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from abc import ABC
import torch
from Layers.Conformer import Conformer
from Layers.DurationPredictor import DurationPredictor
from Layers.LengthRegulator import LengthRegulator
from Layers.PostNet import PostNet
from Layers.VariancePredictor import VariancePredictor
from Utility.utils import make_non_pad_mask
from Utility.utils import make_pad_mask
class FastSpeech2(torch.nn.Module, ABC):
def __init__(self, # network structure related
weights,
idim=66,
odim=80,
adim=384,
aheads=4,
elayers=6,
eunits=1536,
dlayers=6,
dunits=1536,
postnet_layers=5,
postnet_chans=256,
postnet_filts=5,
positionwise_conv_kernel_size=1,
use_scaled_pos_enc=True,
use_batch_norm=True,
encoder_normalize_before=True,
decoder_normalize_before=True,
encoder_concat_after=False,
decoder_concat_after=False,
reduction_factor=1,
# encoder / decoder
use_macaron_style_in_conformer=True,
use_cnn_in_conformer=True,
conformer_enc_kernel_size=7,
conformer_dec_kernel_size=31,
# duration predictor
duration_predictor_layers=2,
duration_predictor_chans=256,
duration_predictor_kernel_size=3,
# energy predictor
energy_predictor_layers=2,
energy_predictor_chans=256,
energy_predictor_kernel_size=3,
energy_predictor_dropout=0.5,
energy_embed_kernel_size=1,
energy_embed_dropout=0.0,
stop_gradient_from_energy_predictor=True,
# pitch predictor
pitch_predictor_layers=5,
pitch_predictor_chans=256,
pitch_predictor_kernel_size=5,
pitch_predictor_dropout=0.5,
pitch_embed_kernel_size=1,
pitch_embed_dropout=0.0,
stop_gradient_from_pitch_predictor=True,
# training related
transformer_enc_dropout_rate=0.2,
transformer_enc_positional_dropout_rate=0.2,
transformer_enc_attn_dropout_rate=0.2,
transformer_dec_dropout_rate=0.2,
transformer_dec_positional_dropout_rate=0.2,
transformer_dec_attn_dropout_rate=0.2,
duration_predictor_dropout_rate=0.2,
postnet_dropout_rate=0.5,
# additional features
utt_embed_dim=704,
connect_utt_emb_at_encoder_out=True,
lang_embs=100):
super().__init__()
self.idim = idim
self.odim = odim
self.reduction_factor = reduction_factor
self.stop_gradient_from_pitch_predictor = stop_gradient_from_pitch_predictor
self.stop_gradient_from_energy_predictor = stop_gradient_from_energy_predictor
self.use_scaled_pos_enc = use_scaled_pos_enc
embed = torch.nn.Sequential(torch.nn.Linear(idim, 100),
torch.nn.Tanh(),
torch.nn.Linear(100, adim))
self.encoder = Conformer(idim=idim, attention_dim=adim, attention_heads=aheads, linear_units=eunits, num_blocks=elayers,
input_layer=embed, dropout_rate=transformer_enc_dropout_rate,
positional_dropout_rate=transformer_enc_positional_dropout_rate, attention_dropout_rate=transformer_enc_attn_dropout_rate,
normalize_before=encoder_normalize_before, concat_after=encoder_concat_after,
positionwise_conv_kernel_size=positionwise_conv_kernel_size, macaron_style=use_macaron_style_in_conformer,
use_cnn_module=use_cnn_in_conformer, cnn_module_kernel=conformer_enc_kernel_size, zero_triu=False,
utt_embed=utt_embed_dim, connect_utt_emb_at_encoder_out=connect_utt_emb_at_encoder_out, lang_embs=lang_embs)
self.duration_predictor = DurationPredictor(idim=adim, n_layers=duration_predictor_layers,
n_chans=duration_predictor_chans,
kernel_size=duration_predictor_kernel_size,
dropout_rate=duration_predictor_dropout_rate, )
self.pitch_predictor = VariancePredictor(idim=adim, n_layers=pitch_predictor_layers,
n_chans=pitch_predictor_chans,
kernel_size=pitch_predictor_kernel_size,
dropout_rate=pitch_predictor_dropout)
self.pitch_embed = torch.nn.Sequential(torch.nn.Conv1d(in_channels=1, out_channels=adim,
kernel_size=pitch_embed_kernel_size,
padding=(pitch_embed_kernel_size - 1) // 2),
torch.nn.Dropout(pitch_embed_dropout))
self.energy_predictor = VariancePredictor(idim=adim, n_layers=energy_predictor_layers,
n_chans=energy_predictor_chans,
kernel_size=energy_predictor_kernel_size,
dropout_rate=energy_predictor_dropout)
self.energy_embed = torch.nn.Sequential(torch.nn.Conv1d(in_channels=1, out_channels=adim,
kernel_size=energy_embed_kernel_size,
padding=(energy_embed_kernel_size - 1) // 2),
torch.nn.Dropout(energy_embed_dropout))
self.length_regulator = LengthRegulator()
self.decoder = Conformer(idim=0,
attention_dim=adim,
attention_heads=aheads,
linear_units=dunits,
num_blocks=dlayers,
input_layer=None,
dropout_rate=transformer_dec_dropout_rate,
positional_dropout_rate=transformer_dec_positional_dropout_rate,
attention_dropout_rate=transformer_dec_attn_dropout_rate,
normalize_before=decoder_normalize_before,
concat_after=decoder_concat_after,
positionwise_conv_kernel_size=positionwise_conv_kernel_size,
macaron_style=use_macaron_style_in_conformer,
use_cnn_module=use_cnn_in_conformer,
