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Zero
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"""
ein notation:
b - batch
n - sequence
nt - text sequence
nw - raw wave length
d - dimension
"""
from __future__ import annotations
import torch
from torch import nn
import torch.nn.functional as F
from einops import repeat
from x_transformers.x_transformers import RotaryEmbedding
from cosyvoice.utils.mask import add_optional_chunk_mask
from cosyvoice.flow.DiT.modules import (
TimestepEmbedding,
ConvNeXtV2Block,
CausalConvPositionEmbedding,
DiTBlock,
AdaLayerNormZero_Final,
precompute_freqs_cis,
get_pos_embed_indices,
)
# Text embedding
class TextEmbedding(nn.Module):
def __init__(self, text_num_embeds, text_dim, conv_layers=0, conv_mult=2):
super().__init__()
self.text_embed = nn.Embedding(text_num_embeds + 1, text_dim) # use 0 as filler token
if conv_layers > 0:
self.extra_modeling = True
self.precompute_max_pos = 4096 # ~44s of 24khz audio
self.register_buffer("freqs_cis", precompute_freqs_cis(text_dim, self.precompute_max_pos), persistent=False)
self.text_blocks = nn.Sequential(
*[ConvNeXtV2Block(text_dim, text_dim * conv_mult) for _ in range(conv_layers)]
)
else:
self.extra_modeling = False
def forward(self, text: int["b nt"], seq_len, drop_text=False): # noqa: F722
batch, text_len = text.shape[0], text.shape[1]
text = text + 1 # use 0 as filler token. preprocess of batch pad -1, see list_str_to_idx()
text = text[:, :seq_len] # curtail if character tokens are more than the mel spec tokens
text = F.pad(text, (0, seq_len - text_len), value=0)
if drop_text: # cfg for text
text = torch.zeros_like(text)
text = self.text_embed(text) # b n -> b n d
# possible extra modeling
if self.extra_modeling:
# sinus pos emb
batch_start = torch.zeros((batch,), dtype=torch.long)
pos_idx = get_pos_embed_indices(batch_start, seq_len, max_pos=self.precompute_max_pos)
text_pos_embed = self.freqs_cis[pos_idx]
text = text + text_pos_embed
# convnextv2 blocks
text = self.text_blocks(text)
return text
# noised input audio and context mixing embedding
class InputEmbedding(nn.Module):
def __init__(self, mel_dim, text_dim, out_dim, spk_dim=None):
super().__init__()
spk_dim = 0 if spk_dim is None else spk_dim
self.spk_dim = spk_dim
self.proj = nn.Linear(mel_dim * 2 + text_dim + spk_dim, out_dim)
self.conv_pos_embed = CausalConvPositionEmbedding(dim=out_dim)
def forward(
self,
x: float["b n d"],
cond: float["b n d"],
text_embed: float["b n d"],
spks: float["b d"],
):
to_cat = [x, cond, text_embed]
if self.spk_dim > 0:
spks = repeat(spks, "b c -> b t c", t=x.shape[1])
to_cat.append(spks)
x = self.proj(torch.cat(to_cat, dim=-1))
x = self.conv_pos_embed(x) + x
return x
# Transformer backbone using DiT blocks
class DiT(nn.Module):
def __init__(
self,
*,
dim,
depth=8,
heads=8,
dim_head=64,
dropout=0.1,
ff_mult=4,
mel_dim=80,
mu_dim=None,
long_skip_connection=False,
spk_dim=None,
out_channels=None,
static_chunk_size=50,
num_decoding_left_chunks=2
):
super().__init__()
self.time_embed = TimestepEmbedding(dim)
if mu_dim is None:
mu_dim = mel_dim
self.input_embed = InputEmbedding(mel_dim, mu_dim, dim, spk_dim)
self.rotary_embed = RotaryEmbedding(dim_head)
self.dim = dim
self.depth = depth
self.transformer_blocks = nn.ModuleList(
[DiTBlock(dim=dim, heads=heads, dim_head=dim_head, ff_mult=ff_mult, dropout=dropout) for _ in range(depth)]
)
self.long_skip_connection = nn.Linear(dim * 2, dim, bias=False) if long_skip_connection else None
self.norm_out = AdaLayerNormZero_Final(dim) # final modulation
self.proj_out = nn.Linear(dim, mel_dim)
self.out_channels = out_channels
self.static_chunk_size = static_chunk_size
self.num_decoding_left_chunks = num_decoding_left_chunks
def forward(self, x, mask, mu, t, spks=None, cond=None, streaming=False):
x = x.transpose(1, 2)
mu = mu.transpose(1, 2)
cond = cond.transpose(1, 2)
spks = spks.unsqueeze(dim=1)
batch, seq_len = x.shape[0], x.shape[1]
if t.ndim == 0:
t = t.repeat(batch)
# t: conditioning time, c: context (text + masked cond audio), x: noised input audio
t = self.time_embed(t)
x = self.input_embed(x, cond, mu, spks.squeeze(1))
rope = self.rotary_embed.forward_from_seq_len(seq_len)
if self.long_skip_connection is not None:
residual = x
if streaming is True:
attn_mask = add_optional_chunk_mask(x, mask.bool(), False, False, 0, self.static_chunk_size, -1).unsqueeze(dim=1)
else:
attn_mask = add_optional_chunk_mask(x, mask.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1).unsqueeze(dim=1)
for block in self.transformer_blocks:
x = block(x, t, mask=attn_mask.bool(), rope=rope)
if self.long_skip_connection is not None:
x = self.long_skip_connection(torch.cat((x, residual), dim=-1))
x = self.norm_out(x, t)
output = self.proj_out(x).transpose(1, 2)
return output
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