ONNXVITS_infer.py

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)