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lib_v5/attend.py
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110
lib_v5/attend.py
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@ -0,0 +1,110 @@
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from functools import wraps
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from packaging import version
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from collections import namedtuple
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import torch
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from torch import nn, einsum
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import torch.nn.functional as F
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from einops import rearrange, reduce
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# constants
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FlashAttentionConfig = namedtuple('FlashAttentionConfig', ['enable_flash', 'enable_math', 'enable_mem_efficient'])
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# helpers
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def exists(val):
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return val is not None
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def once(fn):
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called = False
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@wraps(fn)
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def inner(x):
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nonlocal called
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if called:
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return
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called = True
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return fn(x)
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return inner
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print_once = once(print)
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# main class
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class Attend(nn.Module):
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def __init__(
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self,
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dropout = 0.,
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flash = False
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):
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super().__init__()
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self.dropout = dropout
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self.attn_dropout = nn.Dropout(dropout)
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self.flash = flash
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assert not (flash and version.parse(torch.__version__) < version.parse('2.0.0')), 'in order to use flash attention, you must be using pytorch 2.0 or above'
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# determine efficient attention configs for cuda and cpu
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self.cpu_config = FlashAttentionConfig(True, True, True)
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self.cuda_config = None
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if not torch.cuda.is_available() or not flash:
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return
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device_properties = torch.cuda.get_device_properties(torch.device('cuda'))
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if device_properties.major == 8 and device_properties.minor == 0:
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print_once('A100 GPU detected, using flash attention if input tensor is on cuda')
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self.cuda_config = FlashAttentionConfig(True, False, False)
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else:
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self.cuda_config = FlashAttentionConfig(False, True, True)
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def flash_attn(self, q, k, v):
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_, heads, q_len, _, k_len, is_cuda, device = *q.shape, k.shape[-2], q.is_cuda, q.device
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# Check if there is a compatible device for flash attention
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config = self.cuda_config if is_cuda else self.cpu_config
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# pytorch 2.0 flash attn: q, k, v, mask, dropout, softmax_scale
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with torch.backends.cuda.sdp_kernel(**config._asdict()):
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out = F.scaled_dot_product_attention(
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q, k, v,
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dropout_p = self.dropout if self.training else 0.
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)
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return out
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def forward(self, q, k, v):
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"""
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einstein notation
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b - batch
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h - heads
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n, i, j - sequence length (base sequence length, source, target)
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d - feature dimension
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"""
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q_len, k_len, device = q.shape[-2], k.shape[-2], q.device
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scale = q.shape[-1] ** -0.5
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if self.flash:
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return self.flash_attn(q, k, v)
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# similarity
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sim = einsum(f"b h i d, b h j d -> b h i j", q, k) * scale
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# attention
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attn = sim.softmax(dim=-1)
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attn = self.attn_dropout(attn)
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# aggregate values
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out = einsum(f"b h i j, b h j d -> b h i d", attn, v)
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return out
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607
lib_v5/bs_roformer.py
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607
lib_v5/bs_roformer.py
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@ -0,0 +1,607 @@
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from functools import partial
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import torch
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from torch import nn, einsum, Tensor
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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 .attend import Attend
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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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x = x.to(self.gamma.device)
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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__(
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self,
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dim,
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mult=4,
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dropout=0.
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):
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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__(
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self,
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dim,
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heads=8,
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dim_head=64,
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dropout=0.,
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rotary_embed=None,
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flash=True
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):
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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(
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nn.Linear(dim_inner, dim, bias=False),
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nn.Dropout(dropout)
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)
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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__(
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self,
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*,
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dim,
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dim_head=32,
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heads=8,
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scale=8,
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flash=False,
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dropout=0.
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):
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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),
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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(
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scale=scale,
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dropout=dropout,
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flash=flash
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)
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self.to_out = nn.Sequential(
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Rearrange('b h d n -> b n (h d)'),
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nn.Linear(dim_inner, dim, bias=False)
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)
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def forward(
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self,
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x
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):
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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.,
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ff_dropout=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(dim=dim, dim_head=dim_head, heads=heads, dropout=attn_dropout,
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rotary_embed=rotary_embed, flash=flash_attn)
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self.layers.append(ModuleList([
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attn,
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FeedForward(dim=dim, mult=ff_mult, dropout=ff_dropout)
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]))
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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__(
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self,
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dim,
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dim_inputs: Tuple[int, ...]
