def __init__(self, hparams):
super(Tacotron2, self).__init__()
self.mask_padding = hparams.mask_padding
self.fp16_run = hparams.fp16_run
self.use_vae = hparams.use_vae
self.embedding_variation = hparams.embedding_variation
self.label_type = hparams.label_type
self.n_mel_channels = hparams.n_mel_channels
self.n_frames_per_step = hparams.n_frames_per_step
self.symbols_embedding_dim = hparams.symbols_embedding_dim
self.speaker_embedding_dim = hparams.speaker_embedding_dim
self.emotion_embedding_dim = hparams.emotion_embedding_dim
self.transcript_embedding = nn.Embedding(hparams.n_symbols,
hparams.symbols_embedding_dim)
if self.use_vae:
if self.label_type == 'one-hot':
self.speaker_embedding = LinearNorm(
hparams.n_speakers,
hparams.speaker_embedding_dim,
bias=True,
w_init_gain='tanh')
self.emotion_embedding = LinearNorm(
hparams.n_emotions,
hparams.emotion_embedding_dim,
bias=True,
w_init_gain='tanh')
elif self.label_type == 'id':
self.speaker_embedding = nn.Embedding(
hparams.n_speakers, hparams.speaker_embedding_dim)
self.emotion_embedding = nn.Embedding(
hparams.n_emotions, hparams.emotion_embedding_dim)
self.vae_input_type = hparams.vae_input_type
std = sqrt(2.0 / (hparams.n_symbols + hparams.symbols_embedding_dim))
val = sqrt(3.0) * std # uniform bounds for std
self.transcript_embedding.weight.data.uniform_(-val, val)
self.encoder = Encoder(hparams)
self.decoder = Decoder(hparams)
self.postnet = Postnet(hparams)
self.vae_gst = VAE_GST(hparams)
def __init__(self, hparams):
super(Decoder, self).__init__()
self.n_mel_channels = hparams.n_mel_channels
self.n_frames_per_step = hparams.n_frames_per_step
self.encoder_embedding_dim = hparams.encoder_embedding_dim
self.attention_rnn_dim = hparams.attention_rnn_dim
self.decoder_rnn_dim = hparams.decoder_rnn_dim
self.prenet_dim = hparams.prenet_dim
self.max_decoder_steps = hparams.max_decoder_steps
self.gate_threshold = hparams.gate_threshold
self.p_attention_dropout = hparams.p_attention_dropout
self.p_decoder_dropout = hparams.p_decoder_dropout
self.prenet = Prenet(
hparams.n_mel_channels * hparams.n_frames_per_step,
[hparams.prenet_dim, hparams.prenet_dim])
self.attention_rnn = nn.LSTMCell(
hparams.prenet_dim + hparams.encoder_embedding_dim,
hparams.attention_rnn_dim)
self.attention_layer = Attention(
hparams.attention_rnn_dim, hparams.encoder_embedding_dim,
hparams.attention_dim, hparams.attention_location_n_filters,
hparams.attention_location_kernel_size)
self.decoder_rnn = nn.LSTMCell(
hparams.attention_rnn_dim + hparams.encoder_embedding_dim,
hparams.decoder_rnn_dim, 1)
self.linear_projection = LinearNorm(
hparams.decoder_rnn_dim + hparams.encoder_embedding_dim,
hparams.n_mel_channels * hparams.n_frames_per_step)
self.gate_layer = LinearNorm(hparams.decoder_rnn_dim +
hparams.encoder_embedding_dim,
1,
bias=True,
w_init_gain='sigmoid')
def __init__(self, config):
super(Decoder, self).__init__()
self.n_mel_channels = config["n_mel_channels"]
self.n_frames_per_step = config["n_frames_per_step"]
self.encoder_embedding_dim = config["encoder_embedding_dim"]
self.attention_rnn_dim = config["attention_rnn_dim"]
self.decoder_rnn_dim = config["decoder_rnn_dim"]
