Source code for TTS.tts.models.vits

import math
from dataclasses import dataclass, field
from itertools import chain
from typing import Dict, List, Tuple

import torch

import torchaudio
from coqpit import Coqpit
from torch import nn
from torch.cuda.amp.autocast_mode import autocast
from torch.nn import functional as F

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.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


[docs]@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_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 "". 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. """ 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 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 = "" freeze_encoder: bool = False freeze_DP: bool = False freeze_PE: bool = False freeze_flow_decoder: bool = False freeze_waveform_decoder: bool = False
[docs]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, language_manager: LanguageManager = None, ): super().__init__(config) self.END2END = True self.speaker_manager = speaker_manager self.language_manager = language_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.init_multilingual(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, language_emb_dim=self.embedded_language_dim, ) 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, language_emb_dim=self.embedded_language_dim, ) else: self.duration_predictor = DurationPredictor( args.hidden_channels, 256, 3, args.dropout_p_duration_predictor, cond_channels=self.embedded_speaker_dim, language_emb_dim=self.embedded_language_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)
[docs] 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 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.speaker_encoder is None and ( not config.speaker_encoder_model_path or not config.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.speaker_encoder.eval() print(" > External Speaker Encoder Loaded !!") if ( hasattr(self.speaker_manager.speaker_encoder, "audio_config") and self.config.audio["sample_rate"] != self.speaker_manager.speaker_encoder.audio_config["sample_rate"] ): self.audio_transform = torchaudio.transforms.Resample( orig_freq=self.audio_config["sample_rate"], new_freq=self.speaker_manager.speaker_encoder.audio_config["sample_rate"], ) else: self.audio_transform = None
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
[docs] 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 self.emb_l = None
@staticmethod def _set_cond_input(aux_input: Dict): """Set the speaker conditioning input based on the multi-speaker mode.""" sid, g, lid = 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) return sid, g, lid 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 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_d_vector() else: d_vector = self.speaker_manager.get_mean_d_vector(speaker_name, num_samples=1, randomize=False) elif config.use_speaker_embedding: if speaker_name is None: speaker_id = self.speaker_manager.get_random_speaker_id() else: speaker_id = self.speaker_manager.speaker_ids[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.language_id_mapping[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, }
[docs] def forward( 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, T_wav, 1]` - d_vectors: :math:`[B, C, 1]` - speaker_ids: :math:`[B]` - language_ids: :math:`[B]` """ 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) # 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, 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 # 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) wav_seg = segment( waveform, slice_ids * self.config.audio.hop_length, self.args.spec_segment_size * self.config.audio.hop_length, ) if self.args.use_speaker_encoder_as_loss and self.speaker_manager.speaker_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.speaker_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), "z": z, "z_p": z_p, "m_p": m_p, "logs_p": logs_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, } ) return outputs
[docs] def inference(self, x, aux_input={"d_vectors": None, "speaker_ids": None, "language_ids": None}): """ Shapes: - x: :math:`[B, T_seq]` - d_vectors: :math:`[B, C, 1]` - speaker_ids: :math:`[B]` """ sid, g, lid = self._set_cond_input(aux_input) x_lengths = torch.tensor(x.shape[1:2]).to(x.device) # 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 self.args.use_sdp: logw = self.duration_predictor( x, x_mask, g=g, reverse=True, noise_scale=self.inference_noise_scale_dp, lang_emb=lang_emb ) else: logw = self.duration_predictor(x, x_mask, g=g, lang_emb=lang_emb) 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
[docs] 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(speaker_cond_src).unsqueeze(-1) g_tgt = self.emb_g(speaker_cond_tgt).unsqueeze(-1) elif 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.transpose(1, 2), 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)
[docs] 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 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 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"] language_ids = batch["language_ids"] waveform = batch["waveform"] # generator pass outputs = self.forward( text_input, text_lengths, linear_input.transpose(1, 2), mel_lengths, waveform.transpose(1, 2), aux_input={"d_vectors": d_vectors, "speaker_ids": speaker_ids, "language_ids": language_ids}, ) # cache tensors for the discriminator self.y_disc_cache = None self.wav_seg_disc_cache = None self.y_disc_cache = outputs["model_outputs"] self.wav_seg_disc_cache = outputs["waveform_seg"] # compute discriminator scores and features outputs["scores_disc_fake"], outputs["feats_disc_fake"], _, outputs["feats_disc_real"] = self.disc( outputs["model_outputs"], outputs["waveform_seg"] ) # compute losses with autocast(enabled=False): # use float32 for the criterion loss_dict = criterion[optimizer_idx]( waveform_hat=outputs["model_outputs"].float(), waveform=outputs["waveform_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"], use_speaker_encoder_as_loss=self.args.use_speaker_encoder_as_loss, gt_spk_emb=outputs["gt_spk_emb"], syn_spk_emb=outputs["syn_spk_emb"], ) 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
[docs] 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")
[docs] @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 for idx, s_info in enumerate(test_sentences): try: 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), ap, speaker_id=aux_inputs["speaker_id"], d_vector=aux_inputs["d_vector"], style_wav=aux_inputs["style_wav"], language_id=aux_inputs["language_id"], language_name=aux_inputs["language_name"], 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) except: # pylint: disable=bare-except print(" !! Error creating Test Sentence -", idx) return test_figures, test_audios
[docs] 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") and self.args.use_speaker_embedding and not self.args.use_d_vector_file: gen_parameters = chain(gen_parameters, self.emb_g.parameters()) # add the language embedding layer if hasattr(self, "emb_l") and self.args.use_language_embedding: gen_parameters = chain(gen_parameters, self.emb_l.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]
[docs] 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]
[docs] 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]
[docs] 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)]
[docs] @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
[docs] 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")) # 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} self.load_state_dict(state["model"]) if eval: self.eval() assert not self.training