 -> 300 -> 150 for ~2secs@22050Hz
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channel_mult=(1, 1, 2, 2, 4, 8, 16, 32, 64),
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spectrogram_conditioning_resolutions=(4,8,16,32),
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attention_resolutions=(64,128,256),
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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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use_new_attention_order=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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conditioning_input_dim=80,
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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.num_res_blocks = num_res_blocks
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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 * 4
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self.time_embed = nn.Sequential(
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linear(model_channels, time_embed_dim),
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nn.SiLU(),
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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(conditioning_input_dim, time_embed_dim)
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self.query_gen = 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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self.embedding_combiner = EmbeddingCombiner(time_embed_dim)
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self.input_blocks = nn.ModuleList(
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[
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TimestepEmbedSequential(
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conv_nd(dims, in_channels, model_channels, kernel_size, padding=padding)
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)
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]
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)
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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 in enumerate(channel_mult):
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if ds in spectrogram_conditioning_resolutions:
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self.input_blocks.append(DiscreteSpectrogramConditioningBlock(discrete_codes, ch))
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ch *= 2
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for _ in range(num_res_blocks):
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layers = [
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ResBlock(
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ch,
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time_embed_dim,
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dropout,
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out_channels=mult * model_channels,
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dims=dims,
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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 = 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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use_new_attention_order=use_new_attention_order,
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)
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)
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self.input_blocks.append(TimestepEmbedSequential(*layers))
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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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self.input_blocks.append(
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TimestepEmbedSequential(
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ResBlock(
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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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dims=dims,
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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, dims=dims, out_channels=out_ch, factor=scale_factor
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)
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)
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)
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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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ResBlock(
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ch,
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time_embed_dim,
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dropout,
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dims=dims,
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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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use_new_attention_order=use_new_attention_order,
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),
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ResBlock(
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ch,
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time_embed_dim,
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dropout,
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dims=dims,
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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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self._feature_size += ch
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self.output_blocks = nn.ModuleList([])
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for level, mult in list(enumerate(channel_mult))[::-1]:
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for i in range(num_res_blocks + 1):
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ich = input_block_chans.pop()
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layers = [
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ResBlock(
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ch + ich,
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time_embed_dim,
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dropout,
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out_channels=model_channels * mult,
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dims=dims,
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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 = model_channels * mult
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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_upsample,
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num_head_channels=num_head_channels,
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use_new_attention_order=use_new_attention_order,
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)
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)
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if level and i == num_res_blocks:
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out_ch = ch
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layers.append(
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ResBlock(
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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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dims=dims,
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use_scale_shift_norm=use_scale_shift_norm,
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up=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 Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor)
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)
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ds //= 2
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self.output_blocks.append(TimestepEmbedSequential(*layers))
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self._feature_size += ch
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self.out = nn.Sequential(
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normalization(ch),
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nn.SiLU(),
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zero_module(conv_nd(dims, model_channels, out_channels, kernel_size, padding=padding)),
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)
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def convert_to_fp16(self):
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"""
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Convert the torso of the model to float16.
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"""
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self.input_blocks.apply(convert_module_to_f16)
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self.middle_block.apply(convert_module_to_f16)
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self.output_blocks.apply(convert_module_to_f16)
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def convert_to_fp32(self):
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"""
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Convert the torso of the model to float32.
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"""
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self.input_blocks.apply(convert_module_to_f32)
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self.middle_block.apply(convert_module_to_f32)
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self.output_blocks.apply(convert_module_to_f32)
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def forward(self, x, timesteps, discrete_spectrogram, conditioning_inputs=None, num_conditioning_signals=None):
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"""
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Apply the model to an input batch.
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:param x: an [N x C x ...] Tensor of inputs.
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:param timesteps: a 1-D batch of timesteps.
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:param y: an [N] Tensor of labels, if class-conditional.
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:return: an [N x C x ...] Tensor of outputs.
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"""
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assert x.shape[-1] % 4096 == 0 # This model operates at base//4096 at it's bottom levels, thus this requirement.
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if self.conditioning_enabled:
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assert conditioning_inputs is not None
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assert num_conditioning_signals is not None
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hs = []
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emb1 = self.time_embed(timestep_embedding(timesteps, self.model_channels))
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if self.conditioning_enabled:
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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)
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emb = torch.cat([emb1.unsqueeze(1), emb2], dim=1)
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emb = self.embedding_combiner(emb, None, self.query_gen(x))
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else:
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emb = emb1
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h = x.type(self.dtype)
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for k, module in enumerate(self.input_blocks):
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if isinstance(module, DiscreteSpectrogramConditioningBlock):
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h = module(h, discrete_spectrogram)
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else:
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h = module(h, emb)
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hs.append(h)
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h = self.middle_block(h, emb)
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for module in self.output_blocks:
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h = torch.cat([h, hs.pop()], dim=1)
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h = module(h, emb)
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h = h.type(x.dtype)
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return self.out(h)
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@register_model
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def register_unet_diffusion_vocoder_with_ref(opt_net, opt):
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return DiffusionVocoderWithRef(**opt_net['kwargs'])
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# Test for ~4 second audio clip at 22050Hz
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if __name__ == '__main__':
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clip = torch.randn(2, 1, 81920)
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spec = torch.randint(8192, (2, 500,))
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cond = torch.randn(2, 4, 80, 600)
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ts = torch.LongTensor([555, 556])
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model = DiffusionVocoderWithRef(32, 2)
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print(model(clip, ts, spec, cond, 4).shape)
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