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Mods to support unet diffusion vocoder with conditioning

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This commit is contained in:
James Betker 2021-10-13 21:23:18 -06:00
parent c861054218
commit 83798887a8
4 changed files with 347 additions and 11 deletions
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4
codes/models/diffusion/gaussian_diffusion.py
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@ -770,9 +770,11 @@ class GaussianDiffusion:
terms["loss"] *= self.num_timesteps
elif self.loss_type == LossType.MSE or self.loss_type == LossType.RESCALED_MSE:
model_outputs = model(x_t, self._scale_timesteps(t), **model_kwargs)
if isinstance(model_outputs, tuple):
model_output = model_outputs[0]
if len(model_outputs) > 1:
terms['extra_outputs'] = model_outputs[1:]
else:
model_output = model_outputs
if self.model_var_type in [
ModelVarType.LEARNED,

15
codes/models/gpt_voice/mini_encoder.py
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@ -3,8 +3,7 @@ import torch.nn as nn
from models.diffusion.nn import normalization, conv_nd, zero_module
from models.diffusion.unet_diffusion import Downsample, AttentionBlock, QKVAttention, QKVAttentionLegacy
from models.diffusion.unet_diffusion import Downsample, AttentionBlock, QKVAttention, QKVAttentionLegacy, Upsample
# Combined resnet & full-attention encoder for converting an audio clip into an embedding.
from utils.util import checkpoint
@ -90,17 +89,17 @@ class ResBlock(nn.Module):
class AudioMiniEncoder(nn.Module):
def __init__(self, spec_dim, embedding_dim, resnet_blocks=2, attn_blocks=4, num_attn_heads=4, dropout=0):
def __init__(self, spec_dim, embedding_dim, base_channels=128, depth=2, resnet_blocks=2, attn_blocks=4, num_attn_heads=4, dropout=0, downsample_factor=2, kernel_size=3):
super().__init__()
self.init = nn.Sequential(
conv_nd(1, spec_dim, 128, 3, padding=1)
conv_nd(1, spec_dim, base_channels, 3, padding=1)
)
ch = 128
ch = base_channels
res = []
for l in range(2):
for l in range(depth):
for r in range(resnet_blocks):
res.append(ResBlock(ch, dropout, dims=1, do_checkpoint=False))
res.append(Downsample(ch, use_conv=True, dims=1, out_channels=ch*2, factor=2))
res.append(ResBlock(ch, dropout, dims=1, do_checkpoint=False, kernel_size=kernel_size))
res.append(Downsample(ch, use_conv=True, dims=1, out_channels=ch*2, factor=downsample_factor))
ch *= 2
self.res = nn.Sequential(*res)
self.final = nn.Sequential(

