import os
import random
from typing import Dict, List, Tuple, Union
import torch
import torch.distributed as dist
from coqpit import Coqpit
from torch import nn
from torch.utils.data import DataLoader
from torch.utils.data.sampler import WeightedRandomSampler
from trainer.torch import DistributedSampler, DistributedSamplerWrapper
from TTS.model import BaseTrainerModel
from TTS.tts.datasets.dataset import TTSDataset
from TTS.tts.utils.data import get_length_balancer_weights
from TTS.tts.utils.languages import LanguageManager, get_language_balancer_weights
from TTS.tts.utils.speakers import SpeakerManager, get_speaker_balancer_weights
from TTS.tts.utils.synthesis import synthesis
from TTS.tts.utils.visual import plot_alignment, plot_spectrogram
# pylint: skip-file
class BaseVC(BaseTrainerModel):
"""Base `vc` class. Every new `vc` model must inherit this.
It defines common `vc` specific functions on top of `Model` implementation.
"""
MODEL_TYPE = "vc"
def __init__(
self,
config: Coqpit,
ap: "AudioProcessor",
speaker_manager: SpeakerManager = None,
language_manager: LanguageManager = None,
):
super().__init__()
self.config = config
self.ap = ap
self.speaker_manager = speaker_manager
self.language_manager = language_manager
self._set_model_args(config)
def _set_model_args(self, config: Coqpit):
"""Setup model args based on the config type (`ModelConfig` or `ModelArgs`).
`ModelArgs` has all the fields reuqired to initialize the model architecture.
`ModelConfig` has all the fields required for training, inference and containes `ModelArgs`.
If the config is for training with a name like "*Config", then the model args are embeded in the
config.model_args
If the config is for the model with a name like "*Args", then we assign the directly.
"""
# don't use isintance not to import recursively
if "Config" in config.__class__.__name__:
self.config = config
self.args = config.model_args
elif "Args" in config.__class__.__name__:
self.args = config
else:
raise ValueError("config must be either a *Config or *Args")
def init_multispeaker(self, config: Coqpit, data: List = None):
"""Initialize a speaker embedding layer if needen and define expected embedding channel size for defining
`in_channels` size of the connected layers.
This implementation yields 3 possible outcomes:
1. If `config.use_speaker_embedding` and `config.use_d_vector_file are False, do nothing.
2. If `config.use_d_vector_file` is True, set expected embedding channel size to `config.d_vector_dim` or 512.
3. If `config.use_speaker_embedding`, initialize a speaker embedding layer with channel size of
`config.d_vector_dim` or 512.
You can override this function for new models.
Args:
config (Coqpit): Model configuration.
"""
# set number of speakers
if self.speaker_manager is not None:
self.num_speakers = self.speaker_manager.num_speakers
elif hasattr(config, "num_speakers"):
self.num_speakers = config.num_speakers
# set ultimate speaker embedding size
if config.use_speaker_embedding or config.use_d_vector_file:
self.embedded_speaker_dim = (
config.d_vector_dim if "d_vector_dim" in config and config.d_vector_dim is not None else 512
)
# init speaker embedding layer
if config.use_speaker_embedding and not config.use_d_vector_file:
print(" > Init speaker_embedding layer.")
self.speaker_embedding = nn.Embedding(self.num_speakers, self.embedded_speaker_dim)
self.speaker_embedding.weight.data.normal_(0, 0.3)
def get_aux_input(self, **kwargs) -> Dict:
"""Prepare and return `aux_input` used by `forward()`"""
return {"speaker_id": None, "style_wav": None, "d_vector": None, "language_id": None}
def get_aux_input_from_test_sentences(self, sentence_info):
if hasattr(self.config, "model_args"):
config = self.config.model_args
else:
config = self.config
# extract speaker and language info
text, speaker_name, style_wav, language_name = None, None, None, None
if isinstance(sentence_info, list):
if len(sentence_info) == 1:
text = sentence_info[0]
elif len(sentence_info) == 2:
text, speaker_name = sentence_info
elif len(sentence_info) == 3:
text, speaker_name, style_wav = sentence_info
elif len(sentence_info) == 4:
text, speaker_name, style_wav, language_name = sentence_info
else:
text = sentence_info
# get speaker id/d_vector
speaker_id, d_vector, language_id = None, None, None
if self.speaker_manager is not None:
if config.use_d_vector_file:
if speaker_name is None:
d_vector = self.speaker_manager.get_random_embedding()
else:
d_vector = self.speaker_manager.get_d_vector_by_name(speaker_name)
elif config.use_speaker_embedding:
if speaker_name is None:
speaker_id = self.speaker_manager.get_random_id()
else:
speaker_id = self.speaker_manager.name_to_id[speaker_name]
# get language id
if self.language_manager is not None and config.use_language_embedding and language_name is not None:
language_id = self.language_manager.name_to_id[language_name]
return {
"text": text,
"speaker_id": speaker_id,
"style_wav": style_wav,
"d_vector": d_vector,
"language_id": language_id,
}
def format_batch(self, batch: Dict) -> Dict:
"""Generic batch formatting for `VCDataset`.
