ken11o2/tortoise-tts
1
0
Fork 0
forked from mrq/tortoise-tts
Code Pull Requests Activity

Some renaming

Browse Source
This commit is contained in:
James Betker 2022-01-27 23:21:44 -07:00
parent e16ab82597
commit 86004ad967
3 changed files with 2 additions and 393 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

5
do_tts.py
View File

@ -5,14 +5,13 @@ import random
import torch import torch
import torch.nn.functional as F import torch.nn.functional as F
import torchaudio import torchaudio
import yaml from models.dvae import DiscreteVAE
from models.autoregressive import UnifiedVoice
from tqdm import tqdm from tqdm import tqdm
from models.arch_util import TorchMelSpectrogram from models.arch_util import TorchMelSpectrogram
from models.discrete_diffusion_vocoder import DiscreteDiffusionVocoder from models.discrete_diffusion_vocoder import DiscreteDiffusionVocoder
from models.lucidrains_dvae import DiscreteVAE
from models.text_voice_clip import VoiceCLIP from models.text_voice_clip import VoiceCLIP
from models.unified_voice import UnifiedVoice
from utils.audio import load_audio from utils.audio import load_audio
from utils.diffusion import SpacedDiffusion, space_timesteps, get_named_beta_schedule from utils.diffusion import SpacedDiffusion, space_timesteps, get_named_beta_schedule
from utils.tokenizer import VoiceBpeTokenizer from utils.tokenizer import VoiceBpeTokenizer

