import copy
from abc import abstractmethod
from typing import Dict, Tuple
import torch
from coqpit import Coqpit
from torch import nn
from TTS.tts.layers.losses import TacotronLoss
from TTS.tts.models.base_tts import BaseTTS
from TTS.tts.utils.helpers import sequence_mask
from TTS.tts.utils.speakers import SpeakerManager
from TTS.tts.utils.synthesis import synthesis
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.tts.utils.visual import plot_alignment, plot_spectrogram
from TTS.utils.generic_utils import format_aux_input
from TTS.utils.io import load_fsspec
from TTS.utils.training import gradual_training_scheduler
class BaseTacotron(BaseTTS):
"""Base class shared by Tacotron and Tacotron2"""
def __init__(
self,
config: "TacotronConfig",
ap: "AudioProcessor",
tokenizer: "TTSTokenizer",
speaker_manager: SpeakerManager = None,
):
super().__init__(config, ap, tokenizer, speaker_manager)
# pass all config fields as class attributes
for key in config:
setattr(self, key, config[key])
# layers
self.embedding = None
self.encoder = None
self.decoder = None
self.postnet = None
# init tensors
self.embedded_speakers = None
self.embedded_speakers_projected = None
# global style token
if self.gst and self.use_gst:
self.decoder_in_features += self.gst.gst_embedding_dim # add gst embedding dim
self.gst_layer = None
# Capacitron
if self.capacitron_vae and self.use_capacitron_vae:
self.decoder_in_features += self.capacitron_vae.capacitron_VAE_embedding_dim # add capacitron embedding dim
self.capacitron_vae_layer = None
# additional layers
self.decoder_backward = None
self.coarse_decoder = None
@staticmethod
def _format_aux_input(aux_input: Dict) -> Dict:
"""Set missing fields to their default values"""
if aux_input:
return format_aux_input({"d_vectors": None, "speaker_ids": None}, aux_input)
return None
#############################
# INIT FUNCTIONS
#############################
def _init_backward_decoder(self):
"""Init the backward decoder for Forward-Backward decoding."""
self.decoder_backward = copy.deepcopy(self.decoder)
def _init_coarse_decoder(self):
"""Init the coarse decoder for Double-Decoder Consistency."""
self.coarse_decoder = copy.deepcopy(self.decoder)
self.coarse_decoder.r_init = self.ddc_r
self.coarse_decoder.set_r(self.ddc_r)
#############################
# CORE FUNCTIONS
#############################
@abstractmethod
def forward(self):
pass
@abstractmethod
def inference(self):
pass
def load_checkpoint(
self, config, checkpoint_path, eval=False, cache=False
): # pylint: disable=unused-argument, redefined-builtin
"""Load model checkpoint and set up internals.
Args:
config (Coqpi): model configuration.
checkpoint_path (str): path to checkpoint file.
eval (bool, optional): whether to load model for evaluation.
cache (bool, optional): If True, cache the file locally for subsequent calls. It is cached under `get_user_data_dir()/tts_cache`. Defaults to False.
"""
state = load_fsspec(checkpoint_path, map_location=torch.device("cpu"), cache=cache)
self.load_state_dict(state["model"])
# TODO: set r in run-time by taking it from the new config
if "r" in state:
# set r from the state (for compatibility with older checkpoints)
self.decoder.set_r(state["r"])
elif "config" in state:
# set r from config used at training time (for inference)
self.decoder.set_r(state["config"]["r"])
else:
# set r from the new config (for new-models)
self.decoder.set_r(config.r)
if eval:
self.eval()
print(f" > Model's reduction rate `r` is set to: {self.decoder.r}")
assert not self.training
def get_criterion(self) -> nn.Module:
"""Get the model criterion used in training."""
return TacotronLoss(self.config)
@staticmethod
def init_from_config(config: Coqpit):
"""Initialize model from config."""
from TTS.utils.audio import AudioProcessor
ap = AudioProcessor.init_from_config(config)
tokenizer = TTSTokenizer.init_from_config(config)
speaker_manager = SpeakerManager.init_from_config(config)
return BaseTacotron(config, ap, tokenizer, speaker_manager)
##########################
# TEST AND LOG FUNCTIONS #
##########################
def test_run(self, assets: Dict) -> Tuple[Dict, Dict]:
"""Generic test run for `tts` models used by `Trainer`.
You can override this for a different behaviour.
Args:
assets (dict): A dict of training assets. For `tts` models, it must include `{'audio_processor': ap}`.
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_test_aux_input()
for idx, sen in enumerate(test_sentences):
outputs_dict = synthesis(
self,
sen,
self.config,
"cuda" in str(next(self.parameters()).device),
speaker_id=aux_inputs["speaker_id"],
d_vector=aux_inputs["d_vector"],
style_wav=aux_inputs["style_wav"],
use_griffin_lim=True,
do_trim_silence=False,
)
test_audios["{}-audio".format(idx)] = outputs_dict["wav"]
test_figures["{}-prediction".format(idx)] = plot_spectrogram(
outputs_dict["outputs"]["model_outputs"], self.ap, output_fig=False
)
test_figures["{}-alignment".format(idx)] = plot_alignment(
outputs_dict["outputs"]["alignments"], output_fig=False
)
return {"figures": test_figures, "audios": test_audios}
def test_log(
self, outputs: dict, logger: "Logger", assets: dict, steps: int # pylint: disable=unused-argument
) -> None:
logger.test_audios(steps, outputs["audios"], self.ap.sample_rate)
logger.test_figures(steps, outputs["figures"])
#############################
# COMMON COMPUTE FUNCTIONS
#############################
def compute_masks(self, text_lengths, mel_lengths):
"""Compute masks against sequence paddings."""
