def __init__(self, config: GlowTTSConfig):
super().__init__()
# pass all config fields to `self`
# for fewer code change
self.config = config
for key in config:
setattr(self, key, config[key])
_, self.config, self.num_chars = self.get_characters(config)
self.decoder_output_dim = config.out_channels
self.init_multispeaker(config)
# if is a multispeaker and c_in_channels is 0, set to 256
self.c_in_channels = 0
if self.num_speakers > 1:
if self.d_vector_dim:
self.c_in_channels = self.d_vector_dim
elif self.c_in_channels == 0 and not self.d_vector_dim:
# TODO: make this adjustable
self.c_in_channels = 256
self.run_data_dep_init = config.data_dep_init_steps > 0
self.encoder = Encoder(
self.num_chars,
out_channels=self.out_channels,
hidden_channels=self.hidden_channels_enc,
hidden_channels_dp=self.hidden_channels_dp,
encoder_type=self.encoder_type,
encoder_params=self.encoder_params,
mean_only=self.mean_only,
use_prenet=self.use_encoder_prenet,
dropout_p_dp=self.dropout_p_dp,
c_in_channels=self.c_in_channels,
)
self.decoder = Decoder(
self.out_channels,
self.hidden_channels_dec,
self.kernel_size_dec,
self.dilation_rate,
self.num_flow_blocks_dec,
self.num_block_layers,
dropout_p=self.dropout_p_dec,
num_splits=self.num_splits,
num_squeeze=self.num_squeeze,
sigmoid_scale=self.sigmoid_scale,
c_in_channels=self.c_in_channels,
)
def __init__(
self,
config: GlowTTSConfig,
ap: "AudioProcessor" = None,
tokenizer: "TTSTokenizer" = None,
speaker_manager: SpeakerManager = None,
):
super().__init__(config, ap, tokenizer, speaker_manager)
# pass all config fields to `self`
# for fewer code change
self.config = config
for key in config:
setattr(self, key, config[key])
self.decoder_output_dim = config.out_channels
# init multi-speaker layers if necessary
self.init_multispeaker(config)
self.run_data_dep_init = config.data_dep_init_steps > 0
self.encoder = Encoder(
self.num_chars,
out_channels=self.out_channels,
hidden_channels=self.hidden_channels_enc,
hidden_channels_dp=self.hidden_channels_dp,
encoder_type=self.encoder_type,
encoder_params=self.encoder_params,
mean_only=self.mean_only,
use_prenet=self.use_encoder_prenet,
dropout_p_dp=self.dropout_p_dp,
c_in_channels=self.c_in_channels,
)
self.decoder = Decoder(
self.out_channels,
self.hidden_channels_dec,
self.kernel_size_dec,
self.dilation_rate,
self.num_flow_blocks_dec,
self.num_block_layers,
dropout_p=self.dropout_p_dec,
num_splits=self.num_splits,
num_squeeze=self.num_squeeze,
sigmoid_scale=self.sigmoid_scale,
c_in_channels=self.c_in_channels,
)
def __init__(
self,
num_chars,
hidden_channels_enc,
hidden_channels_dec,
use_encoder_prenet,
hidden_channels_dp,
out_channels,
num_flow_blocks_dec=12,
inference_noise_scale=0.33,
kernel_size_dec=5,
dilation_rate=5,
num_block_layers=4,
dropout_p_dp=0.1,
dropout_p_dec=0.05,
num_speakers=0,
c_in_channels=0,
num_splits=4,
num_squeeze=1,
sigmoid_scale=False,
mean_only=False,
encoder_type="transformer",
encoder_params=None,
speaker_embedding_dim=None,
):
super().__init__()
self.num_chars = num_chars
self.hidden_channels_dp = hidden_channels_dp
self.hidden_channels_enc = hidden_channels_enc
self.hidden_channels_dec = hidden_channels_dec
self.out_channels = out_channels
self.num_flow_blocks_dec = num_flow_blocks_dec
self.kernel_size_dec = kernel_size_dec
self.dilation_rate = dilation_rate
self.num_block_layers = num_block_layers
self.dropout_p_dec = dropout_p_dec
self.num_speakers = num_speakers
self.c_in_channels = c_in_channels
self.num_splits = num_splits
self.num_squeeze = num_squeeze
self.sigmoid_scale = sigmoid_scale
self.mean_only = mean_only
self.use_encoder_prenet = use_encoder_prenet
self.inference_noise_scale = inference_noise_scale
# model constants.
self.noise_scale = 0.33 # defines the noise variance applied to the random z vector at inference.
self.length_scale = 1.0 # scaler for the duration predictor. The larger it is, the slower the speech.
self.speaker_embedding_dim = speaker_embedding_dim
# if is a multispeaker and c_in_channels is 0, set to 256
if num_speakers > 1:
if self.c_in_channels == 0 and not self.speaker_embedding_dim:
# TODO: make this adjustable
self.c_in_channels = 256
elif self.speaker_embedding_dim:
self.c_in_channels = self.speaker_embedding_dim
self.encoder = Encoder(
num_chars,
out_channels=out_channels,
hidden_channels=hidden_channels_enc,
hidden_channels_dp=hidden_channels_dp,
encoder_type=encoder_type,
encoder_params=encoder_params,
mean_only=mean_only,
use_prenet=use_encoder_prenet,
dropout_p_dp=dropout_p_dp,
c_in_channels=self.c_in_channels,
)
self.decoder = Decoder(
out_channels,
hidden_channels_dec,
kernel_size_dec,
dilation_rate,
num_flow_blocks_dec,
num_block_layers,
dropout_p=dropout_p_dec,
num_splits=num_splits,
num_squeeze=num_squeeze,
sigmoid_scale=sigmoid_scale,
c_in_channels=self.c_in_channels,
)
if num_speakers > 1 and not speaker_embedding_dim:
# speaker embedding layer
self.emb_g = nn.Embedding(num_speakers, self.c_in_channels)
nn.init.uniform_(self.emb_g.weight, -0.1, 0.1)
class GlowTTS(nn.Module):
"""Glow TTS models from https://arxiv.org/abs/2005.11129
Args:
num_chars (int): number of embedding characters.