0
models/unified_voice.py → models/autoregressive.py
View File

390
models/lucidrains_dvae.py
View File

@ -1,390 +0,0 @@
import functools
from math import sqrt
import torch
import torch.distributed as distributed
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
def default(val, d):
return val if val is not None else d
def eval_decorator(fn):
def inner(model, *args, **kwargs):
was_training = model.training
model.eval()
out = fn(model, *args, **kwargs)
model.train(was_training)
return out
return inner
# Quantizer implemented by the rosinality vqvae repo.
# Credit: https://github.com/rosinality/vq-vae-2-pytorch
class Quantize(nn.Module):
def __init__(self, dim, n_embed, decay=0.99, eps=1e-5, balancing_heuristic=False, new_return_order=False):
super().__init__()
self.dim = dim
self.n_embed = n_embed
self.decay = decay
self.eps = eps
self.balancing_heuristic = balancing_heuristic
self.codes = None
self.max_codes = 64000
self.codes_full = False
self.new_return_order = new_return_order
embed = torch.randn(dim, n_embed)
self.register_buffer("embed", embed)
self.register_buffer("cluster_size", torch.zeros(n_embed))
self.register_buffer("embed_avg", embed.clone())
def forward(self, input, return_soft_codes=False):
if self.balancing_heuristic and self.codes_full:
h = torch.histc(self.codes, bins=self.n_embed, min=0, max=self.n_embed) / len(self.codes)
mask = torch.logical_or(h > .9, h < .01).unsqueeze(1)
ep = self.embed.permute(1,0)
ea = self.embed_avg.permute(1,0)
rand_embed = torch.randn_like(ep) * mask
self.embed = (ep * ~mask + rand_embed).permute(1,0)
self.embed_avg = (ea * ~mask + rand_embed).permute(1,0)
self.cluster_size = self.cluster_size * ~mask.squeeze()
if torch.any(mask):
print(f"Reset {torch.sum(mask)} embedding codes.")
self.codes = None
self.codes_full = False
flatten = input.reshape(-1, self.dim)
dist = (
flatten.pow(2).sum(1, keepdim=True)
- 2 * flatten @ self.embed
+ self.embed.pow(2).sum(0, keepdim=True)
)
soft_codes = -dist
_, embed_ind = soft_codes.max(1)
embed_onehot = F.one_hot(embed_ind, self.n_embed).type(flatten.dtype)
embed_ind = embed_ind.view(*input.shape[:-1])
quantize = self.embed_code(embed_ind)
if self.balancing_heuristic:
if self.codes is None:
self.codes = embed_ind.flatten()
else:
self.codes = torch.cat([self.codes, embed_ind.flatten()])
if len(self.codes) > self.max_codes:
self.codes = self.codes[-self.max_codes:]
self.codes_full = True
if self.training:
embed_onehot_sum = embed_onehot.sum(0)
embed_sum = flatten.transpose(0, 1) @ embed_onehot
if distributed.is_initialized() and distributed.get_world_size() > 1:
distributed.all_reduce(embed_onehot_sum)
distributed.all_reduce(embed_sum)
self.cluster_size.data.mul_(self.decay).add_(
embed_onehot_sum, alpha=1 - self.decay
)
self.embed_avg.data.mul_(self.decay).add_(embed_sum, alpha=1 - self.decay)
n = self.cluster_size.sum()
cluster_size = (
(self.cluster_size + self.eps) / (n + self.n_embed * self.eps) * n
)
embed_normalized = self.embed_avg / cluster_size.unsqueeze(0)
self.embed.data.copy_(embed_normalized)
diff = (quantize.detach() - input).pow(2).mean()
quantize = input + (quantize - input).detach()
if return_soft_codes:
return quantize, diff, embed_ind, soft_codes.view(input.shape[:-1] + (-1,))
elif self.new_return_order:
return quantize, embed_ind, diff
else:
return quantize, diff, embed_ind
def embed_code(self, embed_id):
return F.embedding(embed_id, self.embed.transpose(0, 1))
# Fits a soft-discretized input to a normal-PDF across the specified dimension.
# In other words, attempts to force the discretization function to have a mean equal utilization across all discrete
# values with the specified expected variance.
class DiscretizationLoss(nn.Module):
def __init__(self, discrete_bins, dim, expected_variance, store_past=0):
super().__init__()
self.discrete_bins = discrete_bins
self.dim = dim
self.dist = torch.distributions.Normal(0, scale=expected_variance)
if store_past > 0:
self.record_past = True
self.register_buffer("accumulator_index", torch.zeros(1, dtype=torch.long, device='cpu'))
self.register_buffer("accumulator_filled", torch.zeros(1, dtype=torch.long, device='cpu'))
self.register_buffer("accumulator", torch.zeros(store_past, discrete_bins))
else:
self.record_past = False
def forward(self, x):
other_dims = set(range(len(x.shape)))-set([self.dim])
averaged = x.sum(dim=tuple(other_dims)) / x.sum()
averaged = averaged - averaged.mean()
if self.record_past:
acc_count = self.accumulator.shape[0]
avg = averaged.detach().clone()
if self.accumulator_filled > 0:
averaged = torch.mean(self.accumulator, dim=0) * (acc_count-1) / acc_count + \
averaged / acc_count
# Also push averaged into the accumulator.
self.accumulator[self.accumulator_index] = avg
self.accumulator_index += 1
if self.accumulator_index >= acc_count:
self.accumulator_index *= 0
if self.accumulator_filled <= 0:
self.accumulator_filled += 1
return torch.sum(-self.dist.log_prob(averaged))
class ResBlock(nn.Module):
def __init__(self, chan, conv, activation):
super().__init__()
self.net = nn.Sequential(
conv(chan, chan, 3, padding = 1),
activation(),
conv(chan, chan, 3, padding = 1),
activation(),
conv(chan, chan, 1)
)
def forward(self, x):
return self.net(x) + x
class UpsampledConv(nn.Module):
def __init__(self, conv, *args, **kwargs):
super().__init__()
assert 'stride' in kwargs.keys()
self.stride = kwargs['stride']
del kwargs['stride']
self.conv = conv(*args, **kwargs)
def forward(self, x):
up = nn.functional.interpolate(x, scale_factor=self.stride, mode='nearest')
return self.conv(up)
# DiscreteVAE partially derived from lucidrains DALLE implementation
# Credit: https://github.com/lucidrains/DALLE-pytorch
class DiscreteVAE(nn.Module):
def __init__(
self,
positional_dims=2,
num_tokens = 512,
codebook_dim = 512,
num_layers = 3,
num_resnet_blocks = 0,
hidden_dim = 64,
channels = 3,
stride = 2,
kernel_size = 4,
use_transposed_convs = True,
encoder_norm = False,
activation = 'relu',
