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Integrate new diffusion network

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This commit is contained in:
James Betker 2022-04-01 14:15:17 -06:00
parent d89c51a71c
commit 4747fae381
3 changed files with 189 additions and 390 deletions
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49
api.py
View File

@ -49,6 +49,15 @@ def download_models():
print('Done.')
def pad_or_truncate(t, length):
if t.shape[-1] == length:
return t
elif t.shape[-1] < length:
return F.pad(t, (0, length-t.shape[-1]))
else:
return t[..., :length]
def load_discrete_vocoder_diffuser(trained_diffusion_steps=4000, desired_diffusion_steps=200, cond_free=True, cond_free_k=1):
"""
Helper function to load a GaussianDiffusion instance configured for use as a vocoder.
@ -96,26 +105,25 @@ def fix_autoregressive_output(codes, stop_token):
return codes
def do_spectrogram_diffusion(diffusion_model, diffuser, mel_codes, conditioning_input, temperature=1):
def do_spectrogram_diffusion(diffusion_model, diffuser, mel_codes, conditioning_samples, temperature=1):
"""
Uses the specified diffusion model and DVAE model to convert the provided MEL & conditioning inputs into an audio clip.
Uses the specified diffusion model to convert discrete codes into a spectrogram.
"""
with torch.no_grad():
cond_mel = wav_to_univnet_mel(conditioning_input.squeeze(1), do_normalization=False)
# Pad MEL to multiples of 32
msl = mel_codes.shape[-1]
dsl = 32
gap = dsl - (msl % dsl)
if gap > 0:
mel = torch.nn.functional.pad(mel_codes, (0, gap))
cond_mels = []
for sample in conditioning_samples:
sample = pad_or_truncate(sample, 102400)
cond_mel = wav_to_univnet_mel(sample.to(mel_codes.device), do_normalization=False)
cond_mels.append(cond_mel)
cond_mels = torch.stack(cond_mels, dim=1)
output_shape = (mel.shape[0], 100, mel.shape[-1]*4)
precomputed_embeddings = diffusion_model.timestep_independent(mel_codes, cond_mel)
output_shape = (mel_codes.shape[0], 100, mel_codes.shape[-1]*4)
precomputed_embeddings = diffusion_model.timestep_independent(mel_codes, cond_mels, False)
noise = torch.randn(output_shape, device=mel_codes.device) * temperature
mel = diffuser.p_sample_loop(diffusion_model, output_shape, noise=noise,
model_kwargs={'precomputed_aligned_embeddings': precomputed_embeddings})
return denormalize_tacotron_mel(mel)[:,:,:msl*4]
return denormalize_tacotron_mel(mel)[:,:,:mel_codes.shape[-1]*4]
class TextToSpeech:
@ -137,12 +145,9 @@ class TextToSpeech:
use_xformers=True).cpu().eval()
self.clip.load_state_dict(torch.load('.models/clip.pth'))
self.diffusion = DiffusionTts(model_channels=512, in_channels=100, out_channels=200, in_latent_channels=1024,
channel_mult=[1, 2, 3, 4], num_res_blocks=[3, 3, 3, 3],
token_conditioning_resolutions=[1, 4, 8],
dropout=0, attention_resolutions=[4, 8], num_heads=8, kernel_size=3, scale_factor=2,
time_embed_dim_multiplier=4, unconditioned_percentage=0, conditioning_dim_factor=2,
conditioning_expansion=1).cpu().eval()
self.diffusion = DiffusionTts(model_channels=1024, num_layers=10, in_channels=100, out_channels=200,
in_latent_channels=1024, in_tokens=8193, dropout=0, use_fp16=False, num_heads=16,
layer_drop=0, unconditioned_percentage=0).cpu().eval()
self.diffusion.load_state_dict(torch.load('.models/diffusion.pth'))
self.vocoder = UnivNetGenerator().cpu()
@ -164,12 +169,6 @@ class TextToSpeech:
for vs in voice_samples:
conds.append(load_conditioning(vs))
conds = torch.stack(conds, dim=1)
cond_diffusion = voice_samples[0].cuda()
