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import torch
from torch.nn import Linear
from torch.nn import Sequential
from torch.nn import Tanh
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 Preprocessing.articulatory_features import get_feature_to_index_lookup
from TrainingInterfaces.Text_to_Spectrogram.ToucanTTS.Glow import Glow
from Utility.utils import make_non_pad_mask
class ToucanTTS(torch.nn.Module):
def __init__(self,
# network structure related
input_feature_dimensions=62,
output_spectrogram_channels=80,
attention_dimension=192,
attention_heads=4,
positionwise_conv_kernel_size=1,
use_scaled_positional_encoding=True,
use_macaron_style_in_conformer=True,
use_cnn_in_conformer=True,
# encoder
encoder_layers=6,
encoder_units=1536,
encoder_normalize_before=True,
encoder_concat_after=False,
conformer_encoder_kernel_size=7,
transformer_enc_dropout_rate=0.2,
transformer_enc_positional_dropout_rate=0.2,
transformer_enc_attn_dropout_rate=0.2,
# decoder
decoder_layers=6,
decoder_units=1536,
decoder_concat_after=False,
conformer_decoder_kernel_size=31,
decoder_normalize_before=True,
transformer_dec_dropout_rate=0.2,
transformer_dec_positional_dropout_rate=0.2,
transformer_dec_attn_dropout_rate=0.2,
# duration predictor
duration_predictor_layers=3,
duration_predictor_chans=256,
duration_predictor_kernel_size=3,
duration_predictor_dropout_rate=0.2,
# pitch predictor
pitch_predictor_layers=7, # 5 in espnet
pitch_predictor_chans=256,
pitch_predictor_kernel_size=5,
pitch_predictor_dropout=0.5,
pitch_embed_kernel_size=1,
pitch_embed_dropout=0.0,
# 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,
# additional features
utt_embed_dim=64,
detach_postflow=True,
lang_embs=8000,
weights=None):
super().__init__()
self.input_feature_dimensions = input_feature_dimensions
self.output_spectrogram_channels = output_spectrogram_channels
self.attention_dimension = attention_dimension
self.detach_postflow = detach_postflow
self.use_scaled_pos_enc = use_scaled_positional_encoding
self.multilingual_model = lang_embs is not None
self.multispeaker_model = utt_embed_dim is not None
articulatory_feature_embedding = Sequential(Linear(input_feature_dimensions, 100), Tanh(), Linear(100, attention_dimension))
self.encoder = Conformer(idim=input_feature_dimensions,
attention_dim=attention_dimension,
attention_heads=attention_heads,
linear_units=encoder_units,
num_blocks=encoder_layers,
input_layer=articulatory_feature_embedding,
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_encoder_kernel_size,
zero_triu=False,
utt_embed=utt_embed_dim,
lang_embs=lang_embs,
use_output_norm=True)
self.duration_predictor = DurationPredictor(idim=attention_dimension, n_layers=duration_predictor_layers,
n_chans=duration_predictor_chans,
kernel_size=duration_predictor_kernel_size,
dropout_rate=duration_predictor_dropout_rate,
utt_embed_dim=utt_embed_dim)
self.pitch_predictor = VariancePredictor(idim=attention_dimension, n_layers=pitch_predictor_layers,
n_chans=pitch_predictor_chans,
kernel_size=pitch_predictor_kernel_size,
dropout_rate=pitch_predictor_dropout,
utt_embed_dim=utt_embed_dim)
self.energy_predictor = VariancePredictor(idim=attention_dimension, n_layers=energy_predictor_layers,
n_chans=energy_predictor_chans,
kernel_size=energy_predictor_kernel_size,
dropout_rate=energy_predictor_dropout,
utt_embed_dim=utt_embed_dim)
self.pitch_embed = Sequential(torch.nn.Conv1d(in_channels=1,
out_channels=attention_dimension,
kernel_size=pitch_embed_kernel_size,
padding=(pitch_embed_kernel_size - 1) // 2),
torch.nn.Dropout(pitch_embed_dropout))
self.energy_embed = Sequential(torch.nn.Conv1d(in_channels=1, out_channels=attention_dimension, 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=attention_dimension,
attention_heads=attention_heads,
linear_units=decoder_units,
num_blocks=decoder_layers,
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_decoder_kernel_size,
use_output_norm=False)
self.feat_out = Linear(attention_dimension, output_spectrogram_channels)
self.conv_postnet = PostNet(idim=0,
odim=output_spectrogram_channels,
n_layers=5,
n_chans=256,
n_filts=5,
use_batch_norm=True,
dropout_rate=0.5)
self.post_flow = Glow(
in_channels=output_spectrogram_channels,
hidden_channels=192, # post_glow_hidden