smooth_l1_loss = False,
straight_through = False,
normalization = None, # ((0.5,) * 3, (0.5,) * 3),
record_codes = False,
discretization_loss_averaging_steps = 100,
lr_quantizer_args = {},
):
super().__init__()
has_resblocks = num_resnet_blocks > 0
self.num_tokens = num_tokens
self.num_layers = num_layers
self.straight_through = straight_through
self.positional_dims = positional_dims
self.discrete_loss = DiscretizationLoss(num_tokens, 2, 1 / (num_tokens*2), discretization_loss_averaging_steps)
assert positional_dims > 0 and positional_dims < 3 # This VAE only supports 1d and 2d inputs for now.
if positional_dims == 2:
conv = nn.Conv2d
conv_transpose = nn.ConvTranspose2d
else:
conv = nn.Conv1d
conv_transpose = nn.ConvTranspose1d
if not use_transposed_convs:
conv_transpose = functools.partial(UpsampledConv, conv)
if activation == 'relu':
act = nn.ReLU
elif activation == 'silu':
act = nn.SiLU
else:
assert NotImplementedError()
enc_layers = []
dec_layers = []
if num_layers > 0:
enc_chans = [hidden_dim * 2 ** i for i in range(num_layers)]
dec_chans = list(reversed(enc_chans))
enc_chans = [channels, *enc_chans]
dec_init_chan = codebook_dim if not has_resblocks else dec_chans[0]
dec_chans = [dec_init_chan, *dec_chans]
enc_chans_io, dec_chans_io = map(lambda t: list(zip(t[:-1], t[1:])), (enc_chans, dec_chans))
pad = (kernel_size - 1) // 2
for (enc_in, enc_out), (dec_in, dec_out) in zip(enc_chans_io, dec_chans_io):
enc_layers.append(nn.Sequential(conv(enc_in, enc_out, kernel_size, stride = stride, padding = pad), act()))
if encoder_norm:
enc_layers.append(nn.GroupNorm(8, enc_out))
dec_layers.append(nn.Sequential(conv_transpose(dec_in, dec_out, kernel_size, stride = stride, padding = pad), act()))
dec_out_chans = dec_chans[-1]
innermost_dim = dec_chans[0]
else:
enc_layers.append(nn.Sequential(conv(channels, hidden_dim, 1), act()))
dec_out_chans = hidden_dim
innermost_dim = hidden_dim
for _ in range(num_resnet_blocks):
dec_layers.insert(0, ResBlock(innermost_dim, conv, act))
enc_layers.append(ResBlock(innermost_dim, conv, act))
if num_resnet_blocks > 0:
dec_layers.insert(0, conv(codebook_dim, innermost_dim, 1))
enc_layers.append(conv(innermost_dim, codebook_dim, 1))
dec_layers.append(conv(dec_out_chans, channels, 1))
self.encoder = nn.Sequential(*enc_layers)
self.decoder = nn.Sequential(*dec_layers)
self.loss_fn = F.smooth_l1_loss if smooth_l1_loss else F.mse_loss
self.codebook = Quantize(codebook_dim, num_tokens, new_return_order=True)
# take care of normalization within class
self.normalization = normalization
self.record_codes = record_codes
if record_codes:
self.codes = torch.zeros((1228800,), dtype=torch.long)
self.code_ind = 0
self.total_codes = 0
self.internal_step = 0
def norm(self, images):
if not self.normalization is not None:
return images
means, stds = map(lambda t: torch.as_tensor(t).to(images), self.normalization)
arrange = 'c -> () c () ()' if self.positional_dims == 2 else 'c -> () c ()'
means, stds = map(lambda t: rearrange(t, arrange), (means, stds))
images = images.clone()
images.sub_(means).div_(stds)
return images
def get_debug_values(self, step, __):
if self.record_codes and self.total_codes > 0:
# Report annealing schedule
return {'histogram_codes': self.codes[:self.total_codes]}
else:
return {}
@torch.no_grad()
@eval_decorator
def get_codebook_indices(self, images):
img = self.norm(images)
logits = self.encoder(img).permute((0,2,3,1) if len(img.shape) == 4 else (0,2,1))
sampled, codes, _ = self.codebook(logits)
self.log_codes(codes)
return codes
def decode(
self,
img_seq
):
self.log_codes(img_seq)
if hasattr(self.codebook, 'embed_code'):
image_embeds = self.codebook.embed_code(img_seq)
else:
image_embeds = F.embedding(img_seq, self.codebook.codebook)
b, n, d = image_embeds.shape
kwargs = {}
if self.positional_dims == 1:
arrange = 'b n d -> b d n'
else:
h = w = int(sqrt(n))
arrange = 'b (h w) d -> b d h w'
kwargs = {'h': h, 'w': w}
image_embeds = rearrange(image_embeds, arrange, **kwargs)
images = [image_embeds]
for layer in self.decoder:
images.append(layer(images[-1]))
return images[-1], images[-2]
def infer(self, img):
img = self.norm(img)
logits = self.encoder(img).permute((0,2,3,1) if len(img.shape) == 4 else (0,2,1))
sampled, codes, commitment_loss = self.codebook(logits)
return self.decode(codes)
# Note: This module is not meant to be run in forward() except while training. It has special logic which performs
# evaluation using quantized values when it detects that it is being run in eval() mode, which will be substantially
# more lossy (but useful for determining network performance).
def forward(
self,
img
):
img = self.norm(img)
logits = self.encoder(img).permute((0,2,3,1) if len(img.shape) == 4 else (0,2,1))
sampled, codes, commitment_loss = self.codebook(logits)
sampled = sampled.permute((0,3,1,2) if len(img.shape) == 4 else (0,2,1))
if self.training:
out = sampled
for d in self.decoder:
out = d(out)
self.log_codes(codes)
else:
# This is non-differentiable, but gives a better idea of how the network is actually performing.
out, _ = self.decode(codes)
# reconstruction loss
recon_loss = self.loss_fn(img, out, reduction='none')
return recon_loss, commitment_loss, out
def log_codes(self, codes):
# This is so we can debug the distribution of codes being learned.
if self.record_codes and self.internal_step % 10 == 0:
codes = codes.flatten()
l = codes.shape[0]
i = self.code_ind if (self.codes.shape[0] - self.code_ind) > l else self.codes.shape[0] - l
self.codes[i:i+l] = codes.cpu()
self.code_ind = self.code_ind + l
if self.code_ind >= self.codes.shape[0]:
self.code_ind = 0
self.total_codes += 1
self.internal_step += 1
if __name__ == '__main__':
v = DiscreteVAE(channels=80, normalization=None, positional_dims=1, num_tokens=8192, codebook_dim=2048,
hidden_dim=512, num_resnet_blocks=3, kernel_size=3, num_layers=1, use_transposed_convs=False)
r,l,o=v(torch.randn(1,80,256))
v.decode(torch.randint(0,8192,(1,256)))
print(o.shape, l.shape)