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Code Issues Projects Releases Wiki Activity

Make it backward compatible

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This commit is contained in:
babysor00 2021-10-12 09:12:58 +08:00
parent 37f11ab9ce
commit 43c86eb411
6 changed files with 28 additions and 16 deletions
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6
.vscode/launch.json vendored
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@ -17,7 +17,7 @@
"request": "launch",
"program": "vocoder_preprocess.py",
"console": "integratedTerminal",
"args": ["..\\..\\chs1"]
"args": ["..\\audiodata"]
},
{
"name": "Python: Vocoder Train",
@ -25,7 +25,7 @@
"request": "launch",
"program": "vocoder_train.py",
"console": "integratedTerminal",
"args": ["dev", "..\\..\\chs1"]
"args": ["dev", "..\\audiodata"]
},
{
"name": "Python: Demo Box",
@ -33,7 +33,7 @@
"request": "launch",
"program": "demo_toolbox.py",
"console": "integratedTerminal",
"args": ["-d","..\\..\\chs"]
"args": ["-d","..\\audiodata"]
},
{
"name": "Python: Synth Train",

4
synthesizer/inference.py
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@ -70,7 +70,7 @@ class Synthesizer:
def synthesize_spectrograms(self, texts: List[str],
embeddings: Union[np.ndarray, List[np.ndarray]],
return_alignments=False):
return_alignments=False, style_idx=0):
"""
Synthesizes mel spectrograms from texts and speaker embeddings.
@ -125,7 +125,7 @@ class Synthesizer:
speaker_embeddings = torch.tensor(speaker_embeds).float().to(self.device)
# Inference
_, mels, alignments = self._model.generate(chars, speaker_embeddings)
_, mels, alignments = self._model.generate(chars, speaker_embeddings, style_idx=style_idx)
mels = mels.detach().cpu().numpy()
for m in mels:
# Trim silence from end of each spectrogram

2
synthesizer/global_style_token.py → synthesizer/models/global_style_token.py
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@ -90,7 +90,7 @@ class STL(nn.Module):
keys = tFunctional.tanh(self.embed).unsqueeze(0).expand(N, -1, -1) # [N, token_num, E // num_heads]
style_embed = self.attention(query, keys)
return style_embed, keys
return style_embed
class MultiHeadAttention(nn.Module):

23
synthesizer/models/tacotron.py
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@ -3,7 +3,7 @@ import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from synthesizer.global_style_token import GlobalStyleToken
from synthesizer.models.global_style_token import GlobalStyleToken
class HighwayNetwork(nn.Module):
@ -385,10 +385,10 @@ class Tacotron(nn.Module):
# The projection avoids unnecessary matmuls in the decoder loop
encoder_seq = self.encoder(texts, speaker_embedding)
# put after encoder
style_embed, _ = self.gst(speaker_embedding) # [N, 256]
style_embed = style_embed.expand_as(encoder_seq)
encoder_seq = encoder_seq + style_embed
if self.gst is not None:
style_embed = self.gst(speaker_embedding)
style_embed = style_embed.expand_as(encoder_seq)
encoder_seq = encoder_seq + style_embed
encoder_seq_proj = self.encoder_proj(encoder_seq)
# Need a couple of lists for outputs
@ -419,7 +419,7 @@ class Tacotron(nn.Module):
return mel_outputs, linear, attn_scores, stop_outputs
def generate(self, x, speaker_embedding=None, steps=200):
def generate(self, x, speaker_embedding=None, steps=200, style_idx=0):
self.eval()
device = next(self.parameters()).device # use same device as parameters
@ -447,7 +447,14 @@ class Tacotron(nn.Module):
encoder_seq = self.encoder(x, speaker_embedding)
# put after encoder
style_embed = self.gst(speaker_embedding) # [N, 256]
if self.gst is not None and style_idx >= 0 and style_idx < 10:
gst_embed = self.gst.stl.embed.cpu().data.numpy() #[0, number_token]
gst_embed = np.tile(gst_embed, (1, 8))
scale = np.zeros(512)
scale[:] = 0.3
speaker_embedding = (gst_embed[style_idx] * scale).astype(np.float32)
speaker_embedding = torch.from_numpy(np.tile(speaker_embedding, (x.shape[0], 1))).to(device)
style_embed = self.gst(speaker_embedding)
style_embed = style_embed.expand_as(encoder_seq)
encoder_seq = encoder_seq + style_embed
encoder_seq_proj = self.encoder_proj(encoder_seq)
@ -504,7 +511,7 @@ class Tacotron(nn.Module):
# Use device of model params as location for loaded state
device = next(self.parameters()).device
checkpoint = torch.load(str(path), map_location=device)
self.load_state_dict(checkpoint["model_state"])
self.load_state_dict(checkpoint["model_state"], strict=False)
if "optimizer_state" in checkpoint and optimizer is not None:
optimizer.load_state_dict(checkpoint["optimizer_state"])

3
toolbox/__init__.py
View File

@ -71,6 +71,7 @@ class Toolbox:
# Initialize the events and the interface
self.ui = UI()
self.style_idx = 0
self.reset_ui(enc_models_dir, syn_models_dir, voc_models_dir, seed)
self.setup_events()
self.ui.start()
@ -233,7 +234,7 @@ class Toolbox:
texts = processed_texts
embed = self.ui.selected_utterance.embed
embeds = [embed] * len(texts)
specs = self.synthesizer.synthesize_spectrograms(texts, embeds)
specs = self.synthesizer.synthesize_spectrograms(texts, embeds, style_idx=int(self.ui.style_idx_textbox.text()))
breaks = [spec.shape[1] for spec in specs]
spec = np.concatenate(specs, axis=1)

6
toolbox/ui.py
View File

@ -574,10 +574,14 @@ class UI(QDialog):
self.seed_textbox = QLineEdit()
self.seed_textbox.setMaximumWidth(80)
layout_seed.addWidget(self.seed_textbox, 0, 1)
layout_seed.addWidget(QLabel("Style#:(0~9)"), 0, 2)
self.style_idx_textbox = QLineEdit("-1")
self.style_idx_textbox.setMaximumWidth(80)
layout_seed.addWidget(self.style_idx_textbox, 0, 3)
self.trim_silences_checkbox = QCheckBox("Enhance vocoder output")
self.trim_silences_checkbox.setToolTip("When checked, trims excess silence in vocoder output."
" This feature requires `webrtcvad` to be installed.")
layout_seed.addWidget(self.trim_silences_checkbox, 0, 2, 1, 2)
layout_seed.addWidget(self.trim_silences_checkbox, 0, 4, 1, 2)
gen_layout.addLayout(layout_seed)
self.loading_bar = QProgressBar()