self.prenet_dim = config["prenet_dim"]
self.max_decoder_steps = config["max_decoder_steps"]
self.gate_threshold = config["gate_threshold"]
self.p_attention_dropout = config["p_attention_dropout"]
self.p_decoder_dropout = config["p_decoder_dropout"]
self.prenet = Prenet(
config["n_mel_channels"] * config["n_frames_per_step"],
[config["prenet_dim"], config["prenet_dim"]])
self.attention_rnn = nn.LSTMCell(
config["prenet_dim"] + config["encoder_embedding_dim"],
config["attention_rnn_dim"])
self.attention_layer = Attention(
config["attention_rnn_dim"], config["encoder_embedding_dim"],
config["attention_dim"], config["attention_location_n_filters"],
config["attention_location_kernel_size"])
self.decoder_rnn = nn.LSTMCell(
config["attention_rnn_dim"] + config["encoder_embedding_dim"],
config["decoder_rnn_dim"], 1)
self.linear_projection = LinearNorm(
config["decoder_rnn_dim"] + config["encoder_embedding_dim"],
config["n_mel_channels"] * config["n_frames_per_step"])
self.gate_layer = LinearNorm(config["decoder_rnn_dim"] +
config["encoder_embedding_dim"],
1,
bias=True,
w_init_gain='sigmoid')
def __init__(self,
attention_rnn_dim,
embedding_dim,
attention_dim,
attention_location_n_filters,
attention_location_kernel_size,
prune_rate=0):
super(Attention, self).__init__()
self.query_layer = LinearNorm(attention_rnn_dim,
attention_dim,
bias=False,
w_init_gain='tanh')
self.memory_layer = LinearNorm(embedding_dim,
attention_dim,
bias=False,
w_init_gain='tanh')
self.v = LinearNorm(attention_dim, 1, bias=False)
self.location_layer = LocationLayer(attention_location_n_filters,
attention_location_kernel_size,
attention_dim)
self.score_mask_value = -float("inf")
self.prune_rate = prune_rate
def __init__(self, attention_rnn_dim, embedding_dim, attention_dim,
attention_location_n_filters, attention_location_kernel_size):
super(Attention, self).__init__()
self.query_layer = LinearNorm(attention_rnn_dim,
attention_dim,
bias=False,
w_init_gain='tanh')
# if hparams.style == 'speaker_encoder':
# embedding_dim += 256
# elif hparams.style == 'style_embedding':
# embedding_dim += 128
# elif hparams.style == 'both':
# embedding_dim += 256 + 128
self.memory_layer = LinearNorm(embedding_dim,
attention_dim,
bias=False,
w_init_gain='tanh')
self.v = LinearNorm(attention_dim, 1, bias=False)
self.location_layer = LocationLayer(attention_location_n_filters,
attention_location_kernel_size,
attention_dim)
self.score_mask_value = -float("inf")
def __init__(self, in_dim, sizes, hparams):
super(Prenet, self).__init__()
in_sizes = [in_dim] + sizes[:-1]
self.layers = nn.ModuleList([
LinearNorm(in_size, out_size, bias=False)
for (in_size, out_size) in zip(in_sizes, sizes)
])
self.convolutions = nn.Sequential(
ConvNorm(hparams.prenet_dim,
hparams.prenet_dim,
kernel_size=hparams.audio_kernel_size,
stride=hparams.audio_stride,
w_init_gain='relu'), nn.BatchNorm1d(hparams.prenet_dim))
def __init__(self, query_dim, keys_dim, attention_dim,
attention_location_n_filters, attention_location_kernel_size):
super(Attention, self).__init__()
# 传统attention需要query和keys做线性变换再v^T.*tanh(W * query + V * keys)
# 这个query_layer和memory_layer分别得到 W * query 和 V * keys
# w_init_gain='tanh'是因为他们包在tanh(W * query + V * keys)函数中
self.query_layer = LinearNorm(query_dim,