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):
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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(
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RMSNorm(dim_in),
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nn.Linear(dim_in, dim)
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)
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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(
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dim_in,
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dim_out,
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dim_hidden=None,
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depth=1,
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activation=nn.Tanh
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):
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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__(
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self,
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dim,
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dim_inputs: Tuple[int, ...],
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depth,
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mlp_expansion_factor=4
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):
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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(
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MLP(dim, dim_in * 2, dim_hidden=dim_hidden, depth=depth),
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nn.GLU(dim=-1)
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)
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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, 2, 2, 2, 2, 2, 2, 2, 2, 2,
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2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
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2, 2, 2, 2,
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4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
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12, 12, 12, 12, 12, 12, 12, 12,
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24, 24, 24, 24, 24, 24, 24, 24,
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48, 48, 48, 48, 48, 48, 48, 48,
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128, 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.,
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ff_dropout=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.,
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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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super().__init__()
|
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|
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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.layers = ModuleList([])
|
||||
|
||||
transformer_kwargs = dict(
|
||||
dim=dim,
|
||||
heads=heads,
|
||||
dim_head=dim_head,
|
||||
attn_dropout=attn_dropout,
|
||||
ff_dropout=ff_dropout,
|
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flash_attn=flash_attn,
|
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norm_output=False
|
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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))
|
||||
tran_modules.append(
|
||||
Transformer(depth=time_transformer_depth, rotary_embed=time_rotary_embed, **transformer_kwargs)
|
||||
)
|
||||
tran_modules.append(
|
||||
Transformer(depth=freq_transformer_depth, rotary_embed=freq_rotary_embed, **transformer_kwargs)
|
||||
)
|
||||
self.layers.append(nn.ModuleList(tran_modules))
|
||||
|
||||
self.final_norm = RMSNorm(dim)
|
||||
|
||||
self.stft_kwargs = dict(
|
||||
n_fft=stft_n_fft,
|
||||
hop_length=stft_hop_length,
|
||||
win_length=stft_win_length,
|
||||
normalized=stft_normalized
|
||||
)
|
||||
|
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self.stft_window_fn = partial(default(stft_window_fn, torch.hann_window), stft_win_length)
|
||||
|
||||
freqs = torch.stft(torch.randn(1, 4096), **self.stft_kwargs, return_complex=True).shape[1]
|
||||
|
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assert len(freqs_per_bands) > 1
|
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assert sum(
|
||||
freqs_per_bands) == freqs, f'the number of freqs in the bands must equal {freqs} based on the STFT settings, but got {sum(freqs_per_bands)}'
