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import torch
import commons
import models
class SynthesizerTrn(models.SynthesizerTrn):
"""
Synthesizer for Training
"""
def __init__(self,
n_vocab,
spec_channels,
segment_size,
inter_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
resblock,
resblock_kernel_sizes,
resblock_dilation_sizes,
upsample_rates,
upsample_initial_channel,
upsample_kernel_sizes,
n_speakers=0,
gin_channels=0,
use_sdp=True,
**kwargs):
super().__init__(
n_vocab,
spec_channels,
segment_size,
inter_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
resblock,
resblock_kernel_sizes,
resblock_dilation_sizes,
upsample_rates,
upsample_initial_channel,
upsample_kernel_sizes,
n_speakers=n_speakers,
gin_channels=gin_channels,
use_sdp=use_sdp,
**kwargs
)
def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None):
from ONNXVITS_utils import runonnx
#x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
x, m_p, logs_p, x_mask = runonnx("ONNX_net/enc_p.onnx", x=x.numpy(), x_lengths=x_lengths.numpy())
x = torch.from_numpy(x)
m_p = torch.from_numpy(m_p)
logs_p = torch.from_numpy(logs_p)
x_mask = torch.from_numpy(x_mask)
if self.n_speakers > 0:
g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
else:
g = None
#logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
logw = runonnx("ONNX_net/dp.onnx", x=x.numpy(), x_mask=x_mask.numpy(), g=g.numpy())
logw = torch.from_numpy(logw[0])
w = torch.exp(logw) * x_mask * length_scale
w_ceil = torch.ceil(w)
y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
attn = commons.generate_path(w_ceil, attn_mask)
m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
#z = self.flow(z_p, y_mask, g=g, reverse=True)
z = runonnx("ONNX_net/flow.onnx", z_p=z_p.numpy(), y_mask=y_mask.numpy(), g=g.numpy())
z = torch.from_numpy(z[0])
#o = self.dec((z * y_mask)[:,:,:max_len], g=g)
o = runonnx("ONNX_net/dec.onnx", z_in=(z * y_mask)[:,:,:max_len].numpy(), g=g.numpy())
o = torch.from_numpy(o[0])
return o, attn, y_mask, (z, z_p, m_p, logs_p)
def predict_duration(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None,
emotion_embedding=None):
from ONNXVITS_utils import runonnx
#x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
x, m_p, logs_p, x_mask = runonnx("ONNX_net/enc_p.onnx", x=x.numpy(), x_lengths=x_lengths.numpy())
x = torch.from_numpy(x)
m_p = torch.from_numpy(m_p)
logs_p = torch.from_numpy(logs_p)
x_mask = torch.from_numpy(x_mask)
if self.n_speakers > 0:
g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
else:
g = None
#logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
logw = runonnx("ONNX_net/dp.onnx", x=x.numpy(), x_mask=x_mask.numpy(), g=g.numpy())
logw = torch.from_numpy(logw[0])
w = torch.exp(logw) * x_mask * length_scale
w_ceil = torch.ceil(w)
return list(w_ceil.squeeze())
def infer_with_duration(self, x, x_lengths, w_ceil, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None,
emotion_embedding=None):
from ONNXVITS_utils import runonnx
#x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
x, m_p, logs_p, x_mask = runonnx("ONNX_net/enc_p.onnx", x=x.numpy(), x_lengths=x_lengths.numpy())
x = torch.from_numpy(x)
m_p = torch.from_numpy(m_p)
logs_p = torch.from_numpy(logs_p)
x_mask = torch.from_numpy(x_mask)
if self.n_speakers > 0:
g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
else:
g = None
assert len(w_ceil) == x.shape[2]
w_ceil = torch.FloatTensor(w_ceil).reshape(1, 1, -1)
y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
attn = commons.generate_path(w_ceil, attn_mask)
m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
#z = self.flow(z_p, y_mask, g=g, reverse=True)
z = runonnx("ONNX_net/flow.onnx", z_p=z_p.numpy(), y_mask=y_mask.numpy(), g=g.numpy())
z = torch.from_numpy(z[0])
#o = self.dec((z * y_mask)[:,:,:max_len], g=g)
o = runonnx("ONNX_net/dec.onnx", z_in=(z * y_mask)[:,:,:max_len].numpy(), g=g.numpy())
o = torch.from_numpy(o[0])
return o, attn, y_mask, (z, z_p, m_p, logs_p) |