import math
import random
from dataclasses import dataclass, field
from itertools import chain
from typing import Dict, List, Tuple
import torch
from coqpit import Coqpit
from torch import nn
from torch.cuda.amp.autocast_mode import autocast
from TTS.tts.layers.glow_tts.duration_predictor import DurationPredictor
from TTS.tts.layers.vits.discriminator import VitsDiscriminator
from TTS.tts.layers.vits.networks import PosteriorEncoder, ResidualCouplingBlocks, TextEncoder
from TTS.tts.layers.vits.stochastic_duration_predictor import StochasticDurationPredictor
from TTS.tts.models.base_tts import BaseTTS
from TTS.tts.utils.helpers import generate_path, maximum_path, rand_segments, segment, sequence_mask
from TTS.tts.utils.speakers import SpeakerManager
from TTS.tts.utils.synthesis import synthesis
from TTS.tts.utils.visual import plot_alignment
from TTS.utils.trainer_utils import get_optimizer, get_scheduler
from TTS.vocoder.models.hifigan_generator import HifiganGenerator
from TTS.vocoder.utils.generic_utils import plot_results
@dataclass
class VitsArgs(Coqpit):
"""VITS model arguments.
Args:
num_chars (int):
Number of characters in the vocabulary. Defaults to 100.
out_channels (int):
Number of output channels. Defaults to 513.
spec_segment_size (int):
Decoder input segment size. Defaults to 32 `(32 * hoplength = waveform length)`.
hidden_channels (int):
Number of hidden channels of the model. Defaults to 192.
hidden_channels_ffn_text_encoder (int):
Number of hidden channels of the feed-forward layers of the text encoder transformer. Defaults to 256.
num_heads_text_encoder (int):
Number of attention heads of the text encoder transformer. Defaults to 2.
num_layers_text_encoder (int):
Number of transformer layers in the text encoder. Defaults to 6.
kernel_size_text_encoder (int):
Kernel size of the text encoder transformer FFN layers. Defaults to 3.
dropout_p_text_encoder (float):
Dropout rate of the text encoder. Defaults to 0.1.
dropout_p_duration_predictor (float):
Dropout rate of the duration predictor. Defaults to 0.1.
kernel_size_posterior_encoder (int):
Kernel size of the posterior encoder's WaveNet layers. Defaults to 5.
dilatation_posterior_encoder (int):
Dilation rate of the posterior encoder's WaveNet layers. Defaults to 1.
num_layers_posterior_encoder (int):
Number of posterior encoder's WaveNet layers. Defaults to 16.
kernel_size_flow (int):
Kernel size of the Residual Coupling layers of the flow network. Defaults to 5.
dilatation_flow (int):
Dilation rate of the Residual Coupling WaveNet layers of the flow network. Defaults to 1.
num_layers_flow (int):
Number of Residual Coupling WaveNet layers of the flow network. Defaults to 6.
resblock_type_decoder (str):
Type of the residual block in the decoder network. Defaults to "1".
resblock_kernel_sizes_decoder (List[int]):
Kernel sizes of the residual blocks in the decoder network. Defaults to `[3, 7, 11]`.
resblock_dilation_sizes_decoder (List[List[int]]):
Dilation sizes of the residual blocks in the decoder network. Defaults to `[[1, 3, 5], [1, 3, 5], [1, 3, 5]]`.
upsample_rates_decoder (List[int]):
Upsampling rates for each concecutive upsampling layer in the decoder network. The multiply of these
values must be equal to the kop length used for computing spectrograms. Defaults to `[8, 8, 2, 2]`.
upsample_initial_channel_decoder (int):
Number of hidden channels of the first upsampling convolution layer of the decoder network. Defaults to 512.
upsample_kernel_sizes_decoder (List[int]):
Kernel sizes for each upsampling layer of the decoder network. Defaults to `[16, 16, 4, 4]`.
use_sdp (bool):
Use Stochastic Duration Predictor. Defaults to True.