attention_dim,
bias=False,
w_init_gain='tanh')
self.memory_layer = LinearNorm(keys_dim,
attention_dim,
bias=False,
w_init_gain='tanh')
# 当前attention除了传统参数还包括对注意力权重做卷积处理
self.location_layer = LocationLayer(attention_location_n_filters,
attention_location_kernel_size,
attention_dim)
self.EOS_embedding_layer = nn.Embedding(1, attention_dim)
self.v = LinearNorm(attention_dim, 1, bias=False)
self.score_mask_value = -float("inf")
def __init__(self, in_dim, sizes, p_prenet_dropout, prenet_batchnorm):
super(Prenet, self).__init__()
in_sizes = [in_dim] + sizes[:-1]
self.layers = nn.ModuleList([
LinearNorm(in_size, out_size, bias=False)
for (in_size, out_size) in zip(in_sizes, sizes)
])
self.p_prenet_dropout = p_prenet_dropout
self.prenet_batchnorm = prenet_batchnorm
self.p_prenet_input_dropout = 0
if self.prenet_batchnorm:
self.batchnorms = nn.ModuleList(
[nn.BatchNorm1d(size) for size in sizes])
def __init__(self, attention_n_filters, attention_kernel_size,
attention_dim):
super(LocationLayer, self).__init__()
padding = int((attention_kernel_size - 1) / 2)
self.location_conv = ConvNorm(2,
attention_n_filters,
kernel_size=attention_kernel_size,
padding=padding,
bias=False,
stride=1,
dilation=1)
self.location_dense = LinearNorm(attention_n_filters,
attention_dim,
bias=False,
w_init_gain='tanh')
def __init__(self, hparams):
super(BERT_Tacotron2, self).__init__()
self.mask_padding = hparams.mask_padding
self.n_mel_channels = hparams.n_mel_channels
self.n_frames_per_step = hparams.n_frames_per_step
self.embedding = nn.Embedding(hparams.n_symbols,
hparams.symbols_embedding_dim)
std = sqrt(2.0 / (hparams.n_symbols + hparams.symbols_embedding_dim))
val = sqrt(3.0) * std # uniform bounds for std
self.embedding.weight.data.uniform_(-val, val)
self.encoder = Encoder(hparams)
self.linear_converter = LinearNorm(
hparams.encoder_embedding_dim + hparams.BERT_embedding_dim,
hparams.encoder_embedding_dim)
self.decoder = Decoder(hparams)
self.postnet = Postnet(hparams)
def __init__(self, hparams, supervised=False):
super(GMVAE_revised, self).__init__()
self.latent_embedding_dim = hparams.latent_embedding_dim
self.supervised = supervised
convolutions = []
conv_layer_1 = nn.Sequential(
ConvNorm(hparams.n_mel_channels,
hparams.latent_embedding_dim,
kernel_size=hparams.latent_kernel_size,
stride=1,
padding=int((hparams.latent_kernel_size - 1) / 2),
dilation=1,
w_init_gain='relu'),
nn.BatchNorm1d(hparams.latent_embedding_dim))
convolutions.append(conv_layer_1)
conv_layer_2 = nn.Sequential(
ConvNorm(hparams.latent_embedding_dim,
hparams.latent_embedding_dim,
kernel_size=hparams.latent_kernel_size,
stride=1,
padding=int((hparams.latent_kernel_size - 1) / 2),
dilation=1,
w_init_gain='relu'),
nn.BatchNorm1d(hparams.latent_embedding_dim))
convolutions.append(conv_layer_2)
self.convolutions = nn.ModuleList(convolutions)
self.lstm = nn.LSTM(hparams.latent_embedding_dim,
int(hparams.latent_embedding_dim / 2),
1,
batch_first=True,
bidirectional=True)
# self.mean_pool = nn.AvgPool1d(hparams.latent_kernel_size, stride=1)
#
# self.mean_pool_out_size = hparams.latent_embedding_dim - hparams.latent_kernel_size + 1
self.linear_projection = LinearNorm(