|
||||
|
||||
freqs_per_bands_with_complex = tuple(2 * f * self.audio_channels for f in freqs_per_bands)
|
||||
|
||||
self.band_split = BandSplit(
|
||||
dim=dim,
|
||||
dim_inputs=freqs_per_bands_with_complex
|
||||
)
|
||||
|
||||
self.mask_estimators = nn.ModuleList([])
|
||||
|
||||
for _ in range(num_stems):
|
||||
mask_estimator = MaskEstimator(
|
||||
dim=dim,
|
||||
dim_inputs=freqs_per_bands_with_complex,
|
||||
depth=mask_estimator_depth
|
||||
)
|
||||
|
||||
self.mask_estimators.append(mask_estimator)
|
||||
|
||||
# for the multi-resolution stft loss
|
||||
|
||||
self.multi_stft_resolution_loss_weight = multi_stft_resolution_loss_weight
|
||||
self.multi_stft_resolutions_window_sizes = multi_stft_resolutions_window_sizes
|
||||
self.multi_stft_n_fft = stft_n_fft
|
||||
self.multi_stft_window_fn = multi_stft_window_fn
|
||||
|
||||
self.multi_stft_kwargs = dict(
|
||||
hop_length=multi_stft_hop_size,
|
||||
normalized=multi_stft_normalized
|
||||
)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
raw_audio,
|
||||
target=None,
|
||||
return_loss_breakdown=False
|
||||
):
|
||||
"""
|
||||
einops
|
||||
|
||||
b - batch
|
||||
f - freq
|
||||
t - time
|
||||
s - audio channel (1 for mono, 2 for stereo)
|
||||
n - number of 'stems'
|
||||
c - complex (2)
|
||||
d - feature dimension
|
||||
"""
|
||||
|
||||
original_device = raw_audio.device
|
||||
|
||||
x_is_mps = True if original_device.type == 'mps' else False
|
||||
|
||||
if x_is_mps:
|
||||
raw_audio = raw_audio.cpu()
|
||||
|
||||
device = raw_audio.device
|
||||
|
||||
if raw_audio.ndim == 2:
|
||||
raw_audio = rearrange(raw_audio, 'b t -> b 1 t')
|
||||
|
||||
channels = raw_audio.shape[1]
|
||||
assert (not self.stereo and channels == 1) or (
|
||||
self.stereo and channels == 2), '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)'
|
||||
|
||||
# to stft
|
||||
|
||||
raw_audio, batch_audio_channel_packed_shape = pack_one(raw_audio, '* t')
|
||||
|
||||
stft_window = self.stft_window_fn(device=device)
|
||||
|
||||
stft_repr = torch.stft(raw_audio, **self.stft_kwargs, window=stft_window, return_complex=True)
|
||||
stft_repr = torch.view_as_real(stft_repr)
|
||||
|
||||
stft_repr = unpack_one(stft_repr, batch_audio_channel_packed_shape, '* f t c')
|
||||
stft_repr = rearrange(stft_repr,
|
||||
'b s f t c -> b (f s) t c') # merge stereo / mono into the frequency, with frequency leading dimension, for band splitting
|
||||
|
||||
x = rearrange(stft_repr, 'b f t c -> b t (f c)')
|
||||
|
||||
x = self.band_split(x)
|
||||
|
||||
# axial / hierarchical attention
|
||||
|
||||
for transformer_block in self.layers:
|
||||
|
||||
if len(transformer_block) == 3:
|
||||
linear_transformer, time_transformer, freq_transformer = transformer_block
|
||||
|
||||
x, ft_ps = pack([x], 'b * d')
|
||||
x = linear_transformer(x)
|
||||
x, = unpack(x, ft_ps, 'b * d')
|
||||
else:
|
||||
time_transformer, freq_transformer = transformer_block
|
||||
|
||||
x = rearrange(x, 'b t f d -> b f t d')
|
||||
x, ps = pack([x], '* t d')
|
||||
|
||||
x = time_transformer(x)
|
||||
|
||||
x, = unpack(x, ps, '* t d')
|
||||
x = rearrange(x, 'b f t d -> b t f d')
|
||||
x, ps = pack([x], '* f d')
|
||||
|
||||
x = freq_transformer(x)
|
||||
|
||||
x, = unpack(x, ps, '* f d')
|
||||
|
||||
x = self.final_norm(x)
|
||||
|
||||
num_stems = len(self.mask_estimators)
|
||||
|
||||
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)
|
||||
|
||||
if x_is_mps:
|
||||
mask = mask.to('cpu')
|
||||
|
||||
# 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)
|
||||
|
||||
recon_audio = torch.istft(stft_repr, **self.stft_kwargs, window=stft_window, return_complex=False)
|
||||
|
||||
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]]
|
||||
|
||||
loss = F.l1_loss(recon_audio, target)
|
||||
|
||||
multi_stft_resolution_loss = 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),
|
||||
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:
|
||||
# Move the result back to the original device if it was moved to CPU for MPS compatibility
|
||||
if x_is_mps:
|
||||
total_loss = total_loss.to(original_device)
|
||||
return total_loss
|
||||
|
||||
# For detailed loss breakdown, ensure all components are moved back to the original device for MPS
|
||||
if x_is_mps:
|
||||
loss = loss.to(original_device)
|
||||
multi_stft_resolution_loss = multi_stft_resolution_loss.to(original_device)
|
||||
weighted_multi_resolution_loss = weighted_multi_resolution_loss.to(original_device)
