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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
from x_transformers.x_transformers import RotaryEmbedding
from f5_tts.model.modules import (
TimestepEmbedding,
ConvPositionEmbedding,
MMDiTBlock,
AdaLayerNormZero_Final,
precompute_freqs_cis,
get_pos_embed_indices,
)
# text embedding
class TextEmbedding(nn.Module):
def __init__(self, out_dim, text_num_embeds):
super().__init__()
self.text_embed = nn.Embedding(text_num_embeds + 1, out_dim) # will use 0 as filler token
self.precompute_max_pos = 1024
self.register_buffer("freqs_cis", precompute_freqs_cis(out_dim, self.precompute_max_pos), persistent=False)
def forward(self, text: int["b nt"], drop_text=False) -> int["b nt d"]: # noqa: F722
text = text + 1
if drop_text:
text = torch.zeros_like(text)
text = self.text_embed(text)
# sinus pos emb
batch_start = torch.zeros((text.shape[0],), dtype=torch.long)
batch_text_len = text.shape[1]
pos_idx = get_pos_embed_indices(batch_start, batch_text_len, max_pos=self.precompute_max_pos)
text_pos_embed = self.freqs_cis[pos_idx]
text = text + text_pos_embed
return text
# noised input & masked cond audio embedding
class AudioEmbedding(nn.Module):
def __init__(self, in_dim, out_dim):
super().__init__()
self.linear = nn.Linear(2 * in_dim, out_dim)
self.conv_pos_embed = ConvPositionEmbedding(out_dim)
def forward(self, x: float["b n d"], cond: float["b n d"], drop_audio_cond=False): # noqa: F722
if drop_audio_cond:
cond = torch.zeros_like(cond)
x = torch.cat((x, cond), dim=-1)
x = self.linear(x)
x = self.conv_pos_embed(x) + x
return x
# Transformer backbone using MM-DiT blocks
class MMDiT(nn.Module):
def __init__(
self,
*,
dim,
depth=8,
heads=8,
dim_head=64,
dropout=0.1,
ff_mult=4,
text_num_embeds=256,
mel_dim=100,
):
super().__init__()
self.time_embed = TimestepEmbedding(dim)
self.text_embed = TextEmbedding(dim, text_num_embeds)
self.audio_embed = AudioEmbedding(mel_dim, dim)
self.rotary_embed = RotaryEmbedding(dim_head)
self.dim = dim
self.depth = depth
self.transformer_blocks = nn.ModuleList(
[
MMDiTBlock(
dim=dim,
heads=heads,
dim_head=dim_head,
dropout=dropout,
ff_mult=ff_mult,
context_pre_only=i == depth - 1,
)
for i in range(depth)
]
)
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 = x.shape[0]
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)
c = self.text_embed(text, drop_text=drop_text)
x = self.audio_embed(x, cond, drop_audio_cond=drop_audio_cond)
seq_len = x.shape[1]
text_len = text.shape[1]
rope_audio = self.rotary_embed.forward_from_seq_len(seq_len)
rope_text = self.rotary_embed.forward_from_seq_len(text_len)
for block in self.transformer_blocks:
c, x = block(x, c, t, mask=mask, rope=rope_audio, c_rope=rope_text)
x = self.norm_out(x, t)
output = self.proj_out(x)
return output
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