import math
import os
from dataclasses import dataclass, field, replace
from itertools import chain
from typing import Dict, List, Tuple, Union
import numpy as np
import torch
import torch.distributed as dist
import torchaudio
from coqpit import Coqpit
from librosa.filters import mel as librosa_mel_fn
from torch import nn
from torch.cuda.amp.autocast_mode import autocast
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torch.utils.data.sampler import WeightedRandomSampler
from trainer.torch import DistributedSampler, DistributedSamplerWrapper
from trainer.trainer_utils import get_optimizer, get_scheduler
from TTS.tts.configs.shared_configs import CharactersConfig
from TTS.tts.datasets.dataset import TTSDataset, _parse_sample
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.languages import LanguageManager
from TTS.tts.utils.speakers import SpeakerManager
from TTS.tts.utils.synthesis import synthesis
from TTS.tts.utils.text.characters import BaseCharacters, _characters, _pad, _phonemes, _punctuations
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.tts.utils.visual import plot_alignment
from TTS.utils.io import load_fsspec
from TTS.utils.samplers import BucketBatchSampler
from TTS.vocoder.models.hifigan_generator import HifiganGenerator
from TTS.vocoder.utils.generic_utils import plot_results
##############################
# IO / Feature extraction
##############################
# pylint: disable=global-statement
hann_window = {}
mel_basis = {}
@torch.no_grad()
def weights_reset(m: nn.Module):
# check if the current module has reset_parameters and if it is reset the weight
reset_parameters = getattr(m, "reset_parameters", None)
if callable(reset_parameters):
m.reset_parameters()
def get_module_weights_sum(mdl: nn.Module):
dict_sums = {}
for name, w in mdl.named_parameters():
if "weight" in name:
value = w.data.sum().item()
dict_sums[name] = value
return dict_sums
def load_audio(file_path):
"""Load the audio file normalized in [-1, 1]
Return Shapes:
- x: :math:`[1, T]`
"""
x, sr = torchaudio.load(file_path)
assert (x > 1).sum() + (x < -1).sum() == 0
return x, sr
def _amp_to_db(x, C=1, clip_val=1e-5):
return torch.log(torch.clamp(x, min=clip_val) * C)
def _db_to_amp(x, C=1):
return torch.exp(x) / C
def amp_to_db(magnitudes):
output = _amp_to_db(magnitudes)
return output
def db_to_amp(magnitudes):
output = _db_to_amp(magnitudes)
return output
def wav_to_spec(y, n_fft, hop_length, win_length, center=False):
"""
Args Shapes:
- y : :math:`[B, 1, T]`
Return Shapes:
- spec : :math:`[B,C,T]`
"""
y = y.squeeze(1)
if torch.min(y) < -1.0:
print("min value is ", torch.min(y))
if torch.max(y) > 1.0:
print("max value is ", torch.max(y))
global hann_window
dtype_device = str(y.dtype) + "_" + str(y.device)
wnsize_dtype_device = str(win_length) + "_" + dtype_device
if wnsize_dtype_device not in hann_window:
hann_window[wnsize_dtype_device] = torch.hann_window(win_length).to(dtype=y.dtype, device=y.device)
y = torch.nn.functional.pad(
y.unsqueeze(1),
(int((n_fft - hop_length) / 2), int((n_fft - hop_length) / 2)),
mode="reflect",
)
y = y.squeeze(1)
spec = torch.stft(
y,
n_fft,
hop_length=hop_length,
win_length=win_length,
window=hann_window[wnsize_dtype_device],
center=center,
pad_mode="reflect",
normalized=False,
onesided=True,
)
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
return spec
def spec_to_mel(spec, n_fft, num_mels, sample_rate, fmin, fmax):
"""
Args Shapes:
- spec : :math:`[B,C,T]`
Return Shapes:
- mel : :math:`[B,C,T]`
"""
global mel_basis
dtype_device = str(spec.dtype) + "_" + str(spec.device)
fmax_dtype_device = str(fmax) + "_" + dtype_device
if fmax_dtype_device not in mel_basis:
mel = librosa_mel_fn(sample_rate, n_fft, num_mels, fmin, fmax)
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
mel = torch.matmul(mel_basis[fmax_dtype_device], spec)
mel = amp_to_db(mel)
return mel
def wav_to_mel(y, n_fft, num_mels, sample_rate, hop_length, win_length, fmin, fmax, center=False):
"""
Args Shapes:
- y : :math:`[B, 1, T]`
Return Shapes:
- spec : :math:`[B,C,T]`
"""
y = y.squeeze(1)
if torch.min(y) < -1.0:
print("min value is ", torch.min(y))
if torch.max(y) > 1.0:
print("max value is ", torch.max(y))
global mel_basis, hann_window
dtype_device = str(y.dtype) + "_" + str(y.device)
fmax_dtype_device = str(fmax) + "_" + dtype_device
wnsize_dtype_device = str(win_length) + "_" + dtype_device
if fmax_dtype_device not in mel_basis:
mel = librosa_mel_fn(sample_rate, n_fft, num_mels, fmin, fmax)
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
if wnsize_dtype_device not in hann_window:
hann_window[wnsize_dtype_device] = torch.hann_window(win_length).to(dtype=y.dtype, device=y.device)
y = torch.nn.functional.pad(
y.unsqueeze(1),
(int((n_fft - hop_length) / 2), int((n_fft - hop_length) / 2)),
mode="reflect",
)
y = y.squeeze(1)
spec = torch.stft(
y,
n_fft,
hop_length=hop_length,
win_length=win_length,
window=hann_window[wnsize_dtype_device],
center=center,
pad_mode="reflect",
normalized=False,
onesided=True,
)
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
spec = amp_to_db(spec)
return spec
#############################
# CONFIGS
#############################
@dataclass
class VitsAudioConfig(Coqpit):
fft_size: int = 1024
sample_rate: int = 22050
win_length: int = 1024
hop_length: int = 256
num_mels: int = 80
mel_fmin: int = 0
mel_fmax: int = None
##############################
# DATASET
##############################
def get_attribute_balancer_weights(items: list, attr_name: str, multi_dict: dict = None):
"""Create inverse frequency weights for balancing the dataset.
