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"""
ein notation:
b - batch
n - sequence
nt - text sequence
nw - raw wave length
d - dimension
"""
from __future__ import annotations
from typing import Literal
import torch
import torch.nn.functional as F
from torch import nn
from x_transformers import RMSNorm
from x_transformers.x_transformers import RotaryEmbedding
from f5_tts.model_new.modules import (Attention, AttnProcessor,
ConvNeXtV2Block, ConvPositionEmbedding,
FeedForward, TimestepEmbedding,
get_pos_embed_indices,
precompute_freqs_cis)
# Text embedding
class TextEmbedding(nn.Module):
def __init__(
self, text_num_embeds, text_dim, mask_padding=True, conv_layers=0, conv_mult=2
):
super().__init__()
self.text_embed = nn.Embedding(
text_num_embeds + 1, text_dim
) # use 0 as filler token
self.mask_padding = mask_padding # mask filler and batch padding tokens or not
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
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
batch, text_len = text.shape[0], text.shape[1]
text = F.pad(text, (0, seq_len - text_len), value=0)
if self.mask_padding:
text_mask = text == 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
if self.mask_padding:
text = text.masked_fill(
text_mask.unsqueeze(-1).expand(-1, -1, text.size(-1)), 0.0
)
for block in self.text_blocks:
text = block(text)
text = text.masked_fill(
text_mask.unsqueeze(-1).expand(-1, -1, text.size(-1)), 0.0
)
else:
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,
): # noqa: F722
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
# Flat UNet Transformer backbone
class UNetT(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,
text_mask_padding=True,
qk_norm=None,
conv_layers=0,
pe_attn_head=None,
skip_connect_type: Literal["add", "concat", "none"] = "concat",
):
super().__init__()
assert depth % 2 == 0, "UNet-Transformer's depth should be even."
self.time_embed = TimestepEmbedding(dim)
if text_dim is None:
text_dim = mel_dim
self.text_embed = TextEmbedding(
text_num_embeds,
text_dim,
mask_padding=text_mask_padding,
conv_layers=conv_layers,
)
self.text_cond, self.text_uncond = None, None # text cache
self.input_embed = InputEmbedding(mel_dim, text_dim, dim)
self.rotary_embed = RotaryEmbedding(dim_head)
# transformer layers & skip connections
self.dim = dim
self.skip_connect_type = skip_connect_type
needs_skip_proj = skip_connect_type == "concat"
self.depth = depth
self.layers = nn.ModuleList([])
for idx in range(depth):
is_later_half = idx >= (depth // 2)
attn_norm = RMSNorm(dim)
attn = Attention(
processor=AttnProcessor(pe_attn_head=pe_attn_head),
dim=dim,
heads=heads,
dim_head=dim_head,
dropout=dropout,
qk_norm=qk_norm,
)
ff_norm = RMSNorm(dim)
ff = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh")
skip_proj = (
nn.Linear(dim * 2, dim, bias=False)
if needs_skip_proj and is_later_half
else None
)
self.layers.append(
nn.ModuleList(
[
skip_proj,
attn_norm,
attn,
ff_norm,
ff,
]
)
)
self.norm_out = RMSNorm(dim)
self.proj_out = nn.Linear(dim, mel_dim)
def get_input_embed(
self,
x, # b n d
cond, # b n d
text, # b nt
drop_audio_cond: bool = False,
drop_text: bool = False,
cache: bool = True,
):
seq_len = x.shape[1]
if cache:
if drop_text:
if self.text_uncond is None:
self.text_uncond = self.text_embed(text, seq_len, drop_text=True)
text_embed = self.text_uncond
else:
if self.text_cond is None:
self.text_cond = self.text_embed(text, seq_len, drop_text=False)
text_embed = self.text_cond
else:
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)
return x
def clear_cache(self):
self.text_cond, self.text_uncond = None, None
def forward(
self,
x: float["b n d"], # nosied input audio # noqa: F722
cond: float["b n d"], # masked cond audio # noqa: F722
text: int["b nt"], # text # noqa: F722
time: float["b"] | float[""], # time step # noqa: F821 F722
mask: bool["b n"] | None = None, # noqa: F722
drop_audio_cond: bool = False, # cfg for cond audio
drop_text: bool = False, # cfg for text
cfg_infer: bool = False, # cfg inference, pack cond & uncond forward
cache: bool = False,
):
batch, seq_len = x.shape[0], x.shape[1]
if time.ndim == 0:
time = time.repeat(batch)
# t: conditioning time, c: context (text + masked cond audio), x: noised input audio
t = self.time_embed(time)
if cfg_infer: # pack cond & uncond forward: b n d -> 2b n d
x_cond = self.get_input_embed(
x, cond, text, drop_audio_cond=False, drop_text=False, cache=cache
)
x_uncond = self.get_input_embed(
x, cond, text, drop_audio_cond=True, drop_text=True, cache=cache
)
x = torch.cat((x_cond, x_uncond), dim=0)
t = torch.cat((t, t), dim=0)
mask = torch.cat((mask, mask), dim=0) if mask is not None else None
else:
x = self.get_input_embed(
x,
cond,
text,
drop_audio_cond=drop_audio_cond,
drop_text=drop_text,
cache=cache,
)
# postfix time t to input x, [b n d] -> [b n+1 d]
x = torch.cat([t.unsqueeze(1), x], dim=1) # pack t to x
if mask is not None:
mask = F.pad(mask, (1, 0), value=1)
rope = self.rotary_embed.forward_from_seq_len(seq_len + 1)
# flat unet transformer
skip_connect_type = self.skip_connect_type
skips = []
for idx, (maybe_skip_proj, attn_norm, attn, ff_norm, ff) in enumerate(
self.layers
):
layer = idx + 1
# skip connection logic
is_first_half = layer <= (self.depth // 2)
is_later_half = not is_first_half
if is_first_half:
skips.append(x)
if is_later_half:
skip = skips.pop()
if skip_connect_type == "concat":
x = torch.cat((x, skip), dim=-1)
x = maybe_skip_proj(x)
elif skip_connect_type == "add":
x = x + skip
# attention and feedforward blocks
x = attn(attn_norm(x), rope=rope, mask=mask) + x
x = ff(ff_norm(x)) + x
assert len(skips) == 0
x = self.norm_out(x)[:, 1:, :] # unpack t from x
return self.proj_out(x)
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