hidden_channels_enc (int): number of embedding and encoder channels.
hidden_channels_dec (int): number of decoder channels.
use_encoder_prenet (bool): enable/disable prenet for encoder. Prenet modules are hard-coded for each alternative encoder.
hidden_channels_dp (int): number of duration predictor channels.
out_channels (int): number of output channels. It should be equal to the number of spectrogram filter.
num_flow_blocks_dec (int): number of decoder blocks.
kernel_size_dec (int): decoder kernel size.
dilation_rate (int): rate to increase dilation by each layer in a decoder block.
num_block_layers (int): number of decoder layers in each decoder block.
dropout_p_dec (float): dropout rate for decoder.
num_speaker (int): number of speaker to define the size of speaker embedding layer.
c_in_channels (int): number of speaker embedding channels. It is set to 512 if embeddings are learned.
num_splits (int): number of split levels in inversible conv1x1 operation.
num_squeeze (int): number of squeeze levels. When squeezing channels increases and time steps reduces by the factor 'num_squeeze'.
sigmoid_scale (bool): enable/disable sigmoid scaling in decoder.
mean_only (bool): if True, encoder only computes mean value and uses constant variance for each time step.
encoder_type (str): encoder module type.
encoder_params (dict): encoder module parameters.
speaker_embedding_dim (int): channels of external speaker embedding vectors.
"""
def __init__(
self,
num_chars,
hidden_channels_enc,
hidden_channels_dec,
use_encoder_prenet,
hidden_channels_dp,
out_channels,
num_flow_blocks_dec=12,
inference_noise_scale=0.33,
kernel_size_dec=5,
dilation_rate=5,
num_block_layers=4,
dropout_p_dp=0.1,
dropout_p_dec=0.05,
num_speakers=0,
c_in_channels=0,
num_splits=4,
num_squeeze=1,
sigmoid_scale=False,
mean_only=False,
encoder_type="transformer",
encoder_params=None,
speaker_embedding_dim=None,
):
super().__init__()
self.num_chars = num_chars
self.hidden_channels_dp = hidden_channels_dp
self.hidden_channels_enc = hidden_channels_enc
self.hidden_channels_dec = hidden_channels_dec
self.out_channels = out_channels
self.num_flow_blocks_dec = num_flow_blocks_dec
self.kernel_size_dec = kernel_size_dec
self.dilation_rate = dilation_rate
self.num_block_layers = num_block_layers
self.dropout_p_dec = dropout_p_dec
self.num_speakers = num_speakers
self.c_in_channels = c_in_channels
self.num_splits = num_splits
self.num_squeeze = num_squeeze
self.sigmoid_scale = sigmoid_scale
self.mean_only = mean_only
self.use_encoder_prenet = use_encoder_prenet
self.inference_noise_scale = inference_noise_scale
# model constants.
self.noise_scale = 0.33 # defines the noise variance applied to the random z vector at inference.
self.length_scale = 1.0 # scaler for the duration predictor. The larger it is, the slower the speech.
self.speaker_embedding_dim = speaker_embedding_dim
# if is a multispeaker and c_in_channels is 0, set to 256
if num_speakers > 1:
if self.c_in_channels == 0 and not self.speaker_embedding_dim:
# TODO: make this adjustable
self.c_in_channels = 256
elif self.speaker_embedding_dim:
self.c_in_channels = self.speaker_embedding_dim
self.encoder = Encoder(
num_chars,
out_channels=out_channels,
hidden_channels=hidden_channels_enc,
hidden_channels_dp=hidden_channels_dp,
encoder_type=encoder_type,
encoder_params=encoder_params,
mean_only=mean_only,
use_prenet=use_encoder_prenet,
dropout_p_dp=dropout_p_dp,
c_in_channels=self.c_in_channels,
)
self.decoder = Decoder(
out_channels,
hidden_channels_dec,
kernel_size_dec,
dilation_rate,
num_flow_blocks_dec,
num_block_layers,
dropout_p=dropout_p_dec,
num_splits=num_splits,
num_squeeze=num_squeeze,
sigmoid_scale=sigmoid_scale,
c_in_channels=self.c_in_channels,
)
if num_speakers > 1 and not speaker_embedding_dim:
# speaker embedding layer
self.emb_g = nn.Embedding(num_speakers, self.c_in_channels)
nn.init.uniform_(self.emb_g.weight, -0.1, 0.1)
@staticmethod
def compute_outputs(attn, o_mean, o_log_scale, x_mask):
# compute final values with the computed alignment
y_mean = torch.matmul(
attn.squeeze(1).transpose(1, 2), o_mean.transpose(1, 2)).transpose(
1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
y_log_scale = torch.matmul(
attn.squeeze(1).transpose(1, 2), o_log_scale.transpose(
1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
# compute total duration with adjustment
o_attn_dur = torch.log(1 + torch.sum(attn, -1)) * x_mask
return y_mean, y_log_scale, o_attn_dur
def forward(self, x, x_lengths, y=None, y_lengths=None, attn=None, g=None):
"""
Shapes:
x: [B, T]
x_lenghts: B
y: [B, C, T]
y_lengths: B
g: [B, C] or B
"""
y_max_length = y.size(2)
# norm speaker embeddings
if g is not None:
if self.speaker_embedding_dim:
g = F.normalize(g).unsqueeze(-1)
else:
g = F.normalize(self.emb_g(g)).unsqueeze(-1) # [b, h, 1]
# embedding pass
o_mean, o_log_scale, o_dur_log, x_mask = self.encoder(x,
x_lengths,
g=g)
# drop redisual frames wrt num_squeeze and set y_lengths.
y, y_lengths, y_max_length, attn = self.preprocess(
y, y_lengths, y_max_length, None)
# create masks
y_mask = torch.unsqueeze(sequence_mask(y_lengths, y_max_length),
1).to(x_mask.dtype)
attn_mask = torch.unsqueeze(x_mask, -1) * torch.unsqueeze(y_mask, 2)
# decoder pass
z, logdet = self.decoder(y, y_mask, g=g, reverse=False)
# find the alignment path
with torch.no_grad():
o_scale = torch.exp(-2 * o_log_scale)
logp1 = torch.sum(-0.5 * math.log(2 * math.pi) - o_log_scale,
[1]).unsqueeze(-1) # [b, t, 1]
logp2 = torch.matmul(o_scale.transpose(1, 2), -0.5 *
(z**2)) # [b, t, d] x [b, d, t'] = [b, t, t']
logp3 = torch.matmul((o_mean * o_scale).transpose(1, 2),
z) # [b, t, d] x [b, d, t'] = [b, t, t']
logp4 = torch.sum(-0.5 * (o_mean**2) * o_scale,
[1]).unsqueeze(-1) # [b, t, 1]
logp = logp1 + logp2 + logp3 + logp4 # [b, t, t']
attn = maximum_path(logp,
attn_mask.squeeze(1)).unsqueeze(1).detach()
y_mean, y_log_scale, o_attn_dur = self.compute_outputs(
attn, o_mean, o_log_scale, x_mask)
attn = attn.squeeze(1).permute(0, 2, 1)
return z, logdet, y_mean, y_log_scale, attn, o_dur_log, o_attn_dur
@torch.no_grad()
def inference_with_MAS(self,
x,
x_lengths,
y=None,
y_lengths=None,
attn=None,
g=None):
"""
It's similar to the teacher forcing in Tacotron.
It was proposed in: https://arxiv.org/abs/2104.05557
Shapes:
x: [B, T]
x_lenghts: B
y: [B, C, T]
y_lengths: B
g: [B, C] or B
"""
y_max_length = y.size(2)
# norm speaker embeddings
if g is not None:
if self.external_speaker_embedding_dim:
g = F.normalize(g).unsqueeze(-1)
else:
g = F.normalize(self.emb_g(g)).unsqueeze(-1) # [b, h, 1]
# embedding pass
o_mean, o_log_scale, o_dur_log, x_mask = self.encoder(x,
x_lengths,
g=g)
# drop redisual frames wrt num_squeeze and set y_lengths.
y, y_lengths, y_max_length, attn = self.preprocess(
y, y_lengths, y_max_length, None)
# create masks
y_mask = torch.unsqueeze(sequence_mask(y_lengths, y_max_length),
1).to(x_mask.dtype)
attn_mask = torch.unsqueeze(x_mask, -1) * torch.unsqueeze(y_mask, 2)
# decoder pass
z, logdet = self.decoder(y, y_mask, g=g, reverse=False)
# find the alignment path between z and encoder output
o_scale = torch.exp(-2 * o_log_scale)
logp1 = torch.sum(-0.5 * math.log(2 * math.pi) - o_log_scale,
[1]).unsqueeze(-1) # [b, t, 1]
logp2 = torch.matmul(o_scale.transpose(1, 2), -0.5 *
(z**2)) # [b, t, d] x [b, d, t'] = [b, t, t']
logp3 = torch.matmul((o_mean * o_scale).transpose(1, 2),
z) # [b, t, d] x [b, d, t'] = [b, t, t']
logp4 = torch.sum(-0.5 * (o_mean**2) * o_scale,
[1]).unsqueeze(-1) # [b, t, 1]
logp = logp1 + logp2 + logp3 + logp4 # [b, t, t']
attn = maximum_path(logp, attn_mask.squeeze(1)).unsqueeze(1).detach()
y_mean, y_log_scale, o_attn_dur = self.compute_outputs(
attn, o_mean, o_log_scale, x_mask)
attn = attn.squeeze(1).permute(0, 2, 1)
# get predited aligned distribution
z = y_mean * y_mask
# reverse the decoder and predict using the aligned distribution
y, logdet = self.decoder(z, y_mask, g=g, reverse=True)
return y, logdet, y_mean, y_log_scale, attn, o_dur_log, o_attn_dur
@torch.no_grad()
def decoder_inference(self, y, y_lengths=None, g=None):
"""
Shapes:
y: [B, C, T]
y_lengths: B
g: [B, C] or B
"""
y_max_length = y.size(2)
# norm speaker embeddings
if g is not None:
if self.external_speaker_embedding_dim:
g = F.normalize(g).unsqueeze(-1)
else:
g = F.normalize(self.emb_g(g)).unsqueeze(-1) # [b, h, 1]
y_mask = torch.unsqueeze(sequence_mask(y_lengths, y_max_length),
1).to(y.dtype)
# decoder pass
z, logdet = self.decoder(y, y_mask, g=g, reverse=False)
# reverse decoder and predict
y, logdet = self.decoder(z, y_mask, g=g, reverse=True)
return y, logdet
@torch.no_grad()
def inference(self, x, x_lengths, g=None):
if g is not None:
if self.speaker_embedding_dim:
g = F.normalize(g).unsqueeze(-1)
else:
g = F.normalize(self.emb_g(g)).unsqueeze(-1) # [b, h]
# embedding pass
o_mean, o_log_scale, o_dur_log, x_mask = self.encoder(x,
x_lengths,
g=g)
# compute output durations
w = (torch.exp(o_dur_log) - 1) * x_mask * self.length_scale
w_ceil = torch.ceil(w)
y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
y_max_length = None
# compute masks
y_mask = torch.unsqueeze(sequence_mask(y_lengths, y_max_length),
1).to(x_mask.dtype)
attn_mask = torch.unsqueeze(x_mask, -1) * torch.unsqueeze(y_mask, 2)
# compute attention mask
attn = generate_path(w_ceil.squeeze(1),
attn_mask.squeeze(1)).unsqueeze(1)
y_mean, y_log_scale, o_attn_dur = self.compute_outputs(
attn, o_mean, o_log_scale, x_mask)
z = (y_mean + torch.exp(y_log_scale) * torch.randn_like(y_mean) *
self.inference_noise_scale) * y_mask
# decoder pass
y, logdet = self.decoder(z, y_mask, g=g, reverse=True)
attn = attn.squeeze(1).permute(0, 2, 1)
return y, logdet, y_mean, y_log_scale, attn, o_dur_log, o_attn_dur
def preprocess(self, y, y_lengths, y_max_length, attn=None):
if y_max_length is not None:
y_max_length = (y_max_length //
self.num_squeeze) * self.num_squeeze
y = y[:, :, :y_max_length]
if attn is not None:
attn = attn[:, :, :, :y_max_length]
y_lengths = (y_lengths // self.num_squeeze) * self.num_squeeze
return y, y_lengths, y_max_length, attn
def store_inverse(self):
self.decoder.store_inverse()
def load_checkpoint(self, config, checkpoint_path, eval=False): # pylint: disable=unused-argument, redefined-builtin
state = torch.load(checkpoint_path, map_location=torch.device("cpu"))
self.load_state_dict(state["model"])
if eval:
self.eval()
self.store_inverse()
assert not self.training
class GlowTTS(BaseTTS):
"""GlowTTS model.