hparams.latent_embedding_dim,
int(hparams.latent_embedding_dim / 2))
self.linear_projection_mean = LinearNorm(
int(hparams.latent_embedding_dim / 2), hparams.latent_out_dim)
self.linear_projection_variance = LinearNorm(
int(hparams.latent_embedding_dim / 2), hparams.latent_out_dim)
self.fc3 = nn.Linear(hparams.latent_out_dim,
int(hparams.latent_embedding_dim / 2))
self.fc4 = nn.Linear(int(hparams.latent_embedding_dim / 2),
hparams.latent_embedding_dim)
def __init__(self, attention_n_filters, attention_kernel_size,
attention_dim):
super(LocationLayer, self).__init__() ##每个类的__init__都要加这一句？
padding = int((attention_kernel_size - 1) / 2)
self.location_conv = ConvNorm(
2,
attention_n_filters, ##ConvNorm是layers.py里定义的一个类
kernel_size=attention_kernel_size,
padding=padding,
bias=False,
stride=1,
dilation=1)
self.location_dense = LinearNorm(
attention_n_filters,
attention_dim, ##LinearNorm是layers.py里定义的一个类
bias=False,
w_init_gain='tanh')
def __init__(self, num_mixtures, attention_rnn_dim, embedding_dim, attention_dim,
attention_location_n_filters, attention_location_kernel_size):
super(GMMAttention, self).__init__()
self.num_mixtures = num_mixtures
lin = nn.Linear(attention_dim, 3*num_mixtures, bias=True)
lin.weight.data.mul_(0.001)
lin.bias.data.mul_(0.001)
lin.bias.data.sub_(2.)
self.F = nn.Sequential(
LinearNorm(attention_rnn_dim, attention_dim, bias=True, w_init_gain='tanh'),
nn.Tanh(),
lin)
# LinearNorm(attention_dim, 3*num_mixtures, bias=False, w_init_gain='linear'))
self.score_mask_value = 0 # -float("inf")
self.register_buffer('pos', torch.arange(
0, 2000, dtype=torch.float).view(1, -1, 1).data)
def __init__(self, hparams):
super(GST, self).__init__()
self.token_embedding_size = hparams.token_embedding_size
self.token_num = hparams.token_num
self.torchMoji_linear = hparams.torchMoji_linear
if hparams.token_activation_func == 'softmax': self.activation_fn = 0
elif hparams.token_activation_func == 'sigmoid': self.activation_fn = 1
elif hparams.token_activation_func == 'tanh': self.activation_fn = 2
elif hparams.token_activation_func == 'absolute':
self.activation_fn = 3
else:
print(
f'token_activation_func of {hparams.token_activation_func} is invalid\nPlease use "softmax", "sigmoid" or "tanh"'
)
raise
self.token_embedding = nn.Parameter(
torch.zeros([self.token_num,
self.token_embedding_size])) # (token_num, Embedding)
init.normal_(self.token_embedding, mean=0., std=0.5)
# init.orthogonal_(self.token_embedding)
self.ref_encoder = ReferenceEncoder(hparams, activation_fn=torch.tanh)
self.att = MultiHeadAttention(hparams)
# torchMoji
if self.torchMoji_linear:
self.map_lin = LinearNorm(hparams.torchMoji_attDim, self.token_num)
self.p_drop_tokens = hparams.p_drop_tokens
self.drop_tokens_mode = hparams.drop_tokens_mode
if self.drop_tokens_mode == 'embedding':
self.embedding = nn.Embedding(1, self.token_num)
elif self.drop_tokens_mode == 'speaker_embedding':
self.speaker_embedding = nn.Embedding(hparams.n_speakers,
self.token_num)
def __init__(self, hparams):
super().__init__()
self.hidden1 = LinearNorm(64, 256)
self.output = LinearNorm(256, hparams.speaker_num)
def __init__(self, in_dim, sizes):