|
||||
|
||||
return total_loss, (loss, multi_stft_resolution_loss)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# if not return_loss_breakdown:
|
||||
# return total_loss
|
||||
|
||||
# return total_loss, (loss, multi_stft_resolution_loss)
|
||||
528
lib_v5/mel_band_roformer.py
Normal file
528
lib_v5/mel_band_roformer.py
Normal file
@ -0,0 +1,528 @@
|
||||
from functools import partial
|
||||
|
||||
import torch
|
||||
from torch import nn, einsum, Tensor
|
||||
from torch.nn import Module, ModuleList
|
||||
import torch.nn.functional as F
|
||||
|
||||
from .attend import Attend
|
||||
|
||||
from beartype.typing import Tuple, Optional, List, Callable
|
||||
from beartype import beartype
|
||||
|
||||
from rotary_embedding_torch import RotaryEmbedding
|
||||
|
||||
from einops import rearrange, pack, unpack, reduce, repeat
|
||||
|
||||
from librosa import filters
|
||||
|
||||
def exists(val):
|
||||
return val is not None
|
||||
|
||||
|
||||
def default(v, d):
|
||||
return v if exists(v) else d
|
||||
|
||||
|
||||
def pack_one(t, pattern):
|
||||
return pack([t], pattern)
|
||||
|
||||
|
||||
def unpack_one(t, ps, pattern):
|
||||
return unpack(t, ps, pattern)[0]
|
||||
|
||||
|
||||
def pad_at_dim(t, pad, dim=-1, value=0.):
|
||||
dims_from_right = (- dim - 1) if dim < 0 else (t.ndim - dim - 1)
|
||||
zeros = ((0, 0) * dims_from_right)
|
||||
return F.pad(t, (*zeros, *pad), value=value)
|
||||
|
||||
class RMSNorm(Module):
|
||||
def __init__(self, dim):
|
||||
super().__init__()
|
||||
self.scale = dim ** 0.5
|
||||
self.gamma = nn.Parameter(torch.ones(dim))
|
||||
|
||||
def forward(self, x):
|
||||
x = x.to(self.gamma.device)
|
||||
return F.normalize(x, dim=-1) * self.scale * self.gamma
|
||||
|
||||
class FeedForward(Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim,
|
||||
mult=4,
|
||||
dropout=0.
|
||||
):
|
||||
super().__init__()
|
||||
dim_inner = int(dim * mult)
|
||||
self.net = nn.Sequential(
|
||||
RMSNorm(dim),
|
||||
nn.Linear(dim, dim_inner),
|
||||
nn.GELU(),
|
||||
nn.Dropout(dropout),
|
||||
nn.Linear(dim_inner, dim),
|
||||
nn.Dropout(dropout)
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
return self.net(x)
|
||||
|
||||
|
||||
class Attention(Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim,
|
||||
heads=8,
|
||||
dim_head=64,
|
||||
dropout=0.,
|
||||
rotary_embed=None,
|
||||
flash=True
|
||||
):
|
||||
super().__init__()
|
||||
self.heads = heads
|
||||
self.scale = dim_head ** -0.5
|
||||
dim_inner = heads * dim_head
|
||||
|
||||
self.rotary_embed = rotary_embed
|
||||
|
||||
self.attend = Attend(flash=flash, dropout=dropout)
|
||||
|
||||
self.norm = RMSNorm(dim)
|
||||
self.to_qkv = nn.Linear(dim, dim_inner * 3, bias=False)
|
||||
|
||||
self.to_gates = nn.Linear(dim, heads)
|
||||
|
||||
self.to_out = nn.Sequential(
|
||||
nn.Linear(dim_inner, dim, bias=False),
|
||||
nn.Dropout(dropout)
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.norm(x)
|
||||
|
||||
q, k, v = rearrange(self.to_qkv(x), 'b n (qkv h d) -> qkv b h n d', qkv=3, h=self.heads)
|
||||
|
||||
if exists(self.rotary_embed):
|
||||
q = self.rotary_embed.rotate_queries_or_keys(q)
|
||||
k = self.rotary_embed.rotate_queries_or_keys(k)
|
||||
|
||||
out = self.attend(q, k, v)
|
||||
|
||||
gates = self.to_gates(x)
|
||||
out = out * rearrange(gates, 'b n h -> b h n 1').sigmoid()
|
||||
|
||||
out = rearrange(out, 'b h n d -> b n (h d)')
|
||||
return self.to_out(out)
|
||||
|
||||
|
||||
class Transformer(Module):
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
dim,
|
||||
depth,
|
||||
dim_head=64,
|
||||
heads=8,
|
||||
attn_dropout=0.,
|
||||
ff_dropout=0.,
|
||||
ff_mult=4,
|
||||
norm_output=True,
|
||||
rotary_embed=None,
|
||||
flash_attn=True
|
||||
):
|
||||
super().__init__()
|
||||
self.layers = ModuleList([])
|
||||
|
||||
for _ in range(depth):
|
||||
self.layers.append(ModuleList([
|
||||
Attention(dim=dim, dim_head=dim_head, heads=heads, dropout=attn_dropout, rotary_embed=rotary_embed,
|
||||
flash=flash_attn),
|
||||
FeedForward(dim=dim, mult=ff_mult, dropout=ff_dropout)
|
||||
]))
|
||||
|
||||
self.norm = RMSNorm(dim) if norm_output else nn.Identity()
|
||||
|
||||
def forward(self, x):
|
||||
|
||||
for attn, ff in self.layers:
|
||||
x = attn(x) + x
|
||||
x = ff(x) + x
|
||||
|
||||
return self.norm(x)
|
||||
|
||||
class BandSplit(Module):
|
||||
@beartype
|
||||
def __init__(
|
||||
self,
|
||||
dim,
|
||||
dim_inputs: Tuple[int, ...]
