tts9 initial commit
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@ -6,7 +6,6 @@ import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch import autocast
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from x_transformers.x_transformers import AbsolutePositionalEmbedding, AttentionLayers, CrossAttender
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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 AttentionBlock, TimestepEmbedSequential, \
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@ -14,63 +13,7 @@ from models.diffusion.unet_diffusion import AttentionBlock, TimestepEmbedSequent
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from models.gpt_voice.mini_encoder import AudioMiniEncoder
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from scripts.audio.gen.use_diffuse_tts import ceil_multiple
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from trainer.networks import register_model
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from utils.util import checkpoint
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from x_transformers import Encoder, ContinuousTransformerWrapper
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def clustered_mask(probability, shape, dev, lateral_expansion_radius_max=3, inverted=False):
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"""
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Produces a masking vector of the specified shape where each element has probability to be zero.
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lateral_expansion_radius_max neighbors of any element that is zero also have a 50% chance to be zero.
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Effectively, this produces clusters of masks tending to be lateral_expansion_radius_max wide.
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"""
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# Each masked token spreads out to 1+lateral_expansion_radius_max on average, therefore reduce the probability in
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# kind
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probability = probability / (1+lateral_expansion_radius_max)
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mask = torch.rand(shape, device=dev)
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mask = (mask < probability).float()
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kernel = torch.tensor([.5 for _ in range(lateral_expansion_radius_max)] + [1] + [.5 for _ in range(lateral_expansion_radius_max)], device=dev)
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mask = F.conv1d(mask.unsqueeze(1), kernel.view(1,1,2*lateral_expansion_radius_max+1), padding=lateral_expansion_radius_max).squeeze(1)
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if inverted:
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return torch.bernoulli(torch.clamp(mask, 0, 1)) != 0
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else:
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return torch.bernoulli(torch.clamp(mask, 0, 1)) == 0
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class CheckpointedLayer(nn.Module):
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"""
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Wraps a module. When forward() is called, passes kwargs that require_grad through torch.checkpoint() and bypasses
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checkpoint for all other args.
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"""
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def __init__(self, wrap):
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super().__init__()
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self.wrap = wrap
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def forward(self, x, *args, **kwargs):
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for k, v in kwargs.items():
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assert not (isinstance(v, torch.Tensor) and v.requires_grad) # This would screw up checkpointing.
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partial = functools.partial(self.wrap, **kwargs)
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return torch.utils.checkpoint.checkpoint(partial, x, *args)
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class CheckpointedXTransformerEncoder(nn.Module):
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"""
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Wraps a ContinuousTransformerWrapper and applies CheckpointedLayer to each layer and permutes from channels-mid
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to channels-last that XTransformer expects.
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"""
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def __init__(self, **xtransformer_kwargs):
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super().__init__()
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self.transformer = ContinuousTransformerWrapper(**xtransformer_kwargs)
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for i in range(len(self.transformer.attn_layers.layers)):
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n, b, r = self.transformer.attn_layers.layers[i]
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self.transformer.attn_layers.layers[i] = nn.ModuleList([n, CheckpointedLayer(b), r])
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def forward(self, x, **kwargs):
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x = x.permute(0,2,1)
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h = self.transformer(x, **kwargs)
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return h.permute(0,2,1)
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from utils.util import checkpoint, opt_get
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class ResBlock(TimestepBlock):
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@ -172,7 +115,7 @@ class DiffusionTts(nn.Module):
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def __init__(
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self,
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model_channels,
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model_channels=1024,
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in_channels=1,
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in_latent_channels=1024,
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out_channels=2, # mean and variance
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@ -193,8 +136,6 @@ class DiffusionTts(nn.Module):
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kernel_size=3,
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scale_factor=2,
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time_embed_dim_multiplier=4,
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cond_transformer_depth=8,
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mid_transformer_depth=8,
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# Parameters for regularization.
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unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training.
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# Parameters for super-sampling.
