forked from mrq/tortoise-tts
371 lines
13 KiB
Python
371 lines
13 KiB
Python
import os
|
|
import functools
|
|
import math
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
import torchaudio
|
|
from tortoise.models.xtransformers import ContinuousTransformerWrapper, RelativePositionBias
|
|
|
|
|
|
def zero_module(module):
|
|
"""
|
|
Zero out the parameters of a module and return it.
|
|
"""
|
|
for p in module.parameters():
|
|
p.detach().zero_()
|
|
return module
|
|
|
|
|
|
class GroupNorm32(nn.GroupNorm):
|
|
def forward(self, x):
|
|
return super().forward(x.float()).type(x.dtype)
|
|
|
|
|
|
def normalization(channels):
|
|
"""
|
|
Make a standard normalization layer.
|
|
|
|
:param channels: number of input channels.
|
|
:return: an nn.Module for normalization.
|
|
"""
|
|
groups = 32
|
|
if channels <= 16:
|
|
groups = 8
|
|
elif channels <= 64:
|
|
groups = 16
|
|
while channels % groups != 0:
|
|
groups = int(groups / 2)
|
|
assert groups > 2
|
|
return GroupNorm32(groups, channels)
|
|
|
|
|
|
class QKVAttentionLegacy(nn.Module):
|
|
"""
|
|
A module which performs QKV attention. Matches legacy QKVAttention + input/output heads shaping
|
|
"""
|
|
|
|
def __init__(self, n_heads):
|
|
super().__init__()
|
|
self.n_heads = n_heads
|
|
|
|
def forward(self, qkv, mask=None, rel_pos=None):
|
|
"""
|
|
Apply QKV attention.
|
|
|
|
:param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
|
|
:return: an [N x (H * C) x T] tensor after attention.
|
|
"""
|
|
bs, width, length = qkv.shape
|
|
assert width % (3 * self.n_heads) == 0
|
|
ch = width // (3 * self.n_heads)
|
|
q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
|
|
scale = 1 / math.sqrt(math.sqrt(ch))
|
|
weight = torch.einsum(
|
|
"bct,bcs->bts", q * scale, k * scale
|
|
) # More stable with f16 than dividing afterwards
|
|
if rel_pos is not None:
|
|
weight = rel_pos(weight.reshape(bs, self.n_heads, weight.shape[-2], weight.shape[-1])).reshape(bs * self.n_heads, weight.shape[-2], weight.shape[-1])
|
|
weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
|
|
if mask is not None:
|
|
# The proper way to do this is to mask before the softmax using -inf, but that doesn't work properly on CPUs.
|
|
mask = mask.repeat(self.n_heads, 1).unsqueeze(1)
|
|
weight = weight * mask
|
|
a = torch.einsum("bts,bcs->bct", weight, v)
|
|
|
|
return a.reshape(bs, -1, length)
|
|
|
|
|
|
class AttentionBlock(nn.Module):
|
|
"""
|
|
An attention block that allows spatial positions to attend to each other.
|
|
|
|
Originally ported from here, but adapted to the N-d case.
|
|
https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
channels,
|
|
num_heads=1,
|
|
num_head_channels=-1,
|
|
do_checkpoint=True,
|
|
relative_pos_embeddings=False,
|
|
):
|
|
super().__init__()
|
|
self.channels = channels
|
|
self.do_checkpoint = do_checkpoint
|
|
if num_head_channels == -1:
|
|
self.num_heads = num_heads
|
|
else:
|
|
assert (
|
|
channels % num_head_channels == 0
|
|
), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
|
|
self.num_heads = channels // num_head_channels
|
|
self.norm = normalization(channels)
|
|
self.qkv = nn.Conv1d(channels, channels * 3, 1)
|
|
# split heads before split qkv
|
|
self.attention = QKVAttentionLegacy(self.num_heads)
|
|
|
|
self.proj_out = zero_module(nn.Conv1d(channels, channels, 1))
|
|
if relative_pos_embeddings:
|
|
self.relative_pos_embeddings = RelativePositionBias(scale=(channels // self.num_heads) ** .5, causal=False, heads=num_heads, num_buckets=32, max_distance=64)
|
|
else:
|
|
self.relative_pos_embeddings = None
|
|
|
|
def forward(self, x, mask=None):
|
|
b, c, *spatial = x.shape
|
|
x = x.reshape(b, c, -1)
|
|
qkv = self.qkv(self.norm(x))
|
|
h = self.attention(qkv, mask, self.relative_pos_embeddings)
|
|
h = self.proj_out(h)
|
|
return (x + h).reshape(b, c, *spatial)
|
|
|
|
|
|
class Upsample(nn.Module):
|
|
"""
|
|
An upsampling layer with an optional convolution.
|
|
|
|
:param channels: channels in the inputs and outputs.
|
|
:param use_conv: a bool determining if a convolution is applied.