Use `multi_dict` to scale relative weights."""
attr_names_samples = np.array([item[attr_name] for item in items])
unique_attr_names = np.unique(attr_names_samples).tolist()
attr_idx = [unique_attr_names.index(l) for l in attr_names_samples]
attr_count = np.array([len(np.where(attr_names_samples == l)[0]) for l in unique_attr_names])
weight_attr = 1.0 / attr_count
dataset_samples_weight = np.array([weight_attr[l] for l in attr_idx])
dataset_samples_weight = dataset_samples_weight / np.linalg.norm(dataset_samples_weight)
if multi_dict is not None:
# check if all keys are in the multi_dict
for k in multi_dict:
assert k in unique_attr_names, f"{k} not in {unique_attr_names}"
# scale weights
multiplier_samples = np.array([multi_dict.get(item[attr_name], 1.0) for item in items])
dataset_samples_weight *= multiplier_samples
return (
torch.from_numpy(dataset_samples_weight).float(),
unique_attr_names,
np.unique(dataset_samples_weight).tolist(),
)
class VitsDataset(TTSDataset):
def __init__(self, model_args, *args, **kwargs):
super().__init__(*args, **kwargs)
self.pad_id = self.tokenizer.characters.pad_id
self.model_args = model_args
def __getitem__(self, idx):
item = self.samples[idx]
raw_text = item["text"]
wav, _ = load_audio(item["audio_file"])
if self.model_args.encoder_sample_rate is not None:
if wav.size(1) % self.model_args.encoder_sample_rate != 0:
wav = wav[:, : -int(wav.size(1) % self.model_args.encoder_sample_rate)]
wav_filename = os.path.basename(item["audio_file"])
token_ids = self.get_token_ids(idx, item["text"])
# after phonemization the text length may change
# this is a shameful 🤠hack to prevent longer phonemes
# TODO: find a better fix
if len(token_ids) > self.max_text_len or wav.shape[1] < self.min_audio_len:
self.rescue_item_idx += 1
return self.__getitem__(self.rescue_item_idx)
return {
"raw_text": raw_text,
"token_ids": token_ids,
"token_len": len(token_ids),
"wav": wav,
"wav_file": wav_filename,
"speaker_name": item["speaker_name"],
"language_name": item["language"],
"audio_unique_name": item["audio_unique_name"],
}
@property
def lengths(self):
lens = []
for item in self.samples:
_, wav_file, *_ = _parse_sample(item)
audio_len = os.path.getsize(wav_file) / 16 * 8 # assuming 16bit audio
lens.append(audio_len)
return lens
def collate_fn(self, batch):
"""
Return Shapes:
- tokens: :math:`[B, T]`
- token_lens :math:`[B]`
- token_rel_lens :math:`[B]`
- waveform: :math:`[B, 1, T]`
- waveform_lens: :math:`[B]`
- waveform_rel_lens: :math:`[B]`
- speaker_names: :math:`[B]`
- language_names: :math:`[B]`
- audiofile_paths: :math:`[B]`
- raw_texts: :math:`[B]`
- audio_unique_names: :math:`[B]`
"""
# convert list of dicts to dict of lists
B = len(batch)
batch = {k: [dic[k] for dic in batch] for k in batch[0]}
_, ids_sorted_decreasing = torch.sort(
torch.LongTensor([x.size(1) for x in batch["wav"]]), dim=0, descending=True
)
max_text_len = max([len(x) for x in batch["token_ids"]])
token_lens = torch.LongTensor(batch["token_len"])
token_rel_lens = token_lens / token_lens.max()
wav_lens = [w.shape[1] for w in batch["wav"]]
wav_lens = torch.LongTensor(wav_lens)
wav_lens_max = torch.max(wav_lens)
wav_rel_lens = wav_lens / wav_lens_max
token_padded = torch.LongTensor(B, max_text_len)
wav_padded = torch.FloatTensor(B, 1, wav_lens_max)
token_padded = token_padded.zero_() + self.pad_id
wav_padded = wav_padded.zero_() + self.pad_id
for i in range(len(ids_sorted_decreasing)):
token_ids = batch["token_ids"][i]
token_padded[i, : batch["token_len"][i]] = torch.LongTensor(token_ids)
wav = batch["wav"][i]
wav_padded[i, :, : wav.size(1)] = torch.FloatTensor(wav)
return {
"tokens": token_padded,
"token_lens": token_lens,
"token_rel_lens": token_rel_lens,
"waveform": wav_padded, # (B x T)
"waveform_lens": wav_lens, # (B)
"waveform_rel_lens": wav_rel_lens,
"speaker_names": batch["speaker_name"],
"language_names": batch["language_name"],
"audio_files": batch["wav_file"],
"raw_text": batch["raw_text"],
"audio_unique_names": batch["audio_unique_name"],
}
##############################
# MODEL DEFINITION
##############################
@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 of the decoder. 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]`.
periods_multi_period_discriminator (List[int]):
Periods values for Vits Multi-Period Discriminator. Defaults to `[2, 3, 5, 7, 11]`.
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_file (str):
Path to the file including pre-computed speaker embeddings. Defaults to None.
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.
use_language_embedding (bool):
Enable/Disable language embedding for multilingual models. Defaults to False.
embedded_language_dim (int):
Number of language embedding channels. Defaults to 4.
num_languages (int):
Number of languages for the language embedding layer. Defaults to 0.
language_ids_file (str):
Path to the language mapping file for the Language Manager. Defaults to None.
use_speaker_encoder_as_loss (bool):
Enable/Disable Speaker Consistency Loss (SCL). Defaults to False.
speaker_encoder_config_path (str):
Path to the file speaker encoder config file, to use for SCL. Defaults to "".
speaker_encoder_model_path (str):
Path to the file speaker encoder checkpoint file, to use for SCL. Defaults to "".
condition_dp_on_speaker (bool):
Condition the duration predictor on the speaker embedding. Defaults to True.
freeze_encoder (bool):
Freeze the encoder weigths during training. Defaults to False.
freeze_DP (bool):
Freeze the duration predictor weigths during training. Defaults to False.
freeze_PE (bool):
Freeze the posterior encoder weigths during training. Defaults to False.
freeze_flow_encoder (bool):
Freeze the flow encoder weigths during training. Defaults to False.