Paper::
https://arxiv.org/abs/2005.11129
Paper abstract::
Recently, text-to-speech (TTS) models such as FastSpeech and ParaNet have been proposed to generate
mel-spectrograms from text in parallel. Despite the advantage, the parallel TTS models cannot be trained
without guidance from autoregressive TTS models as their external aligners. In this work, we propose Glow-TTS,
a flow-based generative model for parallel TTS that does not require any external aligner. By combining the
properties of flows and dynamic programming, the proposed model searches for the most probable monotonic
alignment between text and the latent representation of speech on its own. We demonstrate that enforcing hard
monotonic alignments enables robust TTS, which generalizes to long utterances, and employing generative flows
enables fast, diverse, and controllable speech synthesis. Glow-TTS obtains an order-of-magnitude speed-up over
the autoregressive model, Tacotron 2, at synthesis with comparable speech quality. We further show that our
model can be easily extended to a multi-speaker setting.
Check :class:`TTS.tts.configs.glow_tts_config.GlowTTSConfig` for class arguments.
Examples:
>>> from TTS.tts.configs import GlowTTSConfig
>>> from TTS.tts.models.glow_tts import GlowTTS
>>> config = GlowTTSConfig()
>>> model = GlowTTS(config)
"""
def __init__(self, config: GlowTTSConfig):
super().__init__()
# pass all config fields to `self`
# for fewer code change
self.config = config
for key in config:
setattr(self, key, config[key])
_, self.config, self.num_chars = self.get_characters(config)
self.decoder_output_dim = config.out_channels
self.init_multispeaker(config)
# if is a multispeaker and c_in_channels is 0, set to 256
self.c_in_channels = 0
if self.num_speakers > 1:
if self.d_vector_dim:
self.c_in_channels = self.d_vector_dim
elif self.c_in_channels == 0 and not self.d_vector_dim:
# TODO: make this adjustable
self.c_in_channels = 256
self.run_data_dep_init = config.data_dep_init_steps > 0
self.encoder = Encoder(
self.num_chars,
out_channels=self.out_channels,
hidden_channels=self.hidden_channels_enc,
hidden_channels_dp=self.hidden_channels_dp,
encoder_type=self.encoder_type,
encoder_params=self.encoder_params,
mean_only=self.mean_only,
use_prenet=self.use_encoder_prenet,
dropout_p_dp=self.dropout_p_dp,
c_in_channels=self.c_in_channels,
)
self.decoder = Decoder(
self.out_channels,
self.hidden_channels_dec,
self.kernel_size_dec,
self.dilation_rate,
self.num_flow_blocks_dec,
self.num_block_layers,
dropout_p=self.dropout_p_dec,
num_splits=self.num_splits,
num_squeeze=self.num_squeeze,
sigmoid_scale=self.sigmoid_scale,
c_in_channels=self.c_in_channels,
)
def init_multispeaker(self, config: "Coqpit", data: list = None) -> None:
"""Initialize multi-speaker modules of a model. A model can be trained either with a speaker embedding layer
or with external `d_vectors` computed from a speaker encoder model.
If you need a different behaviour, override this function for your model.
Args:
config (Coqpit): Model configuration.
data (List, optional): Dataset items to infer number of speakers. Defaults to None.
"""
# init speaker manager
self.speaker_manager = get_speaker_manager(config, data=data)
self.num_speakers = self.speaker_manager.num_speakers
if config.use_d_vector_file:
self.external_d_vector_dim = config.d_vector_dim
else:
self.external_d_vector_dim = 0
# init speaker embedding layer
if config.use_speaker_embedding and not config.use_d_vector_file:
self.embedded_speaker_dim = self.c_in_channels
self.emb_g = nn.Embedding(self.num_speakers,
self.embedded_speaker_dim)
nn.init.uniform_(self.emb_g.weight, -0.1, 0.1)
@staticmethod
def compute_outputs(attn, o_mean, o_log_scale, x_mask):
"""Compute and format the mode outputs with the given alignment map"""
y_mean = torch.matmul(
attn.squeeze(1).transpose(1, 2), o_mean.transpose(1, 2)).transpose(
1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
y_log_scale = torch.matmul(
attn.squeeze(1).transpose(1, 2), o_log_scale.transpose(
1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
# compute total duration with adjustment
o_attn_dur = torch.log(1 + torch.sum(attn, -1)) * x_mask
return y_mean, y_log_scale, o_attn_dur
def unlock_act_norm_layers(self):
"""Unlock activation normalization layers for data depended initalization."""
for f in self.decoder.flows:
if getattr(f, "set_ddi", False):
f.set_ddi(True)
def lock_act_norm_layers(self):
"""Lock activation normalization layers."""
for f in self.decoder.flows:
if getattr(f, "set_ddi", False):
f.set_ddi(False)
def forward(self,
x,
x_lengths,
y,
y_lengths=None,
aux_input={
"d_vectors": None,
"speaker_ids": None
}): # pylint: disable=dangerous-default-value
"""
Shapes:
- x: :math:`[B, T]`
- x_lenghts::math:`B`
- y: :math:`[B, T, C]`
- y_lengths::math:`B`
- g: :math:`[B, C] or B`
"""
# [B, T, C] -> [B, C, T]
y = y.transpose(1, 2)
y_max_length = y.size(2)
# norm speaker embeddings
g = aux_input[
"d_vectors"] if aux_input is not None and "d_vectors" in aux_input else None
if self.use_speaker_embedding or self.use_d_vector_file:
if not self.use_d_vector_file:
g = F.normalize(g).unsqueeze(-1)
else:
g = F.normalize(self.emb_g(g)).unsqueeze(-1) # [b, h, 1]
# embedding pass
o_mean, o_log_scale, o_dur_log, x_mask = self.encoder(x,
x_lengths,
g=g)
# drop redisual frames wrt num_squeeze and set y_lengths.
y, y_lengths, y_max_length, attn = self.preprocess(
y, y_lengths, y_max_length, None)
# create masks
y_mask = torch.unsqueeze(sequence_mask(y_lengths, y_max_length),
1).to(x_mask.dtype)
# [B, 1, T_en, T_de]
attn_mask = torch.unsqueeze(x_mask, -1) * torch.unsqueeze(y_mask, 2)
# decoder pass
z, logdet = self.decoder(y, y_mask, g=g, reverse=False)
# find the alignment path
with torch.no_grad():
o_scale = torch.exp(-2 * o_log_scale)
logp1 = torch.sum(-0.5 * math.log(2 * math.pi) - o_log_scale,
[1]).unsqueeze(-1) # [b, t, 1]
logp2 = torch.matmul(o_scale.transpose(1, 2), -0.5 *
(z**2)) # [b, t, d] x [b, d, t'] = [b, t, t']
logp3 = torch.matmul((o_mean * o_scale).transpose(1, 2),
z) # [b, t, d] x [b, d, t'] = [b, t, t']
logp4 = torch.sum(-0.5 * (o_mean**2) * o_scale,
[1]).unsqueeze(-1) # [b, t, 1]
logp = logp1 + logp2 + logp3 + logp4 # [b, t, t']
attn = maximum_path(logp,
attn_mask.squeeze(1)).unsqueeze(1).detach()
y_mean, y_log_scale, o_attn_dur = self.compute_outputs(
attn, o_mean, o_log_scale, x_mask)
attn = attn.squeeze(1).permute(0, 2, 1)
outputs = {
"z": z.transpose(1, 2),
"logdet": logdet,
"y_mean": y_mean.transpose(1, 2),
"y_log_scale": y_log_scale.transpose(1, 2),
"alignments": attn,
"durations_log": o_dur_log.transpose(1, 2),
"total_durations_log": o_attn_dur.transpose(1, 2),
}
return outputs
@torch.no_grad()
def inference_with_MAS(self,
x,
x_lengths,
y=None,
y_lengths=None,
aux_input={
"d_vectors": None,
"speaker_ids": None
}): # pylint: disable=dangerous-default-value
"""
It's similar to the teacher forcing in Tacotron.
It was proposed in: https://arxiv.org/abs/2104.05557
Shapes:
- x: :math:`[B, T]`
- x_lenghts: :math:`B`
- y: :math:`[B, T, C]`
- y_lengths: :math:`B`
- g: :math:`[B, C] or B`
"""
y = y.transpose(1, 2)
y_max_length = y.size(2)
# norm speaker embeddings
g = aux_input[
"d_vectors"] if aux_input is not None and "d_vectors" in aux_input else None
if self.use_speaker_embedding or self.use_d_vector_file:
if not self.use_d_vector_file:
g = F.normalize(g).unsqueeze(-1)
else:
g = F.normalize(self.emb_g(g)).unsqueeze(-1) # [b, h, 1]
# embedding pass
o_mean, o_log_scale, o_dur_log, x_mask = self.encoder(x,
x_lengths,
g=g)
# drop redisual frames wrt num_squeeze and set y_lengths.
y, y_lengths, y_max_length, attn = self.preprocess(
y, y_lengths, y_max_length, None)
# create masks
y_mask = torch.unsqueeze(sequence_mask(y_lengths, y_max_length),
1).to(x_mask.dtype)
attn_mask = torch.unsqueeze(x_mask, -1) * torch.unsqueeze(y_mask, 2)
# decoder pass
z, logdet = self.decoder(y, y_mask, g=g, reverse=False)
# find the alignment path between z and encoder output
o_scale = torch.exp(-2 * o_log_scale)
logp1 = torch.sum(-0.5 * math.log(2 * math.pi) - o_log_scale,
[1]).unsqueeze(-1) # [b, t, 1]
logp2 = torch.matmul(o_scale.transpose(1, 2), -0.5 *
(z**2)) # [b, t, d] x [b, d, t'] = [b, t, t']
logp3 = torch.matmul((o_mean * o_scale).transpose(1, 2),
z) # [b, t, d] x [b, d, t'] = [b, t, t']
logp4 = torch.sum(-0.5 * (o_mean**2) * o_scale,
[1]).unsqueeze(-1) # [b, t, 1]
logp = logp1 + logp2 + logp3 + logp4 # [b, t, t']
attn = maximum_path(logp, attn_mask.squeeze(1)).unsqueeze(1).detach()
y_mean, y_log_scale, o_attn_dur = self.compute_outputs(
attn, o_mean, o_log_scale, x_mask)
attn = attn.squeeze(1).permute(0, 2, 1)
# get predited aligned distribution
z = y_mean * y_mask
# reverse the decoder and predict using the aligned distribution
y, logdet = self.decoder(z, y_mask, g=g, reverse=True)
outputs = {
"model_outputs": z.transpose(1, 2),
"logdet": logdet,
"y_mean": y_mean.transpose(1, 2),
"y_log_scale": y_log_scale.transpose(1, 2),
"alignments": attn,
"durations_log": o_dur_log.transpose(1, 2),
"total_durations_log": o_attn_dur.transpose(1, 2),
}
return outputs
@torch.no_grad()
def decoder_inference(self,
y,
y_lengths=None,
aux_input={
"d_vectors": None,
"speaker_ids": None
}): # pylint: disable=dangerous-default-value
"""
Shapes:
- y: :math:`[B, T, C]`
- y_lengths: :math:`B`
- g: :math:`[B, C] or B`
"""
y = y.transpose(1, 2)
y_max_length = y.size(2)
g = aux_input[