|
|
"""
|
|
|
|
def __init__(self, channels, use_conv, out_channels=None, factor=4):
|
|
super().__init__()
|
|
self.channels = channels
|
|
self.out_channels = out_channels or channels
|
|
self.use_conv = use_conv
|
|
self.factor = factor
|
|
if use_conv:
|
|
ksize = 5
|
|
pad = 2
|
|
self.conv = nn.Conv1d(self.channels, self.out_channels, ksize, padding=pad)
|
|
|
|
def forward(self, x):
|
|
assert x.shape[1] == self.channels
|
|
x = F.interpolate(x, scale_factor=self.factor, mode="nearest")
|
|
if self.use_conv:
|
|
x = self.conv(x)
|
|
return x
|
|
|
|
|
|
class Downsample(nn.Module):
|
|
"""
|
|
A downsampling layer with an optional convolution.
|
|
|
|
:param channels: channels in the inputs and outputs.
|
|
:param use_conv: a bool determining if a convolution is applied.
|
|
"""
|
|
|
|
def __init__(self, channels, use_conv, out_channels=None, factor=4, ksize=5, pad=2):
|
|
super().__init__()
|
|
self.channels = channels
|
|
self.out_channels = out_channels or channels
|
|
self.use_conv = use_conv
|
|
|
|
stride = factor
|
|
if use_conv:
|
|
self.op = nn.Conv1d(
|
|
self.channels, self.out_channels, ksize, stride=stride, padding=pad
|
|
)
|
|
else:
|
|
assert self.channels == self.out_channels
|
|
self.op = nn.AvgPool1d(kernel_size=stride, stride=stride)
|
|
|
|
def forward(self, x):
|
|
assert x.shape[1] == self.channels
|
|
return self.op(x)
|
|
|
|
|
|
class ResBlock(nn.Module):
|
|
def __init__(
|
|
self,
|
|
channels,
|
|
dropout,
|
|
out_channels=None,
|
|
use_conv=False,
|
|
use_scale_shift_norm=False,
|
|
up=False,
|
|
down=False,
|
|
kernel_size=3,
|
|
):
|
|
super().__init__()
|
|
self.channels = channels
|
|
self.dropout = dropout
|
|
self.out_channels = out_channels or channels
|
|
self.use_conv = use_conv
|
|
self.use_scale_shift_norm = use_scale_shift_norm
|
|
padding = 1 if kernel_size == 3 else 2
|
|
|
|
self.in_layers = nn.Sequential(
|
|
normalization(channels),
|
|
nn.SiLU(),
|
|
nn.Conv1d(channels, self.out_channels, kernel_size, padding=padding),
|
|
)
|
|
|
|
self.updown = up or down
|
|
|
|
if up:
|
|
self.h_upd = Upsample(channels, False)
|
|
self.x_upd = Upsample(channels, False)
|
|
elif down:
|
|
self.h_upd = Downsample(channels, False)
|
|
self.x_upd = Downsample(channels, False)
|
|
else:
|
|
self.h_upd = self.x_upd = nn.Identity()
|
|
|
|
self.out_layers = nn.Sequential(
|
|
normalization(self.out_channels),
|
|
nn.SiLU(),
|
|
nn.Dropout(p=dropout),
|
|
zero_module(
|
|
nn.Conv1d(self.out_channels, self.out_channels, kernel_size, padding=padding)
|
|
),
|
|
)
|
|
|
|
if self.out_channels == channels:
|
|
self.skip_connection = nn.Identity()
|
|
elif use_conv:
|
|
self.skip_connection = nn.Conv1d(
|
|
channels, self.out_channels, kernel_size, padding=padding
|
|
)
|
|
else:
|
|
self.skip_connection = nn.Conv1d(channels, self.out_channels, 1)
|
|
|
|
def forward(self, x):
|
|
if self.updown:
|
|
in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
|
|
h = in_rest(x)
|
|
h = self.h_upd(h)
|
|
x = self.x_upd(x)
|
|
h = in_conv(h)
|
|
else:
|
|
h = self.in_layers(x)
|
|
h = self.out_layers(h)
|
|
return self.skip_connection(x) + h
|
|
|
|
|
|
class AudioMiniEncoder(nn.Module):
|
|
def __init__(self,
|
|
spec_dim,
|
|
embedding_dim,
|
|
base_channels=128,
|
|
depth=2,
|
|
resnet_blocks=2,
|
|
attn_blocks=4,
|
|
num_attn_heads=4,
|
|
dropout=0,
|
|
downsample_factor=2,
|
|
kernel_size=3):
|
|
super().__init__()
|
|
self.init = nn.Sequential(
|
|
nn.Conv1d(spec_dim, base_channels, 3, padding=1)
|
|
)
|
|
ch = base_channels
|
|
res = []
|
|
for l in range(depth):
|
|
for r in range(resnet_blocks):
|
|
res.append(ResBlock(ch, dropout, kernel_size=kernel_size))
|
|
res.append(Downsample(ch, use_conv=True, out_channels=ch*2, factor=downsample_factor))
|
|
ch *= 2
|
|
self.res = nn.Sequential(*res)
|
|
self.final = nn.Sequential(
|
|
normalization(ch),
|
|
nn.SiLU(),
|
|
nn.Conv1d(ch, embedding_dim, 1)
|
|
)
|
|
attn = []
|
|
for a in range(attn_blocks):
|
|
attn.append(AttentionBlock(embedding_dim, num_attn_heads,))
|
|
self.attn = nn.Sequential(*attn)
|
|
self.dim = embedding_dim
|
|
|
|
def forward(self, x):
|
|
h = self.init(x)
|
|
h = self.res(h)
|
|
h = self.final(h)
|
|
h = self.attn(h)
|
|
return h[:, :, 0]
|
|
|
|
|
|
DEFAULT_MEL_NORM_FILE = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../data/mel_norms.pth')
|
|
|
|
|
|
class TorchMelSpectrogram(nn.Module):
|
|
def __init__(self, filter_length=1024, hop_length=256, win_length=1024, n_mel_channels=80, mel_fmin=0, mel_fmax=8000,
|
|
sampling_rate=22050, normalize=False, mel_norm_file=DEFAULT_MEL_NORM_FILE):
|
|
super().__init__()