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@ -234,26 +175,16 @@ class DiffusionTts(nn.Module):
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conditioning_dim = model_channels * 8
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self.latent_converter = nn.Conv1d(in_latent_channels, conditioning_dim, 1)
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self.aligned_latent_padding_embedding = nn.Parameter(torch.randn(1,conditioning_dim,1))
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self.aligned_latent_padding_embedding = nn.Parameter(torch.randn(1,in_latent_channels,1))
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self.contextual_embedder = AudioMiniEncoder(1, conditioning_dim, base_channels=32, depth=6, resnet_blocks=1,
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attn_blocks=4, num_attn_heads=8, dropout=dropout, downsample_factor=4, kernel_size=5)
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attn_blocks=3, num_attn_heads=8, dropout=dropout, downsample_factor=4, kernel_size=5)
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self.conditioning_conv = nn.Conv1d(conditioning_dim*2, conditioning_dim, 1)
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self.conditioning_encoder = CheckpointedXTransformerEncoder(
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max_seq_len=-1, # Should be unused
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use_pos_emb=False,
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attn_layers=Encoder(
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dim=conditioning_dim,
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depth=cond_transformer_depth,
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heads=num_heads,
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ff_dropout=dropout,
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attn_dropout=dropout,
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ff_glu=True,
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rotary_pos_emb=True
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)
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)
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self.unconditioned_embedding = nn.Parameter(torch.randn(1,conditioning_dim,1))
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self.conditioning_timestep_integrator = TimestepEmbedSequential(
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ResBlock(conditioning_dim, time_embed_dim, dropout, out_channels=conditioning_dim, dims=dims, kernel_size=1),
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AttentionBlock(conditioning_dim, num_heads=num_heads, num_head_channels=num_head_channels),
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ResBlock(conditioning_dim, time_embed_dim, dropout, out_channels=conditioning_dim, dims=dims, kernel_size=1),
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AttentionBlock(conditioning_dim, num_heads=num_heads, num_head_channels=num_head_channels),
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ResBlock(conditioning_dim, time_embed_dim, dropout, out_channels=conditioning_dim, dims=dims, kernel_size=1),
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)
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@ -314,20 +245,6 @@ class DiffusionTts(nn.Module):
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ds *= 2
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self._feature_size += ch
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mid_transformer = CheckpointedXTransformerEncoder(
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max_seq_len=-1, # Should be unused
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use_pos_emb=False,
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attn_layers=Encoder(
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dim=ch,
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depth=mid_transformer_depth,
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heads=num_heads,
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ff_dropout=dropout,
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attn_dropout=dropout,
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use_rmsnorm=True,
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ff_glu=True,
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rotary_pos_emb=True,
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)
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)
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self.middle_block = TimestepEmbedSequential(
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ResBlock(
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ch,
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@ -336,7 +253,11 @@ class DiffusionTts(nn.Module):
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dims=dims,
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kernel_size=kernel_size,
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),
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mid_transformer,
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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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ResBlock(
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ch,
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time_embed_dim,
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@ -391,10 +312,60 @@ class DiffusionTts(nn.Module):
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'input_blocks': list(self.input_blocks.parameters()),
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'output_blocks': list(self.output_blocks.parameters()),
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'middle_transformer': list(self.middle_block.parameters()),
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'conditioning_encoder': list(self.conditioning_encoder.parameters())
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}
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return groups
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def forward(self, x, timesteps, aligned_latent, conditioning_input, conditioning_free):
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hs = []
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time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
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# Note: this block does not need to repeated on inference, since it is not timestep-dependent.
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if conditioning_free:
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code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, 1)
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else:
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cond_emb = self.contextual_embedder(conditioning_input)
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code_emb = self.latent_converter(aligned_latent)
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cond_emb = cond_emb.unsqueeze(-1).repeat(1,1,code_emb.shape[-1])
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code_emb = self.conditioning_conv(torch.cat([cond_emb, code_emb], dim=1))
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# Mask out the conditioning branch for whole batch elements, implementing something similar to classifier-free guidance.
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if self.training and self.unconditioned_percentage > 0:
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unconditioned_batches = torch.rand((code_emb.shape[0],1,1), device=code_emb.device) < self.unconditioned_percentage
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code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(x.shape[0], 1, 1), code_emb)
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# Everything after this comment is timestep dependent.
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code_emb = self.conditioning_timestep_integrator(code_emb, time_emb)
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time_emb = time_emb.float()
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h = x
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for k, module in enumerate(self.input_blocks):
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if isinstance(module, nn.Conv1d):
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h_tok = F.interpolate(module(code_emb), size=(h.shape[-1]), mode='nearest')
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h = h + h_tok
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else:
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h = module(h, time_emb)
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hs.append(h)
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h = self.middle_block(h, time_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, time_emb)
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# Last block also has autocast disabled for high-precision outputs.
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h = h.float()
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out = self.out(h)
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return out
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class DiffusionTtsWrapper(nn.Module):
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"""
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Wraps the above module with some set-up logic such that the above module can be traced by the PyTorch JIT.
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"""
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def __init__(self, jit_enabled=False, **kwargs):
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super().__init__()
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self.jit_enabled = jit_enabled
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self.jit_forward = None
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self.underlying = DiffusionTts(**kwargs)
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def forward(self, x, timesteps, aligned_latent, conditioning_input, lr_input=None, conditioning_free=False):
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"""
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Apply the model to an input batch.