kernel_size=5, # post_glow_kernel_size
dilation_rate=1,
n_blocks=18, # post_glow_n_blocks (original 12 in paper)
n_layers=4, # post_glow_n_block_layers (original 3 in paper)
n_split=4,
n_sqz=2,
text_condition_channels=attention_dimension,
share_cond_layers=False, # post_share_cond_layers
share_wn_layers=4,
sigmoid_scale=False,
condition_integration_projection=torch.nn.Conv1d(output_spectrogram_channels + attention_dimension, attention_dimension, 5, padding=2)
)
self.load_state_dict(weights)
self.eval()
def _forward(self,
text_tensors,
text_lengths,
gold_durations=None,
gold_pitch=None,
gold_energy=None,
duration_scaling_factor=1.0,
utterance_embedding=None,
lang_ids=None,
pitch_variance_scale=1.0,
energy_variance_scale=1.0,
pause_duration_scaling_factor=1.0):
if not self.multilingual_model:
lang_ids = None
if not self.multispeaker_model:
utterance_embedding = None
else:
utterance_embedding = torch.nn.functional.normalize(utterance_embedding)
# encoding the texts
text_masks = make_non_pad_mask(text_lengths, device=text_lengths.device).unsqueeze(-2)
encoded_texts, _ = self.encoder(text_tensors, text_masks, utterance_embedding=utterance_embedding, lang_ids=lang_ids)
# predicting pitch, energy and durations
pitch_predictions = self.pitch_predictor(encoded_texts, padding_mask=None, utt_embed=utterance_embedding) if gold_pitch is None else gold_pitch
energy_predictions = self.energy_predictor(encoded_texts, padding_mask=None, utt_embed=utterance_embedding) if gold_energy is None else gold_energy
predicted_durations = self.duration_predictor.inference(encoded_texts, padding_mask=None, utt_embed=utterance_embedding) if gold_durations is None else gold_durations
# modifying the predictions with linguistic knowledge and control parameters
for phoneme_index, phoneme_vector in enumerate(text_tensors.squeeze(0)):
if phoneme_vector[get_feature_to_index_lookup()["voiced"]] == 0:
pitch_predictions[0][phoneme_index] = 0.0
if phoneme_vector[get_feature_to_index_lookup()["phoneme"]] == 0:
energy_predictions[0][phoneme_index] = 0.0
if phoneme_vector[get_feature_to_index_lookup()["word-boundary"]] == 1:
predicted_durations[0][phoneme_index] = 0
if phoneme_vector[get_feature_to_index_lookup()["silence"]] == 1 and pause_duration_scaling_factor != 1.0:
predicted_durations[0][phoneme_index] = torch.round(predicted_durations[0][phoneme_index].float() * pause_duration_scaling_factor).long()
if duration_scaling_factor != 1.0:
assert duration_scaling_factor > 0
predicted_durations = torch.round(predicted_durations.float() * duration_scaling_factor).long()
pitch_predictions = _scale_variance(pitch_predictions, pitch_variance_scale)
energy_predictions = _scale_variance(energy_predictions, energy_variance_scale)
# enriching the text with pitch and energy info
embedded_pitch_curve = self.pitch_embed(pitch_predictions.transpose(1, 2)).transpose(1, 2)
embedded_energy_curve = self.energy_embed(energy_predictions.transpose(1, 2)).transpose(1, 2)
enriched_encoded_texts = encoded_texts + embedded_pitch_curve + embedded_energy_curve
# predicting durations for text and upsampling accordingly
upsampled_enriched_encoded_texts = self.length_regulator(enriched_encoded_texts, predicted_durations)
# decoding spectrogram
decoded_speech, _ = self.decoder(upsampled_enriched_encoded_texts, None)
decoded_spectrogram = self.feat_out(decoded_speech).view(decoded_speech.size(0), -1, self.output_spectrogram_channels)
refined_spectrogram = decoded_spectrogram + self.conv_postnet(decoded_spectrogram.transpose(1, 2)).transpose(1, 2)
# refine spectrogram
refined_spectrogram = self.post_flow(tgt_mels=None,
infer=True,
mel_out=refined_spectrogram,
encoded_texts=upsampled_enriched_encoded_texts,
tgt_nonpadding=None).squeeze()
return decoded_spectrogram.squeeze(), refined_spectrogram.squeeze(), predicted_durations.squeeze(), pitch_predictions.squeeze(), energy_predictions.squeeze()
@torch.inference_mode()
def forward(self,
text,
durations=None,
pitch=None,
energy=None,
utterance_embedding=None,
return_duration_pitch_energy=False,
lang_id=None,
duration_scaling_factor=1.0,
pitch_variance_scale=1.0,
energy_variance_scale=1.0,
pause_duration_scaling_factor=1.0):
"""
Generate the sequence of spectrogram frames given the sequence of vectorized phonemes.
