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integrade concatinative speker embedding to tacotron

pull/10/head
Eren Golge 2019-09-12 10:39:15 +02:00
parent d45d963dc1
commit a1322530df
3 changed files with 91 additions and 27 deletions
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17
layers/tacotron.py
View File

@ -273,7 +273,7 @@ class Decoder(nn.Module):
def __init__(self, in_features, memory_dim, r, memory_size, attn_windowing,
attn_norm, prenet_type, prenet_dropout, forward_attn,
trans_agent, forward_attn_mask, location_attn,
separate_stopnet):
separate_stopnet, speaker_embedding_dim):
super(Decoder, self).__init__()
self.r_init = r
self.r = r
@ -285,8 +285,9 @@ class Decoder(nn.Module):
self.separate_stopnet = separate_stopnet
self.query_dim = 256
# memory -> |Prenet| -> processed_memory
prenet_dim = memory_dim * self.memory_size + speaker_embedding_dim if self.use_memory_queue else memory_dim + speaker_embedding_dim
self.prenet = Prenet(
memory_dim * self.memory_size if self.use_memory_queue else memory_dim,
prenet_dim,
prenet_type,
prenet_dropout,
out_features=[256, 128])
@ -407,7 +408,7 @@ class Decoder(nn.Module):
# use only the last frame prediction
self.memory_input = new_memory[:, :self.memory_dim]
def forward(self, inputs, memory, mask):
def forward(self, inputs, memory, mask, speaker_embeddings=None):
"""
Args:
inputs: Encoder outputs.
@ -432,6 +433,8 @@ class Decoder(nn.Module):
if t > 0:
new_memory = memory[t - 1]
self._update_memory_input(new_memory)
if speaker_embeddings is not None:
self.memory_input = torch.cat([self.memory_input, speaker_embeddings], dim=-1)
output, stop_token, attention = self.decode(inputs, mask)
outputs += [output]
attentions += [attention]
@ -440,13 +443,15 @@ class Decoder(nn.Module):
return self._parse_outputs(outputs, attentions, stop_tokens)
def inference(self, inputs):
def inference(self, inputs, speaker_embeddings=None):
"""
Args:
inputs: Encoder outputs.
inputs: encoder outputs.
speaker_embeddings: speaker vectors.
Shapes:
- inputs: batch x time x encoder_out_dim
- speaker_embeddings: batch x embed_dim
"""
outputs = []
attentions = []
@ -459,6 +464,8 @@ class Decoder(nn.Module):
if t > 0:
new_memory = outputs[-1]
self._update_memory_input(new_memory)
if speaker_embeddings is not None:
self.memory_input = torch.cat([self.memory_input, speaker_embeddings], dim=-1)
output, stop_token, attention = self.decode(inputs, None)
stop_token = torch.sigmoid(stop_token.data)
outputs += [output]