noise_scale (float):
Noise scale used for the sample noise tensor in training. Defaults to 1.0.
inference_noise_scale (float):
Noise scale used for the sample noise tensor in inference. Defaults to 0.667.
length_scale (float):
Scale factor for the predicted duration values. Smaller values result faster speech. Defaults to 1.
noise_scale_dp (float):
Noise scale used by the Stochastic Duration Predictor sample noise in training. Defaults to 1.0.
inference_noise_scale_dp (float):
Noise scale for the Stochastic Duration Predictor in inference. Defaults to 0.8.
max_inference_len (int):
Maximum inference length to limit the memory use. Defaults to None.
init_discriminator (bool):
Initialize the disciminator network if set True. Set False for inference. Defaults to True.
use_spectral_norm_disriminator (bool):
Use spectral normalization over weight norm in the discriminator. Defaults to False.
use_speaker_embedding (bool):
Enable/Disable speaker embedding for multi-speaker models. Defaults to False.
num_speakers (int):
Number of speakers for the speaker embedding layer. Defaults to 0.
speakers_file (str):
Path to the speaker mapping file for the Speaker Manager. Defaults to None.
speaker_embedding_channels (int):
Number of speaker embedding channels. Defaults to 256.
use_d_vector_file (bool):
Enable/Disable the use of d-vectors for multi-speaker training. Defaults to False.
d_vector_dim (int):
Number of d-vector channels. Defaults to 0.
detach_dp_input (bool):
Detach duration predictor's input from the network for stopping the gradients. Defaults to True.
"""
num_chars: int = 100
out_channels: int = 513
spec_segment_size: int = 32
hidden_channels: int = 192
hidden_channels_ffn_text_encoder: int = 768
num_heads_text_encoder: int = 2
num_layers_text_encoder: int = 6
kernel_size_text_encoder: int = 3
dropout_p_text_encoder: float = 0.1
dropout_p_duration_predictor: float = 0.5
kernel_size_posterior_encoder: int = 5
dilation_rate_posterior_encoder: int = 1
num_layers_posterior_encoder: int = 16
kernel_size_flow: int = 5
dilation_rate_flow: int = 1
num_layers_flow: int = 4
resblock_type_decoder: str = "1"
resblock_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [3, 7, 11])
resblock_dilation_sizes_decoder: List[List[int]] = field(default_factory=lambda: [[1, 3, 5], [1, 3, 5], [1, 3, 5]])
upsample_rates_decoder: List[int] = field(default_factory=lambda: [8, 8, 2, 2])
upsample_initial_channel_decoder: int = 512
upsample_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [16, 16, 4, 4])
use_sdp: bool = True
noise_scale: float = 1.0
inference_noise_scale: float = 0.667
length_scale: float = 1
noise_scale_dp: float = 1.0
inference_noise_scale_dp: float = 1.0
max_inference_len: int = None
init_discriminator: bool = True
use_spectral_norm_disriminator: bool = False
use_speaker_embedding: bool = False
num_speakers: int = 0
speakers_file: str = None
speaker_embedding_channels: int = 256
use_d_vector_file: bool = False
d_vector_file: str = None
d_vector_dim: int = 0
detach_dp_input: bool = True
class Vits(BaseTTS):
"""VITS TTS model
Paper::
https://arxiv.org/pdf/2106.06103.pdf
Paper Abstract::
Several recent end-to-end text-to-speech (TTS) models enabling single-stage training and parallel
sampling have been proposed, but their sample quality does not match that of two-stage TTS systems.
In this work, we present a parallel endto-end TTS method that generates more natural sounding audio than
current two-stage models. Our method adopts variational inference augmented with normalizing flows and
an adversarial training process, which improves the expressive power of generative modeling. We also propose a
stochastic duration predictor to synthesize speech with diverse rhythms from input text. With the
uncertainty modeling over latent variables and the stochastic duration predictor, our method expresses the
natural one-to-many relationship in which a text input can be spoken in multiple ways
with different pitches and rhythms. A subjective human evaluation (mean opinion score, or MOS)
on the LJ Speech, a single speaker dataset, shows that our method outperforms the best publicly
available TTS systems and achieves a MOS comparable to ground truth.
