Source code for TTS.tts.models.forward_tts

from dataclasses import dataclass, field
from typing import Dict, List, Tuple, Union

import torch
from coqpit import Coqpit
from torch import nn
from torch.cuda.amp.autocast_mode import autocast

from TTS.tts.layers.feed_forward.decoder import Decoder
from TTS.tts.layers.feed_forward.encoder import Encoder
from TTS.tts.layers.generic.aligner import AlignmentNetwork
from TTS.tts.layers.generic.pos_encoding import PositionalEncoding
from TTS.tts.layers.glow_tts.duration_predictor import DurationPredictor
from TTS.tts.models.base_tts import BaseTTS
from TTS.tts.utils.helpers import average_over_durations, generate_path, maximum_path, sequence_mask
from TTS.tts.utils.speakers import SpeakerManager
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.tts.utils.visual import plot_alignment, plot_avg_pitch, plot_spectrogram


[docs]@dataclass class ForwardTTSArgs(Coqpit): """ForwardTTS Model arguments. Args: num_chars (int): Number of characters in the vocabulary. Defaults to 100. out_channels (int): Number of output channels. Defaults to 80. hidden_channels (int): Number of base hidden channels of the model. Defaults to 512. use_aligner (bool): Whether to use aligner network to learn the text to speech alignment or use pre-computed durations. If set False, durations should be computed by `TTS/bin/compute_attention_masks.py` and path to the pre-computed durations must be provided to `config.datasets[0].meta_file_attn_mask`. Defaults to True. use_pitch (bool): Use pitch predictor to learn the pitch. Defaults to True. duration_predictor_hidden_channels (int): Number of hidden channels in the duration predictor. Defaults to 256. duration_predictor_dropout_p (float): Dropout rate for the duration predictor. Defaults to 0.1. duration_predictor_kernel_size (int): Kernel size of conv layers in the duration predictor. Defaults to 3. pitch_predictor_hidden_channels (int): Number of hidden channels in the pitch predictor. Defaults to 256. pitch_predictor_dropout_p (float): Dropout rate for the pitch predictor. Defaults to 0.1. pitch_predictor_kernel_size (int): Kernel size of conv layers in the pitch predictor. Defaults to 3. pitch_embedding_kernel_size (int): Kernel size of the projection layer in the pitch predictor. Defaults to 3. positional_encoding (bool): Whether to use positional encoding. Defaults to True. positional_encoding_use_scale (bool): Whether to use a learnable scale coeff in the positional encoding. Defaults to True. length_scale (int): Length scale that multiplies the predicted durations. Larger values result slower speech. Defaults to 1.0. encoder_type (str): Type of the encoder module. One of the encoders available in :class:`TTS.tts.layers.feed_forward.encoder`. Defaults to `fftransformer` as in the paper. encoder_params (dict): Parameters of the encoder module. Defaults to ```{"hidden_channels_ffn": 1024, "num_heads": 1, "num_layers": 6, "dropout_p": 0.1}``` decoder_type (str): Type of the decoder module. One of the decoders available in :class:`TTS.tts.layers.feed_forward.decoder`. Defaults to `fftransformer` as in the paper. decoder_params (str): Parameters of the decoder module. Defaults to ```{"hidden_channels_ffn": 1024, "num_heads": 1, "num_layers": 6, "dropout_p": 0.1}``` detach_duration_predictor (bool): Detach the input to the duration predictor from the earlier computation graph so that the duraiton loss does not pass to the earlier layers. Defaults to True. max_duration (int): Maximum duration accepted by the model. Defaults to 75. num_speakers (int): Number of speakers for the speaker embedding layer. Defaults to 0. speakers_file (str): Path to the speaker mapping file for the Speaker Manager. Defaults to None. speaker_embedding_channels (int): Number of speaker embedding channels. Defaults to 256. use_d_vector_file (bool): Enable/Disable the use of d-vectors for multi-speaker training. Defaults to False. d_vector_dim (int): Number of d-vector channels. Defaults to 0. """ num_chars: int = None out_channels: int = 80 