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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
from torch import nn
import torch.nn.functional as F
from x_transformers import RMSNorm
from x_transformers.x_transformers import RotaryEmbedding
from f5_tts.model.modules import (
TimestepEmbedding,
ConvNeXtV2Block,
ConvPositionEmbedding,
Attention,
AttnProcessor,
FeedForward,
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
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 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): # 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,
conv_layers=0,
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, conv_layers=conv_layers)
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(),
dim=dim,
heads=heads,
dim_head=dim_head,
dropout=dropout,
)
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 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
drop_audio_cond, # cfg for cond audio
drop_text, # cfg for text
mask: bool["b n"] | None = None, # noqa: F722
):
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)
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)
# 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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