File size: 7,031 Bytes
2ccf6b5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 |
import datetime as dt
import math
import random
import torch
import torch.nn.functional as F
from pflow.models.baselightningmodule import BaseLightningClass
from pflow.models.components.flow_matching import CFM
from pflow.models.components.speech_prompt_encoder import TextEncoder
from pflow.utils.model import (
denormalize,
duration_loss,
fix_len_compatibility,
generate_path,
sequence_mask,
)
from pflow.models.components import commons
from pflow.models.components.aligner import Aligner, ForwardSumLoss, BinLoss
class pflowTTS(BaseLightningClass): #
def __init__(
self,
n_vocab,
n_feats,
encoder,
decoder,
cfm,
data_statistics,
prompt_size=264,
optimizer=None,
scheduler=None,
**kwargs,
):
super().__init__()
self.save_hyperparameters(logger=False)
self.n_vocab = n_vocab
self.n_feats = n_feats
self.prompt_size = prompt_size
speech_in_channels = n_feats
self.encoder = TextEncoder(
encoder.encoder_type,
encoder.encoder_params,
encoder.duration_predictor_params,
n_vocab,
speech_in_channels,
)
# self.aligner = Aligner(
# dim_in=encoder.encoder_params.n_feats,
# dim_hidden=encoder.encoder_params.n_feats,
# attn_channels=encoder.encoder_params.n_feats,
# )
# self.aligner_loss = ForwardSumLoss()
# self.bin_loss = BinLoss()
# self.aligner_bin_loss_weight = 0.0
self.decoder = CFM(
in_channels=encoder.encoder_params.n_feats,
out_channel=encoder.encoder_params.n_feats,
cfm_params=cfm,
decoder_params=decoder,
)
self.proj_prompt = torch.nn.Conv1d(encoder.encoder_params.n_channels, self.n_feats, 1)
self.update_data_statistics(data_statistics)
@torch.inference_mode()
def synthesise(self, x, x_lengths, prompt, n_timesteps, temperature=1.0, length_scale=1.0, guidance_scale=0.0):
# For RTF computation
t = dt.datetime.now()
assert prompt is not None, "Prompt must be provided for synthesis"
# Get encoder_outputs `mu_x` and log-scaled token durations `logw`
mu_x, logw, x_mask = self.encoder(x, x_lengths, prompt)
w = torch.exp(logw) * x_mask
w_ceil = torch.ceil(w) * length_scale
y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
y_max_length = y_lengths.max()
y_max_length_ = fix_len_compatibility(y_max_length)
# Using obtained durations `w` construct alignment map `attn`
y_mask = sequence_mask(y_lengths, y_max_length_).unsqueeze(1).to(x_mask.dtype)
attn_mask = x_mask.unsqueeze(-1) * y_mask.unsqueeze(2)
attn = generate_path(w_ceil.squeeze(1), attn_mask.squeeze(1)).unsqueeze(1)
# Align encoded text and get mu_y
mu_y = torch.matmul(attn.squeeze(1).transpose(1, 2), mu_x.transpose(1, 2))
mu_y = mu_y.transpose(1, 2)
encoder_outputs = mu_y[:, :, :y_max_length]
# Generate sample tracing the probability flow
decoder_outputs = self.decoder(mu_y, y_mask, n_timesteps, temperature, guidance_scale=guidance_scale)
decoder_outputs = decoder_outputs[:, :, :y_max_length]
t = (dt.datetime.now() - t).total_seconds()
rtf = t * 22050 / (decoder_outputs.shape[-1] * 256)
return {
"encoder_outputs": encoder_outputs,
"decoder_outputs": decoder_outputs,
"attn": attn[:, :, :y_max_length],
"mel": denormalize(decoder_outputs, self.mel_mean, self.mel_std),
"mel_lengths": y_lengths,
"rtf": rtf,
}
def forward(self, x, x_lengths, y, y_lengths, prompt=None, cond=None, **kwargs):
if prompt is None:
prompt_slice, ids_slice = commons.rand_slice_segments(
y, y_lengths, self.prompt_size
)
else:
prompt_slice = prompt
mu_x, logw, x_mask = self.encoder(x, x_lengths, prompt_slice)
y_max_length = y.shape[-1]
y_mask = sequence_mask(y_lengths, y_max_length).unsqueeze(1).to(x_mask)
attn_mask = x_mask.unsqueeze(-1) * y_mask.unsqueeze(2)
with torch.no_grad():
# negative cross-entropy
s_p_sq_r = torch.ones_like(mu_x) # [b, d, t]
# s_p_sq_r = torch.exp(-2 * logx)
neg_cent1 = torch.sum(
-0.5 * math.log(2 * math.pi)- torch.zeros_like(mu_x), [1], keepdim=True
)
# neg_cent1 = torch.sum(
# -0.5 * math.log(2 * math.pi) - logx, [1], keepdim=True
# ) # [b, 1, t_s]
neg_cent2 = torch.einsum("bdt, bds -> bts", -0.5 * (y**2), s_p_sq_r)
neg_cent3 = torch.einsum("bdt, bds -> bts", y, (mu_x * s_p_sq_r))
neg_cent4 = torch.sum(
-0.5 * (mu_x**2) * s_p_sq_r, [1], keepdim=True
)
neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
from pflow.utils.monotonic_align import maximum_path
attn = (
maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
)
logw_ = torch.log(1e-8 + attn.sum(2)) * x_mask
dur_loss = duration_loss(logw, logw_, x_lengths)
# aln_hard, aln_soft, aln_log, aln_mask = self.aligner(
# mu_x.transpose(1,2), x_mask, y, y_mask
# )
# attn = aln_mask.transpose(1,2).unsqueeze(1)
# align_loss = self.aligner_loss(aln_log, x_lengths, y_lengths)
# if self.aligner_bin_loss_weight > 0.:
# align_bin_loss = self.bin_loss(aln_mask, aln_log, x_lengths) * self.aligner_bin_loss_weight
# align_loss = align_loss + align_bin_loss
# dur_loss = F.l1_loss(logw, attn.sum(2))
# dur_loss = dur_loss + align_loss
# Align encoded text with mel-spectrogram and get mu_y segment
attn = attn.squeeze(1).transpose(1,2)
mu_y = torch.matmul(attn.squeeze(1).transpose(1, 2), mu_x.transpose(1, 2))
mu_y = mu_y.transpose(1, 2)
y_loss_mask = sequence_mask(y_lengths, y_max_length).unsqueeze(1).to(x_mask)
if prompt is None:
for i in range(y.size(0)):
y_loss_mask[i,:,ids_slice[i]:ids_slice[i] + self.prompt_size] = False
# Compute loss of the decoder
diff_loss, _ = self.decoder.compute_loss(x1=y.detach(), mask=y_mask, mu=mu_y, cond=cond, loss_mask=y_loss_mask)
prior_loss = torch.sum(0.5 * ((y - mu_y) ** 2 + math.log(2 * math.pi)) * y_loss_mask)
prior_loss = prior_loss / (torch.sum(y_loss_mask) * self.n_feats)
return dur_loss, prior_loss, diff_loss, attn |