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Running
on
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 model.modules import (
TimestepEmbedding,
ConvNeXtV2Block,
ConvPositionEmbedding,
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):
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: # cfg for cond audio
cond = torch.zeros_like(cond)
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 DiT(nn.Module):
def __init__(self, *,
dim, depth = 8, heads = 8, dim_head = 64, dropout = 0.1, ff_mult = 4,
mel_dim = 100, text_num_embeds = 256, text_dim = None, conv_layers = 0,
long_skip_connection = False,
):
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.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)
def forward(
self,
x: float['b n d'], # nosied input audio
cond: float['b n d'], # masked cond audio
text: int['b nt'], # text
time: float['b'] | float[''], # time step
drop_audio_cond, # cfg for cond audio
drop_text, # cfg for text
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: conditioning time, c: context (text + masked cond audio), x: noised input audio
t = self.time_embed(time)
text_embed = self.text_embed(text, seq_len, drop_text = drop_text)
x = self.input_embed(x, cond, text_embed, drop_audio_cond = drop_audio_cond)
rope = self.rotary_embed.forward_from_seq_len(seq_len)
if self.long_skip_connection is not None:
residual = x
for block in self.transformer_blocks:
x = block(x, t, mask = mask, 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)
return output
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