cnn_module_kernel=conformer_dec_kernel_size)
self.feat_out = torch.nn.Linear(adim, odim * reduction_factor)
self.postnet = PostNet(idim=idim,
odim=odim,
n_layers=postnet_layers,
n_chans=postnet_chans,
n_filts=postnet_filts,
use_batch_norm=use_batch_norm,
dropout_rate=postnet_dropout_rate)
self.load_state_dict(weights)
def _forward(self, text_tensors, text_lens, gold_speech=None, speech_lens=None,
gold_durations=None, gold_pitch=None, gold_energy=None,
is_inference=False, alpha=1.0, utterance_embedding=None, lang_ids=None):
# forward encoder
text_masks = self._source_mask(text_lens)
encoded_texts, _ = self.encoder(text_tensors, text_masks, utterance_embedding=utterance_embedding, lang_ids=lang_ids) # (B, Tmax, adim)
# forward duration predictor and variance predictors
duration_masks = make_pad_mask(text_lens, device=text_lens.device)
if self.stop_gradient_from_pitch_predictor:
pitch_predictions = self.pitch_predictor(encoded_texts.detach(), duration_masks.unsqueeze(-1))
else:
pitch_predictions = self.pitch_predictor(encoded_texts, duration_masks.unsqueeze(-1))
if self.stop_gradient_from_energy_predictor:
energy_predictions = self.energy_predictor(encoded_texts.detach(), duration_masks.unsqueeze(-1))
else:
energy_predictions = self.energy_predictor(encoded_texts, duration_masks.unsqueeze(-1))
if is_inference:
if gold_durations is not None:
duration_predictions = gold_durations
else:
duration_predictions = self.duration_predictor.inference(encoded_texts, duration_masks)
if gold_pitch is not None:
pitch_predictions = gold_pitch
if gold_energy is not None:
energy_predictions = gold_energy
pitch_embeddings = self.pitch_embed(pitch_predictions.transpose(1, 2)).transpose(1, 2)
energy_embeddings = self.energy_embed(energy_predictions.transpose(1, 2)).transpose(1, 2)
encoded_texts = encoded_texts + energy_embeddings + pitch_embeddings
encoded_texts = self.length_regulator(encoded_texts, duration_predictions, alpha)
else:
duration_predictions = self.duration_predictor(encoded_texts, duration_masks)
# use groundtruth in training
pitch_embeddings = self.pitch_embed(gold_pitch.transpose(1, 2)).transpose(1, 2)
energy_embeddings = self.energy_embed(gold_energy.transpose(1, 2)).transpose(1, 2)
encoded_texts = encoded_texts + energy_embeddings + pitch_embeddings
encoded_texts = self.length_regulator(encoded_texts, gold_durations) # (B, Lmax, adim)
# forward decoder
if speech_lens is not None and not is_inference:
if self.reduction_factor > 1:
olens_in = speech_lens.new([olen // self.reduction_factor for olen in speech_lens])
else:
olens_in = speech_lens
h_masks = self._source_mask(olens_in)
else:
h_masks = None
zs, _ = self.decoder(encoded_texts, h_masks) # (B, Lmax, adim)
before_outs = self.feat_out(zs).view(zs.size(0), -1, self.odim) # (B, Lmax, odim)
# postnet -> (B, Lmax//r * r, odim)
after_outs = before_outs + self.postnet(before_outs.transpose(1, 2)).transpose(1, 2)
return before_outs, after_outs, duration_predictions, pitch_predictions, energy_predictions
@torch.no_grad()
def forward(self,
text,
speech=None,
durations=None,
pitch=None,
energy=None,
utterance_embedding=None,
return_duration_pitch_energy=False,
lang_id=None):
"""
Generate the sequence of features given the sequences of characters.
Args:
text: Input sequence of characters
speech: Feature sequence to extract style
durations: Groundtruth of duration
pitch: Groundtruth of token-averaged pitch
energy: Groundtruth of token-averaged energy
return_duration_pitch_energy: whether to return the list of predicted durations for nicer plotting
utterance_embedding: embedding of utterance wide parameters
Returns:
Mel Spectrogram
"""
self.eval()
# setup batch axis
ilens = torch.tensor([text.shape[0]], dtype=torch.long, device=text.device)
if speech is not None:
gold_speech = speech.unsqueeze(0)
else:
gold_speech = None
if durations is not None:
durations = durations.unsqueeze(0)
if pitch is not None:
pitch = pitch.unsqueeze(0)
if energy is not None:
energy = energy.unsqueeze(0)
if lang_id is not None:
lang_id = lang_id.unsqueeze(0)
before_outs, after_outs, d_outs, pitch_predictions, energy_predictions = self._forward(text.unsqueeze(0),
ilens,
gold_speech=gold_speech,
gold_durations=durations,
is_inference=True,
gold_pitch=pitch,
gold_energy=energy,
utterance_embedding=utterance_embedding.unsqueeze(0),
lang_ids=lang_id)
self.train()
if return_duration_pitch_energy:
return after_outs[0], d_outs[0], pitch_predictions[0], energy_predictions[0]
return after_outs[0]
def _source_mask(self, ilens):
x_masks = make_non_pad_mask(ilens).to(next(self.parameters()).device)
return x_masks.unsqueeze(-2)
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