Check :class:`TTS.tts.configs.vits_config.VitsConfig` for class arguments.
Examples:
>>> from TTS.tts.configs.vits_config import VitsConfig
>>> from TTS.tts.models.vits import Vits
>>> config = VitsConfig()
>>> model = Vits(config)
"""
# pylint: disable=dangerous-default-value
def __init__(self, config: Coqpit, speaker_manager: SpeakerManager = None):
super().__init__(config)
self.END2END = True
self.speaker_manager = speaker_manager
if config.__class__.__name__ == "VitsConfig":
# loading from VitsConfig
if "num_chars" not in config:
_, self.config, num_chars = self.get_characters(config)
config.model_args.num_chars = num_chars
else:
self.config = config
config.model_args.num_chars = config.num_chars
args = self.config.model_args
elif isinstance(config, VitsArgs):
# loading from VitsArgs
self.config = config
args = config
else:
raise ValueError("config must be either a VitsConfig or VitsArgs")
self.args = args
self.init_multispeaker(config)
self.length_scale = args.length_scale
self.noise_scale = args.noise_scale
self.inference_noise_scale = args.inference_noise_scale
self.inference_noise_scale_dp = args.inference_noise_scale_dp
self.noise_scale_dp = args.noise_scale_dp
self.max_inference_len = args.max_inference_len
self.spec_segment_size = args.spec_segment_size
self.text_encoder = TextEncoder(
args.num_chars,
args.hidden_channels,
args.hidden_channels,
args.hidden_channels_ffn_text_encoder,
args.num_heads_text_encoder,
args.num_layers_text_encoder,
args.kernel_size_text_encoder,
args.dropout_p_text_encoder,
)
self.posterior_encoder = PosteriorEncoder(
args.out_channels,
args.hidden_channels,
args.hidden_channels,
kernel_size=args.kernel_size_posterior_encoder,
dilation_rate=args.dilation_rate_posterior_encoder,
num_layers=args.num_layers_posterior_encoder,
cond_channels=self.embedded_speaker_dim,
)
self.flow = ResidualCouplingBlocks(
args.hidden_channels,
args.hidden_channels,
kernel_size=args.kernel_size_flow,
dilation_rate=args.dilation_rate_flow,
num_layers=args.num_layers_flow,
cond_channels=self.embedded_speaker_dim,
)
if args.use_sdp:
self.duration_predictor = StochasticDurationPredictor(
args.hidden_channels,
192,
3,
args.dropout_p_duration_predictor,
4,
cond_channels=self.embedded_speaker_dim,
)
else:
self.duration_predictor = DurationPredictor(
args.hidden_channels, 256, 3, args.dropout_p_duration_predictor, cond_channels=self.embedded_speaker_dim
)
self.waveform_decoder = HifiganGenerator(
args.hidden_channels,
1,
args.resblock_type_decoder,
args.resblock_dilation_sizes_decoder,
args.resblock_kernel_sizes_decoder,
args.upsample_kernel_sizes_decoder,
args.upsample_initial_channel_decoder,
args.upsample_rates_decoder,
inference_padding=0,
cond_channels=self.embedded_speaker_dim,
conv_pre_weight_norm=False,
conv_post_weight_norm=False,
conv_post_bias=False,
)
if args.init_discriminator:
self.disc = VitsDiscriminator(use_spectral_norm=args.use_spectral_norm_disriminator)
def init_multispeaker(self, config: Coqpit):
"""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.
Args:
config (Coqpit): Model configuration.
data (List, optional): Dataset items to infer number of speakers. Defaults to None.