|
||||
):
|
||||
super().__init__()
|
||||
self.dim_inputs = dim_inputs
|
||||
self.to_features = ModuleList([])
|
||||
|
||||
for dim_in in dim_inputs:
|
||||
net = nn.Sequential(
|
||||
RMSNorm(dim_in),
|
||||
nn.Linear(dim_in, dim)
|
||||
)
|
||||
|
||||
self.to_features.append(net)
|
||||
|
||||
def forward(self, x):
|
||||
x = x.split(self.dim_inputs, dim=-1)
|
||||
|
||||
outs = []
|
||||
for split_input, to_feature in zip(x, self.to_features):
|
||||
split_output = to_feature(split_input)
|
||||
outs.append(split_output)
|
||||
|
||||
return torch.stack(outs, dim=-2)
|
||||
|
||||
|
||||
def MLP(
|
||||
dim_in,
|
||||
dim_out,
|
||||
dim_hidden=None,
|
||||
depth=1,
|
||||
activation=nn.Tanh
|
||||
):
|
||||
dim_hidden = default(dim_hidden, dim_in)
|
||||
|
||||
net = []
|
||||
dims = (dim_in, *((dim_hidden,) * depth), dim_out)
|
||||
|
||||
for ind, (layer_dim_in, layer_dim_out) in enumerate(zip(dims[:-1], dims[1:])):
|
||||
is_last = ind == (len(dims) - 2)
|
||||
|
||||
net.append(nn.Linear(layer_dim_in, layer_dim_out))
|
||||
|
||||
if is_last:
|
||||
continue
|
||||
|
||||
net.append(activation())
|
||||
|
||||
return nn.Sequential(*net)
|
||||
|
||||
|
||||
class MaskEstimator(Module):
|
||||
@beartype
|
||||
def __init__(
|
||||
self,
|
||||
dim,
|
||||
dim_inputs: Tuple[int, ...],
|
||||
depth,
|
||||
mlp_expansion_factor=4
|
||||
):
|
||||
super().__init__()
|
||||
self.dim_inputs = dim_inputs
|
||||
self.to_freqs = ModuleList([])
|
||||
dim_hidden = dim * mlp_expansion_factor
|
||||
|
||||
for dim_in in dim_inputs:
|
||||
net = []
|
||||
|
||||
mlp = nn.Sequential(
|
||||
MLP(dim, dim_in * 2, dim_hidden=dim_hidden, depth=depth),
|
||||
nn.GLU(dim=-1)
|
||||
)
|
||||
|
||||
self.to_freqs.append(mlp)
|
||||
|
||||
def forward(self, x):
|
||||
x = x.unbind(dim=-2)
|
||||
|
||||
outs = []
|
||||
|
||||
for band_features, mlp in zip(x, self.to_freqs):
|
||||
freq_out = mlp(band_features)
|
||||
outs.append(freq_out)
|
||||
|
||||
return torch.cat(outs, dim=-1)
|
||||
|
||||
class MelBandRoformer(Module):
|
||||
|
||||
@beartype
|
||||
def __init__(
|
||||
self,
|
||||
dim,
|
||||
*,
|
||||
depth,
|
||||
stereo=False,
|
||||
num_stems=1,
|
||||
