mirror of
https://github.com/SWivid/F5-TTS.git
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137 lines
4.1 KiB
Python
137 lines
4.1 KiB
Python
"""
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ein notation:
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b - batch
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n - sequence
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nt - text sequence
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nw - raw wave length
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d - dimension
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"""
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from __future__ import annotations
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import torch
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from torch import nn
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from einops import repeat
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from x_transformers.x_transformers import RotaryEmbedding
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from model.modules import (
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TimestepEmbedding,
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ConvPositionEmbedding,
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MMDiTBlock,
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AdaLayerNormZero_Final,
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precompute_freqs_cis, get_pos_embed_indices,
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)
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# text embedding
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class TextEmbedding(nn.Module):
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def __init__(self, out_dim, text_num_embeds):
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super().__init__()
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self.text_embed = nn.Embedding(text_num_embeds + 1, out_dim) # will use 0 as filler token
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self.precompute_max_pos = 1024
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self.register_buffer("freqs_cis", precompute_freqs_cis(out_dim, self.precompute_max_pos), persistent=False)
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def forward(self, text: int['b nt'], drop_text = False) -> int['b nt d']:
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text = text + 1
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if drop_text:
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text = torch.zeros_like(text)
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text = self.text_embed(text)
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# sinus pos emb
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batch_start = torch.zeros((text.shape[0],), dtype=torch.long)
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batch_text_len = text.shape[1]
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pos_idx = get_pos_embed_indices(batch_start, batch_text_len, max_pos=self.precompute_max_pos)
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text_pos_embed = self.freqs_cis[pos_idx]
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text = text + text_pos_embed
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return text
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# noised input & masked cond audio embedding
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class AudioEmbedding(nn.Module):
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def __init__(self, in_dim, out_dim):
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super().__init__()
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self.linear = nn.Linear(2 * in_dim, out_dim)
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self.conv_pos_embed = ConvPositionEmbedding(out_dim)
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def forward(self, x: float['b n d'], cond: float['b n d'], drop_audio_cond = False):
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if drop_audio_cond:
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cond = torch.zeros_like(cond)
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x = torch.cat((x, cond), dim = -1)
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x = self.linear(x)
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x = self.conv_pos_embed(x) + x
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return x
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# Transformer backbone using MM-DiT blocks
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class MMDiT(nn.Module):
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def __init__(self, *,
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dim, depth = 8, heads = 8, dim_head = 64, dropout = 0.1, ff_mult = 4,
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text_num_embeds = 256, mel_dim = 100,
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):
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super().__init__()
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self.time_embed = TimestepEmbedding(dim)
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self.text_embed = TextEmbedding(dim, text_num_embeds)
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self.audio_embed = AudioEmbedding(mel_dim, dim)
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self.rotary_embed = RotaryEmbedding(dim_head)
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self.dim = dim
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self.depth = depth
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self.transformer_blocks = nn.ModuleList(
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[
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MMDiTBlock(
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dim = dim,
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heads = heads,
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dim_head = dim_head,
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dropout = dropout,
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ff_mult = ff_mult,
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context_pre_only = i == depth - 1,
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)
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for i in range(depth)
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]
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)
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self.norm_out = AdaLayerNormZero_Final(dim) # final modulation
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self.proj_out = nn.Linear(dim, mel_dim)
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def forward(
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self,
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x: float['b n d'], # nosied input audio
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cond: float['b n d'], # masked cond audio
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text: int['b nt'], # text
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time: float['b'] | float[''], # time step
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drop_audio_cond, # cfg for cond audio
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drop_text, # cfg for text
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mask: bool['b n'] | None = None,
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):
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batch = x.shape[0]
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if time.ndim == 0:
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time = repeat(time, ' -> b', b = batch)
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# t: conditioning (time), c: context (text + masked cond audio), x: noised input audio
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t = self.time_embed(time)
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c = self.text_embed(text, drop_text = drop_text)
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x = self.audio_embed(x, cond, drop_audio_cond = drop_audio_cond)
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seq_len = x.shape[1]
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text_len = text.shape[1]
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rope_audio = self.rotary_embed.forward_from_seq_len(seq_len)
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rope_text = self.rotary_embed.forward_from_seq_len(text_len)
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for block in self.transformer_blocks:
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c, x = block(x, c, t, mask = mask, rope = rope_audio, c_rope = rope_text)
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x = self.norm_out(x, t)
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output = self.proj_out(x)
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return output
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