# The diffusion model expects = 88200 conditioning samples.
if cond_diffusion.shape[-1] < 88200:
cond_diffusion = F.pad(cond_diffusion, (0, 88200-cond_diffusion.shape[-1]))
else:
cond_diffusion = cond_diffusion[:, :88200]
diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=diffusion_iterations, cond_free=cond_free, cond_free_k=cond_free_k)
@ -211,7 +210,7 @@ class TextToSpeech:
self.vocoder = self.vocoder.cuda()
for b in range(best_results.shape[0]):
code = best_results[b].unsqueeze(0)
mel = do_spectrogram_diffusion(self.diffusion, diffuser, code, cond_diffusion, temperature=diffusion_temperature)
mel = do_spectrogram_diffusion(self.diffusion, diffuser, code, voice_samples, temperature=diffusion_temperature)
wav = self.vocoder.inference(mel)
wav_candidates.append(wav.cpu())
self.diffusion = self.diffusion.cpu()

21
models/arch_util.py
View File

@ -6,6 +6,7 @@ import torch.nn as nn
import torch.nn.functional as F
import torchaudio
from x_transformers import ContinuousTransformerWrapper
from x_transformers.x_transformers import RelativePositionBias
def zero_module(module):
@ -49,7 +50,7 @@ class QKVAttentionLegacy(nn.Module):
super().__init__()
self.n_heads = n_heads
def forward(self, qkv, mask=None):
def forward(self, qkv, mask=None, rel_pos=None):
"""
Apply QKV attention.
@ -64,6 +65,8 @@ class QKVAttentionLegacy(nn.Module):
weight = torch.einsum(
"bct,bcs->bts", q * scale, k * scale
) # More stable with f16 than dividing afterwards
if rel_pos is not None:
weight = rel_pos(weight.reshape(bs, self.n_heads, weight.shape[-2], weight.shape[-1])).reshape(bs * self.n_heads, weight.shape[-2], weight.shape[-1])
weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
if mask is not None:
# The proper way to do this is to mask before the softmax using -inf, but that doesn't work properly on CPUs.
@ -87,9 +90,12 @@ class AttentionBlock(nn.Module):
channels,
num_heads=1,
num_head_channels=-1,
do_checkpoint=True,
relative_pos_embeddings=False,
):
super().__init__()
self.channels = channels
self.do_checkpoint = do_checkpoint
if num_head_channels == -1:
self.num_heads = num_heads
else:
@ -99,21 +105,20 @@ class AttentionBlock(nn.Module):
self.num_heads = channels // num_head_channels
self.norm = normalization(channels)
self.qkv = nn.Conv1d(channels, channels * 3, 1)
# split heads before split qkv
self.attention = QKVAttentionLegacy(self.num_heads)
self.proj_out = zero_module(nn.Conv1d(channels, channels, 1))
if relative_pos_embeddings:
self.relative_pos_embeddings = RelativePositionBias(scale=(channels // self.num_heads) ** .5, causal=False, heads=num_heads, num_buckets=32, max_distance=64)
else:
self.relative_pos_embeddings = None
def forward(self, x, mask=None):
if mask is not None:
return self._forward(x, mask)
else:
return self._forward(x)
def _forward(self, x, mask=None):
b, c, *spatial = x.shape
x = x.reshape(b, c, -1)
qkv = self.qkv(self.norm(x))
h = self.attention(qkv, mask)
h = self.attention(qkv, mask, self.relative_pos_embeddings)
h = self.proj_out(h)
return (x + h).reshape(b, c, *spatial)

493
models/diffusion_decoder.py
View File

@ -1,22 +1,13 @@
"""
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 functools
import math
import random
from abc import abstractmethod
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import autocast
from torch.nn import Linear
from torch.utils.checkpoint import checkpoint
from x_transformers import ContinuousTransformerWrapper, Encoder
from models.arch_util import normalization, zero_module, Downsample, Upsample, AudioMiniEncoder, AttentionBlock, \
CheckpointedXTransformerEncoder
from models.arch_util import normalization, AttentionBlock
def is_latent(t):
@ -27,13 +18,6 @@ def is_sequence(t):
return t.dtype == torch.long
def ceil_multiple(base, multiple):
res = base % multiple
if res == 0:
return base
return base + (multiple - res)
def timestep_embedding(timesteps, dim, max_period=10000):
"""
Create sinusoidal timestep embeddings.
