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模型兼容问题加强 Compatibility Enhance of Pretrained Models and code base #209

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babysor00 2021-11-10 23:23:13 +08:00
parent 902e1eb537
commit a37b26a89c
3 changed files with 26 additions and 11 deletions
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2
synthesizer/hparams.py
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@ -91,4 +91,6 @@ hparams = HParams(
speaker_embedding_size = 256, # Dimension for the speaker embedding speaker_embedding_size = 256, # Dimension for the speaker embedding
silence_min_duration_split = 0.4, # Duration in seconds of a silence for an utterance to be split silence_min_duration_split = 0.4, # Duration in seconds of a silence for an utterance to be split
utterance_min_duration = 1.6, # Duration in seconds below which utterances are discarded utterance_min_duration = 1.6, # Duration in seconds below which utterances are discarded
use_gst = True, # Whether to use global style token
use_ser_for_gst = False, # Whether to use speaker embedding referenced for global style token
) )

5
synthesizer/models/global_style_token.py
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@ -3,6 +3,7 @@ import torch.nn as nn
import torch.nn.init as init import torch.nn.init as init
import torch.nn.functional as tFunctional import torch.nn.functional as tFunctional
from synthesizer.gst_hyperparameters import GSTHyperparameters as hp from synthesizer.gst_hyperparameters import GSTHyperparameters as hp
from synthesizer.hparams import hparams
class GlobalStyleToken(nn.Module): class GlobalStyleToken(nn.Module):
@ -20,7 +21,7 @@ class GlobalStyleToken(nn.Module):
def forward(self, inputs, speaker_embedding=None): def forward(self, inputs, speaker_embedding=None):
enc_out = self.encoder(inputs) enc_out = self.encoder(inputs)
# concat speaker_embedding according to https://github.com/mozilla/TTS/blob/master/TTS/tts/layers/gst_layers.py # concat speaker_embedding according to https://github.com/mozilla/TTS/blob/master/TTS/tts/layers/gst_layers.py
if speaker_embedding is not None: if hparams.use_ser_for_gst and speaker_embedding is not None:
enc_out = torch.cat([enc_out, speaker_embedding], dim=-1) enc_out = torch.cat([enc_out, speaker_embedding], dim=-1)
style_embed = self.stl(enc_out) style_embed = self.stl(enc_out)
@ -87,7 +88,7 @@ class STL(nn.Module):
d_q = hp.E // 2 d_q = hp.E // 2
d_k = hp.E // hp.num_heads d_k = hp.E // hp.num_heads
# self.attention = MultiHeadAttention(hp.num_heads, d_model, d_q, d_v) # self.attention = MultiHeadAttention(hp.num_heads, d_model, d_q, d_v)
if speaker_embedding_dim: if hparams.use_ser_for_gst and speaker_embedding_dim is not None:
d_q += speaker_embedding_dim d_q += speaker_embedding_dim
self.attention = MultiHeadAttention(query_dim=d_q, key_dim=d_k, num_units=hp.E, num_heads=hp.num_heads) self.attention = MultiHeadAttention(query_dim=d_q, key_dim=d_k, num_units=hp.E, num_heads=hp.num_heads)