super(Prenet, self).__init__()
in_sizes = [in_dim] + sizes[:-1]
self.layers = nn.ModuleList(
[LinearNorm(in_size, out_size, bias=False)
for (in_size, out_size) in zip(in_sizes, sizes)])
class GMVAE_revised(nn.Module):
def __init__(self, hparams, supervised=False):
super(GMVAE_revised, self).__init__()
self.latent_embedding_dim = hparams.latent_embedding_dim
self.supervised = supervised
convolutions = []
conv_layer_1 = nn.Sequential(
ConvNorm(hparams.n_mel_channels,
hparams.latent_embedding_dim,
kernel_size=hparams.latent_kernel_size,
stride=1,
padding=int((hparams.latent_kernel_size - 1) / 2),
dilation=1,
w_init_gain='relu'),
nn.BatchNorm1d(hparams.latent_embedding_dim))
convolutions.append(conv_layer_1)
conv_layer_2 = nn.Sequential(
ConvNorm(hparams.latent_embedding_dim,
hparams.latent_embedding_dim,
kernel_size=hparams.latent_kernel_size,
stride=1,
padding=int((hparams.latent_kernel_size - 1) / 2),
dilation=1,
w_init_gain='relu'),
nn.BatchNorm1d(hparams.latent_embedding_dim))
convolutions.append(conv_layer_2)
self.convolutions = nn.ModuleList(convolutions)
self.lstm = nn.LSTM(hparams.latent_embedding_dim,
int(hparams.latent_embedding_dim / 2),
1,
batch_first=True,
bidirectional=True)
# self.mean_pool = nn.AvgPool1d(hparams.latent_kernel_size, stride=1)
#
# self.mean_pool_out_size = hparams.latent_embedding_dim - hparams.latent_kernel_size + 1
self.linear_projection = LinearNorm(
hparams.latent_embedding_dim,
int(hparams.latent_embedding_dim / 2))
self.linear_projection_mean = LinearNorm(
int(hparams.latent_embedding_dim / 2), hparams.latent_out_dim)
self.linear_projection_variance = LinearNorm(
int(hparams.latent_embedding_dim / 2), hparams.latent_out_dim)
self.fc3 = nn.Linear(hparams.latent_out_dim,
int(hparams.latent_embedding_dim / 2))
self.fc4 = nn.Linear(int(hparams.latent_embedding_dim / 2),
hparams.latent_embedding_dim)
def parse_batch(self, batch):
if self.supervised:
text_padded, input_lengths, mel_padded, gate_padded, output_lengths, mel_padded_512, gate_padded_512, output_lengths_512, labels = batch
else:
text_padded, input_lengths, mel_padded, gate_padded, output_lengths, mel_padded_512, gate_padded_512, output_lengths_512 = batch
text_padded = to_gpu(text_padded).long()
input_lengths = to_gpu(input_lengths).long()
max_len = torch.max(input_lengths.data).item()
mel_padded = to_gpu(mel_padded).float()
gate_padded = to_gpu(gate_padded).float()
output_lengths = to_gpu(output_lengths).long()
mel_padded_512 = to_gpu(mel_padded_512).float()
gate_padded_512 = to_gpu(gate_padded_512).float()
output_lengths_512 = to_gpu(output_lengths_512).long()
return ((text_padded, input_lengths, mel_padded, max_len,
output_lengths, mel_padded), (mel_padded, gate_padded))
def vae_encode(self, inputs, label=None):
_, _, x, _, _, _ = inputs
# print('x shape:', x.shape)
# pdb.set_trace()
for conv in self.convolutions:
x = F.dropout(F.relu(conv(x)), 0.5, self.training)
# pdb.set_trace()
x = x.transpose(1, 2)
# print('Just finished convs')
# pdb.set_trace()
out, _ = self.lstm(x)
# print('Just finished lstm', out.shape)
# pdb.set_trace()
out = torch.mean(out, dim=1)
x_after_mean = out