time_transformer_depth=2,
|
||||
freq_transformer_depth=2,
|
||||
num_bands=60,
|
||||
dim_head=64,
|
||||
heads=8,
|
||||
attn_dropout=0.1,
|
||||
ff_dropout=0.1,
|
||||
flash_attn=True,
|
||||
dim_freqs_in=1025,
|
||||
sample_rate=44100,
|
||||
stft_n_fft=2048,
|
||||
stft_hop_length=512,
|
||||
stft_win_length=2048,
|
||||
stft_normalized=False,
|
||||
stft_window_fn: Optional[Callable] = None,
|
||||
mask_estimator_depth=1,
|
||||
multi_stft_resolution_loss_weight=1.,
|
||||
multi_stft_resolutions_window_sizes: Tuple[int, ...] = (4096, 2048, 1024, 512, 256),
|
||||
multi_stft_hop_size=147,
|
||||
multi_stft_normalized=False,
|
||||
multi_stft_window_fn: Callable = torch.hann_window,
|
||||
match_input_audio_length=False,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.stereo = stereo
|
||||
self.audio_channels = 2 if stereo else 1
|
||||
self.num_stems = num_stems
|
||||
|
||||
self.layers = ModuleList([])
|
||||
|
||||
transformer_kwargs = dict(
|
||||
dim=dim,
|
||||
heads=heads,
|
||||
dim_head=dim_head,
|
||||
attn_dropout=attn_dropout,
|
||||
ff_dropout=ff_dropout,
|
||||
flash_attn=flash_attn
|
||||
)
|
||||
|
||||
time_rotary_embed = RotaryEmbedding(dim=dim_head)
|
||||
freq_rotary_embed = RotaryEmbedding(dim=dim_head)
|
||||
|
||||
for _ in range(depth):
|
||||
self.layers.append(nn.ModuleList([
|
||||
Transformer(depth=time_transformer_depth, rotary_embed=time_rotary_embed, **transformer_kwargs),
|
||||
Transformer(depth=freq_transformer_depth, rotary_embed=freq_rotary_embed, **transformer_kwargs)
|
||||
]))
|
||||
|
||||
self.stft_window_fn = partial(default(stft_window_fn, torch.hann_window), stft_win_length)
|
||||
|
||||
self.stft_kwargs = dict(
|
||||
n_fft=stft_n_fft,
|
||||
hop_length=stft_hop_length,
|
||||
win_length=stft_win_length,
|
||||
normalized=stft_normalized
|
||||
)
|
||||
|
||||
freqs = torch.stft(torch.randn(1, 4096), **self.stft_kwargs, return_complex=True).shape[1]
|
||||
|
||||
mel_filter_bank_numpy = filters.mel(sr=sample_rate, n_fft=stft_n_fft, n_mels=num_bands)
|
||||
|
||||
mel_filter_bank = torch.from_numpy(mel_filter_bank_numpy)
|
||||
|
||||
mel_filter_bank[0][0] = 1.
|
||||
|
||||
mel_filter_bank[-1, -1] = 1.