335
codes/models/gpt_voice/unet_diffusion_vocoder_with_ref.py Normal file
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@ -0,0 +1,335 @@
from models.diffusion.fp16_util import convert_module_to_f32, convert_module_to_f16
from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
from models.diffusion.unet_diffusion import AttentionPool2d, AttentionBlock, ResBlock, TimestepEmbedSequential, \
Downsample, Upsample
import torch
import torch.nn as nn
from models.gpt_voice.mini_encoder import AudioMiniEncoder, EmbeddingCombiner
from trainer.networks import register_model
from utils.util import get_mask_from_lengths
class DiscreteSpectrogramConditioningBlock(nn.Module):
def __init__(self, discrete_codes, channels):
super().__init__()
self.emb = nn.Embedding(discrete_codes, channels)
"""
Embeds the given codes and concatenates them onto x. Return shape: bx2cxS
:param x: bxcxS waveform latent
:param codes: bxN discrete codes, N <= S
"""
def forward(self, x, codes):
_, c, S = x.shape
b, N = codes.shape
assert N <= S
emb = self.emb(codes).permute(0,2,1)
emb = nn.functional.interpolate(emb, size=(S,), mode='nearest')
return torch.cat([x, emb], dim=1)
class DiffusionVocoderWithRef(nn.Module):
"""
The full UNet model with attention and timestep embedding.
Customized to be conditioned on a spectrogram prior.
:param in_channels: channels in the input Tensor.
:param spectrogram_channels: channels in the conditioning spectrogram.
:param model_channels: base channel count for the model.
:param out_channels: channels in the output Tensor.
:param num_res_blocks: number of residual blocks per downsample.
:param attention_resolutions: a collection of downsample rates at which
attention will take place. May be a set, list, or tuple.
For example, if this contains 4, then at 4x downsampling, attention
will be used.
:param dropout: the dropout probability.
:param channel_mult: channel multiplier for each level of the UNet.
:param conv_resample: if True, use learned convolutions for upsampling and
downsampling.
:param dims: determines if the signal is 1D, 2D, or 3D.
:param num_heads: the number of attention heads in each attention layer.
:param num_heads_channels: if specified, ignore num_heads and instead use
a fixed channel width per attention head.
:param num_heads_upsample: works with num_heads to set a different number
of heads for upsampling. Deprecated.
:param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
:param resblock_updown: use residual blocks for up/downsampling.
:param use_new_attention_order: use a different attention pattern for potentially
increased efficiency.
"""
def __init__(
self,
model_channels,
num_res_blocks,
in_channels=1,
out_channels=2, # mean and variance
discrete_codes=8192,
dropout=0,
# 38400 -> 19200 -> 9600 -> 4800 -> 2400 -> 1200 -> 600 -> 300 -> 150 for ~2secs@22050Hz
channel_mult=(1, 1, 2, 2, 4, 8, 16, 32, 64),
spectrogram_conditioning_resolutions=(4,8,16,32),
attention_resolutions=(64,128,256),
conv_resample=True,
dims=1,
use_fp16=False,
num_heads=1,
num_head_channels=-1,
num_heads_upsample=-1,
use_scale_shift_norm=False,
resblock_updown=False,
use_new_attention_order=False,
kernel_size=3,
scale_factor=2,
conditioning_inputs_provided=True,
conditioning_input_dim=80,
):
super().__init__()
if num_heads_upsample == -1:
num_heads_upsample = num_heads
self.in_channels = in_channels
self.model_channels = model_channels
self.out_channels = out_channels
self.num_res_blocks = num_res_blocks
self.attention_resolutions = attention_resolutions
self.dropout = dropout
self.channel_mult = channel_mult
self.conv_resample = conv_resample
self.dtype = torch.float16 if use_fp16 else torch.float32
self.num_heads = num_heads
self.num_head_channels = num_head_channels
self.num_heads_upsample = num_heads_upsample
self.dims = dims
padding = 1 if kernel_size == 3 else 2
time_embed_dim = model_channels * 4
self.time_embed = nn.Sequential(
linear(model_channels, time_embed_dim),
nn.SiLU(),
linear(time_embed_dim, time_embed_dim),
)
self.conditioning_enabled = conditioning_inputs_provided
if conditioning_inputs_provided:
self.contextual_embedder = AudioMiniEncoder(conditioning_input_dim, time_embed_dim)
self.query_gen = AudioMiniEncoder(in_channels, time_embed_dim, base_channels=32, depth=6, resnet_blocks=1,