# print('After mean pool', out.shape)
# pdb.set_trace()
# out=torch.cat([out, label],1)
out = self.linear_projection.forward(out)
# print('After linear 1', out.shape)
# pdb.set_trace()
mean = self.linear_projection_mean.forward(out)
variance = self.linear_projection_variance.forward(out)
# mean = torch.mean(torch.mean(self.linear_projection_mean.forward(out),dim=1), dim=0)
# variance = torch.mean(torch.mean(self.linear_projection_variance.forward(out),dim=1), dim=0)
# print('mean', mean.shape)
# print('variance', variance.shape)
# pdb.set_trace()
return mean, variance, x_after_mean
def reparameterize(self, mu, logvar):
std = torch.exp(0.5 * logvar)
eps = torch.randn_like(std)
return mu + eps * std
def decode(self, z, label=None):
# print('shape to be decoded', z.shape)
#z=torch.cat([z, label],1)
h3 = F.relu(self.fc3(z))
# print('shape of the recons',h3.shape)
# pdb.set_trace()
return torch.sigmoid(self.fc4(h3))
def forward(self, x, label=None):
mu, logvar, x_after_mean = self.vae_encode(x, label)
z = self.reparameterize(mu, logvar)
# print('mu shape:', mu.shape)
# print('logvar shape:', logvar.shape)
# pdb.set_trace()
return self.decode(z, label), mu, logvar, x_after_mean
def generate_sample(self, x):
mu, logvar, _ = self.vae_encode(x)
# pdb.set_trace()
# pdb.set_trace()
return Normal(mu, logvar.exp()).sample(
(1, x[2].shape[2])).squeeze(dim=0)
def __init__(self,hparams):
super(speaker_classifier, self).__init__()
self.hidden1 = LinearNorm(hparams.z_speaker_dim,256)
self.output = LinearNorm(256, hparams.speaker_num)
def __init__(self, input_dims, hidden_dims):
super(vectorBased_selfAttention, self).__init__()
self.layers1 = LinearNorm(input_dims, hidden_dims)
self.layers2 = LinearNorm(hidden_dims, input_dims)
def __init__(self, hparams):
super(Decoder, self).__init__()
self.mellotron = hparams.mellotron
self.disable_f0 = hparams.disable_f0
self.n_mel_channels = hparams.n_mel_channels
self.n_frames_per_step = hparams.n_frames_per_step
self.encoder_dim = hparams.encoder_LSTM_dim + hparams.token_embedding_size + hparams.speaker_embedding_dim
self.attention_rnn_dim = hparams.attention_rnn_dim
self.decoder_rnn_dim = hparams.decoder_rnn_dim
self.prenet_dim = hparams.prenet_dim
self.prenet_layers = hparams.prenet_layers
self.prenet_batchnorm = hparams.prenet_batchnorm
self.p_prenet_dropout = hparams.p_prenet_dropout
self.max_decoder_steps = hparams.max_decoder_steps
self.gate_threshold = hparams.gate_threshold
self.AttRNN_extra_decoder_input = hparams.AttRNN_extra_decoder_input
self.AttRNN_hidden_dropout_type = hparams.AttRNN_hidden_dropout_type
self.p_AttRNN_hidden_dropout = hparams.p_AttRNN_hidden_dropout
self.p_AttRNN_cell_dropout = hparams.p_AttRNN_cell_dropout
self.DecRNN_hidden_dropout_type = hparams.DecRNN_hidden_dropout_type
self.p_DecRNN_hidden_dropout = hparams.p_DecRNN_hidden_dropout
self.p_DecRNN_cell_dropout = hparams.p_DecRNN_cell_dropout
self.p_teacher_forcing = hparams.p_teacher_forcing
self.teacher_force_till = hparams.teacher_force_till
self.num_att_mixtures = hparams.num_att_mixtures
self.extra_projection = hparams.extra_projection
self.normalize_attention_input = hparams.normalize_attention_input
self.normalize_AttRNN_output = hparams.normalize_AttRNN_output