"d_vectors"] if aux_input is not None and "d_vectors" in aux_input else None
# norm speaker embeddings
if g is not None:
if self.external_d_vector_dim:
g = F.normalize(g).unsqueeze(-1)
else:
g = F.normalize(self.emb_g(g)).unsqueeze(-1) # [b, h, 1]
y_mask = torch.unsqueeze(sequence_mask(y_lengths, y_max_length),
1).to(y.dtype)
# decoder pass
z, logdet = self.decoder(y, y_mask, g=g, reverse=False)
# reverse decoder and predict
y, logdet = self.decoder(z, y_mask, g=g, reverse=True)
outputs = {}
outputs["model_outputs"] = y.transpose(1, 2)
outputs["logdet"] = logdet
return outputs
@torch.no_grad()
def inference(self,
x,
aux_input={
"x_lengths": None,
"d_vectors": None,
"speaker_ids": None
}): # pylint: disable=dangerous-default-value
x_lengths = aux_input["x_lengths"]
g = aux_input[
"d_vectors"] if aux_input is not None and "d_vectors" in aux_input else None
if g is not None:
if self.d_vector_dim:
g = F.normalize(g).unsqueeze(-1)
else:
g = F.normalize(self.emb_g(g)).unsqueeze(-1) # [b, h]
# embedding pass
o_mean, o_log_scale, o_dur_log, x_mask = self.encoder(x,
x_lengths,
g=g)
# compute output durations
w = (torch.exp(o_dur_log) - 1) * x_mask * self.length_scale
w_ceil = torch.ceil(w)
y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
y_max_length = None
# compute masks
y_mask = torch.unsqueeze(sequence_mask(y_lengths, y_max_length),
1).to(x_mask.dtype)
attn_mask = torch.unsqueeze(x_mask, -1) * torch.unsqueeze(y_mask, 2)
# compute attention mask
attn = generate_path(w_ceil.squeeze(1),
attn_mask.squeeze(1)).unsqueeze(1)
y_mean, y_log_scale, o_attn_dur = self.compute_outputs(
attn, o_mean, o_log_scale, x_mask)
z = (y_mean + torch.exp(y_log_scale) * torch.randn_like(y_mean) *
self.inference_noise_scale) * y_mask
# decoder pass
y, logdet = self.decoder(z, y_mask, g=g, reverse=True)
attn = attn.squeeze(1).permute(0, 2, 1)
outputs = {
"model_outputs": y.transpose(1, 2),
"logdet": logdet,
"y_mean": y_mean.transpose(1, 2),
"y_log_scale": y_log_scale.transpose(1, 2),
"alignments": attn,
"durations_log": o_dur_log.transpose(1, 2),
"total_durations_log": o_attn_dur.transpose(1, 2),
}
return outputs
def train_step(self, batch: dict, criterion: nn.Module):
"""A single training step. Forward pass and loss computation. Run data depended initialization for the
first `config.data_dep_init_steps` steps.
Args:
batch (dict): [description]
criterion (nn.Module): [description]
"""
text_input = batch["text_input"]
text_lengths = batch["text_lengths"]
mel_input = batch["mel_input"]
mel_lengths = batch["mel_lengths"]
d_vectors = batch["d_vectors"]
speaker_ids = batch["speaker_ids"]
if self.run_data_dep_init and self.training:
# compute data-dependent initialization of activation norm layers
self.unlock_act_norm_layers()
with torch.no_grad():
_ = self.forward(
text_input,
text_lengths,
mel_input,
mel_lengths,
aux_input={
"d_vectors": d_vectors,
"speaker_ids": speaker_ids
},
)
outputs = None
loss_dict = None
self.lock_act_norm_layers()
else:
# normal training step
outputs = self.forward(
text_input,
text_lengths,
mel_input,
mel_lengths,
aux_input={
"d_vectors": d_vectors,
"speaker_ids": speaker_ids
},
)
with autocast(enabled=False): # avoid mixed_precision in criterion
loss_dict = criterion(
outputs["z"].float(),
outputs["y_mean"].float(),
outputs["y_log_scale"].float(),
outputs["logdet"].float(),
mel_lengths,
outputs["durations_log"].float(),
outputs["total_durations_log"].float(),
text_lengths,
)
return outputs, loss_dict
def train_log(self, ap: AudioProcessor, batch: dict, outputs: dict): # pylint: disable=no-self-use
alignments = outputs["alignments"]
text_input = batch["text_input"]
text_lengths = batch["text_lengths"]
mel_input = batch["mel_input"]
d_vectors = batch["d_vectors"]
speaker_ids = batch["speaker_ids"]
# model runs reverse flow to predict spectrograms
pred_outputs = self.inference(
text_input[:1],
aux_input={
"x_lengths": text_lengths[:1],
"d_vectors": d_vectors,
"speaker_ids": speaker_ids
},
)
model_outputs = pred_outputs["model_outputs"]
pred_spec = model_outputs[0].data.cpu().numpy()
gt_spec = mel_input[0].data.cpu().numpy()
align_img = alignments[0].data.cpu().numpy()
figures = {
"prediction": plot_spectrogram(pred_spec, ap, output_fig=False),
"ground_truth": plot_spectrogram(gt_spec, ap, output_fig=False),
"alignment": plot_alignment(align_img, output_fig=False),
}
# Sample audio
train_audio = ap.inv_melspectrogram(pred_spec.T)
return figures, {"audio": train_audio}
@torch.no_grad()
def eval_step(self, batch: dict, criterion: nn.Module):
return self.train_step(batch, criterion)
def eval_log(self, ap: AudioProcessor, batch: dict, outputs: dict):
return self.train_log(ap, batch, outputs)
@torch.no_grad()
def test_run(self, ap):
"""Generic test run for `tts` models used by `Trainer`.
You can override this for a different behaviour.
Returns:
Tuple[Dict, Dict]: Test figures and audios to be projected to Tensorboard.