freeze_waveform_decoder (bool):
Freeze the waveform decoder weigths during training. Defaults to False.
encoder_sample_rate (int):
If not None this sample rate will be used for training the Posterior Encoder,
flow, text_encoder and duration predictor. The decoder part (vocoder) will be
trained with the `config.audio.sample_rate`. Defaults to None.
interpolate_z (bool):
If `encoder_sample_rate` not None and this parameter True the nearest interpolation
will be used to upsampling the latent variable z with the sampling rate `encoder_sample_rate`
to the `config.audio.sample_rate`. If it is False you will need to add extra
`upsample_rates_decoder` to match the shape. 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])
periods_multi_period_discriminator: List[int] = field(default_factory=lambda: [2, 3, 5, 7, 11])
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
d_vector_file: str = None
speaker_embedding_channels: int = 256
use_d_vector_file: bool = False
d_vector_dim: int = 0
detach_dp_input: bool = True
use_language_embedding: bool = False
embedded_language_dim: int = 4
num_languages: int = 0
language_ids_file: str = None
use_speaker_encoder_as_loss: bool = False
speaker_encoder_config_path: str = ""
speaker_encoder_model_path: str = ""
condition_dp_on_speaker: bool = True
freeze_encoder: bool = False
freeze_DP: bool = False
freeze_PE: bool = False
freeze_flow_decoder: bool = False
freeze_waveform_decoder: bool = False
encoder_sample_rate: int = None
interpolate_z: bool = True
reinit_DP: bool = False
reinit_text_encoder: bool = False
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)
"""
def __init__(
self,
config: Coqpit,
ap: "AudioProcessor" = None,
tokenizer: "TTSTokenizer" = None,
speaker_manager: SpeakerManager = None,
language_manager: LanguageManager = None,
):
super().__init__(config, ap, tokenizer, speaker_manager, language_manager)
self.init_multispeaker(config)
self.init_multilingual(config)
self.init_upsampling()
self.length_scale = self.args.length_scale
self.noise_scale = self.args.noise_scale
self.inference_noise_scale = self.args.inference_noise_scale
self.inference_noise_scale_dp = self.args.inference_noise_scale_dp
self.noise_scale_dp = self.args.noise_scale_dp
self.max_inference_len = self.args.max_inference_len
self.spec_segment_size = self.args.spec_segment_size
self.text_encoder = TextEncoder(
self.args.num_chars,
self.args.hidden_channels,
self.args.hidden_channels,
self.args.hidden_channels_ffn_text_encoder,
self.args.num_heads_text_encoder,
self.args.num_layers_text_encoder,
self.args.kernel_size_text_encoder,
self.args.dropout_p_text_encoder,
language_emb_dim=self.embedded_language_dim,
)
self.posterior_encoder = PosteriorEncoder(
self.args.out_channels,
self.args.hidden_channels,
self.args.hidden_channels,
kernel_size=self.args.kernel_size_posterior_encoder,
dilation_rate=self.args.dilation_rate_posterior_encoder,
num_layers=self.args.num_layers_posterior_encoder,
cond_channels=self.embedded_speaker_dim,
)
self.flow = ResidualCouplingBlocks(
self.args.hidden_channels,
self.args.hidden_channels,
kernel_size=self.args.kernel_size_flow,
dilation_rate=self.args.dilation_rate_flow,
num_layers=self.args.num_layers_flow,
cond_channels=self.embedded_speaker_dim,
)
if self.args.use_sdp:
self.duration_predictor = StochasticDurationPredictor(
self.args.hidden_channels,
192,
3,
self.args.dropout_p_duration_predictor,
4,
cond_channels=self.embedded_speaker_dim if self.args.condition_dp_on_speaker else 0,
language_emb_dim=self.embedded_language_dim,
)
else:
self.duration_predictor = DurationPredictor(
self.args.hidden_channels,
256,
3,
self.args.dropout_p_duration_predictor,
cond_channels=self.embedded_speaker_dim,
language_emb_dim=self.embedded_language_dim,
)
self.waveform_decoder = HifiganGenerator(
self.args.hidden_channels,
1,
self.args.resblock_type_decoder,
self.args.resblock_dilation_sizes_decoder,
self.args.resblock_kernel_sizes_decoder,
self.args.upsample_kernel_sizes_decoder,
self.args.upsample_initial_channel_decoder,
self.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 self.args.init_discriminator:
self.disc = VitsDiscriminator(
periods=self.args.periods_multi_period_discriminator,
use_spectral_norm=self.args.use_spectral_norm_disriminator,
)
@property
def device(self):
return next(self.parameters()).device
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.
You must provide a `speaker_manager` at initialization to set up the multi-speaker modules.
Args:
config (Coqpit): Model configuration.
data (List, optional): Dataset items to infer number of speakers. Defaults to None.
"""
self.embedded_speaker_dim = 0
self.num_speakers = self.args.num_speakers
self.audio_transform = None
if self.speaker_manager:
self.num_speakers = self.speaker_manager.num_speakers
if self.args.use_speaker_embedding:
self._init_speaker_embedding()
if self.args.use_d_vector_file:
self._init_d_vector()
# TODO: make this a function
if self.args.use_speaker_encoder_as_loss:
if self.speaker_manager.encoder is None and (
not self.args.speaker_encoder_model_path or not self.args.speaker_encoder_config_path
):
raise RuntimeError(
" [!] To use the speaker consistency loss (SCL) you need to specify speaker_encoder_model_path and speaker_encoder_config_path !!"
)
self.speaker_manager.encoder.eval()
print(" > External Speaker Encoder Loaded !!")
if (
hasattr(self.speaker_manager.encoder, "audio_config")
and self.config.audio.sample_rate != self.speaker_manager.encoder.audio_config["sample_rate"]
):
self.audio_transform = torchaudio.transforms.Resample(
orig_freq=self.config.audio.sample_rate,
new_freq=self.speaker_manager.encoder.audio_config["sample_rate"],
)
def _init_speaker_embedding(self):
# pylint: disable=attribute-defined-outside-init
if self.num_speakers > 0:
print(" > initialization of speaker-embedding layers.")
self.embedded_speaker_dim = self.args.speaker_embedding_channels
self.emb_g = nn.Embedding(self.num_speakers, self.embedded_speaker_dim)
def _init_d_vector(self):
# pylint: disable=attribute-defined-outside-init
if hasattr(self, "emb_g"):
raise ValueError("[!] Speaker embedding layer already initialized before d_vector settings.")
self.embedded_speaker_dim = self.args.d_vector_dim
def init_multilingual(self, config: Coqpit):
"""Initialize multilingual modules of a model.