"""
self.embedded_speaker_dim = 0
if hasattr(config, "model_args"):
config = config.model_args
self.num_speakers = config.num_speakers
if config.use_speaker_embedding:
self._init_speaker_embedding(config)
if config.use_d_vector_file:
self._init_d_vector(config)
def _init_speaker_embedding(self, config):
# pylint: disable=attribute-defined-outside-init
if config.speakers_file is not None:
self.speaker_manager = SpeakerManager(speaker_id_file_path=config.speakers_file)
if self.num_speakers > 0:
print(" > initialization of speaker-embedding layers.")
self.embedded_speaker_dim = config.speaker_embedding_channels
self.emb_g = nn.Embedding(self.num_speakers, self.embedded_speaker_dim)
def _init_d_vector(self, config):
# pylint: disable=attribute-defined-outside-init
if hasattr(self, "emb_g"):
raise ValueError("[!] Speaker embedding layer already initialized before d_vector settings.")
self.speaker_manager = SpeakerManager(d_vectors_file_path=config.d_vector_file)
self.embedded_speaker_dim = config.d_vector_dim
@staticmethod
def _set_cond_input(aux_input: Dict):
"""Set the speaker conditioning input based on the multi-speaker mode."""
sid, g = None, None
if "speaker_ids" in aux_input and aux_input["speaker_ids"] is not None:
sid = aux_input["speaker_ids"]
if sid.ndim == 0:
sid = sid.unsqueeze_(0)
if "d_vectors" in aux_input and aux_input["d_vectors"] is not None:
g = aux_input["d_vectors"]
return sid, g
def get_aux_input(self, aux_input: Dict):
sid, g = self._set_cond_input(aux_input)
return {"speaker_id": sid, "style_wav": None, "d_vector": g}
def forward(
self,
x: torch.tensor,
x_lengths: torch.tensor,
y: torch.tensor,
y_lengths: torch.tensor,
aux_input={"d_vectors": None, "speaker_ids": None},
) -> Dict:
"""Forward pass of the model.
Args:
x (torch.tensor): Batch of input character sequence IDs.
x_lengths (torch.tensor): Batch of input character sequence lengths.
y (torch.tensor): Batch of input spectrograms.
y_lengths (torch.tensor): Batch of input spectrogram lengths.
aux_input (dict, optional): Auxiliary inputs for multi-speaker training. Defaults to {"d_vectors": None, "speaker_ids": None}.
Returns:
Dict: model outputs keyed by the output name.
Shapes:
- x: :math:`[B, T_seq]`
- x_lengths: :math:`[B]`
- y: :math:`[B, C, T_spec]`
- y_lengths: :math:`[B]`
- d_vectors: :math:`[B, C, 1]`
- speaker_ids: :math:`[B]`
"""
outputs = {}
sid, g = self._set_cond_input(aux_input)
x, m_p, logs_p, x_mask = self.text_encoder(x, x_lengths)
# speaker embedding
if self.num_speakers > 1 and sid is not None:
g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
# posterior encoder
z, m_q, logs_q, y_mask = self.posterior_encoder(y, y_lengths, g=g)
# flow layers
z_p = self.flow(z, y_mask, g=g)
# find the alignment path
attn_mask = torch.unsqueeze(x_mask, -1) * torch.unsqueeze(y_mask, 2)
with torch.no_grad():
o_scale = torch.exp(-2 * logs_p)
logp1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1]).unsqueeze(-1) # [b, t, 1]
logp2 = torch.einsum("klm, kln -> kmn", [o_scale, -0.5 * (z_p ** 2)])
logp3 = torch.einsum("klm, kln -> kmn", [m_p * o_scale, z_p])
logp4 = torch.sum(-0.5 * (m_p ** 2) * o_scale, [1]).unsqueeze(-1) # [b, t, 1]
logp = logp2 + logp3 + logp1 + logp4
attn = maximum_path(logp, attn_mask.squeeze(1)).unsqueeze(1).detach()
# duration predictor
attn_durations = attn.sum(3)
if self.args.use_sdp:
loss_duration = self.duration_predictor(
