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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 x_transformers.x_transformers import RotaryEmbedding
from f5_tts.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): # 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
# 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 # 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)
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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