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# *****************************************************************************
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of the NVIDIA CORPORATION nor the
# names of its contributors may be used to endorse or promote products
# derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# *****************************************************************************
import torch
import traceback
from torch import nn as nn
from torch.nn.utils.rnn import pad_sequence
from python.common.layers import ConvReLUNorm
from python.common.utils import mask_from_lens
from python.fastpitch.transformer import FFTransformer
def regulate_len(durations, enc_out, pace=1.0, mel_max_len=None):
"""If target=None, then predicted durations are applied"""
reps = torch.round(durations.float() * pace).long()
dec_lens = reps.sum(dim=1)
enc_rep = pad_sequence([torch.repeat_interleave(o, r, dim=0)
for o, r in zip(enc_out, reps)],
batch_first=True)
if mel_max_len:
enc_rep = enc_rep[:, :mel_max_len]
dec_lens = torch.clamp_max(dec_lens, mel_max_len)
return enc_rep, dec_lens
class TemporalPredictor(nn.Module):
"""Predicts a single float per each temporal location"""
def __init__(self, input_size, filter_size, kernel_size, dropout, n_layers=2, device=None):
super(TemporalPredictor, self).__init__()
self.layers = nn.Sequential(*[
ConvReLUNorm(input_size if i == 0 else filter_size, filter_size,
kernel_size=kernel_size, dropout=dropout)
for i in range(n_layers)]
)
self.fc = nn.Linear(filter_size, 1, bias=True)
def forward(self, enc_out, enc_out_mask):
out = enc_out * enc_out_mask
out = self.layers(out.transpose(1, 2)).transpose(1, 2)
out = self.fc(out) * enc_out_mask
return out.squeeze(-1)
class FastPitch(nn.Module):
def __init__(self, n_mel_channels, max_seq_len, n_symbols, padding_idx,
symbols_embedding_dim, in_fft_n_layers, in_fft_n_heads,
in_fft_d_head,
in_fft_conv1d_kernel_size, in_fft_conv1d_filter_size,
in_fft_output_size,
p_in_fft_dropout, p_in_fft_dropatt, p_in_fft_dropemb,
out_fft_n_layers, out_fft_n_heads, out_fft_d_head,
out_fft_conv1d_kernel_size, out_fft_conv1d_filter_size,
out_fft_output_size,
p_out_fft_dropout, p_out_fft_dropatt, p_out_fft_dropemb,
dur_predictor_kernel_size, dur_predictor_filter_size,
p_dur_predictor_dropout, dur_predictor_n_layers,
pitch_predictor_kernel_size, pitch_predictor_filter_size,
p_pitch_predictor_dropout, pitch_predictor_n_layers,
pitch_embedding_kernel_size, n_speakers, speaker_emb_weight, device=None):
super(FastPitch, self).__init__()
del max_seq_len # unused
self.encoder = FFTransformer(
n_layer=in_fft_n_layers, n_head=in_fft_n_heads,
d_model=symbols_embedding_dim,
d_head=in_fft_d_head,
d_inner=in_fft_conv1d_filter_size,
kernel_size=in_fft_conv1d_kernel_size,
dropout=p_in_fft_dropout,
dropatt=p_in_fft_dropatt,
dropemb=p_in_fft_dropemb,
embed_input=True,
d_embed=symbols_embedding_dim,
n_embed=n_symbols,
padding_idx=padding_idx)
if n_speakers > 1:
self.speaker_emb = nn.Embedding(n_speakers, symbols_embedding_dim)
print(f'self.speaker_emb, {self.speaker_emb}')
else:
self.speaker_emb = None
self.speaker_emb_weight = speaker_emb_weight
