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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 capspeech.nar.model.modules import (
TimestepEmbedding,
ConvNeXtV2Block,
ConvPositionEmbedding,
CrossDiTBlock,
DiTBlock,
AdaLayerNormZero_Final,
precompute_freqs_cis, get_pos_embed_indices,
)
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):
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):
super().__init__()
self.proj = nn.Linear(mel_dim * 2 + text_dim, out_dim)
self.conv_pos_embed = ConvPositionEmbedding(dim = out_dim)
def forward(self, x: float['b n d'], cond: float['b n d'],
text_embed: float['b n d'], drop_audio_cond = False):
if drop_audio_cond or cond is None: # cfg for cond audio
cond = torch.zeros_like(x)
x = self.proj(torch.cat((x, cond, text_embed), dim=-1))
x = self.conv_pos_embed(x) + x
return x
# Transformer backbone using DiT blocks
class CrossDiT(nn.Module):
def __init__(self,
dim, depth=8, heads=8, dim_head=64, dropout=0.0, ff_mult=4,
mel_dim=100, t5_dim=512, clap_dim=512,
text_num_embeds=256, text_dim=None, conv_layers=0,
skip=False, use_checkpoint=True, qk_norm=True,
):
super().__init__()
self.time_embed = TimestepEmbedding(dim)
if text_dim is None:
text_dim = mel_dim
self.text_embed = TextEmbedding(text_num_embeds, text_dim, conv_layers=conv_layers)
self.input_embed = InputEmbedding(mel_dim, text_dim, dim)
self.caption_embedding = nn.Sequential(
nn.Linear(t5_dim, dim),
nn.SiLU(),
nn.Linear(dim, dim)
)
self.clap_embedding = nn.Sequential(
nn.Linear(clap_dim, dim),
nn.SiLU(),
nn.Linear(dim, text_dim)
)
# self.null_clap = nn.Parameters
# self.null_prompt = nn.Parameters
self.rotary_embed = RotaryEmbedding(dim_head)
self.dim = dim
self.depth = depth
self.skip = skip
self.in_blocks = nn.ModuleList([
CrossDiTBlock(dim=dim,
heads=heads,
dim_head=dim_head,
ff_mult=ff_mult,
dropout=dropout,
use_checkpoint=use_checkpoint,
qk_norm=qk_norm,
skip=False
)
for _ in range(depth//2)
])
self.mid_block = CrossDiTBlock(dim=dim,
heads=heads,
dim_head=dim_head,
ff_mult=ff_mult,
dropout=dropout,
use_checkpoint=use_checkpoint,
qk_norm=qk_norm,
skip=False)
self.out_blocks = nn.ModuleList([
CrossDiTBlock(dim=dim,
heads=heads,
dim_head=dim_head,
ff_mult=ff_mult,
dropout=dropout,
use_checkpoint=use_checkpoint,
qk_norm=qk_norm,
skip=skip
)
for _ in range(depth//2)
])
self.norm_out = AdaLayerNormZero_Final(dim) # final modulation
self.proj_out = nn.Linear(dim, mel_dim)
def forward(
self,
x: float['b n d'], # nosied input audio
cond: float['b n d'], # masked cond audio
prompt: float['b n d'], # speech caption
clap: float['b n d'], # sound effects
text: int['b nt'], # text
time: float['b'] | float[''], # time step
mask: bool['b n'] | None = None,
prompt_mask: bool['b n'] | None = None,
):
batch, seq_len = x.shape[0], x.shape[1]
if time.ndim == 0:
time = repeat(time, ' -> b', b=batch)
t = self.time_embed(time)
text_embed = self.text_embed(text, seq_len-1)
prompt_embed = self.caption_embedding(prompt)
clap_embed = self.clap_embedding(clap).unsqueeze(1)
text_embed = torch.cat([clap_embed, text_embed], dim=1)
x = self.input_embed(x, cond, text_embed)
rope = self.rotary_embed.forward_from_seq_len(seq_len)
skips = []
for i, block in enumerate(self.in_blocks):
x = block(x, t, mask=mask, rope=rope,
context=prompt_embed, context_mask=prompt_mask)
if self.skip:
skips.append(x)
x = self.mid_block(x, t, mask=mask, rope=rope,
context=prompt_embed, context_mask=prompt_mask)
for i, block in enumerate(self.out_blocks):
if self.skip:
skip = skips.pop()
else:
skip = None
x = block(x, t, mask=mask, rope=rope,
context=prompt_embed, context_mask=prompt_mask, skip=skip)
x = self.norm_out(x, t)
output = self.proj_out(x)
return output
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