hidden_channels: int = 384 use_aligner: bool = True use_pitch: bool = True pitch_predictor_hidden_channels: int = 256 pitch_predictor_kernel_size: int = 3 pitch_predictor_dropout_p: float = 0.1 pitch_embedding_kernel_size: int = 3 duration_predictor_hidden_channels: int = 256 duration_predictor_kernel_size: int = 3 duration_predictor_dropout_p: float = 0.1 positional_encoding: bool = True poisitonal_encoding_use_scale: bool = True length_scale: int = 1 encoder_type: str = "fftransformer" encoder_params: dict = field( default_factory=lambda: {"hidden_channels_ffn": 1024, "num_heads": 1, "num_layers": 6, "dropout_p": 0.1} ) decoder_type: str = "fftransformer" decoder_params: dict = field( default_factory=lambda: {"hidden_channels_ffn": 1024, "num_heads": 1, "num_layers": 6, "dropout_p": 0.1} ) detach_duration_predictor: bool = False max_duration: int = 75 num_speakers: int = 1 use_speaker_embedding: bool = False speakers_file: str = None use_d_vector_file: bool = False d_vector_dim: int = None d_vector_file: str = None
[docs]class ForwardTTS(BaseTTS): """General forward TTS model implementation that uses an encoder-decoder architecture with an optional alignment network and a pitch predictor. If the alignment network is used, the model learns the text-to-speech alignment from the data instead of using pre-computed durations. If the pitch predictor is used, the model trains a pitch predictor that predicts average pitch value for each input character as in the FastPitch model. `ForwardTTS` can be configured to one of these architectures, - FastPitch - SpeedySpeech - FastSpeech - TODO: FastSpeech2 (requires average speech energy predictor) Args: config (Coqpit): Model coqpit class. speaker_manager (SpeakerManager): Speaker manager for multi-speaker training. Only used for multi-speaker models. Defaults to None. Examples: >>> from TTS.tts.models.fast_pitch import ForwardTTS, ForwardTTSArgs >>> config = ForwardTTSArgs() >>> model = ForwardTTS(config) """ # pylint: disable=dangerous-default-value def __init__( self, config: Coqpit, ap: "AudioProcessor" = None, tokenizer: "TTSTokenizer" = None, speaker_manager: SpeakerManager = None, ): super().__init__(config, ap, tokenizer, speaker_manager) self._set_model_args(config) self.init_multispeaker(config) self.max_duration = self.args.max_duration self.use_aligner = self.args.use_aligner self.use_pitch = self.args.use_pitch self.binary_loss_weight = 0.0 self.length_scale = ( float(self.args.length_scale) if isinstance(self.args.length_scale, int) else self.args.length_scale ) self.emb = nn.Embedding(self.args.num_chars, self.args.hidden_channels) self.encoder = Encoder( self.args.hidden_channels, self.args.hidden_channels, self.args.encoder_type, self.args.encoder_params, self.embedded_speaker_dim, ) if self.args.positional_encoding: self.pos_encoder = PositionalEncoding(self.args.hidden_channels) self.decoder = Decoder( self.args.out_channels, self.args.hidden_channels, self.args.decoder_type, self.args.decoder_params, ) self.duration_predictor = DurationPredictor( self.args.hidden_channels + self.embedded_speaker_dim, self.args.duration_predictor_hidden_channels, self.args.duration_predictor_kernel_size, self.args.duration_predictor_dropout_p, ) if self.args.use_pitch: self.pitch_predictor = DurationPredictor( self.args.hidden_channels + self.embedded_speaker_dim, self.args.pitch_predictor_hidden_channels, self.args.pitch_predictor_kernel_size, self.args.pitch_predictor_dropout_p, ) self.pitch_emb = nn.Conv1d( 1, self.args.hidden_channels, kernel_size=self.args.pitch_embedding_kernel_size, padding=int((self.args.pitch_embedding_kernel_size - 1) / 2), ) if self.args.use_aligner: self.aligner = AlignmentNetwork( in_query_channels=self.args.out_channels, in_key_channels=self.args.hidden_channels )