Args:
config (Coqpit): Model configuration.
"""
if self.args.language_ids_file is not None:
self.language_manager = LanguageManager(language_ids_file_path=config.language_ids_file)
if self.args.use_language_embedding and self.language_manager:
print(" > initialization of language-embedding layers.")
self.num_languages = self.language_manager.num_languages
self.embedded_language_dim = self.args.embedded_language_dim
self.emb_l = nn.Embedding(self.num_languages, self.embedded_language_dim)
torch.nn.init.xavier_uniform_(self.emb_l.weight)
else:
self.embedded_language_dim = 0
def init_upsampling(self):
"""
Initialize upsampling modules of a model.
"""
if self.args.encoder_sample_rate:
self.interpolate_factor = self.config.audio["sample_rate"] / self.args.encoder_sample_rate
self.audio_resampler = torchaudio.transforms.Resample(
orig_freq=self.config.audio["sample_rate"], new_freq=self.args.encoder_sample_rate
) # pylint: disable=W0201
def on_epoch_start(self, trainer): # pylint: disable=W0613
"""Freeze layers at the beginning of an epoch"""
self._freeze_layers()
# set the device of speaker encoder
if self.args.use_speaker_encoder_as_loss:
self.speaker_manager.encoder = self.speaker_manager.encoder.to(self.device)
def on_init_end(self, trainer): # pylint: disable=W0613
"""Reinit layes if needed"""
if self.args.reinit_DP:
before_dict = get_module_weights_sum(self.duration_predictor)
# Applies weights_reset recursively to every submodule of the duration predictor
self.duration_predictor.apply(fn=weights_reset)
after_dict = get_module_weights_sum(self.duration_predictor)
for key, value in after_dict.items():
if value == before_dict[key]:
raise RuntimeError(" [!] The weights of Duration Predictor was not reinit check it !")
print(" > Duration Predictor was reinit.")
if self.args.reinit_text_encoder:
before_dict = get_module_weights_sum(self.text_encoder)
# Applies weights_reset recursively to every submodule of the duration predictor
self.text_encoder.apply(fn=weights_reset)
after_dict = get_module_weights_sum(self.text_encoder)
for key, value in after_dict.items():
if value == before_dict[key]:
raise RuntimeError(" [!] The weights of Text Encoder was not reinit check it !")
print(" > Text Encoder was reinit.")
def get_aux_input(self, aux_input: Dict):
sid, g, lid, _ = self._set_cond_input(aux_input)
return {"speaker_ids": sid, "style_wav": None, "d_vectors": g, "language_ids": lid}
def _freeze_layers(self):
if self.args.freeze_encoder:
for param in self.text_encoder.parameters():
param.requires_grad = False
if hasattr(self, "emb_l"):
for param in self.emb_l.parameters():
param.requires_grad = False
if self.args.freeze_PE:
for param in self.posterior_encoder.parameters():
param.requires_grad = False
if self.args.freeze_DP:
for param in self.duration_predictor.parameters():
param.requires_grad = False
if self.args.freeze_flow_decoder:
for param in self.flow.parameters():
param.requires_grad = False
if self.args.freeze_waveform_decoder:
for param in self.waveform_decoder.parameters():
param.requires_grad = False
@staticmethod
def _set_cond_input(aux_input: Dict):
"""Set the speaker conditioning input based on the multi-speaker mode."""
sid, g, lid, durations = None, None, 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 = F.normalize(aux_input["d_vectors"]).unsqueeze(-1)
if g.ndim == 2:
g = g.unsqueeze_(0)
if "language_ids" in aux_input and aux_input["language_ids"] is not None:
lid = aux_input["language_ids"]
if lid.ndim == 0:
lid = lid.unsqueeze_(0)
if "durations" in aux_input and aux_input["durations"] is not None:
durations = aux_input["durations"]
return sid, g, lid, durations
def _set_speaker_input(self, aux_input: Dict):
d_vectors = aux_input.get("d_vectors", None)
speaker_ids = aux_input.get("speaker_ids", None)
if d_vectors is not None and speaker_ids is not None:
raise ValueError("[!] Cannot use d-vectors and speaker-ids together.")
if speaker_ids is not None and not hasattr(self, "emb_g"):
raise ValueError("[!] Cannot use speaker-ids without enabling speaker embedding.")
g = speaker_ids if speaker_ids is not None else d_vectors
return g
def forward_mas(self, outputs, z_p, m_p, logs_p, x, x_mask, y_mask, g, lang_emb):
# 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() # [b, 1, t, t']
# 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,
lang_emb=lang_emb.detach() if self.args.detach_dp_input and lang_emb is not None else lang_emb,
)
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,
lang_emb=lang_emb.detach() if self.args.detach_dp_input and lang_emb is not None else lang_emb,
)
loss_duration = torch.sum((log_durations - attn_log_durations) ** 2, [1, 2]) / torch.sum(x_mask)
outputs["loss_duration"] = loss_duration
return outputs, attn
def upsampling_z(self, z, slice_ids=None, y_lengths=None, y_mask=None):
spec_segment_size = self.spec_segment_size
if self.args.encoder_sample_rate:
# recompute the slices and spec_segment_size if needed
slice_ids = slice_ids * int(self.interpolate_factor) if slice_ids is not None else slice_ids
spec_segment_size = spec_segment_size * int(self.interpolate_factor)
# interpolate z if needed
if self.args.interpolate_z:
z = torch.nn.functional.interpolate(z, scale_factor=[self.interpolate_factor], mode="linear").squeeze(0)
# recompute the mask if needed
if y_lengths is not None and y_mask is not None:
y_mask = (
sequence_mask(y_lengths * self.interpolate_factor, None).to(y_mask.dtype).unsqueeze(1)
) # [B, 1, T_dec_resampled]
return z, spec_segment_size, slice_ids, y_mask
def forward( # pylint: disable=dangerous-default-value
self,
x: torch.tensor,
x_lengths: torch.tensor,
y: torch.tensor,
y_lengths: torch.tensor,
waveform: torch.tensor,
aux_input={"d_vectors": None, "speaker_ids": None, "language_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.
waveform (torch.tensor): Batch of ground truth waveforms per sample.
aux_input (dict, optional): Auxiliary inputs for multi-speaker and multi-lingual training.