self.duration_predictor = TemporalPredictor(
in_fft_output_size,
filter_size=dur_predictor_filter_size,
kernel_size=dur_predictor_kernel_size,
dropout=p_dur_predictor_dropout, n_layers=dur_predictor_n_layers
)
self.decoder = FFTransformer(
n_layer=out_fft_n_layers, n_head=out_fft_n_heads,
d_model=symbols_embedding_dim,
d_head=out_fft_d_head,
d_inner=out_fft_conv1d_filter_size,
kernel_size=out_fft_conv1d_kernel_size,
dropout=p_out_fft_dropout,
dropatt=p_out_fft_dropatt,
dropemb=p_out_fft_dropemb,
embed_input=False,
d_embed=symbols_embedding_dim
)
self.pitch_predictor = TemporalPredictor(
in_fft_output_size,
filter_size=pitch_predictor_filter_size,
kernel_size=pitch_predictor_kernel_size,
dropout=p_pitch_predictor_dropout, n_layers=pitch_predictor_n_layers
)
self.pitch_emb = nn.Conv1d(
1, symbols_embedding_dim,
kernel_size=pitch_embedding_kernel_size,
padding=int((pitch_embedding_kernel_size - 1) / 2))
# Store values precomputed for training data within the model
self.register_buffer('pitch_mean', torch.zeros(1))
self.register_buffer('pitch_std', torch.zeros(1))
self.proj = nn.Linear(out_fft_output_size, n_mel_channels, bias=True)
def forward(self, inputs, use_gt_durations=True, use_gt_pitch=True,
pace=1.0, max_duration=75):
inputs, _, mel_tgt, _, dur_tgt, _, pitch_tgt, speaker = inputs
mel_max_len = mel_tgt.size(2)
# Calculate speaker embedding
if self.speaker_emb is None:
spk_emb = 0
else:
spk_emb = self.speaker_emb(speaker).unsqueeze(1)
spk_emb.mul_(self.speaker_emb_weight)
# Input FFT
enc_out, enc_mask = self.encoder(inputs, conditioning=spk_emb)
# Embedded for predictors
pred_enc_out, pred_enc_mask = enc_out, enc_mask
# Predict durations
log_dur_pred = self.duration_predictor(pred_enc_out, pred_enc_mask)
dur_pred = torch.clamp(torch.exp(log_dur_pred) - 1, 0, max_duration)
# Predict pitch
pitch_pred = self.pitch_predictor(enc_out, enc_mask)
if use_gt_pitch and pitch_tgt is not None:
pitch_emb = self.pitch_emb(pitch_tgt.unsqueeze(1))
else:
pitch_emb = self.pitch_emb(pitch_pred.unsqueeze(1))
enc_out = enc_out + pitch_emb.transpose(1, 2)
len_regulated, dec_lens = regulate_len(
dur_tgt if use_gt_durations else dur_pred,
enc_out, pace, mel_max_len)
# Output FFT
dec_out, dec_mask = self.decoder(len_regulated, dec_lens)
mel_out = self.proj(dec_out)
return mel_out, dec_mask, dur_pred, log_dur_pred, pitch_pred
def infer(self, inputs, input_lens, pace=1.0, dur_tgt=None, pitch_tgt=None,
pitch_transform=None, max_duration=75, speaker=0):
del input_lens # unused
if self.speaker_emb is None:
spk_emb = 0
else:
speaker = torch.ones(inputs.size(0)).long().to(inputs.device) * speaker
spk_emb = self.speaker_emb(speaker).unsqueeze(1)
spk_emb.mul_(self.speaker_emb_weight)
# Input FFT
enc_out, enc_mask = self.encoder(inputs, conditioning=spk_emb)
# Embedded for predictors
pred_enc_out, pred_enc_mask = enc_out, enc_mask
# Predict durations
log_dur_pred = self.duration_predictor(pred_enc_out, pred_enc_mask)
dur_pred = torch.clamp(torch.exp(log_dur_pred) - 1, 0, max_duration)
# Pitch over chars
pitch_pred = self.pitch_predictor(enc_out, enc_mask)
if pitch_transform is not None:
if self.pitch_std[0] == 0.0:
# XXX LJSpeech-1.1 defaults
mean, std = 218.14, 67.24