[docs] def init_multispeaker(self, config: Coqpit): """Init for multi-speaker training. Args: config (Coqpit): Model configuration. """ self.embedded_speaker_dim = 0 # init speaker manager if self.speaker_manager is None and (config.use_d_vector_file or config.use_speaker_embedding): raise ValueError( " > SpeakerManager is not provided. You must provide the SpeakerManager before initializing a multi-speaker model." ) # set number of speakers if self.speaker_manager is not None: self.num_speakers = self.speaker_manager.num_speakers # init d-vector embedding if config.use_d_vector_file: self.embedded_speaker_dim = config.d_vector_dim if self.args.d_vector_dim != self.args.hidden_channels: self.proj_g = nn.Conv1d(self.args.d_vector_dim, self.args.hidden_channels, 1) # init speaker embedding layer if config.use_speaker_embedding and not config.use_d_vector_file: print(" > Init speaker_embedding layer.") self.emb_g = nn.Embedding(self.num_speakers, self.args.hidden_channels) nn.init.uniform_(self.emb_g.weight, -0.1, 0.1)
[docs] @staticmethod def generate_attn(dr, x_mask, y_mask=None): """Generate an attention mask from the durations. Shapes - dr: :math:`(B, T_{en})` - x_mask: :math:`(B, T_{en})` - y_mask: :math:`(B, T_{de})` """ # compute decode mask from the durations if y_mask is None: y_lengths = dr.sum(1).long() y_lengths[y_lengths < 1] = 1 y_mask = torch.unsqueeze(sequence_mask(y_lengths, None), 1).to(dr.dtype) attn_mask = torch.unsqueeze(x_mask, -1) * torch.unsqueeze(y_mask, 2) attn = generate_path(dr, attn_mask.squeeze(1)).to(dr.dtype) return attn
[docs] def expand_encoder_outputs(self, en, dr, x_mask, y_mask): """Generate attention alignment map from durations and expand encoder outputs Shapes: - en: :math:`(B, D_{en}, T_{en})` - dr: :math:`(B, T_{en})` - x_mask: :math:`(B, T_{en})` - y_mask: :math:`(B, T_{de})` Examples:: encoder output: [a,b,c,d] durations: [1, 3, 2, 1] expanded: [a, b, b, b, c, c, d] attention map: [[0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 1, 1, 0], [0, 1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0]] """ attn = self.generate_attn(dr, x_mask, y_mask) o_en_ex = torch.matmul(attn.squeeze(1).transpose(1, 2).to(en.dtype), en.transpose(1, 2)).transpose(1, 2) return o_en_ex, attn
[docs] def format_durations(self, o_dr_log, x_mask): """Format predicted durations. 1. Convert to linear scale from log scale 2. Apply the length scale for speed adjustment 3. Apply masking. 4. Cast 0 durations to 1. 5. Round the duration values. Args: o_dr_log: Log scale durations. x_mask: Input text mask. Shapes: - o_dr_log: :math:`(B, T_{de})` - x_mask: :math:`(B, T_{en})` """ o_dr = (torch.exp(o_dr_log) - 1) * x_mask * self.length_scale o_dr[o_dr < 1] = 1.0 o_dr = torch.round(o_dr) return o_dr
def _forward_encoder( self, x: torch.LongTensor, x_mask: torch.FloatTensor, g: torch.FloatTensor = None ) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]: """Encoding forward pass. 1. Embed speaker IDs if multi-speaker mode. 2. Embed character sequences. 3. Run the encoder network. 4. Sum encoder outputs and speaker embeddings Args: x (torch.LongTensor): Input sequence IDs. x_mask (torch.FloatTensor): Input squence mask. g (torch.FloatTensor, optional): Conditioning vectors. In general speaker embeddings. Defaults to None. Returns: Tuple[torch.tensor, torch.tensor, torch.tensor, torch.tensor, torch.tensor]: encoder output, encoder output for the duration predictor, input sequence mask, speaker embeddings, character embeddings Shapes: - x: :math:`(B, T_{en})` - x_mask: :math:`(B, 1, T_{en})` - g: :math:`(B, C)` """ if hasattr(self, "emb_g"): g = self.emb_g(g) # [B, C, 1] if g is not None: g = g.unsqueeze(-1) # [B, T, C] x_emb = self.emb(x) # encoder pass o_en = self.encoder(torch.transpose(x_emb, 1, -1), x_mask) # speaker conditioning # TODO: try different ways of conditioning if g is not None: o_en = o_en + g return o_en, x_mask, g, x_emb def _forward_decoder( self, o_en: torch.FloatTensor, dr: torch.IntTensor, x_mask: torch.FloatTensor, y_lengths: torch.IntTensor, g: torch.FloatTensor, ) -> Tuple[torch.FloatTensor, torch.FloatTensor]: """Decoding forward pass. 1. Compute the decoder output mask 2. Expand encoder output with the durations. 3. Apply position encoding. 4. Add speaker embeddings if multi-speaker mode. 5. Run the decoder. