mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-06-12 04:29:18 +08:00
53cac93589
* ruff check --fix * ruff format --line-length 120 --target-version py39 * Change the link for G2PW Model * update pytorch version and colab
627 lines
18 KiB
Python
627 lines
18 KiB
Python
from functools import partial
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import torch
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from torch import nn
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from torch.nn import Module, ModuleList
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import torch.nn.functional as F
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from bs_roformer.attend import Attend
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from torch.utils.checkpoint import checkpoint
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from typing import Tuple, Optional, Callable
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# from beartype.typing import Tuple, Optional, List, Callable
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# from beartype import beartype
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from rotary_embedding_torch import RotaryEmbedding
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from einops import rearrange, pack, unpack
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from einops.layers.torch import Rearrange
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# helper functions
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def exists(val):
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return val is not None
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def default(v, d):
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return v if exists(v) else d
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def pack_one(t, pattern):
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return pack([t], pattern)
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def unpack_one(t, ps, pattern):
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return unpack(t, ps, pattern)[0]
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# norm
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def l2norm(t):
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return F.normalize(t, dim=-1, p=2)
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class RMSNorm(Module):
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def __init__(self, dim):
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super().__init__()
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self.scale = dim**0.5
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self.gamma = nn.Parameter(torch.ones(dim))
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def forward(self, x):
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return F.normalize(x, dim=-1) * self.scale * self.gamma
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# attention
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class FeedForward(Module):
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def __init__(self, dim, mult=4, dropout=0.0):
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super().__init__()
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dim_inner = int(dim * mult)
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self.net = nn.Sequential(
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RMSNorm(dim),
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nn.Linear(dim, dim_inner),
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nn.GELU(),
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nn.Dropout(dropout),
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nn.Linear(dim_inner, dim),
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nn.Dropout(dropout),
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)
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def forward(self, x):
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return self.net(x)
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class Attention(Module):
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def __init__(self, dim, heads=8, dim_head=64, dropout=0.0, rotary_embed=None, flash=True):
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super().__init__()
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self.heads = heads
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self.scale = dim_head**-0.5
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dim_inner = heads * dim_head
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self.rotary_embed = rotary_embed
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self.attend = Attend(flash=flash, dropout=dropout)
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self.norm = RMSNorm(dim)
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self.to_qkv = nn.Linear(dim, dim_inner * 3, bias=False)
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self.to_gates = nn.Linear(dim, heads)
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self.to_out = nn.Sequential(nn.Linear(dim_inner, dim, bias=False), nn.Dropout(dropout))
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def forward(self, x):
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x = self.norm(x)
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q, k, v = rearrange(self.to_qkv(x), "b n (qkv h d) -> qkv b h n d", qkv=3, h=self.heads)
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if exists(self.rotary_embed):
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q = self.rotary_embed.rotate_queries_or_keys(q)
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k = self.rotary_embed.rotate_queries_or_keys(k)
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out = self.attend(q, k, v)
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gates = self.to_gates(x)
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out = out * rearrange(gates, "b n h -> b h n 1").sigmoid()
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out = rearrange(out, "b h n d -> b n (h d)")
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return self.to_out(out)
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class LinearAttention(Module):
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"""
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this flavor of linear attention proposed in https://arxiv.org/abs/2106.09681 by El-Nouby et al.