Defaults to {"d_vectors": None, "speaker_ids": None, "language_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]`
- waveform: :math:`[B, 1, T_wav]`
- d_vectors: :math:`[B, C, 1]`
- speaker_ids: :math:`[B]`
- language_ids: :math:`[B]`
Return Shapes:
- model_outputs: :math:`[B, 1, T_wav]`
- alignments: :math:`[B, T_seq, T_dec]`
- z: :math:`[B, C, T_dec]`
- z_p: :math:`[B, C, T_dec]`
- m_p: :math:`[B, C, T_dec]`
- logs_p: :math:`[B, C, T_dec]`
- m_q: :math:`[B, C, T_dec]`
- logs_q: :math:`[B, C, T_dec]`
- waveform_seg: :math:`[B, 1, spec_seg_size * hop_length]`
- gt_spk_emb: :math:`[B, 1, speaker_encoder.proj_dim]`
- syn_spk_emb: :math:`[B, 1, speaker_encoder.proj_dim]`
"""
outputs = {}
sid, g, lid, _ = self._set_cond_input(aux_input)
# speaker embedding
if self.args.use_speaker_embedding and sid is not None:
g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
# language embedding
lang_emb = None
if self.args.use_language_embedding and lid is not None:
lang_emb = self.emb_l(lid).unsqueeze(-1)
x, m_p, logs_p, x_mask = self.text_encoder(x, x_lengths, lang_emb=lang_emb)
# 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)
# duration predictor
outputs, attn = self.forward_mas(outputs, z_p, m_p, logs_p, x, x_mask, y_mask, g=g, lang_emb=lang_emb)
# 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, let_short_samples=True, pad_short=True)
# interpolate z if needed
z_slice, spec_segment_size, slice_ids, _ = self.upsampling_z(z_slice, slice_ids=slice_ids)
o = self.waveform_decoder(z_slice, g=g)
wav_seg = segment(
waveform,
slice_ids * self.config.audio.hop_length,
spec_segment_size * self.config.audio.hop_length,
pad_short=True,
)
if self.args.use_speaker_encoder_as_loss and self.speaker_manager.encoder is not None:
# concate generated and GT waveforms
wavs_batch = torch.cat((wav_seg, o), dim=0)
# resample audio to speaker encoder sample_rate
# pylint: disable=W0105
if self.audio_transform is not None:
wavs_batch = self.audio_transform(wavs_batch)
pred_embs = self.speaker_manager.encoder.forward(wavs_batch, l2_norm=True)
# split generated and GT speaker embeddings
gt_spk_emb, syn_spk_emb = torch.chunk(pred_embs, 2, dim=0)
else:
gt_spk_emb, syn_spk_emb = None, None
outputs.update(
{
"model_outputs": o,
"alignments": attn.squeeze(1),
"m_p": m_p,
"logs_p": logs_p,
"z": z,
"z_p": z_p,
"m_q": m_q,
"logs_q": logs_q,
"waveform_seg": wav_seg,
"gt_spk_emb": gt_spk_emb,
"syn_spk_emb": syn_spk_emb,
"slice_ids": slice_ids,
}
)
return outputs
@staticmethod
def _set_x_lengths(x, aux_input):
if "x_lengths" in aux_input and aux_input["x_lengths"] is not None:
return aux_input["x_lengths"]
return torch.tensor(x.shape[1:2]).to(x.device)
@torch.no_grad()
def inference(
self,
x,
aux_input={"x_lengths": None, "d_vectors": None, "speaker_ids": None, "language_ids": None, "durations": None},
): # pylint: disable=dangerous-default-value
"""
Note:
To run in batch mode, provide `x_lengths` else model assumes that the batch size is 1.
Shapes:
- x: :math:`[B, T_seq]`
- x_lengths: :math:`[B]`
- d_vectors: :math:`[B, C]`
- speaker_ids: :math:`[B]`
Return Shapes:
- model_outputs: :math:`[B, 1, T_wav]`
- alignments: :math:`[B, T_seq, T_dec]`
- z: :math:`[B, C, T_dec]`
- z_p: :math:`[B, C, T_dec]`
- m_p: :math:`[B, C, T_dec]`
- logs_p: :math:`[B, C, T_dec]`
"""
sid, g, lid, durations = self._set_cond_input(aux_input)
x_lengths = self._set_x_lengths(x, aux_input)
# speaker embedding
if self.args.use_speaker_embedding and sid is not None:
g = self.emb_g(sid).unsqueeze(-1)
# language embedding
lang_emb = None
if self.args.use_language_embedding and lid is not None:
lang_emb = self.emb_l(lid).unsqueeze(-1)
x, m_p, logs_p, x_mask = self.text_encoder(x, x_lengths, lang_emb=lang_emb)
if durations is None:
if self.args.use_sdp:
logw = self.duration_predictor(
x,
x_mask,
g=g if self.args.condition_dp_on_speaker else None,
reverse=True,
noise_scale=self.inference_noise_scale_dp,
lang_emb=lang_emb,
)
else:
logw = self.duration_predictor(
x, x_mask, g=g if self.args.condition_dp_on_speaker else None, lang_emb=lang_emb
)
w = torch.exp(logw) * x_mask * self.length_scale
else:
assert durations.shape[-1] == x.shape[-1]
w = durations.unsqueeze(0)
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).unsqueeze(1) # [B, 1, T_dec]
attn_mask = x_mask * y_mask.transpose(1, 2) # [B, 1, T_enc] * [B, T_dec, 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)
# upsampling if needed
z, _, _, y_mask = self.upsampling_z(z, y_lengths=y_lengths, y_mask=y_mask)
o = self.waveform_decoder((z * y_mask)[:, :, : self.max_inference_len], g=g)
outputs = {
"model_outputs": o,
"alignments": attn.squeeze(1),
"durations": w_ceil,
"z": z,
"z_p": z_p,
"m_p": m_p,
"logs_p": logs_p,
"y_mask": y_mask,
}
return outputs
@torch.no_grad()
def inference_voice_conversion(
self, reference_wav, speaker_id=None, d_vector=None, reference_speaker_id=None, reference_d_vector=None
):
"""Inference for voice conversion
Args:
