Voice_Cloning / TTS /tts /layers /tortoise /diffusion_decoder.py
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voice-clone with single audio sample input
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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 TTS.tts.layers.tortoise.arch_utils import AttentionBlock, normalization
def is_latent(t):
return t.dtype == torch.float
def is_sequence(t):
return t.dtype == torch.long
def timestep_embedding(timesteps, dim, max_period=10000):
"""
Create sinusoidal timestep embeddings.
:param timesteps: a 1-D Tensor of N indices, one per batch element.
These may be fractional.
:param dim: the dimension of the output.
:param max_period: controls the minimum frequency of the embeddings.
:return: an [N x dim] Tensor of positional embeddings.
"""
half = dim // 2
freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(
device=timesteps.device
)
args = timesteps[:, None].float() * freqs[None]
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
if dim % 2:
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
return embedding
class TimestepBlock(nn.Module):
@abstractmethod
def forward(self, x, emb):
"""
Apply the module to `x` given `emb` timestep embeddings.
"""
class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
def forward(self, x, emb):
for layer in self:
if isinstance(layer, TimestepBlock):
x = layer(x, emb)
else:
x = layer(x)
return x
class ResBlock(TimestepBlock):
def __init__(
self,
channels,
emb_channels,
dropout,
out_channels=None,
dims=2,
kernel_size=3,
efficient_config=True,
use_scale_shift_norm=False,
):
super().__init__()
self.channels = channels
self.emb_channels = emb_channels
self.dropout = dropout
self.out_channels = out_channels or channels
self.use_scale_shift_norm = use_scale_shift_norm
padding = {1: 0, 3: 1, 5: 2}[kernel_size]
eff_kernel = 1 if efficient_config else 3
eff_padding = 0 if efficient_config else 1
self.in_layers = nn.Sequential(
normalization(channels),
nn.SiLU(),
nn.Conv1d(channels, self.out_channels, eff_kernel, padding=eff_padding),
)
self.emb_layers = nn.Sequential(
nn.SiLU(),
nn.Linear(
emb_channels,
2 * self.out_channels if use_scale_shift_norm else self.out_channels,
),
)
self.out_layers = nn.Sequential(
normalization(self.out_channels),
nn.SiLU(),
nn.Dropout(p=dropout),
nn.Conv1d(self.out_channels, self.out_channels, kernel_size, padding=padding),
)
if self.out_channels == channels:
self.skip_connection = nn.Identity()
else:
self.skip_connection = nn.Conv1d(channels, self.out_channels, eff_kernel, padding=eff_padding)
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):
emb_out = emb_out[..., None]
if self.use_scale_shift_norm:
out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
scale, shift = torch.chunk(emb_out, 2, dim=1)
h = out_norm(h) * (1 + scale) + shift
h = out_rest(h)
else:
h = h + emb_out
h = self.out_layers(h)
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):
def __init__(
self,
model_channels=512,
num_layers=8,
in_channels=100,
in_latent_channels=512,
in_tokens=8193,
out_channels=200, # mean and variance
dropout=0,
use_fp16=False,
num_heads=16,
# Parameters for regularization.
layer_drop=0.1,
unconditioned_percentage=0.1, # This implements a mechanism similar to what is used in classifier-free training.
):
super().__init__()
self.in_channels = in_channels
self.model_channels = model_channels
self.out_channels = out_channels
self.dropout = dropout
self.num_heads = num_heads
self.unconditioned_percentage = unconditioned_percentage
self.enable_fp16 = use_fp16
self.layer_drop = layer_drop
self.inp_block = nn.Conv1d(in_channels, model_channels, 3, 1, 1)
self.time_embed = nn.Sequential(
nn.Linear(model_channels, model_channels),
nn.SiLU(),
nn.Linear(model_channels, model_channels),
)
# 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(
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.code_norm = normalization(model_channels)
self.latent_conditioner = nn.Sequential(
nn.Conv1d(in_latent_channels, model_channels, 3, padding=1),
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),
AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
)
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(
DiffusionLayer(model_channels, dropout, num_heads),
DiffusionLayer(model_channels, dropout, num_heads),
DiffusionLayer(model_channels, dropout, num_heads),
)
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.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(model_channels),
nn.SiLU(),
nn.Conv1d(model_channels, out_channels, 3, padding=1),
)
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_conditioner.parameters())
+ list(self.latent_conditioner.parameters()),
"timestep_integrator": list(self.conditioning_timestep_integrator.parameters())
+ list(self.integrating_conv.parameters()),
"time_embed": list(self.time_embed.parameters()),
}
return groups
def get_conditioning(self, conditioning_input):
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)
conds = conds.mean(dim=-1)
return conds
def timestep_independent(
self,
aligned_conditioning,
conditioning_latent,
expected_seq_len,
return_code_pred,
):
# Shuffle aligned_latent to BxCxS format
if is_latent(aligned_conditioning):
aligned_conditioning = aligned_conditioning.permute(0, 2, 1)
cond_scale, cond_shift = torch.chunk(conditioning_latent, 2, dim=1)
if is_latent(aligned_conditioning):
code_emb = self.latent_conditioner(aligned_conditioning)
else:
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)
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=expected_seq_len, mode="nearest")
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_latent=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_latent: a pre-computed conditioning latent; see get_conditioning().
: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_latent 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_conditioner.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_latent, x.shape[-1], 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_conditioner.parameters()))
unused_params.append(self.unconditioned_embedding)
time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
code_emb = self.conditioning_timestep_integrator(code_emb, time_emb)
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:
# 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)
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, 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(512, layer_drop=0.3, unconditioned_percentage=0.5)
# Test with latent aligned conditioning
# o = model(clip, ts, aligned_latent, cond)
# Test with sequence aligned conditioning
o = model(clip, ts, aligned_sequence, cond)