|
||||
|
||||
freqs_per_band = mel_filter_bank > 0
|
||||
assert freqs_per_band.any(dim=0).all(), 'all frequencies need to be covered by all bands for now'
|
||||
|
||||
repeated_freq_indices = repeat(torch.arange(freqs), 'f -> b f', b=num_bands)
|
||||
freq_indices = repeated_freq_indices[freqs_per_band]
|
||||
|
||||
if stereo:
|
||||
freq_indices = repeat(freq_indices, 'f -> f s', s=2)
|
||||
freq_indices = freq_indices * 2 + torch.arange(2)
|
||||
freq_indices = rearrange(freq_indices, 'f s -> (f s)')
|
||||
|
||||
self.register_buffer('freq_indices', freq_indices, persistent=False)
|
||||
self.register_buffer('freqs_per_band', freqs_per_band, persistent=False)
|
||||
|
||||
num_freqs_per_band = reduce(freqs_per_band, 'b f -> b', 'sum')
|
||||
num_bands_per_freq = reduce(freqs_per_band, 'b f -> f', 'sum')
|
||||
|
||||
self.register_buffer('num_freqs_per_band', num_freqs_per_band, persistent=False)
|
||||
self.register_buffer('num_bands_per_freq', num_bands_per_freq, persistent=False)
|
||||
|
||||
freqs_per_bands_with_complex = tuple(2 * f * self.audio_channels for f in num_freqs_per_band.tolist())
|
||||
|
||||
self.band_split = BandSplit(
|
||||
dim=dim,
|
||||
dim_inputs=freqs_per_bands_with_complex
|
||||
)
|
||||
|
||||
self.mask_estimators = nn.ModuleList([])
|
||||
|
||||
for _ in range(num_stems):
|
||||
mask_estimator = MaskEstimator(
|
||||
dim=dim,
|
||||
dim_inputs=freqs_per_bands_with_complex,
|
||||
depth=mask_estimator_depth
|
||||
)
|
||||
|
||||
self.mask_estimators.append(mask_estimator)
|
||||
|
||||
self.multi_stft_resolution_loss_weight = multi_stft_resolution_loss_weight
|
||||
self.multi_stft_resolutions_window_sizes = multi_stft_resolutions_window_sizes
|
||||
self.multi_stft_n_fft = stft_n_fft
|
||||
self.multi_stft_window_fn = multi_stft_window_fn
|
||||
|
||||
self.multi_stft_kwargs = dict(
|
||||
hop_length=multi_stft_hop_size,
|
||||
normalized=multi_stft_normalized
|
||||
)
|
||||
|
||||
self.match_input_audio_length = match_input_audio_length
|
||||
|
||||
def forward(
|
||||
self,
|
||||
raw_audio,
|
||||
target=None,
|
||||
return_loss_breakdown=False
|
||||
):
|
||||
"""
|
||||
einops
|
||||
|
||||
b - batch
|
||||
f - freq
|
||||
t - time
|
||||
s - audio channel (1 for mono, 2 for stereo)
|
||||
n - number of 'stems'
|
||||
c - complex (2)
|
||||
d - feature dimension
|
||||
"""
|
||||
|
||||
original_device = raw_audio.device
|
||||
x_is_mps = True if original_device.type == 'mps' else False
|
||||
|
||||
if x_is_mps:
|
||||
raw_audio = raw_audio.cpu()
|
||||
|
||||
device = raw_audio.device
|
||||
|
||||
if raw_audio.ndim == 2:
|
||||
raw_audio = rearrange(raw_audio, 'b t -> b 1 t')
|
||||
|
||||
batch, channels, raw_audio_length = raw_audio.shape
|
||||
|
||||
istft_length = raw_audio_length if self.match_input_audio_length else None
|
||||
|
||||
assert (not self.stereo and channels == 1) or (
|
||||
self.stereo and channels == 2), '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)'
|
||||
|
||||
raw_audio, batch_audio_channel_packed_shape = pack_one(raw_audio, '* t')
|
||||
|
||||
stft_window = self.stft_window_fn(device=device)
|
||||
|
||||
stft_repr = torch.stft(raw_audio, **self.stft_kwargs, window=stft_window, return_complex=True)
|
||||
stft_repr = torch.view_as_real(stft_repr)
|
||||
|
||||
stft_repr = unpack_one(stft_repr, batch_audio_channel_packed_shape, '* f t c')
|
||||
stft_repr = rearrange(stft_repr,
|
||||
'b s f t c -> b (f s) t c') # merge stereo / mono into the frequency, with frequency leading dimension, for band splitting
|
||||
|
||||
batch_arange = torch.arange(batch, device=device)[..., None]
|
||||
|
||||
x = stft_repr[batch_arange, self.freq_indices.cpu()] if x_is_mps else stft_repr[batch_arange, self.freq_indices]
|
||||
|
||||