@ -56,10 +40,6 @@ def timestep_embedding(timesteps, dim, max_period=10000):
class TimestepBlock(nn.Module):
"""
Any module where forward() takes timestep embeddings as a second argument.
"""
@abstractmethod
def forward(self, x, emb):
"""
@ -68,11 +48,6 @@ class TimestepBlock(nn.Module):
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):
@ -89,6 +64,7 @@ class ResBlock(TimestepBlock):
emb_channels,
dropout,
out_channels=None,
dims=2,
kernel_size=3,
efficient_config=True,
use_scale_shift_norm=False,
@ -111,7 +87,7 @@ class ResBlock(TimestepBlock):
self.emb_layers = nn.Sequential(
nn.SiLU(),
Linear(
nn.Linear(
emb_channels,
2 * self.out_channels if use_scale_shift_norm else self.out_channels,
),
@ -120,9 +96,7 @@ class ResBlock(TimestepBlock):
normalization(self.out_channels),
nn.SiLU(),
nn.Dropout(p=dropout),
zero_module(
nn.Conv1d(self.out_channels, self.out_channels, kernel_size, padding=padding)
),
nn.Conv1d(self.out_channels, self.out_channels, kernel_size, padding=padding),
)
if self.out_channels == channels:
@ -131,18 +105,6 @@ class ResBlock(TimestepBlock):
self.skip_connection = nn.Conv1d(channels, self.out_channels, eff_kernel, padding=eff_padding)
def forward(self, x, emb):
"""
Apply the block to a Tensor, conditioned on a timestep embedding.
:param x: an [N x C x ...] Tensor of features.
:param emb: an [N x emb_channels] Tensor of timestep embeddings.
:return: an [N x C x ...] Tensor of outputs.
"""
return checkpoint(
self._forward, x, emb
)
def _forward(self, x, emb):
h = self.in_layers(x)
emb_out = self.emb_layers(emb).type(h.dtype)
while len(emb_out.shape) < len(h.shape):
@ -158,372 +120,205 @@ class ResBlock(TimestepBlock):
return self.skip_connection(x) + h
class DiffusionLayer(TimestepBlock):
def __init__(self, model_channels, dropout, num_heads):
super().__init__()
self.resblk = ResBlock(model_channels, model_channels, dropout, model_channels, dims=1, use_scale_shift_norm=True)
self.attn = AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True)
def forward(self, x, time_emb):
y = self.resblk(x, time_emb)
return self.attn(y)
class DiffusionTts(nn.Module):
"""
The full UNet model with attention and timestep embedding.
Customized to be conditioned on an aligned prior derived from a autoregressive
GPT-style model.
:param in_channels: channels in the input Tensor.
:param in_latent_channels: channels from the input latent.
: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 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,
in_latent_channels=1024,
model_channels=512,
num_layers=8,
in_channels=100,
in_latent_channels=512,
in_tokens=8193,
conditioning_dim_factor=8,
conditioning_expansion=4,
out_channels=2, # mean and variance
out_channels=200, # mean and variance
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
token_conditioning_resolutions=(1,16,),
attention_resolutions=(512,1024,2048),
conv_resample=True,
use_fp16=False,
num_heads=1,
num_head_channels=-1,
num_heads_upsample=-1,
kernel_size=3,
scale_factor=2,
time_embed_dim_multiplier=4,
freeze_main_net=False,
efficient_convs=True, # Uses kernels with width of 1 in several places rather than 3.
use_scale_shift_norm=True,
num_heads=16,
# Parameters for regularization.
layer_drop=.1,
unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training.
# Parameters for super-sampling.
super_sampling=False,
super_sampling_max_noising_factor=.1,
):
super().__init__()
if num_heads_upsample == -1:
num_heads_upsample = num_heads
if super_sampling:
in_channels *= 2 # In super-sampling mode, the LR input is concatenated directly onto the input.