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"""
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# @beartype
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def __init__(self, *, dim, dim_head=32, heads=8, scale=8, flash=False, dropout=0.0):
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super().__init__()
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dim_inner = dim_head * heads
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self.norm = RMSNorm(dim)
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self.to_qkv = nn.Sequential(
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nn.Linear(dim, dim_inner * 3, bias=False), Rearrange("b n (qkv h d) -> qkv b h d n", qkv=3, h=heads)
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)
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self.temperature = nn.Parameter(torch.ones(heads, 1, 1))
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self.attend = Attend(scale=scale, dropout=dropout, flash=flash)
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self.to_out = nn.Sequential(Rearrange("b h d n -> b n (h d)"), nn.Linear(dim_inner, dim, bias=False))
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def forward(self, x):
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x = self.norm(x)
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q, k, v = self.to_qkv(x)
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q, k = map(l2norm, (q, k))
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q = q * self.temperature.exp()
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out = self.attend(q, k, v)
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return self.to_out(out)
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class Transformer(Module):
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def __init__(
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self,
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*,
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dim,
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depth,
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dim_head=64,
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heads=8,
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attn_dropout=0.0,
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ff_dropout=0.0,
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ff_mult=4,
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norm_output=True,
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rotary_embed=None,
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flash_attn=True,
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linear_attn=False,
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):
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super().__init__()
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self.layers = ModuleList([])
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for _ in range(depth):
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if linear_attn:
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attn = LinearAttention(dim=dim, dim_head=dim_head, heads=heads, dropout=attn_dropout, flash=flash_attn)
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else:
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attn = Attention(
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dim=dim,
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dim_head=dim_head,
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heads=heads,
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dropout=attn_dropout,
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rotary_embed=rotary_embed,
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flash=flash_attn,
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)
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self.layers.append(ModuleList([attn, FeedForward(dim=dim, mult=ff_mult, dropout=ff_dropout)]))
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self.norm = RMSNorm(dim) if norm_output else nn.Identity()
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def forward(self, x):
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for attn, ff in self.layers:
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x = attn(x) + x
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x = ff(x) + x
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return self.norm(x)
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# bandsplit module
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class BandSplit(Module):
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# @beartype
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def __init__(self, dim, dim_inputs: Tuple[int, ...]):
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super().__init__()
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self.dim_inputs = dim_inputs
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self.to_features = ModuleList([])
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for dim_in in dim_inputs:
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net = nn.Sequential(RMSNorm(dim_in), nn.Linear(dim_in, dim))
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self.to_features.append(net)
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def forward(self, x):
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x = x.split(self.dim_inputs, dim=-1)
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outs = []
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for split_input, to_feature in zip(x, self.to_features):
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split_output = to_feature(split_input)
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outs.append(split_output)
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return torch.stack(outs, dim=-2)
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def MLP(dim_in, dim_out, dim_hidden=None, depth=1, activation=nn.Tanh):
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dim_hidden = default(dim_hidden, dim_in)
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net = []
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dims = (dim_in, *((dim_hidden,) * (depth - 1)), dim_out)
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for ind, (layer_dim_in, layer_dim_out) in enumerate(zip(dims[:-1], dims[1:])):
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is_last = ind == (len(dims) - 2)
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net.append(nn.Linear(layer_dim_in, layer_dim_out))
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if is_last:
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continue
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net.append(activation())
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return nn.Sequential(*net)
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class MaskEstimator(Module):
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# @beartype
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def __init__(self, dim, dim_inputs: Tuple[int, ...], depth, mlp_expansion_factor=4):
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super().__init__()
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self.dim_inputs = dim_inputs
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self.to_freqs = ModuleList([])