Args:
text: input sequence of vectorized phonemes
durations: durations to be used (optional, if not provided, they will be predicted)
pitch: token-averaged pitch curve to be used (optional, if not provided, it will be predicted)
energy: token-averaged energy curve to be used (optional, if not provided, it will be predicted)
return_duration_pitch_energy: whether to return the list of predicted durations for nicer plotting
utterance_embedding: embedding of speaker information
lang_id: id to be fed into the embedding layer that contains language information
duration_scaling_factor: reasonable values are 0.8 < scale < 1.2.
1.0 means no scaling happens, higher values increase durations for the whole
utterance, lower values decrease durations for the whole utterance.
pitch_variance_scale: reasonable values are 0.6 < scale < 1.4.
1.0 means no scaling happens, higher values increase variance of the pitch curve,
lower values decrease variance of the pitch curve.
energy_variance_scale: reasonable values are 0.6 < scale < 1.4.
1.0 means no scaling happens, higher values increase variance of the energy curve,
lower values decrease variance of the energy curve.
pause_duration_scaling_factor: reasonable values are 0.6 < scale < 1.4.
scales the durations of pauses on top of the regular duration scaling
Returns:
mel spectrogram
"""
# setup batch axis
text_length = torch.tensor([text.shape[0]], dtype=torch.long, device=text.device)
if durations is not None:
durations = durations.unsqueeze(0).to(text.device)
if pitch is not None:
pitch = pitch.unsqueeze(0).to(text.device)
if energy is not None:
energy = energy.unsqueeze(0).to(text.device)
if lang_id is not None:
lang_id = lang_id.unsqueeze(0).to(text.device)
before_outs, \
after_outs, \
predicted_durations, \
pitch_predictions, \
energy_predictions = self._forward(text.unsqueeze(0),
text_length,
gold_durations=durations,
gold_pitch=pitch,
gold_energy=energy,
utterance_embedding=utterance_embedding.unsqueeze(0) if utterance_embedding is not None else None, lang_ids=lang_id,
duration_scaling_factor=duration_scaling_factor,
pitch_variance_scale=pitch_variance_scale,
energy_variance_scale=energy_variance_scale,
pause_duration_scaling_factor=pause_duration_scaling_factor)
if return_duration_pitch_energy:
return after_outs, predicted_durations, pitch_predictions, energy_predictions
return after_outs
def store_inverse_all(self):
def remove_weight_norm(m):
try:
if hasattr(m, 'store_inverse'):
m.store_inverse()
torch.nn.utils.remove_weight_norm(m)
except ValueError: # this module didn't have weight norm
return
self.apply(remove_weight_norm)
def _scale_variance(sequence, scale):
if scale == 1.0:
return sequence
average = sequence[0][sequence[0] != 0.0].mean()
sequence = sequence - average # center sequence around 0
sequence = sequence * scale # scale the variance
sequence = sequence + average # move center back to original with changed variance
for sequence_index in range(len(sequence[0])):
if sequence[0][sequence_index] < 0.0:
sequence[0][sequence_index] = 0.0
return sequence
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