x.detach() if self.args.detach_dp_input else x,
x_mask,
attn_durations,
g=g.detach() if self.args.detach_dp_input and g is not None else g,
)
loss_duration = loss_duration / torch.sum(x_mask)
else:
attn_log_durations = torch.log(attn_durations + 1e-6) * x_mask
log_durations = self.duration_predictor(
x.detach() if self.args.detach_dp_input else x,
x_mask,
g=g.detach() if self.args.detach_dp_input and g is not None else g,
)
loss_duration = torch.sum((log_durations - attn_log_durations) ** 2, [1, 2]) / torch.sum(x_mask)
outputs["loss_duration"] = loss_duration
# expand prior
m_p = torch.einsum("klmn, kjm -> kjn", [attn, m_p])
logs_p = torch.einsum("klmn, kjm -> kjn", [attn, logs_p])
# select a random feature segment for the waveform decoder
z_slice, slice_ids = rand_segments(z, y_lengths, self.spec_segment_size)
o = self.waveform_decoder(z_slice, g=g)
outputs.update(
{
"model_outputs": o,
"alignments": attn.squeeze(1),
"slice_ids": slice_ids,
"z": z,
"z_p": z_p,
"m_p": m_p,
"logs_p": logs_p,
"m_q": m_q,
"logs_q": logs_q,
}
)
return outputs
def inference(self, x, aux_input={"d_vectors": None, "speaker_ids": None}):
"""
Shapes:
- x: :math:`[B, T_seq]`
- d_vectors: :math:`[B, C, 1]`
- speaker_ids: :math:`[B]`
"""
sid, g = self._set_cond_input(aux_input)
x_lengths = torch.tensor(x.shape[1:2]).to(x.device)
x, m_p, logs_p, x_mask = self.text_encoder(x, x_lengths)
if self.num_speakers > 0 and sid is not None:
g = self.emb_g(sid).unsqueeze(-1)
if self.args.use_sdp:
logw = self.duration_predictor(x, x_mask, g=g, reverse=True, noise_scale=self.inference_noise_scale_dp)
else:
logw = self.duration_predictor(x, x_mask, g=g)
w = torch.exp(logw) * x_mask * self.length_scale
w_ceil = torch.ceil(w)
y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
y_mask = sequence_mask(y_lengths, None).to(x_mask.dtype)
attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
attn = generate_path(w_ceil.squeeze(1), attn_mask.squeeze(1).transpose(1, 2))
m_p = torch.matmul(attn.transpose(1, 2), m_p.transpose(1, 2)).transpose(1, 2)
logs_p = torch.matmul(attn.transpose(1, 2), logs_p.transpose(1, 2)).transpose(1, 2)
z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * self.inference_noise_scale
z = self.flow(z_p, y_mask, g=g, reverse=True)
o = self.waveform_decoder((z * y_mask)[:, :, : self.max_inference_len], g=g)
outputs = {"model_outputs": o, "alignments": attn.squeeze(1), "z": z, "z_p": z_p, "m_p": m_p, "logs_p": logs_p}
return outputs
def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
"""TODO: create an end-point for voice conversion"""
assert self.num_speakers > 0, "num_speakers have to be larger than 0."
g_src = self.emb_g(sid_src).unsqueeze(-1)
g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
z, _, _, y_mask = self.enc_q(y, y_lengths, g=g_src)
z_p = self.flow(z, y_mask, g=g_src)
z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
o_hat = self.waveform_decoder(z_hat * y_mask, g=g_tgt)
return o_hat, y_mask, (z, z_p, z_hat)
def train_step(self, batch: dict, criterion: nn.Module, optimizer_idx: int) -> Tuple[Dict, Dict]:
"""Perform a single training step. Run the model forward pass and compute losses.
Args:
batch (Dict): Input tensors.
criterion (nn.Module): Loss layer designed for the model.
optimizer_idx (int): Index of optimizer to use. 0 for the generator and 1 for the discriminator networks.
Returns:
Tuple[Dict, Dict]: Model ouputs and computed losses.