# B x T_in_max (boolean)
input_mask = sequence_mask(text_lengths)
output_mask = None
if mel_lengths is not None:
max_len = mel_lengths.max()
r = self.decoder.r
max_len = max_len + (r - (max_len % r)) if max_len % r > 0 else max_len
output_mask = sequence_mask(mel_lengths, max_len=max_len)
return input_mask, output_mask
def _backward_pass(self, mel_specs, encoder_outputs, mask):
"""Run backwards decoder"""
decoder_outputs_b, alignments_b, _ = self.decoder_backward(
encoder_outputs, torch.flip(mel_specs, dims=(1,)), mask
)
decoder_outputs_b = decoder_outputs_b.transpose(1, 2).contiguous()
return decoder_outputs_b, alignments_b
def _coarse_decoder_pass(self, mel_specs, encoder_outputs, alignments, input_mask):
"""Double Decoder Consistency"""
T = mel_specs.shape[1]
if T % self.coarse_decoder.r > 0:
padding_size = self.coarse_decoder.r - (T % self.coarse_decoder.r)
mel_specs = torch.nn.functional.pad(mel_specs, (0, 0, 0, padding_size, 0, 0))
decoder_outputs_backward, alignments_backward, _ = self.coarse_decoder(
encoder_outputs.detach(), mel_specs, input_mask
)
# scale_factor = self.decoder.r_init / self.decoder.r
alignments_backward = torch.nn.functional.interpolate(
alignments_backward.transpose(1, 2),
size=alignments.shape[1],
mode="nearest",
).transpose(1, 2)
decoder_outputs_backward = decoder_outputs_backward.transpose(1, 2)
decoder_outputs_backward = decoder_outputs_backward[:, :T, :]
return decoder_outputs_backward, alignments_backward
#############################
# EMBEDDING FUNCTIONS
#############################
def compute_gst(self, inputs, style_input, speaker_embedding=None):
"""Compute global style token"""
if isinstance(style_input, dict):
# multiply each style token with a weight
query = torch.zeros(1, 1, self.gst.gst_embedding_dim // 2).type_as(inputs)
if speaker_embedding is not None:
query = torch.cat([query, speaker_embedding.reshape(1, 1, -1)], dim=-1)
_GST = torch.tanh(self.gst_layer.style_token_layer.style_tokens)
gst_outputs = torch.zeros(1, 1, self.gst.gst_embedding_dim).type_as(inputs)
for k_token, v_amplifier in style_input.items():
key = _GST[int(k_token)].unsqueeze(0).expand(1, -1, -1)
gst_outputs_att = self.gst_layer.style_token_layer.attention(query, key)
gst_outputs = gst_outputs + gst_outputs_att * v_amplifier
elif style_input is None:
# ignore style token and return zero tensor
gst_outputs = torch.zeros(1, 1, self.gst.gst_embedding_dim).type_as(inputs)
else:
# compute style tokens
gst_outputs = self.gst_layer(style_input, speaker_embedding) # pylint: disable=not-callable
inputs = self._concat_speaker_embedding(inputs, gst_outputs)
return inputs
def compute_capacitron_VAE_embedding(self, inputs, reference_mel_info, text_info=None, speaker_embedding=None):
"""Capacitron Variational Autoencoder"""
(
VAE_outputs,
posterior_distribution,
prior_distribution,
capacitron_beta,
) = self.capacitron_vae_layer(
reference_mel_info,
text_info,
speaker_embedding, # pylint: disable=not-callable
)
VAE_outputs = VAE_outputs.to(inputs.device)
encoder_output = self._concat_speaker_embedding(
inputs, VAE_outputs
) # concatenate to the output of the basic tacotron encoder
return (
encoder_output,
posterior_distribution,
prior_distribution,
capacitron_beta,
)
@staticmethod
def _add_speaker_embedding(outputs, embedded_speakers):
embedded_speakers_ = embedded_speakers.expand(outputs.size(0), outputs.size(1), -1)
outputs = outputs + embedded_speakers_
return outputs
@staticmethod
def _concat_speaker_embedding(outputs, embedded_speakers):
embedded_speakers_ = embedded_speakers.expand(outputs.size(0), outputs.size(1), -1)
outputs = torch.cat([outputs, embedded_speakers_], dim=-1)
return outputs
#############################
# CALLBACKS
#############################
def on_epoch_start(self, trainer):
"""Callback for setting values wrt gradual training schedule.
Args:
trainer (TrainerTTS): TTS trainer object that is used to train this model.
"""
if self.gradual_training:
r, trainer.config.batch_size = gradual_training_scheduler(trainer.total_steps_done, trainer.config)
trainer.config.r = r
self.decoder.set_r(r)
if trainer.config.bidirectional_decoder:
trainer.model.decoder_backward.set_r(r)
print(f"\n > Number of output frames: {self.decoder.r}")