x = rearrange(x, 'b f t c -> b t (f c)')
|
||||
|
||||
x = self.band_split(x)
|
||||
|
||||
for time_transformer, freq_transformer in self.layers:
|
||||
x = rearrange(x, 'b t f d -> b f t d')
|
||||
x, ps = pack([x], '* t d')
|
||||
|
||||
x = time_transformer(x)
|
||||
|
||||
x, = unpack(x, ps, '* t d')
|
||||
x = rearrange(x, 'b f t d -> b t f d')
|
||||
x, ps = pack([x], '* f d')
|
||||
|
||||
x = freq_transformer(x)
|
||||
|
||||
x, = unpack(x, ps, '* f d')
|
||||
|
||||
num_stems = len(self.mask_estimators)
|
||||
|
||||
masks = torch.stack([fn(x) for fn in self.mask_estimators], dim=1)
|
||||
masks = rearrange(masks, 'b n t (f c) -> b n f t c', c=2)
|
||||
if x_is_mps:
|
||||
masks = masks.cpu()
|
||||
|
||||
stft_repr = rearrange(stft_repr, 'b f t c -> b 1 f t c')
|
||||
|
||||
stft_repr = torch.view_as_complex(stft_repr)
|
||||
masks = torch.view_as_complex(masks)
|
||||
|
||||
masks = masks.type(stft_repr.dtype)
|
||||
|
||||
if x_is_mps:
|
||||
scatter_indices = repeat(self.freq_indices.cpu(), 'f -> b n f t', b=batch, n=num_stems, t=stft_repr.shape[-1])
|
||||
else:
|
||||
scatter_indices = repeat(self.freq_indices, 'f -> b n f t', b=batch, n=num_stems, t=stft_repr.shape[-1])
|
||||
stft_repr_expanded_stems = repeat(stft_repr, 'b 1 ... -> b n ...', n=num_stems)
|
||||
masks_summed = torch.zeros_like(stft_repr_expanded_stems).scatter_add_(2, scatter_indices, masks)
|
||||
|
||||
denom = repeat(self.num_bands_per_freq, 'f -> (f r) 1', r=channels)
|
||||
if x_is_mps:
|
||||
denom = denom.cpu()
|
||||
|
||||
masks_averaged = masks_summed / denom.clamp(min=1e-8)
|
||||
|
||||
stft_repr = stft_repr * masks_averaged
|
||||
|
||||
stft_repr = rearrange(stft_repr, 'b n (f s) t -> (b n s) f t', s=self.audio_channels)
|
||||
|
||||
recon_audio = torch.istft(stft_repr, **self.stft_kwargs, window=stft_window, return_complex=False,
|
||||
length=istft_length)
|
||||
|
||||
recon_audio = rearrange(recon_audio, '(b n s) t -> b n s t', b=batch, s=self.audio_channels, n=num_stems)
|
||||
|
||||
if num_stems == 1:
|
||||
recon_audio = rearrange(recon_audio, 'b 1 s t -> b s t')
|
||||
|
||||
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]]
|
||||
|
||||
loss = F.l1_loss(recon_audio, target)
|
||||
|
||||
multi_stft_resolution_loss = 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),
|
||||
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
|
||||
|
||||
|
||||
# Move the total loss back to the original device if necessary
|
||||
if x_is_mps:
|
||||
total_loss = total_loss.to(original_device)
|
||||
|
||||
if not return_loss_breakdown:
|
||||
return total_loss
|
||||
|
||||
# If detailed loss breakdown is requested, ensure all components are on the original device
|
||||
return total_loss, (loss.to(original_device) if x_is_mps else loss,
|
||||
multi_stft_resolution_loss.to(original_device) if x_is_mps else multi_stft_resolution_loss)
|
||||
|
||||
# if not return_loss_breakdown:
|
||||
# return total_loss
|
||||
|
||||
# return total_loss, (loss, multi_stft_resolution_loss)
|
||||
@ -541,7 +541,9 @@ def ensembling(a, inputs, is_wavs=False):
|
||||
if MIN_SPEC == a:
|
||||
input = np.where(np.abs(inputs[i]) <= np.abs(input), inputs[i], input)
|
||||
if MAX_SPEC == a:
|
||||
input = np.where(np.abs(inputs[i]) >= np.abs(input), inputs[i], input)
|
||||
#input = np.array(np.where(np.greater_equal(np.abs(inputs[i]), np.abs(input)), inputs[i], input), dtype=object)
|
||||
input = np.where(np.abs(inputs[i]) >= np.abs(input), inputs[i], input)
|
||||
#max_spec = np.array([np.where(np.greater_equal(np.abs(inputs[i]), np.abs(input)), s, specs[0]) for s in specs[1:]], dtype=object)[-1]
|
||||
|
||||
#linear_ensemble
|
||||
#input = ensemble_wav(inputs, split_size=1)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user