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.num_heads = num_heads
self.num_head_channels = num_head_channels
self.num_heads_upsample = num_heads_upsample
self.super_sampling_enabled = super_sampling
self.super_sampling_max_noising_factor = super_sampling_max_noising_factor
self.unconditioned_percentage = unconditioned_percentage
self.enable_fp16 = use_fp16
self.alignment_size = 2 ** (len(channel_mult)+1)
self.freeze_main_net = freeze_main_net
padding = 1 if kernel_size == 3 else 2
down_kernel = 1 if efficient_convs else 3
self.layer_drop = layer_drop
time_embed_dim = model_channels * time_embed_dim_multiplier
self.inp_block = nn.Conv1d(in_channels, model_channels, 3, 1, 1)
self.time_embed = nn.Sequential(
Linear(model_channels, time_embed_dim),
nn.Linear(model_channels, model_channels),
nn.SiLU(),
Linear(time_embed_dim, time_embed_dim),
nn.Linear(model_channels, model_channels),
)
conditioning_dim = model_channels * conditioning_dim_factor
# Either code_converter or latent_converter is used, depending on what type of conditioning data is fed.
# This model is meant to be able to be trained on both for efficiency purposes - it is far less computationally
# complex to generate tokens, while generating latents will normally mean propagating through a deep autoregressive
# transformer network.
self.code_embedding = nn.Embedding(in_tokens, model_channels)
self.code_converter = nn.Sequential(
nn.Embedding(in_tokens, conditioning_dim),
CheckpointedXTransformerEncoder(
needs_permute=False,
max_seq_len=-1,
use_pos_emb=False,
attn_layers=Encoder(
dim=conditioning_dim,
depth=3,
heads=num_heads,
ff_dropout=dropout,
attn_dropout=dropout,
use_rmsnorm=True,
ff_glu=True,
rotary_emb_dim=True,
AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
)
))
self.latent_converter = nn.Conv1d(in_latent_channels, conditioning_dim, 1)
self.aligned_latent_padding_embedding = nn.Parameter(torch.randn(1,in_latent_channels,1))
if in_channels > 60: # It's a spectrogram.
self.contextual_embedder = nn.Sequential(nn.Conv1d(in_channels,conditioning_dim,3,padding=1,stride=2),
CheckpointedXTransformerEncoder(
needs_permute=True,
max_seq_len=-1,
use_pos_emb=False,
attn_layers=Encoder(
dim=conditioning_dim,
depth=4,
heads=num_heads,
ff_dropout=dropout,
attn_dropout=dropout,
use_rmsnorm=True,
ff_glu=True,
rotary_emb_dim=True,
)
))
else:
self.contextual_embedder = AudioMiniEncoder(1, conditioning_dim, base_channels=32, depth=6, resnet_blocks=1,
attn_blocks=3, num_attn_heads=8, dropout=dropout, downsample_factor=4, kernel_size=5)
self.conditioning_conv = nn.Conv1d(conditioning_dim*2, conditioning_dim, 1)
self.unconditioned_embedding = nn.Parameter(torch.randn(1,conditioning_dim,1))
self.code_norm = normalization(model_channels)
self.latent_converter = nn.Conv1d(in_latent_channels, model_channels, 1)
self.contextual_embedder = nn.Sequential(nn.Conv1d(in_channels,model_channels,3,padding=1,stride=2),
nn.Conv1d(model_channels, model_channels*2,3,padding=1,stride=2),
AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False))
self.unconditioned_embedding = nn.Parameter(torch.randn(1,model_channels,1))
self.conditioning_timestep_integrator = TimestepEmbedSequential(
ResBlock(conditioning_dim, time_embed_dim, dropout, out_channels=conditioning_dim, kernel_size=1, use_scale_shift_norm=use_scale_shift_norm),
AttentionBlock(conditioning_dim, num_heads=num_heads, num_head_channels=num_head_channels),