You must override this if you use a custom dataset.
Args:
batch (Dict): [description]
Returns:
Dict: [description]
"""
# setup input batch
text_input = batch["token_id"]
text_lengths = batch["token_id_lengths"]
speaker_names = batch["speaker_names"]
linear_input = batch["linear"]
mel_input = batch["mel"]
mel_lengths = batch["mel_lengths"]
stop_targets = batch["stop_targets"]
item_idx = batch["item_idxs"]
d_vectors = batch["d_vectors"]
speaker_ids = batch["speaker_ids"]
attn_mask = batch["attns"]
waveform = batch["waveform"]
pitch = batch["pitch"]
energy = batch["energy"]
language_ids = batch["language_ids"]
max_text_length = torch.max(text_lengths.float())
max_spec_length = torch.max(mel_lengths.float())
# compute durations from attention masks
durations = None
if attn_mask is not None:
durations = torch.zeros(attn_mask.shape[0], attn_mask.shape[2])
for idx, am in enumerate(attn_mask):
# compute raw durations
c_idxs = am[:, : text_lengths[idx], : mel_lengths[idx]].max(1)[1]
# c_idxs, counts = torch.unique_consecutive(c_idxs, return_counts=True)
c_idxs, counts = torch.unique(c_idxs, return_counts=True)
dur = torch.ones([text_lengths[idx]]).to(counts.dtype)
dur[c_idxs] = counts
# smooth the durations and set any 0 duration to 1
# by cutting off from the largest duration indeces.
extra_frames = dur.sum() - mel_lengths[idx]
largest_idxs = torch.argsort(-dur)[:extra_frames]
dur[largest_idxs] -= 1
assert (
dur.sum() == mel_lengths[idx]
), f" [!] total duration {dur.sum()} vs spectrogram length {mel_lengths[idx]}"
durations[idx, : text_lengths[idx]] = dur
# set stop targets wrt reduction factor
stop_targets = stop_targets.view(text_input.shape[0], stop_targets.size(1) // self.config.r, -1)
stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze(2)
stop_target_lengths = torch.divide(mel_lengths, self.config.r).ceil_()
return {
"text_input": text_input,
"text_lengths": text_lengths,
"speaker_names": speaker_names,
"mel_input": mel_input,
"mel_lengths": mel_lengths,
"linear_input": linear_input,
"stop_targets": stop_targets,
"stop_target_lengths": stop_target_lengths,
"attn_mask": attn_mask,
"durations": durations,
"speaker_ids": speaker_ids,
"d_vectors": d_vectors,
"max_text_length": float(max_text_length),
"max_spec_length": float(max_spec_length),
"item_idx": item_idx,
"waveform": waveform,
"pitch": pitch,
"energy": energy,
"language_ids": language_ids,
"audio_unique_names": batch["audio_unique_names"],
}
def get_sampler(self, config: Coqpit, dataset: TTSDataset, num_gpus=1):
weights = None
data_items = dataset.samples
if getattr(config, "use_language_weighted_sampler", False):
alpha = getattr(config, "language_weighted_sampler_alpha", 1.0)
print(" > Using Language weighted sampler with alpha:", alpha)
weights = get_language_balancer_weights(data_items) * alpha
if getattr(config, "use_speaker_weighted_sampler", False):
alpha = getattr(config, "speaker_weighted_sampler_alpha", 1.0)
print(" > Using Speaker weighted sampler with alpha:", alpha)
if weights is not None:
weights += get_speaker_balancer_weights(data_items) * alpha
else:
weights = get_speaker_balancer_weights(data_items) * alpha
if getattr(config, "use_length_weighted_sampler", False):
alpha = getattr(config, "length_weighted_sampler_alpha", 1.0)
print(" > Using Length weighted sampler with alpha:", alpha)
if weights is not None:
weights += get_length_balancer_weights(data_items) * alpha
else:
weights = get_length_balancer_weights(data_items) * alpha
if weights is not None:
sampler = WeightedRandomSampler(weights, len(weights))
else:
sampler = None
# sampler for DDP
if sampler is None:
sampler = DistributedSampler(dataset) if num_gpus > 1 else None
else: # If a sampler is already defined use this sampler and DDP sampler together
sampler = DistributedSamplerWrapper(sampler) if num_gpus > 1 else sampler
return sampler
def get_data_loader(
self,
config: Coqpit,
assets: Dict,
is_eval: bool,
samples: Union[List[Dict], List[List]],
verbose: bool,
num_gpus: int,
rank: int = None,
) -> "DataLoader":
if is_eval and not config.run_eval:
loader = None
else:
# setup multi-speaker attributes
if self.speaker_manager is not None:
if hasattr(config, "model_args"):
speaker_id_mapping = (
self.speaker_manager.name_to_id if config.model_args.use_speaker_embedding else None
)
d_vector_mapping = self.speaker_manager.embeddings if config.model_args.use_d_vector_file else None