"""
# pylint: disable=attribute-defined-outside-init
if optimizer_idx not in [0, 1]:
raise ValueError(" [!] Unexpected `optimizer_idx`.")
if optimizer_idx == 0:
text_input = batch["text_input"]
text_lengths = batch["text_lengths"]
mel_lengths = batch["mel_lengths"]
linear_input = batch["linear_input"]
d_vectors = batch["d_vectors"]
speaker_ids = batch["speaker_ids"]
waveform = batch["waveform"]
# generator pass
outputs = self.forward(
text_input,
text_lengths,
linear_input.transpose(1, 2),
mel_lengths,
aux_input={"d_vectors": d_vectors, "speaker_ids": speaker_ids},
)
# cache tensors for the discriminator
self.y_disc_cache = None
self.wav_seg_disc_cache = None
self.y_disc_cache = outputs["model_outputs"]
wav_seg = segment(
waveform.transpose(1, 2),
outputs["slice_ids"] * self.config.audio.hop_length,
self.args.spec_segment_size * self.config.audio.hop_length,
)
self.wav_seg_disc_cache = wav_seg
outputs["waveform_seg"] = wav_seg
# compute discriminator scores and features
outputs["scores_disc_fake"], outputs["feats_disc_fake"], _, outputs["feats_disc_real"] = self.disc(
outputs["model_outputs"], wav_seg
)
# compute losses
with autocast(enabled=False): # use float32 for the criterion
loss_dict = criterion[optimizer_idx](
waveform_hat=outputs["model_outputs"].float(),
waveform=wav_seg.float(),
z_p=outputs["z_p"].float(),
logs_q=outputs["logs_q"].float(),
m_p=outputs["m_p"].float(),
logs_p=outputs["logs_p"].float(),
z_len=mel_lengths,
scores_disc_fake=outputs["scores_disc_fake"],
feats_disc_fake=outputs["feats_disc_fake"],
feats_disc_real=outputs["feats_disc_real"],
loss_duration=outputs["loss_duration"],
)
elif optimizer_idx == 1:
# discriminator pass
outputs = {}
# compute scores and features
outputs["scores_disc_fake"], _, outputs["scores_disc_real"], _ = self.disc(
self.y_disc_cache.detach(), self.wav_seg_disc_cache
)
# compute loss
with autocast(enabled=False): # use float32 for the criterion
loss_dict = criterion[optimizer_idx](
outputs["scores_disc_real"],
outputs["scores_disc_fake"],
)
return outputs, loss_dict
def _log(self, ap, batch, outputs, name_prefix="train"): # pylint: disable=unused-argument,no-self-use
y_hat = outputs[0]["model_outputs"]
y = outputs[0]["waveform_seg"]
figures = plot_results(y_hat, y, ap, name_prefix)
sample_voice = y_hat[0].squeeze(0).detach().cpu().numpy()
audios = {f"{name_prefix}/audio": sample_voice}
alignments = outputs[0]["alignments"]
align_img = alignments[0].data.cpu().numpy().T
figures.update(
{
"alignment": plot_alignment(align_img, output_fig=False),
}
)
return figures, audios
def train_log(
self, batch: dict, outputs: dict, logger: "Logger", assets: dict, steps: int
): # pylint: disable=no-self-use
"""Create visualizations and waveform examples.
For example, here you can plot spectrograms and generate sample sample waveforms from these spectrograms to
be projected onto Tensorboard.
Args:
ap (AudioProcessor): audio processor used at training.
batch (Dict): Model inputs used at the previous training step.
outputs (Dict): Model outputs generated at the previoud training step.
Returns:
Tuple[Dict, np.ndarray]: training plots and output waveform.