ResBlock(conditioning_dim, time_embed_dim, dropout, out_channels=conditioning_dim, kernel_size=1, use_scale_shift_norm=use_scale_shift_norm),
AttentionBlock(conditioning_dim, num_heads=num_heads, num_head_channels=num_head_channels),
ResBlock(conditioning_dim, time_embed_dim, dropout, out_channels=conditioning_dim, kernel_size=1, use_scale_shift_norm=use_scale_shift_norm),
DiffusionLayer(model_channels, dropout, num_heads),
DiffusionLayer(model_channels, dropout, num_heads),
DiffusionLayer(model_channels, dropout, num_heads),
)
self.conditioning_expansion = conditioning_expansion
self.integrating_conv = nn.Conv1d(model_channels*2, model_channels, kernel_size=1)
self.mel_head = nn.Conv1d(model_channels, in_channels, kernel_size=3, padding=1)
self.input_blocks = nn.ModuleList(
[
TimestepEmbedSequential(
nn.Conv1d(in_channels, model_channels, kernel_size, padding=padding)
)
]
)
token_conditioning_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 token_conditioning_resolutions:
token_conditioning_block = nn.Conv1d(conditioning_dim, ch, 1)
token_conditioning_block.weight.data *= .02
self.input_blocks.append(token_conditioning_block)
token_conditioning_blocks.append(token_conditioning_block)
for _ in range(num_blocks):
layers = [
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=int(mult * model_channels),
kernel_size=kernel_size,
efficient_config=efficient_convs,
use_scale_shift_norm=use_scale_shift_norm,
)
]
ch = int(mult * model_channels)
if ds in attention_resolutions:
layers.append(
AttentionBlock(
ch,
num_heads=num_heads,
num_head_channels=num_head_channels,
)
)
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(
Downsample(
ch, conv_resample, out_channels=out_ch, factor=scale_factor, ksize=down_kernel, pad=0 if down_kernel == 1 else 1
)
)
)
ch = out_ch
input_block_chans.append(ch)
ds *= 2
self._feature_size += ch
self.middle_block = TimestepEmbedSequential(
ResBlock(
ch,
time_embed_dim,
dropout,
kernel_size=kernel_size,
efficient_config=efficient_convs,
use_scale_shift_norm=use_scale_shift_norm,
),
AttentionBlock(
ch,
num_heads=num_heads,
num_head_channels=num_head_channels,
),
ResBlock(
ch,
time_embed_dim,
dropout,
kernel_size=kernel_size,
efficient_config=efficient_convs,
use_scale_shift_norm=use_scale_shift_norm,
),
)
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),
kernel_size=kernel_size,
efficient_config=efficient_convs,
use_scale_shift_norm=use_scale_shift_norm,
)
]
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(
Upsample(ch, conv_resample, out_channels=out_ch, factor=scale_factor)
)
ds //= 2
self.output_blocks.append(TimestepEmbedSequential(*layers))
self._feature_size += ch
self.layers = nn.ModuleList([DiffusionLayer(model_channels, dropout, num_heads) for _ in range(num_layers)] +
[ResBlock(model_channels, model_channels, dropout, dims=1, use_scale_shift_norm=True) for _ in range(3)])
self.out = nn.Sequential(
normalization(ch),
normalization(model_channels),
nn.SiLU(),
zero_module(nn.Conv1d(model_channels, out_channels, kernel_size, padding=padding)),
nn.Conv1d(model_channels, out_channels, 3, padding=1),
)
def fix_alignment(self, x, aligned_conditioning):
"""
The UNet requires that the input <x> is a certain multiple of 2, defined by the UNet depth. Enforce this by
padding both <x> and <aligned_conditioning> before forward propagation and removing the padding before returning.