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dim_hidden = dim * mlp_expansion_factor
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for dim_in in dim_inputs:
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net = []
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mlp = nn.Sequential(MLP(dim, dim_in * 2, dim_hidden=dim_hidden, depth=depth), nn.GLU(dim=-1))
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self.to_freqs.append(mlp)
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def forward(self, x):
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x = x.unbind(dim=-2)
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outs = []
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for band_features, mlp in zip(x, self.to_freqs):
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freq_out = mlp(band_features)
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outs.append(freq_out)
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return torch.cat(outs, dim=-1)
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# main class
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DEFAULT_FREQS_PER_BANDS = (
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2,
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2,
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2,
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2,
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2,
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2,
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2,
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2,
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2,
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2,
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2,
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2,
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2,
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2,
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2,
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2,
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2,
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2,
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2,
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2,
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2,
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2,
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2,
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2,
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4,
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4,
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4,
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4,
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4,
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4,
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4,
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4,
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4,
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4,
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4,
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4,
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12,
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12,
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12,
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12,
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12,
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12,
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12,
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12,
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24,
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24,
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24,
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24,
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24,
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24,
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24,
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24,
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48,
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48,
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48,
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48,
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48,
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48,
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48,
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48,
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128,
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129,
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)
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class BSRoformer(Module):
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# @beartype
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def __init__(
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self,
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dim,
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*,
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depth,
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stereo=False,
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num_stems=1,
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time_transformer_depth=2,
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freq_transformer_depth=2,
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linear_transformer_depth=0,
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freqs_per_bands: Tuple[int, ...] = DEFAULT_FREQS_PER_BANDS,
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# in the paper, they divide into ~60 bands, test with 1 for starters
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dim_head=64,
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heads=8,
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attn_dropout=0.0,
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ff_dropout=0.0,
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flash_attn=True,
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dim_freqs_in=1025,
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stft_n_fft=2048,
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stft_hop_length=512,
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# 10ms at 44100Hz, from sections 4.1, 4.4 in the paper - @faroit recommends // 2 or // 4 for better reconstruction
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stft_win_length=2048,
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stft_normalized=False,
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stft_window_fn: Optional[Callable] = None,
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mask_estimator_depth=2,
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multi_stft_resolution_loss_weight=1.0,
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multi_stft_resolutions_window_sizes: Tuple[int, ...] = (4096, 2048, 1024, 512, 256),
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multi_stft_hop_size=147,
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multi_stft_normalized=False,
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multi_stft_window_fn: Callable = torch.hann_window,
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mlp_expansion_factor=4,
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use_torch_checkpoint=False,