attn_blocks=2, num_attn_heads=2, dropout=dropout, downsample_factor=4, kernel_size=5)
self.embedding_combiner = EmbeddingCombiner(time_embed_dim)
self.input_blocks = nn.ModuleList(
[
TimestepEmbedSequential(
conv_nd(dims, in_channels, model_channels, kernel_size, padding=padding)
)
]
)
self._feature_size = model_channels
input_block_chans = [model_channels]
ch = model_channels
ds = 1
for level, mult in enumerate(channel_mult):
if ds in spectrogram_conditioning_resolutions:
self.input_blocks.append(DiscreteSpectrogramConditioningBlock(discrete_codes, ch))
ch *= 2
for _ in range(num_res_blocks):
layers = [
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=mult * model_channels,
dims=dims,
use_scale_shift_norm=use_scale_shift_norm,
kernel_size=kernel_size,
)
]
ch = 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 in list(enumerate(channel_mult))[::-1]:
for i in range(num_res_blocks + 1):
ich = input_block_chans.pop()
layers = [
ResBlock(
ch + ich,
time_embed_dim,
dropout,
out_channels=model_channels * mult,
dims=dims,
use_scale_shift_norm=use_scale_shift_norm,
kernel_size=kernel_size,
)
]
ch = 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_res_blocks:
out_ch = ch
layers.append(
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=out_ch,
dims=dims,
use_scale_shift_norm=use_scale_shift_norm,
up=True,
kernel_size=kernel_size,
)
if resblock_updown
else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor)
)
ds //= 2
self.output_blocks.append(TimestepEmbedSequential(*layers))
self._feature_size += ch
self.out = nn.Sequential(
normalization(ch),
nn.SiLU(),
zero_module(conv_nd(dims, model_channels, out_channels, kernel_size, padding=padding)),
)
def convert_to_fp16(self):
"""
Convert the torso of the model to float16.
"""
self.input_blocks.apply(convert_module_to_f16)
self.middle_block.apply(convert_module_to_f16)
self.output_blocks.apply(convert_module_to_f16)
def convert_to_fp32(self):
"""
Convert the torso of the model to float32.
"""
self.input_blocks.apply(convert_module_to_f32)
self.middle_block.apply(convert_module_to_f32)
self.output_blocks.apply(convert_module_to_f32)
def forward(self, x, timesteps, discrete_spectrogram, conditioning_inputs=None, num_conditioning_signals=None):
"""
Apply the model to an input batch.
:param x: an [N x C x ...] Tensor of inputs.
:param timesteps: a 1-D batch of timesteps.
:param y: an [N] Tensor of labels, if class-conditional.
:return: an [N x C x ...] Tensor of outputs.
"""
assert x.shape[-1] % 4096 == 0 # This model operates at base//4096 at it's bottom levels, thus this requirement.
if self.conditioning_enabled:
assert conditioning_inputs is not None
assert num_conditioning_signals is not None
hs = []
emb1 = self.time_embed(timestep_embedding(timesteps, self.model_channels))
if self.conditioning_enabled:
emb2 = torch.stack([self.contextual_embedder(ci.squeeze(1)) for ci in list(torch.chunk(conditioning_inputs, conditioning_inputs.shape[1], dim=1))], dim=1)
emb = torch.cat([emb1.unsqueeze(1), emb2], dim=1)
emb = self.embedding_combiner(emb, None, self.query_gen(x))
else:
emb = emb1
h = x.type(self.dtype)
for k, module in enumerate(self.input_blocks):
if isinstance(module, DiscreteSpectrogramConditioningBlock):
h = module(h, discrete_spectrogram)
else:
h = module(h, emb)
hs.append(h)
h = self.middle_block(h, emb)
for module in self.output_blocks:
h = torch.cat([h, hs.pop()], dim=1)
h = module(h, emb)
h = h.type(x.dtype)
return self.out(h)
@register_model
def register_unet_diffusion_vocoder_with_ref(opt_net, opt):
return DiffusionVocoderWithRef(**opt_net['kwargs'])
# Test for ~4 second audio clip at 22050Hz
if __name__ == '__main__':
clip = torch.randn(2, 1, 81920)
spec = torch.randint(8192, (2, 500,))
cond = torch.randn(2, 4, 80, 600)
ts = torch.LongTensor([555, 556])
model = DiffusionVocoderWithRef(32, 2)
print(model(clip, ts, spec, cond, 4).shape)

2
codes/train.py
View File

@ -284,7 +284,7 @@ class Trainer:
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_dvae_clips_with_discretization_loss.yml')
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_diffusion_vocoder_clips.yml')
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
parser.add_argument('--local_rank', type=int, default=0)
args = parser.parse_args()