else:
mean, std = self.pitch_mean[0], self.pitch_std[0]
pitch_pred = pitch_transform(pitch_pred, enc_mask.sum(dim=(1,2)), mean, std)
if pitch_tgt is None:
pitch_emb = self.pitch_emb(pitch_pred.unsqueeze(1)).transpose(1, 2)
else:
pitch_emb = self.pitch_emb(pitch_tgt.unsqueeze(1)).transpose(1, 2)
enc_out = enc_out + pitch_emb
len_regulated, dec_lens = regulate_len(
dur_pred if dur_tgt is None else dur_tgt,
enc_out, pace, mel_max_len=None)
dec_out, dec_mask = self.decoder(len_regulated, dec_lens)
mel_out = self.proj(dec_out)
# mel_lens = dec_mask.squeeze(2).sum(axis=1).long()
mel_out = mel_out.permute(0, 2, 1) # For inference.py
return mel_out, dec_lens, dur_pred, pitch_pred
def infer_using_vals (self, logger, plugin_manager, sequence, pace, enc_out, max_duration, enc_mask, dur_pred_existing=None, pitch_pred_existing=None, old_sequence=None, new_sequence=None, pitch_amp=None):
start_index = None
end_index = None
# Calculate text splicing bounds, if needed
if old_sequence is not None:
old_sequence_np = old_sequence.cpu().detach().numpy()
old_sequence_np = list(old_sequence_np[0])
new_sequence_np = new_sequence.cpu().detach().numpy()
new_sequence_np = list(new_sequence_np[0])
# Get the index of the first changed value
if old_sequence_np[0]==new_sequence_np[0]: # If the start of both sequences is the same, then the change is not at the start
for i in range(len(old_sequence_np)):
if i<len(new_sequence_np):
if old_sequence_np[i]!=new_sequence_np[i]:
start_index = i-1
break
else:
start_index = i-1
break
if start_index is None:
start_index = len(old_sequence_np)-1
# Get the index of the last changed value
old_sequence_np.reverse()
new_sequence_np.reverse()
if old_sequence_np[0]==new_sequence_np[0]: # If the end of both reversed sequences is the same, then the change is not at the end
for i in range(len(old_sequence_np)):
if i<len(new_sequence_np):
if old_sequence_np[i]!=new_sequence_np[i]:
end_index = len(old_sequence_np)-1-i+1
break
else:
end_index = len(old_sequence_np)-1-i+1
break
old_sequence_np.reverse()
new_sequence_np.reverse()
# Calculate its own pitch and duration vals if these were not already provided
if (dur_pred_existing is None or pitch_pred_existing is None) or old_sequence is not None:
# Embedded for predictors
pred_enc_out, pred_enc_mask = enc_out, enc_mask
# Predict durations
log_dur_pred = self.duration_predictor(pred_enc_out, pred_enc_mask)
dur_pred = torch.clamp(torch.exp(log_dur_pred) - 1, 0, max_duration)
dur_pred = torch.clamp(dur_pred, 0.25)
# Pitch over chars
pitch_pred = self.pitch_predictor(enc_out, enc_mask)
else:
dur_pred = dur_pred_existing
pitch_pred = pitch_pred_existing
# Splice/replace pitch/duration values from the old input if simulating only a partial re-generation
if start_index is not None or end_index is not None:
dur_pred_np = list(dur_pred.cpu().detach().numpy())[0]
pitch_pred_np = list(pitch_pred.cpu().detach().numpy())[0]
dur_pred_existing_np = list(dur_pred_existing.cpu().detach().numpy())[0]
pitch_pred_existing_np = list(pitch_pred_existing.cpu().detach().numpy())[0]
if start_index is not None: # Replace starting values
for i in range(start_index+1):