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@ -408,15 +379,14 @@ class DiffusionTts(nn.Module):
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:return: an [N x C x ...] Tensor of outputs.
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"""
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assert conditioning_input is not None
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if self.super_sampling_enabled:
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if self.underlying.super_sampling_enabled:
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assert lr_input is not None
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if self.training and self.super_sampling_max_noising_factor > 0:
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noising_factor = random.uniform(0,self.super_sampling_max_noising_factor)
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noising_factor = random.uniform(0,self.underlying.super_sampling_max_noising_factor)
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lr_input = torch.randn_like(lr_input) * noising_factor + lr_input
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lr_input = F.interpolate(lr_input, size=(x.shape[-1],), mode='nearest')
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x = torch.cat([x, lr_input], dim=1)
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with autocast(x.device.type, enabled=self.enable_fp16):
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# Shuffle aligned_latent to BxCxS format
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aligned_latent = aligned_latent.permute(0,2,1)
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@ -428,55 +398,21 @@ class DiffusionTts(nn.Module):
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x = F.pad(x, (0,cm-x.shape[-1]))
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# Also fix aligned_latent, which is aligned to x.
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aligned_latent = torch.cat([aligned_latent,
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self.aligned_latent_padding_embedding.repeat(x.shape[0],1,int(pc*aligned_latent.shape[-1]))], dim=-1)
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self.underlying.aligned_latent_padding_embedding.repeat(x.shape[0],1,int(pc*aligned_latent.shape[-1]))], dim=-1)
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hs = []
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time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
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# Note: this block does not need to repeated on inference, since it is not timestep-dependent.
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if conditioning_free:
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code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, 1)
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with autocast(x.device.type, enabled=self.underlying.enable_fp16):
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if self.jit_enabled:
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if self.jit_forward is None:
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self.jit_forward = torch.jit.script(self.underlying, (x, timesteps, aligned_latent, conditioning_input, conditioning_free))
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out = self.jit_forward(x, timesteps, aligned_latent, conditioning_input, conditioning_free)
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else:
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cond_emb = self.contextual_embedder(conditioning_input)
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code_emb = self.latent_converter(aligned_latent)
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cond_emb = cond_emb.unsqueeze(-1).repeat(1,1,code_emb.shape[-1])
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code_emb = self.conditioning_conv(torch.cat([cond_emb, code_emb], dim=1))
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code_emb = self.conditioning_encoder(code_emb)
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# Mask out the conditioning branch for whole batch elements, implementing something similar to classifier-free guidance.
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if self.training and self.unconditioned_percentage > 0:
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unconditioned_batches = torch.rand((code_emb.shape[0],1,1), device=code_emb.device) < self.unconditioned_percentage
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code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(x.shape[0], 1, 1), code_emb)
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# Everything after this comment is timestep dependent.
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code_emb = self.conditioning_timestep_integrator(code_emb, time_emb)
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first = True
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time_emb = time_emb.float()
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h = x
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for k, module in enumerate(self.input_blocks):
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if isinstance(module, nn.Conv1d):
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h_tok = F.interpolate(module(code_emb), size=(h.shape[-1]), mode='nearest')
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h = h + h_tok
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else:
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with autocast(x.device.type, enabled=self.enable_fp16 and not first):
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# First block has autocast disabled to allow a high precision signal to be properly vectorized.
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h = module(h, time_emb)
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hs.append(h)
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first = False
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h = self.middle_block(h, time_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, time_emb)
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# Last block also has autocast disabled for high-precision outputs.
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h = h.float()
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out = self.out(h)
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out = self.underlying(x, timesteps, aligned_latent, conditioning_input, conditioning_free)
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return out[:, :, :orig_x_shape]
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@register_model
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def register_diffusion_tts9(opt_net, opt):
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return DiffusionTts(**opt_net['kwargs'])
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return DiffusionTtsWrapper(**opt_net['kwargs'])
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if __name__ == '__main__':
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@ -484,7 +420,7 @@ if __name__ == '__main__':
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aligned_latent = torch.randn(2,388,1024)
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cond = torch.randn(2, 1, 44000)
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ts = torch.LongTensor([600, 600])
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model = DiffusionTts(128,
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model = DiffusionTtsWrapper(128,
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channel_mult=[1,1.5,2, 3, 4, 6, 8],
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num_res_blocks=[2, 2, 2, 2, 2, 2, 1],
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token_conditioning_resolutions=[1,4,16,64],
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