"""
ap = assets["audio_processor"]
self._log(ap, batch, outputs, "train")
@torch.no_grad()
def eval_step(self, batch: dict, criterion: nn.Module, optimizer_idx: int):
return self.train_step(batch, criterion, optimizer_idx)
def eval_log(self, batch: dict, outputs: dict, logger: "Logger", assets: dict, steps: int) -> None:
ap = assets["audio_processor"]
return self._log(ap, batch, outputs, "eval")
@torch.no_grad()
def test_run(self, ap) -> Tuple[Dict, Dict]:
"""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 = {
"speaker_id": None
if not self.config.use_speaker_embedding
else random.sample(sorted(self.speaker_manager.speaker_ids.values()), 1),
"d_vector": None
if not self.config.use_d_vector_file
else random.samples(sorted(self.speaker_manager.d_vectors.values()), 1),
"style_wav": None,
}
for idx, sen in enumerate(test_sentences):
wav, alignment, _, _ = 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,
).values()
test_audios["{}-audio".format(idx)] = wav
test_figures["{}-alignment".format(idx)] = plot_alignment(alignment.T, output_fig=False)
return test_figures, test_audios
def get_optimizer(self) -> List:
"""Initiate and return the GAN optimizers based on the config parameters.
It returnes 2 optimizers in a list. First one is for the generator and the second one is for the discriminator.
Returns:
List: optimizers.
"""
gen_parameters = chain(
self.text_encoder.parameters(),
self.posterior_encoder.parameters(),
self.flow.parameters(),
self.duration_predictor.parameters(),
self.waveform_decoder.parameters(),
)
# add the speaker embedding layer
if hasattr(self, "emb_g"):
gen_parameters = chain(gen_parameters, self.emb_g.parameters())
optimizer0 = get_optimizer(
self.config.optimizer, self.config.optimizer_params, self.config.lr_gen, parameters=gen_parameters
)
optimizer1 = get_optimizer(self.config.optimizer, self.config.optimizer_params, self.config.lr_disc, self.disc)
return [optimizer0, optimizer1]
def get_lr(self) -> List:
"""Set the initial learning rates for each optimizer.
Returns:
List: learning rates for each optimizer.
"""
return [self.config.lr_gen, self.config.lr_disc]
def get_scheduler(self, optimizer) -> List:
"""Set the schedulers for each optimizer.
Args:
optimizer (List[`torch.optim.Optimizer`]): List of optimizers.
Returns:
List: Schedulers, one for each optimizer.
"""
scheduler0 = get_scheduler(self.config.lr_scheduler_gen, self.config.lr_scheduler_gen_params, optimizer[0])
scheduler1 = get_scheduler(self.config.lr_scheduler_disc, self.config.lr_scheduler_disc_params, optimizer[1])
return [scheduler0, scheduler1]
def get_criterion(self):
"""Get criterions for each optimizer. The index in the output list matches the optimizer idx used in
`train_step()`"""
from TTS.tts.layers.losses import ( # pylint: disable=import-outside-toplevel
VitsDiscriminatorLoss,
VitsGeneratorLoss,
)
return [VitsGeneratorLoss(self.config), VitsDiscriminatorLoss(self.config)]
@staticmethod
def make_symbols(config):
"""Create a custom arrangement of symbols used by the model. The output list of symbols propagate along the
whole training and inference steps."""
_pad = config.characters["pad"]
_punctuations = config.characters["punctuations"]
_letters = config.characters["characters"]
_letters_ipa = config.characters["phonemes"]
symbols = [_pad] + list(_punctuations) + list(_letters)
if config.use_phonemes:
symbols += list(_letters_ipa)
return symbols
@staticmethod
def get_characters(config: Coqpit):
if config.characters is not None:
symbols = Vits.make_symbols(config)
else:
from TTS.tts.utils.text.symbols import ( # pylint: disable=import-outside-toplevel
parse_symbols,
phonemes,
symbols,
)
config.characters = parse_symbols()
if config.use_phonemes:
symbols = phonemes
num_chars = len(symbols) + getattr(config, "add_blank", False)
return symbols, config, num_chars
def load_checkpoint(
self, config, checkpoint_path, eval=False
): # pylint: disable=unused-argument, redefined-builtin
"""Load the model checkpoint and setup for training or inference"""
state = torch.load(checkpoint_path, map_location=torch.device("cpu"))
self.load_state_dict(state["model"])
if eval:
self.eval()
assert not self.training