reference_wav (Tensor): Reference wavform. Tensor of shape [B, T]
speaker_id (Tensor): speaker_id of the target speaker. Tensor of shape [B]
d_vector (Tensor): d_vector embedding of target speaker. Tensor of shape `[B, C]`
reference_speaker_id (Tensor): speaker_id of the reference_wav speaker. Tensor of shape [B]
reference_d_vector (Tensor): d_vector embedding of the reference_wav speaker. Tensor of shape `[B, C]`
"""
# compute spectrograms
y = wav_to_spec(
reference_wav,
self.config.audio.fft_size,
self.config.audio.hop_length,
self.config.audio.win_length,
center=False,
)
y_lengths = torch.tensor([y.size(-1)]).to(y.device)
speaker_cond_src = reference_speaker_id if reference_speaker_id is not None else reference_d_vector
speaker_cond_tgt = speaker_id if speaker_id is not None else d_vector
wav, _, _ = self.voice_conversion(y, y_lengths, speaker_cond_src, speaker_cond_tgt)
return wav
def voice_conversion(self, y, y_lengths, speaker_cond_src, speaker_cond_tgt):
"""Forward pass for voice conversion
TODO: create an end-point for voice conversion
Args:
y (Tensor): Reference spectrograms. Tensor of shape [B, T, C]
y_lengths (Tensor): Length of each reference spectrogram. Tensor of shape [B]
speaker_cond_src (Tensor): Reference speaker ID. Tensor of shape [B,]
speaker_cond_tgt (Tensor): Target speaker ID. Tensor of shape [B,]
"""
assert self.num_speakers > 0, "num_speakers have to be larger than 0."
# speaker embedding
if self.args.use_speaker_embedding and not self.args.use_d_vector_file:
g_src = self.emb_g(torch.from_numpy((np.array(speaker_cond_src))).unsqueeze(0)).unsqueeze(-1)
g_tgt = self.emb_g(torch.from_numpy((np.array(speaker_cond_tgt))).unsqueeze(0)).unsqueeze(-1)
elif not self.args.use_speaker_embedding and self.args.use_d_vector_file:
g_src = F.normalize(speaker_cond_src).unsqueeze(-1)
g_tgt = F.normalize(speaker_cond_tgt).unsqueeze(-1)
else:
raise RuntimeError(" [!] Voice conversion is only supported on multi-speaker models.")
z, _, _, y_mask = self.posterior_encoder(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.
"""
spec_lens = batch["spec_lens"]
if optimizer_idx == 0:
tokens = batch["tokens"]
token_lenghts = batch["token_lens"]
spec = batch["spec"]
d_vectors = batch["d_vectors"]
speaker_ids = batch["speaker_ids"]
language_ids = batch["language_ids"]
waveform = batch["waveform"]
# generator pass
outputs = self.forward(
tokens,
token_lenghts,
spec,
spec_lens,
waveform,
aux_input={"d_vectors": d_vectors, "speaker_ids": speaker_ids, "language_ids": language_ids},
)
# cache tensors for the generator pass
self.model_outputs_cache = outputs # pylint: disable=attribute-defined-outside-init
# compute scores and features
scores_disc_fake, _, scores_disc_real, _ = self.disc(
outputs["model_outputs"].detach(), outputs["waveform_seg"]
)
# compute loss
with autocast(enabled=False): # use float32 for the criterion
loss_dict = criterion[optimizer_idx](
scores_disc_real,
scores_disc_fake,
)
return outputs, loss_dict
if optimizer_idx == 1:
mel = batch["mel"]
# compute melspec segment
with autocast(enabled=False):
if self.args.encoder_sample_rate:
spec_segment_size = self.spec_segment_size * int(self.interpolate_factor)
else:
spec_segment_size = self.spec_segment_size
mel_slice = segment(
mel.float(), self.model_outputs_cache["slice_ids"], spec_segment_size, pad_short=True
)
mel_slice_hat = wav_to_mel(
y=self.model_outputs_cache["model_outputs"].float(),
n_fft=self.config.audio.fft_size,
sample_rate=self.config.audio.sample_rate,
num_mels=self.config.audio.num_mels,
hop_length=self.config.audio.hop_length,
win_length=self.config.audio.win_length,
fmin=self.config.audio.mel_fmin,
fmax=self.config.audio.mel_fmax,
center=False,
)
# compute discriminator scores and features
scores_disc_fake, feats_disc_fake, _, feats_disc_real = self.disc(
self.model_outputs_cache["model_outputs"], self.model_outputs_cache["waveform_seg"]
)
# compute losses
with autocast(enabled=False): # use float32 for the criterion
loss_dict = criterion[optimizer_idx](
mel_slice_hat=mel_slice.float(),
mel_slice=mel_slice_hat.float(),
z_p=self.model_outputs_cache["z_p"].float(),
logs_q=self.model_outputs_cache["logs_q"].float(),
m_p=self.model_outputs_cache["m_p"].float(),
logs_p=self.model_outputs_cache["logs_p"].float(),
z_len=spec_lens,
scores_disc_fake=scores_disc_fake,
feats_disc_fake=feats_disc_fake,
feats_disc_real=feats_disc_real,
loss_duration=self.model_outputs_cache["loss_duration"],
use_speaker_encoder_as_loss=self.args.use_speaker_encoder_as_loss,
gt_spk_emb=self.model_outputs_cache["gt_spk_emb"],
syn_spk_emb=self.model_outputs_cache["syn_spk_emb"],
)
return self.model_outputs_cache, loss_dict
raise ValueError(" [!] Unexpected `optimizer_idx`.")
def _log(self, ap, batch, outputs, name_prefix="train"): # pylint: disable=unused-argument,no-self-use
y_hat = outputs[1]["model_outputs"]
y = outputs[1]["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[1]["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.