|
|
# These are the default tacotron values for the MEL spectrogram.
|
|
self.filter_length = filter_length
|
|
self.hop_length = hop_length
|
|
self.win_length = win_length
|
|
self.n_mel_channels = n_mel_channels
|
|
self.mel_fmin = mel_fmin
|
|
self.mel_fmax = mel_fmax
|
|
self.sampling_rate = sampling_rate
|
|
self.mel_stft = torchaudio.transforms.MelSpectrogram(n_fft=self.filter_length, hop_length=self.hop_length,
|
|
win_length=self.win_length, power=2, normalized=normalize,
|
|
sample_rate=self.sampling_rate, f_min=self.mel_fmin,
|
|
f_max=self.mel_fmax, n_mels=self.n_mel_channels,
|
|
norm="slaney")
|
|
self.mel_norm_file = mel_norm_file
|
|
if self.mel_norm_file is not None:
|
|
self.mel_norms = torch.load(self.mel_norm_file)
|
|
else:
|
|
self.mel_norms = None
|
|
|
|
def forward(self, inp):
|
|
if len(inp.shape) == 3: # Automatically squeeze out the channels dimension if it is present (assuming mono-audio)
|
|
inp = inp.squeeze(1)
|
|
assert len(inp.shape) == 2
|
|
self.mel_stft = self.mel_stft.to(inp.device)
|
|
mel = self.mel_stft(inp)
|
|
# Perform dynamic range compression
|
|
mel = torch.log(torch.clamp(mel, min=1e-5))
|
|
if self.mel_norms is not None:
|
|
self.mel_norms = self.mel_norms.to(mel.device)
|
|
mel = mel / self.mel_norms.unsqueeze(0).unsqueeze(-1)
|
|
return mel
|
|
|
|
|
|
class CheckpointedLayer(nn.Module):
|
|
"""
|
|
Wraps a module. When forward() is called, passes kwargs that require_grad through torch.checkpoint() and bypasses
|
|
checkpoint for all other args.
|
|
"""
|
|
def __init__(self, wrap):
|
|
super().__init__()
|
|
self.wrap = wrap
|
|
|
|
def forward(self, x, *args, **kwargs):
|
|
for k, v in kwargs.items():
|
|
assert not (isinstance(v, torch.Tensor) and v.requires_grad) # This would screw up checkpointing.
|
|
partial = functools.partial(self.wrap, **kwargs)
|
|
return partial(x, *args)
|
|
|
|
|
|
class CheckpointedXTransformerEncoder(nn.Module):
|
|
"""
|
|
Wraps a ContinuousTransformerWrapper and applies CheckpointedLayer to each layer and permutes from channels-mid
|
|
to channels-last that XTransformer expects.
|
|
"""
|
|
def __init__(self, needs_permute=True, exit_permute=True, checkpoint=True, **xtransformer_kwargs):
|
|
super().__init__()
|
|
self.transformer = ContinuousTransformerWrapper(**xtransformer_kwargs)
|
|
self.needs_permute = needs_permute
|
|
self.exit_permute = exit_permute
|
|
|
|
if not checkpoint:
|
|
return
|
|
for i in range(len(self.transformer.attn_layers.layers)):
|
|
n, b, r = self.transformer.attn_layers.layers[i]
|
|
self.transformer.attn_layers.layers[i] = nn.ModuleList([n, CheckpointedLayer(b), r])
|
|
|
|
def forward(self, x, **kwargs):
|
|
if self.needs_permute:
|
|
x = x.permute(0,2,1)
|
|
h = self.transformer(x, **kwargs)
|
|
if self.exit_permute:
|
|
h = h.permute(0,2,1)
|
|
return h |