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import os
import json
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformer import Encoder, Decoder, PostNet
from .modules import VarianceAdaptor
from utils.tools import get_mask_from_lengths, get_roberta_emotion_embeddings
class FastSpeech2(nn.Module):
""" FastSpeech2 """
def __init__(self, preprocess_config, model_config):
super(FastSpeech2, self).__init__()
self.model_config = model_config
self.encoder = Encoder(model_config)
self.variance_adaptor = VarianceAdaptor(
preprocess_config, model_config)
self.decoder = Decoder(model_config)
self.mel_linear = nn.Linear(
model_config["transformer"]["decoder_hidden"],
preprocess_config["preprocessing"]["mel"]["n_mel_channels"],
)
self.postnet = PostNet()
self.speaker_emb = None
if model_config["multi_speaker"]:
with open(
os.path.join(
preprocess_config["path"]["preprocessed_path"], "speakers.json"
),
"r",
) as f:
n_speaker = len(json.load(f))
self.speaker_emb = nn.Embedding(
n_speaker,
model_config["transformer"]["encoder_hidden"],
)
self.emotion_emb = None
if model_config["multi_emotion"]:
with open(
os.path.join(
preprocess_config["path"]["preprocessed_path"], "emotions.json"
),
"r",
) as f:
n_emotion = len(json.load(f))
self.emotion_emb = nn.Embedding(
n_emotion,
model_config["transformer"]["encoder_hidden"],
)
self.emotion_linear = nn.Sequential(
nn.Linear(model_config["transformer"]["encoder_hidden"],
model_config["transformer"]["encoder_hidden"]),
nn.ReLU()
)
def forward(
self,
speakers,
texts,
src_lens,
max_src_len,
emotions,
mels=None,
mel_lens=None,
max_mel_len=None,
p_targets=None,
e_targets=None,
d_targets=None,
p_control=1.0,
e_control=1.0,
d_control=1.0,
):
src_masks = get_mask_from_lengths(src_lens, max_src_len)
mel_masks = (
get_mask_from_lengths(mel_lens, max_mel_len)
if mel_lens is not None
else None
)
output = self.encoder(texts, src_masks)
if self.speaker_emb is not None:
output = output + self.speaker_emb(speakers).unsqueeze(1).expand(
-1, max_src_len, -1
)
if self.emotion_emb is not None:
output = output + self.emotion_linear(self.emotion_emb(emotions)).unsqueeze(1).expand(
-1, max_src_len, -1
)
(
output,
p_predictions,
e_predictions,
log_d_predictions,
d_rounded,
mel_lens,
mel_masks,
) = self.variance_adaptor(
output,
src_masks,
mel_masks,
max_mel_len,
p_targets,
e_targets,
d_targets,
p_control,
e_control,
d_control,
)
output, mel_masks = self.decoder(output, mel_masks)
output = self.mel_linear(output)
postnet_output = self.postnet(output) + output
return (
output,
postnet_output,
p_predictions,
e_predictions,
log_d_predictions,
d_rounded,
src_masks,
mel_masks,
src_lens,
mel_lens,
)
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