"""
figures, audios = self._log(self.ap, batch, outputs, "train")
logger.train_figures(steps, figures)
logger.train_audios(steps, audios, self.ap.sample_rate)
@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:
figures, audios = self._log(self.ap, batch, outputs, "eval")
logger.eval_figures(steps, figures)
logger.eval_audios(steps, audios, self.ap.sample_rate)
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 hasattr(self, "speaker_manager"):
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_mean_embedding(speaker_name, num_samples=None, randomize=False)
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 hasattr(self, "language_manager") 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,
"language_name": language_name,
}
@torch.no_grad()
def test_run(self, assets) -> 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
for idx, s_info in enumerate(test_sentences):
aux_inputs = self.get_aux_input_from_test_sentences(s_info)
wav, alignment, _, _ = synthesis(
self,
aux_inputs["text"],
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"],
language_id=aux_inputs["language_id"],
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 {"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"])
def format_batch(self, batch: Dict) -> Dict:
"""Compute speaker, langugage IDs and d_vector for the batch if necessary."""
speaker_ids = None
language_ids = None
d_vectors = None
# get numerical speaker ids from speaker names
if self.speaker_manager is not None and self.speaker_manager.name_to_id and self.args.use_speaker_embedding:
speaker_ids = [self.speaker_manager.name_to_id[sn] for sn in batch["speaker_names"]]
if speaker_ids is not None:
speaker_ids = torch.LongTensor(speaker_ids)
batch["speaker_ids"] = speaker_ids
# get d_vectors from audio file names
if self.speaker_manager is not None and self.speaker_manager.embeddings and self.args.use_d_vector_file:
d_vector_mapping = self.speaker_manager.embeddings
d_vectors = [d_vector_mapping[w]["embedding"] for w in batch["audio_unique_names"]]
d_vectors = torch.FloatTensor(d_vectors)
# get language ids from language names
if self.language_manager is not None and self.language_manager.name_to_id and self.args.use_language_embedding:
language_ids = [self.language_manager.name_to_id[ln] for ln in batch["language_names"]]
if language_ids is not None:
language_ids = torch.LongTensor(language_ids)
batch["language_ids"] = language_ids
batch["d_vectors"] = d_vectors
batch["speaker_ids"] = speaker_ids
return batch
def format_batch_on_device(self, batch):
"""Compute spectrograms on the device."""
ac = self.config.audio
if self.args.encoder_sample_rate:
wav = self.audio_resampler(batch["waveform"])
else:
wav = batch["waveform"]
# compute spectrograms
batch["spec"] = wav_to_spec(wav, ac.fft_size, ac.hop_length, ac.win_length, center=False)
if self.args.encoder_sample_rate:
# recompute spec with high sampling rate to the loss
spec_mel = wav_to_spec(batch["waveform"], ac.fft_size, ac.hop_length, ac.win_length, center=False)
# remove extra stft frames if needed
if spec_mel.size(2) > int(batch["spec"].size(2) * self.interpolate_factor):
spec_mel = spec_mel[:, :, : int(batch["spec"].size(2) * self.interpolate_factor)]
else:
batch["spec"] = batch["spec"][:, :, : int(spec_mel.size(2) / self.interpolate_factor)]
else:
spec_mel = batch["spec"]
batch["mel"] = spec_to_mel(
spec=spec_mel,
n_fft=ac.fft_size,
num_mels=ac.num_mels,
sample_rate=ac.sample_rate,
fmin=ac.mel_fmin,
fmax=ac.mel_fmax,
)
if self.args.encoder_sample_rate:
assert batch["spec"].shape[2] == int(
batch["mel"].shape[2] / self.interpolate_factor
), f"{batch['spec'].shape[2]}, {batch['mel'].shape[2]}"
else:
assert batch["spec"].shape[2] == batch["mel"].shape[2], f"{batch['spec'].shape[2]}, {batch['mel'].shape[2]}"
# compute spectrogram frame lengths
batch["spec_lens"] = (batch["spec"].shape[2] * batch["waveform_rel_lens"]).int()
batch["mel_lens"] = (batch["mel"].shape[2] * batch["waveform_rel_lens"]).int()
if self.args.encoder_sample_rate:
assert (batch["spec_lens"] - (batch["mel_lens"] / self.interpolate_factor).int()).sum() == 0
else:
assert (batch["spec_lens"] - batch["mel_lens"]).sum() == 0
# zero the padding frames
batch["spec"] = batch["spec"] * sequence_mask(batch["spec_lens"]).unsqueeze(1)
batch["mel"] = batch["mel"] * sequence_mask(batch["mel_lens"]).unsqueeze(1)
return batch
def get_sampler(self, config: Coqpit, dataset: TTSDataset, num_gpus=1, is_eval=False):
weights = None
data_items = dataset.samples
if getattr(config, "use_weighted_sampler", False):
for attr_name, alpha in config.weighted_sampler_attrs.items():
print(f" > Using weighted sampler for attribute '{attr_name}' with alpha '{alpha}'")
multi_dict = config.weighted_sampler_multipliers.get(attr_name, None)
print(multi_dict)
weights, attr_names, attr_weights = get_attribute_balancer_weights(
attr_name=attr_name, items=data_items, multi_dict=multi_dict
)
weights = weights * alpha
print(f" > Attribute weights for '{attr_names}' \n | > {attr_weights}")
# input_audio_lenghts = [os.path.getsize(x["audio_file"]) for x in data_items]
if weights is not None:
w_sampler = WeightedRandomSampler(weights, len(weights))
batch_sampler = BucketBatchSampler(
w_sampler,
data=data_items,
batch_size=config.eval_batch_size if is_eval else config.batch_size,
sort_key=lambda x: os.path.getsize(x["audio_file"]),
drop_last=True,
)
else:
batch_sampler = None
# sampler for DDP
if batch_sampler is None:
batch_sampler = DistributedSampler(dataset) if num_gpus > 1 else None
else: # If a sampler is already defined use this sampler and DDP sampler together
batch_sampler = (
DistributedSamplerWrapper(batch_sampler) if num_gpus > 1 else batch_sampler
) # TODO: check batch_sampler with multi-gpu
return batch_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:
# init dataloader
dataset = VitsDataset(
model_args=self.args,
samples=samples,
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,
verbose=verbose,
tokenizer=self.tokenizer,
start_by_longest=config.start_by_longest,
)
# 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)
if sampler is None:
loader = DataLoader(
dataset,
batch_size=config.eval_batch_size if is_eval else config.batch_size,
shuffle=False, # shuffle is done in the dataset.
collate_fn=dataset.collate_fn,
drop_last=False, # setting this False might cause issues in AMP training.
num_workers=config.num_eval_loader_workers if is_eval else config.num_loader_workers,
pin_memory=False,
)
else:
loader = DataLoader(
dataset,
batch_sampler=sampler,
collate_fn=dataset.collate_fn,
num_workers=config.num_eval_loader_workers if is_eval else config.num_loader_workers,
pin_memory=False,
)
return loader
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.