"""
print(" | > Synthesizing test sentences.")
test_audios = {}
test_figures = {}
test_sentences = self.config.test_sentences
aux_inputs = self.get_aux_input()
if len(test_sentences) == 0:
print(" | [!] No test sentences provided.")
else:
for idx, sen in enumerate(test_sentences):
outputs = synthesis(
self,
sen,
self.config,
"cuda" in str(next(self.parameters()).device),
ap,
speaker_id=aux_inputs["speaker_id"],
d_vector=aux_inputs["d_vector"],
style_wav=aux_inputs["style_wav"],
enable_eos_bos_chars=self.config.enable_eos_bos_chars,
use_griffin_lim=True,
do_trim_silence=False,
)
test_audios["{}-audio".format(idx)] = outputs["wav"]
test_figures["{}-prediction".format(idx)] = plot_spectrogram(
outputs["outputs"]["model_outputs"], ap, output_fig=False)
test_figures["{}-alignment".format(idx)] = plot_alignment(
outputs["alignments"], output_fig=False)
return test_figures, test_audios
def preprocess(self, y, y_lengths, y_max_length, attn=None):
if y_max_length is not None:
y_max_length = (y_max_length //
self.num_squeeze) * self.num_squeeze
y = y[:, :, :y_max_length]
if attn is not None:
attn = attn[:, :, :, :y_max_length]
y_lengths = (y_lengths // self.num_squeeze) * self.num_squeeze
return y, y_lengths, y_max_length, attn
def store_inverse(self):
self.decoder.store_inverse()
def load_checkpoint(self, config, checkpoint_path, eval=False): # pylint: disable=unused-argument, redefined-builtin
state = load_fsspec(checkpoint_path, map_location=torch.device("cpu"))
self.load_state_dict(state["model"])
if eval:
self.eval()
self.store_inverse()
assert not self.training
def get_criterion(self):
from TTS.tts.layers.losses import GlowTTSLoss # pylint: disable=import-outside-toplevel
return GlowTTSLoss()
def on_train_step_start(self, trainer):
"""Decide on every training step wheter enable/disable data depended initialization."""
self.run_data_dep_init = trainer.total_steps_done < self.data_dep_init_steps
def __init__(self,
num_chars,
hidden_channels,
filter_channels,
filter_channels_dp,
out_channels,
kernel_size=3,
num_heads=2,
num_layers_enc=6,
dropout_p=0.1,
num_flow_blocks_dec=12,
kernel_size_dec=5,
dilation_rate=5,
num_block_layers=4,
dropout_p_dec=0.,
num_speakers=0,
c_in_channels=0,
num_splits=4,
num_sqz=1,
sigmoid_scale=False,
rel_attn_window_size=None,
input_length=None,
mean_only=False,
hidden_channels_enc=None,
hidden_channels_dec=None,
use_encoder_prenet=False,
encoder_type="transformer"):
super().__init__()
self.num_chars = num_chars
self.hidden_channels = hidden_channels
self.filter_channels = filter_channels
self.filter_channels_dp = filter_channels_dp
self.out_channels = out_channels
self.kernel_size = kernel_size
self.num_heads = num_heads
self.num_layers_enc = num_layers_enc
self.dropout_p = dropout_p
self.num_flow_blocks_dec = num_flow_blocks_dec
self.kernel_size_dec = kernel_size_dec
self.dilation_rate = dilation_rate
self.num_block_layers = num_block_layers
self.dropout_p_dec = dropout_p_dec
self.num_speakers = num_speakers
self.c_in_channels = c_in_channels
self.num_splits = num_splits
self.num_sqz = num_sqz
self.sigmoid_scale = sigmoid_scale
self.rel_attn_window_size = rel_attn_window_size
self.input_length = input_length
self.mean_only = mean_only
self.hidden_channels_enc = hidden_channels_enc
self.hidden_channels_dec = hidden_channels_dec
self.use_encoder_prenet = use_encoder_prenet
self.noise_scale = 0.66
self.length_scale = 1.
self.encoder = Encoder(num_chars,
out_channels=out_channels,
hidden_channels=hidden_channels,
filter_channels=filter_channels,
filter_channels_dp=filter_channels_dp,
encoder_type=encoder_type,
num_heads=num_heads,
num_layers=num_layers_enc,
kernel_size=kernel_size,
dropout_p=dropout_p,
mean_only=mean_only,
use_prenet=use_encoder_prenet,
c_in_channels=c_in_channels)
self.decoder = Decoder(out_channels,
hidden_channels_dec or hidden_channels,
kernel_size_dec,
dilation_rate,
num_flow_blocks_dec,
num_block_layers,
dropout_p=dropout_p_dec,
num_splits=num_splits,
num_sqz=num_sqz,
sigmoid_scale=sigmoid_scale,
c_in_channels=c_in_channels)
if num_speakers > 1:
self.emb_g = nn.Embedding(num_speakers, c_in_channels)
nn.init.uniform_(self.emb_g.weight, -0.1, 0.1)
class GlowTts(nn.Module):
"""Glow TTS models from https://arxiv.org/abs/2005.11129"""
def __init__(self,
num_chars,
hidden_channels,
filter_channels,
filter_channels_dp,
out_channels,
kernel_size=3,
num_heads=2,
num_layers_enc=6,
dropout_p=0.1,
num_flow_blocks_dec=12,
kernel_size_dec=5,
dilation_rate=5,
num_block_layers=4,
dropout_p_dec=0.,
num_speakers=0,
c_in_channels=0,
num_splits=4,
num_sqz=1,
sigmoid_scale=False,
rel_attn_window_size=None,
input_length=None,
mean_only=False,
hidden_channels_enc=None,
hidden_channels_dec=None,
use_encoder_prenet=False,
encoder_type="transformer"):
super().__init__()
self.num_chars = num_chars
self.hidden_channels = hidden_channels
self.filter_channels = filter_channels
self.filter_channels_dp = filter_channels_dp
self.out_channels = out_channels
self.kernel_size = kernel_size
self.num_heads = num_heads
self.num_layers_enc = num_layers_enc
self.dropout_p = dropout_p
self.num_flow_blocks_dec = num_flow_blocks_dec
self.kernel_size_dec = kernel_size_dec
self.dilation_rate = dilation_rate
self.num_block_layers = num_block_layers
self.dropout_p_dec = dropout_p_dec
self.num_speakers = num_speakers
self.c_in_channels = c_in_channels
self.num_splits = num_splits
self.num_sqz = num_sqz
self.sigmoid_scale = sigmoid_scale
self.rel_attn_window_size = rel_attn_window_size
self.input_length = input_length
self.mean_only = mean_only
self.hidden_channels_enc = hidden_channels_enc
self.hidden_channels_dec = hidden_channels_dec
self.use_encoder_prenet = use_encoder_prenet
self.noise_scale = 0.66
self.length_scale = 1.