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skip_connection=False,
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):
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super().__init__()
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self.stereo = stereo
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self.audio_channels = 2 if stereo else 1
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self.num_stems = num_stems
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self.use_torch_checkpoint = use_torch_checkpoint
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self.skip_connection = skip_connection
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self.layers = ModuleList([])
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transformer_kwargs = dict(
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dim=dim,
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heads=heads,
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dim_head=dim_head,
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attn_dropout=attn_dropout,
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ff_dropout=ff_dropout,
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flash_attn=flash_attn,
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norm_output=False,
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)
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time_rotary_embed = RotaryEmbedding(dim=dim_head)
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freq_rotary_embed = RotaryEmbedding(dim=dim_head)
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for _ in range(depth):
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tran_modules = []
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if linear_transformer_depth > 0:
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tran_modules.append(Transformer(depth=linear_transformer_depth, linear_attn=True, **transformer_kwargs))
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tran_modules.append(
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Transformer(depth=time_transformer_depth, rotary_embed=time_rotary_embed, **transformer_kwargs)
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)
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tran_modules.append(
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Transformer(depth=freq_transformer_depth, rotary_embed=freq_rotary_embed, **transformer_kwargs)
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)
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self.layers.append(nn.ModuleList(tran_modules))
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self.final_norm = RMSNorm(dim)
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self.stft_kwargs = dict(
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n_fft=stft_n_fft, hop_length=stft_hop_length, win_length=stft_win_length, normalized=stft_normalized
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)
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self.stft_window_fn = partial(default(stft_window_fn, torch.hann_window), stft_win_length)
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freqs = torch.stft(
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torch.randn(1, 4096), **self.stft_kwargs, window=torch.ones(stft_win_length), return_complex=True
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).shape[1]
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assert len(freqs_per_bands) > 1
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assert sum(freqs_per_bands) == freqs, (
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f"the number of freqs in the bands must equal {freqs} based on the STFT settings, but got {sum(freqs_per_bands)}"
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)
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freqs_per_bands_with_complex = tuple(2 * f * self.audio_channels for f in freqs_per_bands)
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self.band_split = BandSplit(dim=dim, dim_inputs=freqs_per_bands_with_complex)
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self.mask_estimators = nn.ModuleList([])
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for _ in range(num_stems):
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mask_estimator = MaskEstimator(
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dim=dim,
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dim_inputs=freqs_per_bands_with_complex,
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depth=mask_estimator_depth,
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mlp_expansion_factor=mlp_expansion_factor,
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)
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self.mask_estimators.append(mask_estimator)
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# for the multi-resolution stft loss
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self.multi_stft_resolution_loss_weight = multi_stft_resolution_loss_weight
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self.multi_stft_resolutions_window_sizes = multi_stft_resolutions_window_sizes
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self.multi_stft_n_fft = stft_n_fft
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self.multi_stft_window_fn = multi_stft_window_fn
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self.multi_stft_kwargs = dict(hop_length=multi_stft_hop_size, normalized=multi_stft_normalized)
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def forward(self, raw_audio, target=None, return_loss_breakdown=False):
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"""
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einops
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b - batch
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f - freq
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t - time
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s - audio channel (1 for mono, 2 for stereo)
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n - number of 'stems'
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c - complex (2)
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d - feature dimension
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"""
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device = raw_audio.device
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# defining whether model is loaded on MPS (MacOS GPU accelerator)
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x_is_mps = True if device.type == "mps" else False
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if raw_audio.ndim == 2:
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raw_audio = rearrange(raw_audio, "b t -> b 1 t")
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channels = raw_audio.shape[1]
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assert (not self.stereo and channels == 1) or (self.stereo and channels == 2), (
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"stereo needs to be set to True if passing in audio signal that is stereo (channel dimension of 2). also need to be False if mono (channel dimension of 1)"
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)
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# to stft