config.use_d_vector_file = config.model_args.use_d_vector_file
else:
speaker_id_mapping = self.speaker_manager.name_to_id if config.use_speaker_embedding else None
d_vector_mapping = self.speaker_manager.embeddings if config.use_d_vector_file else None
else:
speaker_id_mapping = None
d_vector_mapping = None
# setup multi-lingual attributes
if self.language_manager is not None:
language_id_mapping = self.language_manager.name_to_id if self.args.use_language_embedding else None
else:
language_id_mapping = None
# init dataloader
dataset = TTSDataset(
outputs_per_step=config.r if "r" in config else 1,
compute_linear_spec=config.model.lower() == "tacotron" or config.compute_linear_spec,
compute_f0=config.get("compute_f0", False),
f0_cache_path=config.get("f0_cache_path", None),
compute_energy=config.get("compute_energy", False),
energy_cache_path=config.get("energy_cache_path", None),
samples=samples,
ap=self.ap,
return_wav=config.return_wav if "return_wav" in config else False,
batch_group_size=0 if is_eval else config.batch_group_size * config.batch_size,
min_text_len=config.min_text_len,
max_text_len=config.max_text_len,
min_audio_len=config.min_audio_len,
max_audio_len=config.max_audio_len,
phoneme_cache_path=config.phoneme_cache_path,
precompute_num_workers=config.precompute_num_workers,
use_noise_augment=False if is_eval else config.use_noise_augment,
verbose=verbose,
speaker_id_mapping=speaker_id_mapping,
d_vector_mapping=d_vector_mapping if config.use_d_vector_file else None,
tokenizer=None,
start_by_longest=config.start_by_longest,
language_id_mapping=language_id_mapping,
)
# wait all the DDP process to be ready
if num_gpus > 1:
dist.barrier()
# sort input sequences from short to long
dataset.preprocess_samples()
# get samplers
sampler = self.get_sampler(config, dataset, num_gpus)
loader = DataLoader(
dataset,
batch_size=config.eval_batch_size if is_eval else config.batch_size,
shuffle=config.shuffle if sampler is None else False, # if there is no other sampler
collate_fn=dataset.collate_fn,
drop_last=config.drop_last, # setting this False might cause issues in AMP training.
sampler=sampler,
num_workers=config.num_eval_loader_workers if is_eval else config.num_loader_workers,
pin_memory=False,
)
return loader
def _get_test_aux_input(
self,
) -> Dict:
d_vector = None
if self.config.use_d_vector_file:
d_vector = [self.speaker_manager.embeddings[name]["embedding"] for name in self.speaker_manager.embeddings]
d_vector = (random.sample(sorted(d_vector), 1),)
aux_inputs = {
"speaker_id": None
if not self.config.use_speaker_embedding
else random.sample(sorted(self.speaker_manager.name_to_id.values()), 1),
"d_vector": d_vector,
"style_wav": None, # TODO: handle GST style input
}
return aux_inputs
def test_run(self, assets: Dict) -> Tuple[Dict, Dict]:
"""Generic test run for `vc` models used by `Trainer`.
You can override this for a different behaviour.
Args:
assets (dict): A dict of training assets. For `vc` 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):
if isinstance(sen, list):
aux_inputs = self.get_aux_input_from_test_sentences(sen)
sen = aux_inputs["text"]
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 test_figures, test_audios
def on_init_start(self, trainer):
"""Save the speaker.pth and language_ids.json at the beginning of the training. Also update both paths."""
if self.speaker_manager is not None:
output_path = os.path.join(trainer.output_path, "speakers.pth")
self.speaker_manager.save_ids_to_file(output_path)
trainer.config.speakers_file = output_path
# some models don't have `model_args` set
if hasattr(trainer.config, "model_args"):
trainer.config.model_args.speakers_file = output_path
trainer.config.save_json(os.path.join(trainer.output_path, "config.json"))
print(f" > `speakers.pth` is saved to {output_path}.")
print(" > `speakers_file` is updated in the config.json.")
if self.language_manager is not None:
output_path = os.path.join(trainer.output_path, "language_ids.json")
self.language_manager.save_ids_to_file(output_path)
trainer.config.language_ids_file = output_path
if hasattr(trainer.config, "model_args"):
trainer.config.model_args.language_ids_file = output_path
trainer.config.save_json(os.path.join(trainer.output_path, "config.json"))
print(f" > `language_ids.json` is saved to {output_path}.")
print(" > `language_ids_file` is updated in the config.json.")