Spaces:
Build error
Build error
File size: 13,917 Bytes
9aefa26 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 |
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)
|