self.encoder = Encoder(num_chars,
out_channels=out_channels,
hidden_channels=hidden_channels,
filter_channels=filter_channels,
filter_channels_dp=filter_channels_dp,
encoder_type=encoder_type,
num_heads=num_heads,
num_layers=num_layers_enc,
kernel_size=kernel_size,
dropout_p=dropout_p,
mean_only=mean_only,
use_prenet=use_encoder_prenet,
c_in_channels=c_in_channels)
self.decoder = Decoder(out_channels,
hidden_channels_dec or hidden_channels,
kernel_size_dec,
dilation_rate,
num_flow_blocks_dec,
num_block_layers,
dropout_p=dropout_p_dec,
num_splits=num_splits,
num_sqz=num_sqz,
sigmoid_scale=sigmoid_scale,
c_in_channels=c_in_channels)
if num_speakers > 1:
self.emb_g = nn.Embedding(num_speakers, c_in_channels)
nn.init.uniform_(self.emb_g.weight, -0.1, 0.1)
@staticmethod
def compute_outputs(attn, o_mean, o_log_scale, x_mask):
# compute final values with the computed alignment
y_mean = torch.matmul(
attn.squeeze(1).transpose(1, 2), o_mean.transpose(1, 2)).transpose(
1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
y_log_scale = torch.matmul(
attn.squeeze(1).transpose(1, 2), o_log_scale.transpose(
1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
# compute total duration with adjustment
o_attn_dur = torch.log(1 + torch.sum(attn, -1)) * x_mask
return y_mean, y_log_scale, o_attn_dur
def forward(self, x, x_lengths, y=None, y_lengths=None, attn=None, g=None):
"""
Shapes:
x: B x T
x_lenghts: B
y: B x C x T
y_lengths: B
"""
y_max_length = y.size(2)
# norm speaker embeddings
if g is not None:
g = F.normalize(self.emb_g(g)).unsqueeze(-1) # [b, h]
# embedding pass
o_mean, o_log_scale, o_dur_log, x_mask = self.encoder(x,
x_lengths,
g=g)
# format feature vectors and feature vector lenghts
y, y_lengths, y_max_length, attn = self.preprocess(
y, y_lengths, y_max_length, None)
# create masks
y_mask = torch.unsqueeze(sequence_mask(y_lengths, y_max_length),
1).to(x_mask.dtype)
attn_mask = torch.unsqueeze(x_mask, -1) * torch.unsqueeze(y_mask, 2)
# decoder pass
z, logdet = self.decoder(y, y_mask, g=g, reverse=False)
# find the alignment path
with torch.no_grad():
o_scale = torch.exp(-2 * o_log_scale)
logp1 = torch.sum(-0.5 * math.log(2 * math.pi) - o_log_scale,
[1]).unsqueeze(-1) # [b, t, 1]
logp2 = torch.matmul(o_scale.transpose(1, 2), -0.5 *
(z**2)) # [b, t, d] x [b, d, t'] = [b, t, t']
logp3 = torch.matmul((o_mean * o_scale).transpose(1, 2),
z) # [b, t, d] x [b, d, t'] = [b, t, t']
logp4 = torch.sum(-0.5 * (o_mean**2) * o_scale,
[1]).unsqueeze(-1) # [b, t, 1]
logp = logp1 + logp2 + logp3 + logp4 # [b, t, t']
attn = maximum_path(logp,
attn_mask.squeeze(1)).unsqueeze(1).detach()
y_mean, y_log_scale, o_attn_dur = self.compute_outputs(
attn, o_mean, o_log_scale, x_mask)
attn = attn.squeeze(1).permute(0, 2, 1)
return z, logdet, y_mean, y_log_scale, attn, o_dur_log, o_attn_dur
@torch.no_grad()
def inference(self, x, x_lengths, g=None):
if g is not None:
g = F.normalize(self.emb_g(g)).unsqueeze(-1) # [b, h]
# embedding pass
o_mean, o_log_scale, o_dur_log, x_mask = self.encoder(x,
x_lengths,
g=g)
# compute output durations
w = (torch.exp(o_dur_log) - 1) * x_mask * self.length_scale
w_ceil = torch.ceil(w)
y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
y_max_length = None
# compute masks
y_mask = torch.unsqueeze(sequence_mask(y_lengths, y_max_length),
1).to(x_mask.dtype)
attn_mask = torch.unsqueeze(x_mask, -1) * torch.unsqueeze(y_mask, 2)
# compute attention mask
attn = generate_path(w_ceil.squeeze(1),
attn_mask.squeeze(1)).unsqueeze(1)
y_mean, y_log_scale, o_attn_dur = self.compute_outputs(
attn, o_mean, o_log_scale, x_mask)
z = (y_mean + torch.exp(y_log_scale) * torch.randn_like(y_mean) *
self.noise_scale) * y_mask
# decoder pass
y, logdet = self.decoder(z, y_mask, g=g, reverse=True)
attn = attn.squeeze(1).permute(0, 2, 1)
return y, logdet, y_mean, y_log_scale, attn, o_dur_log, o_attn_dur
def preprocess(self, y, y_lengths, y_max_length, attn=None):
if y_max_length is not None:
y_max_length = (y_max_length // self.num_sqz) * self.num_sqz
y = y[:, :, :y_max_length]
if attn is not None:
attn = attn[:, :, :, :y_max_length]
y_lengths = (y_lengths // self.num_sqz) * self.num_sqz
return y, y_lengths, y_max_length, attn
def store_inverse(self):
self.decoder.store_inverse()