28
synthesizer/models/tacotron.py
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@ -5,6 +5,7 @@ import torch.nn as nn
import torch.nn.functional as F import torch.nn.functional as F
from synthesizer.models.global_style_token import GlobalStyleToken from synthesizer.models.global_style_token import GlobalStyleToken
from synthesizer.gst_hyperparameters import GSTHyperparameters as gst_hp from synthesizer.gst_hyperparameters import GSTHyperparameters as gst_hp
from synthesizer.hparams import hparams
class HighwayNetwork(nn.Module): class HighwayNetwork(nn.Module):
@ -255,12 +256,14 @@ class Decoder(nn.Module):
self.prenet = PreNet(n_mels, fc1_dims=prenet_dims[0], fc2_dims=prenet_dims[1], self.prenet = PreNet(n_mels, fc1_dims=prenet_dims[0], fc2_dims=prenet_dims[1],
dropout=dropout) dropout=dropout)
self.attn_net = LSA(decoder_dims) self.attn_net = LSA(decoder_dims)
self.attn_rnn = nn.GRUCell(encoder_dims + prenet_dims[1] + speaker_embedding_size + gst_hp.E, decoder_dims) if hparams.use_gst:
self.rnn_input = nn.Linear(encoder_dims + decoder_dims + speaker_embedding_size + gst_hp.E, lstm_dims) speaker_embedding_size += gst_hp.E
self.attn_rnn = nn.GRUCell(encoder_dims + prenet_dims[1] + speaker_embedding_size, decoder_dims)
self.rnn_input = nn.Linear(encoder_dims + decoder_dims + speaker_embedding_size, lstm_dims)
self.res_rnn1 = nn.LSTMCell(lstm_dims, lstm_dims) self.res_rnn1 = nn.LSTMCell(lstm_dims, lstm_dims)
self.res_rnn2 = nn.LSTMCell(lstm_dims, lstm_dims) self.res_rnn2 = nn.LSTMCell(lstm_dims, lstm_dims)
self.mel_proj = nn.Linear(lstm_dims, n_mels * self.max_r, bias=False) self.mel_proj = nn.Linear(lstm_dims, n_mels * self.max_r, bias=False)
self.stop_proj = nn.Linear(encoder_dims + speaker_embedding_size + lstm_dims + gst_hp.E, 1) self.stop_proj = nn.Linear(encoder_dims + speaker_embedding_size + lstm_dims, 1)
def zoneout(self, prev, current, p=0.1): def zoneout(self, prev, current, p=0.1):
device = next(self.parameters()).device # Use same device as parameters device = next(self.parameters()).device # Use same device as parameters
@ -337,8 +340,11 @@ class Tacotron(nn.Module):
self.speaker_embedding_size = speaker_embedding_size self.speaker_embedding_size = speaker_embedding_size
self.encoder = Encoder(embed_dims, num_chars, encoder_dims, self.encoder = Encoder(embed_dims, num_chars, encoder_dims,
encoder_K, num_highways, dropout) encoder_K, num_highways, dropout)
self.encoder_proj = nn.Linear(encoder_dims + speaker_embedding_size + gst_hp.E, decoder_dims, bias=False) project_dims = encoder_dims + speaker_embedding_size
if hparams.use_gst:
project_dims += gst_hp.E
self.gst = GlobalStyleToken(speaker_embedding_size) self.gst = GlobalStyleToken(speaker_embedding_size)
self.encoder_proj = nn.Linear(project_dims, decoder_dims, bias=False)
self.decoder = Decoder(n_mels, encoder_dims, decoder_dims, lstm_dims, self.decoder = Decoder(n_mels, encoder_dims, decoder_dims, lstm_dims,
dropout, speaker_embedding_size) dropout, speaker_embedding_size)
self.postnet = CBHG(postnet_K, n_mels, postnet_dims, self.postnet = CBHG(postnet_K, n_mels, postnet_dims,
@ -387,13 +393,16 @@ class Tacotron(nn.Module):
go_frame = torch.zeros(batch_size, self.n_mels, device=device) go_frame = torch.zeros(batch_size, self.n_mels, device=device)
# Need an initial context vector # Need an initial context vector
context_vec = torch.zeros(batch_size, self.encoder_dims + self.speaker_embedding_size + gst_hp.E, device=device) size = self.encoder_dims + self.speaker_embedding_size
if hparams.use_gst:
size += gst_hp.E
context_vec = torch.zeros(batch_size, size, device=device)
# SV2TTS: Run the encoder with the speaker embedding # SV2TTS: Run the encoder with the speaker embedding
# The projection avoids unnecessary matmuls in the decoder loop # The projection avoids unnecessary matmuls in the decoder loop
encoder_seq = self.encoder(texts, speaker_embedding) encoder_seq = self.encoder(texts, speaker_embedding)
# put after encoder # put after encoder
if self.gst is not None: if hparams.use_gst and self.gst is not None:
style_embed = self.gst(speaker_embedding, speaker_embedding) # for training, speaker embedding can represent both style inputs and referenced style_embed = self.gst(speaker_embedding, speaker_embedding) # for training, speaker embedding can represent both style inputs and referenced
# style_embed = style_embed.expand_as(encoder_seq) # style_embed = style_embed.expand_as(encoder_seq)
# encoder_seq = torch.cat((encoder_seq, style_embed), 2) # encoder_seq = torch.cat((encoder_seq, style_embed), 2)
@ -449,14 +458,17 @@ class Tacotron(nn.Module):
go_frame = torch.zeros(batch_size, self.n_mels, device=device) go_frame = torch.zeros(batch_size, self.n_mels, device=device)
# Need an initial context vector # Need an initial context vector
context_vec = torch.zeros(batch_size, self.encoder_dims + self.speaker_embedding_size + gst_hp.E, device=device) size = self.encoder_dims + self.speaker_embedding_size
if hparams.use_gst:
size += gst_hp.E
context_vec = torch.zeros(batch_size, size, device=device)
# SV2TTS: Run the encoder with the speaker embedding # SV2TTS: Run the encoder with the speaker embedding
# The projection avoids unnecessary matmuls in the decoder loop # The projection avoids unnecessary matmuls in the decoder loop
encoder_seq = self.encoder(x, speaker_embedding) encoder_seq = self.encoder(x, speaker_embedding)
# put after encoder # put after encoder
if self.gst is not None: if hparams.use_gst and self.gst is not None:
if style_idx >= 0 and style_idx < 10: if style_idx >= 0 and style_idx < 10:
gst_embed = self.gst.stl.embed.cpu().data.numpy() #[0, number_token] gst_embed = self.gst.stl.embed.cpu().data.numpy() #[0, number_token]
gst_embed = np.tile(gst_embed, (1, 8)) gst_embed = np.tile(gst_embed, (1, 8))