"""
# select generator parameters
optimizer0 = get_optimizer(self.config.optimizer, self.config.optimizer_params, self.config.lr_disc, self.disc)
gen_parameters = chain(params for k, params in self.named_parameters() if not k.startswith("disc."))
optimizer1 = get_optimizer(
self.config.optimizer, self.config.optimizer_params, self.config.lr_gen, parameters=gen_parameters
)
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_disc, self.config.lr_gen]
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.
"""
scheduler_D = get_scheduler(self.config.lr_scheduler_disc, self.config.lr_scheduler_disc_params, optimizer[0])
scheduler_G = get_scheduler(self.config.lr_scheduler_gen, self.config.lr_scheduler_gen_params, optimizer[1])
return [scheduler_D, scheduler_G]
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 [VitsDiscriminatorLoss(self.config), VitsGeneratorLoss(self.config)]
def load_checkpoint(
self, config, checkpoint_path, eval=False, strict=True, cache=False
): # pylint: disable=unused-argument, redefined-builtin
"""Load the model checkpoint and setup for training or inference"""
state = load_fsspec(checkpoint_path, map_location=torch.device("cpu"), cache=cache)
# compat band-aid for the pre-trained models to not use the encoder baked into the model
# TODO: consider baking the speaker encoder into the model and call it from there.
# as it is probably easier for model distribution.
state["model"] = {k: v for k, v in state["model"].items() if "speaker_encoder" not in k}
if self.args.encoder_sample_rate is not None and eval:
# audio resampler is not used in inference time
self.audio_resampler = None
# handle fine-tuning from a checkpoint with additional speakers
if hasattr(self, "emb_g") and state["model"]["emb_g.weight"].shape != self.emb_g.weight.shape:
num_new_speakers = self.emb_g.weight.shape[0] - state["model"]["emb_g.weight"].shape[0]
print(f" > Loading checkpoint with {num_new_speakers} additional speakers.")
emb_g = state["model"]["emb_g.weight"]
new_row = torch.randn(num_new_speakers, emb_g.shape[1])
emb_g = torch.cat([emb_g, new_row], axis=0)
state["model"]["emb_g.weight"] = emb_g
# load the model weights
self.load_state_dict(state["model"], strict=strict)
if eval:
self.eval()
assert not self.training
@staticmethod
def init_from_config(config: "VitsConfig", samples: Union[List[List], List[Dict]] = None, verbose=True):
"""Initiate model from config
Args:
config (VitsConfig): Model config.
samples (Union[List[List], List[Dict]]): Training samples to parse speaker ids for training.
Defaults to None.
"""
from TTS.utils.audio import AudioProcessor
upsample_rate = torch.prod(torch.as_tensor(config.model_args.upsample_rates_decoder)).item()
if not config.model_args.encoder_sample_rate:
assert (
upsample_rate == config.audio.hop_length
), f" [!] Product of upsample rates must be equal to the hop length - {upsample_rate} vs {config.audio.hop_length}"
else:
encoder_to_vocoder_upsampling_factor = config.audio.sample_rate / config.model_args.encoder_sample_rate
effective_hop_length = config.audio.hop_length * encoder_to_vocoder_upsampling_factor
assert (
upsample_rate == effective_hop_length
), f" [!] Product of upsample rates must be equal to the hop length - {upsample_rate} vs {effective_hop_length}"
ap = AudioProcessor.init_from_config(config, verbose=verbose)
tokenizer, new_config = TTSTokenizer.init_from_config(config)
speaker_manager = SpeakerManager.init_from_config(config, samples)
language_manager = LanguageManager.init_from_config(config)
if config.model_args.speaker_encoder_model_path:
speaker_manager.init_encoder(
config.model_args.speaker_encoder_model_path, config.model_args.speaker_encoder_config_path
)
return Vits(new_config, ap, tokenizer, speaker_manager, language_manager)
##################################
# VITS CHARACTERS
##################################
class VitsCharacters(BaseCharacters):
"""Characters class for VITs model for compatibility with pre-trained models"""
def __init__(
self,
graphemes: str = _characters,
punctuations: str = _punctuations,
pad: str = _pad,
ipa_characters: str = _phonemes,
) -> None:
if ipa_characters is not None:
graphemes += ipa_characters
super().__init__(graphemes, punctuations, pad, None, None, "<BLNK>", is_unique=False, is_sorted=True)
def _create_vocab(self):
self._vocab = [self._pad] + list(self._punctuations) + list(self._characters) + [self._blank]
self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)}
# pylint: disable=unnecessary-comprehension
self._id_to_char = {idx: char for idx, char in enumerate(self.vocab)}
@staticmethod
def init_from_config(config: Coqpit):
if config.characters is not None:
_pad = config.characters["pad"]
_punctuations = config.characters["punctuations"]
_letters = config.characters["characters"]
_letters_ipa = config.characters["phonemes"]
return (
VitsCharacters(graphemes=_letters, ipa_characters=_letters_ipa, punctuations=_punctuations, pad=_pad),
config,
)
characters = VitsCharacters()
new_config = replace(config, characters=characters.to_config())
return characters, new_config
def to_config(self) -> "CharactersConfig":
return CharactersConfig(
characters=self._characters,
punctuations=self._punctuations,
pad=self._pad,
eos=None,
bos=None,
blank=self._blank,
is_unique=False,
is_sorted=True,
)