70
models/tacotron.py
View File

@ -1,4 +1,5 @@
# coding: utf-8
import torch
from torch import nn
from TTS.layers.tacotron import Encoder, Decoder, PostCBHG
from TTS.utils.generic_utils import sequence_mask
@ -25,28 +26,50 @@ class Tacotron(nn.Module):
self.r = r
self.mel_dim = mel_dim
self.linear_dim = linear_dim
self.num_speakers = num_speakers
self.embedding = nn.Embedding(num_chars, 256)
self.embedding.weight.data.normal_(0, 0.3)
decoder_dim = 512 if num_speakers > 1 else 256
encoder_dim = 512 if num_speakers > 1 else 256
proj_speaker_dim = 80 if num_speakers > 1 else 0
if num_speakers > 1:
self.speaker_embedding = nn.Embedding(num_speakers, 256)
self.speaker_embedding.weight.data.normal_(0, 0.3)
self.encoder = Encoder(256)
self.decoder = Decoder(256, mel_dim, r, memory_size, attn_win,
self.speaker_project_mel = nn.Sequential(nn.Linear(256, proj_speaker_dim), nn.Tanh())
self.encoder = Encoder(encoder_dim)
self.decoder = Decoder(decoder_dim, mel_dim, r, memory_size, attn_win,
attn_norm, prenet_type, prenet_dropout,
forward_attn, trans_agent, forward_attn_mask,
location_attn, separate_stopnet)
location_attn, separate_stopnet, proj_speaker_dim)
self.postnet = PostCBHG(mel_dim)
self.last_linear = nn.Linear(self.postnet.cbhg.gru_features * 2, linear_dim)
def __init_states(self):
self.speaker_embeddings = None
self.speaker_embeddings_projected = None
def compute_speaker_embedding(self, speaker_ids):
if hasattr(self, "speaker_embedding") and speaker_ids is None:
raise RuntimeError(" [!] Model has speaker embedding layer but speaker_id is not provided")
if hasattr(self, "speaker_embedding") and speaker_ids is not None:
self.speaker_embeddings = self._compute_speaker_embedding(speaker_ids)
self.speaker_embeddings_projected = self.speaker_project_mel(self.speaker_embeddings).squeeze(1)
def forward(self, characters, text_lengths, mel_specs, speaker_ids=None):
B = characters.size(0)
mask = sequence_mask(text_lengths).to(characters.device)
inputs = self.embedding(characters)
self.__init_states()
self.compute_speaker_embedding(speaker_ids)
if self.num_speakers > 1:
inputs = self._concat_speaker_embedding(inputs,
self.speaker_embeddings)
encoder_outputs = self.encoder(inputs)
encoder_outputs = self._add_speaker_embedding(encoder_outputs,
speaker_ids)
if self.num_speakers > 1:
encoder_outputs = self._concat_speaker_embedding(encoder_outputs,
self.speaker_embeddings)
mel_outputs, alignments, stop_tokens = self.decoder(
encoder_outputs, mel_specs, mask)
encoder_outputs, mel_specs, mask, self.speaker_embeddings_projected)
mel_outputs = mel_outputs.view(B, -1, self.mel_dim)
linear_outputs = self.postnet(mel_outputs)
linear_outputs = self.last_linear(linear_outputs)
@ -55,25 +78,30 @@ class Tacotron(nn.Module):
def inference(self, characters, speaker_ids=None):
B = characters.size(0)
inputs = self.embedding(characters)
self.__init_states()
self.compute_speaker_embedding(speaker_ids)
if self.num_speakers > 1:
inputs = self._concat_speaker_embedding(inputs,
self.speaker_embeddings)
encoder_outputs = self.encoder(inputs)
encoder_outputs = self._add_speaker_embedding(encoder_outputs,
speaker_ids)
if self.num_speakers > 1:
encoder_outputs = self._concat_speaker_embedding(encoder_outputs,
self.speaker_embeddings)
mel_outputs, alignments, stop_tokens = self.decoder.inference(
encoder_outputs)
encoder_outputs, self.speaker_embeddings_projected)
mel_outputs = mel_outputs.view(B, -1, self.mel_dim)
linear_outputs = self.postnet(mel_outputs)
linear_outputs = self.last_linear(linear_outputs)
return mel_outputs, linear_outputs, alignments, stop_tokens
def _add_speaker_embedding(self, encoder_outputs, speaker_ids):
if hasattr(self, "speaker_embedding") and speaker_ids is None:
raise RuntimeError(" [!] Model has speaker embedding layer but speaker_id is not provided")
if hasattr(self, "speaker_embedding") and speaker_ids is not None:
speaker_embeddings = self.speaker_embedding(speaker_ids)
def _compute_speaker_embedding(self, speaker_ids):
speaker_embeddings = self.speaker_embedding(speaker_ids)
return speaker_embeddings.unsqueeze_(1)
def _concat_speaker_embedding(self, outputs, speaker_embeddings):
speaker_embeddings_ = speaker_embeddings.expand(outputs.size(0),
outputs.size(1),
-1)
outputs = torch.cat([outputs, speaker_embeddings_], dim=-1)
return outputs
speaker_embeddings.unsqueeze_(1)
speaker_embeddings = speaker_embeddings.expand(encoder_outputs.size(0),
encoder_outputs.size(1),
-1)
encoder_outputs = encoder_outputs + speaker_embeddings
return encoder_outputs

31
tests/test_layers.py
View File

@ -54,7 +54,8 @@ class DecoderTests(unittest.TestCase):
trans_agent=True,
forward_attn_mask=True,
location_attn=True,
separate_stopnet=True)
separate_stopnet=True,
speaker_embedding_dim=0)
dummy_input = T.rand(4, 8, 256)
dummy_memory = T.rand(4, 2, 80)
@ -66,6 +67,34 @@ class DecoderTests(unittest.TestCase):
assert output.shape[2] == 80 * 2, "size not {}".format(output.shape[2])
assert stop_tokens.shape[0] == 4
def test_in_out_multispeaker(self):
layer = Decoder(
in_features=256,
memory_dim=80,
r=2,
memory_size=4,
attn_windowing=False,
attn_norm="sigmoid",
prenet_type='original',
prenet_dropout=True,
forward_attn=True,
trans_agent=True,
forward_attn_mask=True,
location_attn=True,
separate_stopnet=True,
speaker_embedding_dim=80)
dummy_input = T.rand(4, 8, 256)
dummy_memory = T.rand(4, 2, 80)
dummy_embed = T.rand(4, 80)
output, alignment, stop_tokens = layer(
dummy_input, dummy_memory, mask=None, speaker_embeddings=dummy_embed)
assert output.shape[0] == 4
assert output.shape[1] == 1, "size not {}".format(output.shape[1])
assert output.shape[2] == 80 * 2, "size not {}".format(output.shape[2])
assert stop_tokens.shape[0] == 4
class EncoderTests(unittest.TestCase):
def test_in_out(self):