self.attention_type = hparams.attention_type
self.attention_layers = hparams.attention_layers
self.low_vram_inference = hparams.low_vram_inference
self.context_frames = hparams.context_frames
self.hide_startstop_tokens = hparams.hide_startstop_tokens
attention_rnn_in_dim = hparams.prenet_dim + self.encoder_dim
if not self.disable_f0:
self.prenet_f0 = ConvNorm(
1,
hparams.prenet_f0_dim,
kernel_size=hparams.prenet_f0_kernel_size,
padding=max(0, int(hparams.prenet_f0_kernel_size / 2)),
bias=False,
stride=1,
dilation=1)
attention_rnn_in_dim += hparams.prenet_f0_dim
self.prenet = Prenet(
hparams.n_mel_channels * hparams.n_frames_per_step *
self.context_frames, [hparams.prenet_dim] * hparams.prenet_layers,
self.p_prenet_dropout, self.prenet_batchnorm)
if self.AttRNN_extra_decoder_input:
attention_rnn_in_dim += hparams.decoder_rnn_dim
if self.AttRNN_hidden_dropout_type == 'dropout':
self.attention_rnn = nn.LSTMCell(
attention_rnn_in_dim, # input_size
hparams.attention_rnn_dim) # hidden_size)
elif self.AttRNN_hidden_dropout_type == 'zoneout':
self.attention_rnn = LSTMCellWithZoneout(
attention_rnn_in_dim, # input_size
hparams.attention_rnn_dim,
zoneout_prob=self.p_DecRNN_hidden_dropout
) # hidden_size, zoneout)
self.p_AttRNN_hidden_dropout = 0.0 # zoneout assigned inside LSTMCellWithZoneout so don't need normal dropout
if self.attention_type == 0:
self.attention_layer = Attention(
hparams.attention_rnn_dim, self.encoder_dim,
hparams.attention_dim, hparams.attention_location_n_filters,
hparams.attention_location_kernel_size)
elif self.attention_type == 1:
self.attention_layer = GMMAttention(
hparams.num_att_mixtures, hparams.attention_layers,
hparams.attention_rnn_dim, self.encoder_dim,
hparams.attention_dim, hparams.attention_location_n_filters,
hparams.attention_location_kernel_size, hparams)
else:
raise NotImplementedException(
"attention_type invalid, valid values are... 0 and 1")
if self.DecRNN_hidden_dropout_type == 'dropout':
self.decoder_rnn = nn.LSTMCell(
hparams.attention_rnn_dim + self.encoder_dim, # input_size
hparams.decoder_rnn_dim,
1) # hidden_size, bias)
elif self.DecRNN_hidden_dropout_type == 'zoneout':
self.decoder_rnn = LSTMCellWithZoneout(
hparams.attention_rnn_dim + self.encoder_dim, # input_size
hparams.decoder_rnn_dim,
1,
zoneout_prob=self.p_DecRNN_hidden_dropout
) # hidden_size, zoneout)
self.p_DecRNN_hidden_dropout = 0.0 # zoneout assigned inside LSTMCellWithZoneout so don't need normal dropout
if self.extra_projection:
self.linear_projection_pre = LinearNorm(
hparams.decoder_rnn_dim + self.encoder_dim,
hparams.decoder_rnn_dim + self.encoder_dim)
self.linear_projection = LinearNorm(
hparams.decoder_rnn_dim + self.encoder_dim,
hparams.n_mel_channels * hparams.n_frames_per_step)
self.gate_layer = LinearNorm(hparams.decoder_rnn_dim +
self.encoder_dim,
1,
bias=True,
w_init_gain='sigmoid')
def __init__(self):
super().__init__()
self.hidden2 = LinearNorm(64, 256)
self.output = LinearNorm(256, 2)
self.lambd = 1
def __init__(self, hparams):
super(augmentation_classifier, self).__init__()
self.hidden2 = LinearNorm(hparams.z_speaker_dim, 256)
self.output = LinearNorm(256, 2)
self.lambd = 1