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raw_audio, batch_audio_channel_packed_shape = pack_one(raw_audio, "* t")
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stft_window = self.stft_window_fn(device=device)
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# RuntimeError: FFT operations are only supported on MacOS 14+
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# Since it's tedious to define whether we're on correct MacOS version - simple try-catch is used
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try:
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stft_repr = torch.stft(raw_audio, **self.stft_kwargs, window=stft_window, return_complex=True)
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except:
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stft_repr = torch.stft(
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raw_audio.cpu() if x_is_mps else raw_audio,
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**self.stft_kwargs,
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window=stft_window.cpu() if x_is_mps else stft_window,
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return_complex=True,
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).to(device)
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stft_repr = torch.view_as_real(stft_repr)
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stft_repr = unpack_one(stft_repr, batch_audio_channel_packed_shape, "* f t c")
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# merge stereo / mono into the frequency, with frequency leading dimension, for band splitting
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stft_repr = rearrange(stft_repr, "b s f t c -> b (f s) t c")
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x = rearrange(stft_repr, "b f t c -> b t (f c)")
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if self.use_torch_checkpoint:
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x = checkpoint(self.band_split, x, use_reentrant=False)
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else:
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x = self.band_split(x)
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# axial / hierarchical attention
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store = [None] * len(self.layers)
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for i, transformer_block in enumerate(self.layers):
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if len(transformer_block) == 3:
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linear_transformer, time_transformer, freq_transformer = transformer_block
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x, ft_ps = pack([x], "b * d")
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if self.use_torch_checkpoint:
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x = checkpoint(linear_transformer, x, use_reentrant=False)
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else:
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x = linear_transformer(x)
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(x,) = unpack(x, ft_ps, "b * d")
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else:
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time_transformer, freq_transformer = transformer_block
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if self.skip_connection:
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# Sum all previous
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for j in range(i):
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x = x + store[j]
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x = rearrange(x, "b t f d -> b f t d")
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x, ps = pack([x], "* t d")
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if self.use_torch_checkpoint:
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x = checkpoint(time_transformer, x, use_reentrant=False)
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else:
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x = time_transformer(x)
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(x,) = unpack(x, ps, "* t d")
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x = rearrange(x, "b f t d -> b t f d")
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|
x, ps = pack([x], "* f d")
|
|
|
|
if self.use_torch_checkpoint:
|
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x = checkpoint(freq_transformer, x, use_reentrant=False)
|
|
else:
|
|
x = freq_transformer(x)
|
|
|
|
(x,) = unpack(x, ps, "* f d")
|
|
|
|
if self.skip_connection:
|
|
store[i] = x
|
|
|
|
x = self.final_norm(x)
|
|
|
|
num_stems = len(self.mask_estimators)
|
|
|
|
if self.use_torch_checkpoint:
|
|
mask = torch.stack([checkpoint(fn, x, use_reentrant=False) for fn in self.mask_estimators], dim=1)
|
|
else:
|
|
mask = torch.stack([fn(x) for fn in self.mask_estimators], dim=1)
|
|
mask = rearrange(mask, "b n t (f c) -> b n f t c", c=2)
|
|
|
|
# modulate frequency representation
|
|
|
|
stft_repr = rearrange(stft_repr, "b f t c -> b 1 f t c")
|
|
|
|
# complex number multiplication
|
|
|
|
stft_repr = torch.view_as_complex(stft_repr)
|
|
mask = torch.view_as_complex(mask)
|
|
|
|
stft_repr = stft_repr * mask
|
|
|
|
# istft
|
|
|
|
stft_repr = rearrange(stft_repr, "b n (f s) t -> (b n s) f t", s=self.audio_channels)
|
|
|
|
# same as torch.stft() fix for MacOS MPS above
|
|
try:
|
|
recon_audio = torch.istft(
|
|
stft_repr, **self.stft_kwargs, window=stft_window, return_complex=False, length=raw_audio.shape[-1]
|
|
)
|
|
except:
|
|
recon_audio = torch.istft(
|
|
stft_repr.cpu() if x_is_mps else stft_repr,
|
|
**self.stft_kwargs,
|
|
window=stft_window.cpu() if x_is_mps else stft_window,
|
|
return_complex=False,
|
|
length=raw_audio.shape[-1],
|
|
).to(device)
|
|
|
|
recon_audio = rearrange(recon_audio, "(b n s) t -> b n s t", s=self.audio_channels, n=num_stems)
|
|
|
|
if num_stems == 1:
|
|
recon_audio = rearrange(recon_audio, "b 1 s t -> b s t")
|
|
|
|
# if a target is passed in, calculate loss for learning
|
|
|
|
if not exists(target):
|
|
return recon_audio
|
|
|
|
if self.num_stems > 1:
|
|
assert target.ndim == 4 and target.shape[1] == self.num_stems
|
|
|
|
if target.ndim == 2:
|
|
target = rearrange(target, "... t -> ... 1 t")
|
|
|
|
target = target[..., : recon_audio.shape[-1]] # protect against lost length on istft
|
|
|
|
loss = F.l1_loss(recon_audio, target)
|
|
|
|
multi_stft_resolution_loss = 0.0
|
|
|
|
for window_size in self.multi_stft_resolutions_window_sizes:
|
|
res_stft_kwargs = dict(
|
|
n_fft=max(window_size, self.multi_stft_n_fft), # not sure what n_fft is across multi resolution stft
|
|
win_length=window_size,
|
|
return_complex=True,
|
|
window=self.multi_stft_window_fn(window_size, device=device),
|
|
**self.multi_stft_kwargs,
|
|
)
|
|
|
|
recon_Y = torch.stft(rearrange(recon_audio, "... s t -> (... s) t"), **res_stft_kwargs)
|
|
target_Y = torch.stft(rearrange(target, "... s t -> (... s) t"), **res_stft_kwargs)
|
|
|
|
multi_stft_resolution_loss = multi_stft_resolution_loss + F.l1_loss(recon_Y, target_Y)
|
|
|
|
weighted_multi_resolution_loss = multi_stft_resolution_loss * self.multi_stft_resolution_loss_weight
|
|
|
|
total_loss = loss + weighted_multi_resolution_loss
|
|
|
|
if not return_loss_breakdown:
|
|
return total_loss
|
|
|
|
return total_loss, (loss, multi_stft_resolution_loss)
|