"""
cm = ceil_multiple(x.shape[-1], self.alignment_size)
if cm != 0:
pc = (cm-x.shape[-1])/x.shape[-1]
x = F.pad(x, (0,cm-x.shape[-1]))
# Also fix aligned_latent, which is aligned to x.
if is_latent(aligned_conditioning):
aligned_conditioning = torch.cat([aligned_conditioning,
self.aligned_latent_padding_embedding.repeat(x.shape[0], 1, int(pc * aligned_conditioning.shape[-1]))], dim=-1)
else:
aligned_conditioning = F.pad(aligned_conditioning, (0, int(pc*aligned_conditioning.shape[-1])))
return x, aligned_conditioning
def get_grad_norm_parameter_groups(self):
groups = {
'minicoder': list(self.contextual_embedder.parameters()),
'layers': list(self.layers.parameters()),
'code_converters': list(self.code_embedding.parameters()) + list(self.code_converter.parameters()) + list(self.latent_converter.parameters()) + list(self.latent_converter.parameters()),
'timestep_integrator': list(self.conditioning_timestep_integrator.parameters()) + list(self.integrating_conv.parameters()),
'time_embed': list(self.time_embed.parameters()),
}
return groups
def timestep_independent(self, aligned_conditioning, conditioning_input):
def timestep_independent(self, aligned_conditioning, conditioning_input, return_code_pred):
# Shuffle aligned_latent to BxCxS format
if is_latent(aligned_conditioning):
aligned_conditioning = aligned_conditioning.permute(0, 2, 1)
with autocast(aligned_conditioning.device.type, enabled=self.enable_fp16):
cond_emb = self.contextual_embedder(conditioning_input)
if len(cond_emb.shape) == 3: # Just take the first element.
cond_emb = cond_emb[:, :, 0]
# Note: this block does not need to repeated on inference, since it is not timestep-dependent or x-dependent.
speech_conditioning_input = conditioning_input.unsqueeze(1) if len(
conditioning_input.shape) == 3 else conditioning_input
conds = []
for j in range(speech_conditioning_input.shape[1]):
conds.append(self.contextual_embedder(speech_conditioning_input[:, j]))
conds = torch.cat(conds, dim=-1)
cond_emb = conds.mean(dim=-1)
cond_scale, cond_shift = torch.chunk(cond_emb, 2, dim=1)
if is_latent(aligned_conditioning):
code_emb = self.latent_converter(aligned_conditioning)
else:
code_emb = self.code_converter(aligned_conditioning)
cond_emb = cond_emb.unsqueeze(-1).repeat(1, 1, code_emb.shape[-1])
code_emb = self.conditioning_conv(torch.cat([cond_emb, code_emb], dim=1))
return code_emb
code_emb = self.code_embedding(aligned_conditioning).permute(0, 2, 1)
code_emb = self.code_converter(code_emb)
code_emb = self.code_norm(code_emb) * (1 + cond_scale.unsqueeze(-1)) + cond_shift.unsqueeze(-1)
def forward(self, x, timesteps, precomputed_aligned_embeddings, conditioning_free=False):
assert x.shape[-1] % self.alignment_size == 0
unconditioned_batches = torch.zeros((code_emb.shape[0], 1, 1), device=code_emb.device)
# Mask out the conditioning branch for whole batch elements, implementing something similar to classifier-free guidance.
if self.training and self.unconditioned_percentage > 0:
unconditioned_batches = torch.rand((code_emb.shape[0], 1, 1),
device=code_emb.device) < self.unconditioned_percentage
code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(aligned_conditioning.shape[0], 1, 1),
code_emb)
expanded_code_emb = F.interpolate(code_emb, size=aligned_conditioning.shape[-1]*4, mode='nearest')
with autocast(x.device.type, enabled=self.enable_fp16):
if conditioning_free:
code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, 1)
if not return_code_pred:
return expanded_code_emb
else:
mel_pred = self.mel_head(expanded_code_emb)
# Multiply mel_pred by !unconditioned_branches, which drops the gradient on unconditioned branches. This is because we don't want that gradient being used to train parameters through the codes_embedder as it unbalances contributions to that network from the MSE loss.
mel_pred = mel_pred * unconditioned_batches.logical_not()
return expanded_code_emb, mel_pred
def forward(self, x, timesteps, aligned_conditioning=None, conditioning_input=None, precomputed_aligned_embeddings=None, conditioning_free=False, return_code_pred=False):
"""
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 aligned_conditioning: an aligned latent or sequence of tokens providing useful data about the sample to be produced.
:param conditioning_input: a full-resolution audio clip that is used as a reference to the style you want decoded.
