import math
import torch
from torch import nn
from torch.nn import functional as F
from TTS.tts.configs import GlowTTSConfig
from TTS.tts.layers.glow_tts.decoder import Decoder
from TTS.tts.layers.glow_tts.encoder import Encoder
from TTS.tts.layers.glow_tts.monotonic_align import generate_path, maximum_path
from TTS.tts.models.base_tts import BaseTTS
from TTS.tts.utils.data import sequence_mask
from TTS.tts.utils.measures import alignment_diagonal_score
from TTS.tts.utils.speakers import get_speaker_manager
from TTS.tts.utils.synthesis import synthesis
from TTS.tts.utils.visual import plot_alignment, plot_spectrogram
from TTS.utils.audio import AudioProcessor
from TTS.utils.io import load_fsspec
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.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
# 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 forward(
self, x, x_lengths, y, y_lengths=None, aux_input={"d_vectors": 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`
"""
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 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, 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)
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 inference_with_MAS(
self, x, x_lengths, y=None, y_lengths=None, aux_input={"d_vectors": 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 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]
# 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}
): # 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}): # 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):
"""Perform a single training step by fetching the right set if samples from the batch.
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"]
outputs = self.forward(text_input, text_lengths, mel_input, mel_lengths, aux_input={"d_vectors": d_vectors})
loss_dict = criterion(
outputs["model_outputs"],
outputs["y_mean"],
outputs["y_log_scale"],
outputs["logdet"],
mel_lengths,
outputs["durations_log"],
outputs["total_durations_log"],
text_lengths,
)
# compute alignment error (the lower the better )
align_error = 1 - alignment_diagonal_score(outputs["alignments"], binary=True)
loss_dict["align_error"] = align_error
return outputs, loss_dict
def train_log(self, ap: AudioProcessor, batch: dict, outputs: dict): # pylint: disable=no-self-use
model_outputs = outputs["model_outputs"]
alignments = outputs["alignments"]
mel_input = batch["mel_input"]
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()
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()