dur_pred_np[i] = dur_pred_existing_np[i]
pitch_pred_np[i] = pitch_pred_existing_np[i]
if end_index is not None: # Replace end values
for i in range(len(old_sequence_np)-end_index):
dur_pred_np[-i-1] = dur_pred_existing_np[-i-1]
pitch_pred_np[-i-1] = pitch_pred_existing_np[-i-1]
dur_pred = torch.tensor(dur_pred_np).to(self.device).unsqueeze(0)
pitch_pred = torch.tensor(pitch_pred_np).to(self.device).unsqueeze(0)
if pitch_amp is not None:
pitch_pred = pitch_pred * pitch_amp
if plugin_manager and len(plugin_manager.plugins["synth-line"]["mid"]):
plugin_data = {
"duration": dur_pred.cpu().detach().numpy(),
"pitch": pitch_pred.cpu().detach().numpy(),
"text": [val.split("|") for val in sequence],
"is_fresh_synth": pitch_pred_existing is None and dur_pred_existing is None
}
plugin_manager.run_plugins(plist=plugin_manager.plugins["synth-line"]["mid"], event="mid synth-line", data=plugin_data)
dur_pred = torch.tensor(plugin_data["duration"]).to(self.device)
pitch_pred = torch.tensor(plugin_data["pitch"]).to(self.device)
pitch_emb = self.pitch_emb(pitch_pred.unsqueeze(1)).transpose(1, 2)
enc_out = enc_out + pitch_emb
len_regulated, dec_lens = regulate_len(dur_pred, enc_out, pace, mel_max_len=None)
dec_out, dec_mask = self.decoder(len_regulated, dec_lens)
mel_out = self.proj(dec_out)
mel_out = mel_out.permute(0, 2, 1) # For inference.py
start_index = -1 if start_index is None else start_index
end_index = -1 if end_index is None else end_index
return mel_out, dec_lens, dur_pred, pitch_pred, start_index, end_index
def infer_advanced (self, logger, plugin_manager, cleaned_text, inputs, speaker_i, pace=1.0, pitch_data=None, max_duration=75, old_sequence=None, pitch_amp=None):
if speaker_i is not None:
speaker = torch.ones(inputs.size(0)).long().to(inputs.device) * speaker_i
spk_emb = self.speaker_emb(speaker).unsqueeze(1)
spk_emb.mul_(self.speaker_emb_weight)
del speaker
else:
spk_emb = 0
# Input FFT
enc_out, enc_mask = self.encoder(inputs, conditioning=spk_emb)
if pitch_data is not None and pitch_data[0] is not None and len(pitch_data[0]) and pitch_data[1] is not None and len(pitch_data[1]):
pitch_pred, dur_pred, energy_pred, _, _, _, _, _ = pitch_data
dur_pred = torch.tensor(dur_pred)
dur_pred = dur_pred.view((1, dur_pred.shape[0])).float().to(self.device)
pitch_pred = torch.tensor(pitch_pred)
pitch_pred = pitch_pred.view((1, pitch_pred.shape[0])).float().to(self.device)
del spk_emb
# Try using the provided pitch/duration data, but fall back to using its own, otherwise
try:
return self.infer_using_vals(logger, plugin_manager, cleaned_text, pace, enc_out, max_duration, enc_mask, dur_pred_existing=dur_pred, pitch_pred_existing=pitch_pred, old_sequence=old_sequence, new_sequence=inputs, pitch_amp=pitch_amp)
except:
print(traceback.format_exc())
logger.info(traceback.format_exc())
return self.infer_using_vals(logger, plugin_manager, cleaned_text, pace, enc_out, max_duration, enc_mask, None, None, None, pitch_amp=pitch_amp)
else:
del spk_emb
return self.infer_using_vals(logger, plugin_manager, cleaned_text, pace, enc_out, max_duration, enc_mask, None, None, None, pitch_amp=pitch_amp)
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