:param precomputed_aligned_embeddings: Embeddings returned from self.timestep_independent()
:param conditioning_free: When set, all conditioning inputs (including tokens and conditioning_input) will not be considered.
:return: an [N x C x ...] Tensor of outputs.
"""
assert precomputed_aligned_embeddings is not None or (aligned_conditioning is not None and conditioning_input is not None)
assert not (return_code_pred and precomputed_aligned_embeddings is not None) # These two are mutually exclusive.
unused_params = []
if conditioning_free:
code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, x.shape[-1])
unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters()))
unused_params.extend(list(self.latent_converter.parameters()))
else:
if precomputed_aligned_embeddings is not None:
code_emb = precomputed_aligned_embeddings
else:
code_emb, mel_pred = self.timestep_independent(aligned_conditioning, conditioning_input, True)
if is_latent(aligned_conditioning):
unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters()))
else:
unused_params.extend(list(self.latent_converter.parameters()))
unused_params.append(self.unconditioned_embedding)
time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
code_emb = torch.repeat_interleave(code_emb, self.conditioning_expansion, dim=-1)
code_emb = self.conditioning_timestep_integrator(code_emb, time_emb)
first = True
time_emb = time_emb.float()
h = x
hs = []
for k, module in enumerate(self.input_blocks):
if isinstance(module, nn.Conv1d):
h_tok = F.interpolate(module(code_emb), size=(h.shape[-1]), mode='nearest')
h = h + h_tok
x = self.inp_block(x)
x = torch.cat([x, code_emb], dim=1)
x = self.integrating_conv(x)
for i, lyr in enumerate(self.layers):
# Do layer drop where applicable. Do not drop first and last layers.
if self.training and self.layer_drop > 0 and i != 0 and i != (len(self.layers)-1) and random.random() < self.layer_drop:
unused_params.extend(list(lyr.parameters()))
else:
with autocast(x.device.type, enabled=self.enable_fp16 and not first):
# First block has autocast disabled to allow a high precision signal to be properly vectorized.
h = module(h, time_emb)
hs.append(h)
first = False
h = self.middle_block(h, time_emb)
for module in self.output_blocks:
h = torch.cat([h, hs.pop()], dim=1)
h = module(h, time_emb)
# First and last blocks will have autocast disabled for improved precision.
with autocast(x.device.type, enabled=self.enable_fp16 and i != 0):
x = lyr(x, time_emb)
# Last block also has autocast disabled for high-precision outputs.
h = h.float()
out = self.out(h)
x = x.float()
out = self.out(x)
# Involve probabilistic or possibly unused parameters in loss so we don't get DDP errors.
extraneous_addition = 0
for p in unused_params:
extraneous_addition = extraneous_addition + p.mean()
out = out + extraneous_addition * 0
if return_code_pred:
return out, mel_pred
return out
if __name__ == '__main__':
clip = torch.randn(2, 1, 32868)
aligned_latent = torch.randn(2,388,1024)
aligned_sequence = torch.randint(0,8192,(2,388))
cond = torch.randn(2, 1, 44000)
clip = torch.randn(2, 100, 400)
aligned_latent = torch.randn(2,388,512)
aligned_sequence = torch.randint(0,8192,(2,100))
cond = torch.randn(2, 100, 400)
ts = torch.LongTensor([600, 600])
model = DiffusionTts(128,
channel_mult=[1,1.5,2, 3, 4, 6, 8],
num_res_blocks=[2, 2, 2, 2, 2, 2, 1],
token_conditioning_resolutions=[1,4,16,64],
attention_resolutions=[],
num_heads=8,
kernel_size=3,
scale_factor=2,
time_embed_dim_multiplier=4,
super_sampling=False,
efficient_convs=False)
model = DiffusionTts(512, layer_drop=.3, unconditioned_percentage=.5)
# Test with latent aligned conditioning
o = model(clip, ts, aligned_latent, cond)
#o = model(clip, ts, aligned_latent, cond)
# Test with sequence aligned conditioning
o = model(clip, ts, aligned_sequence, cond)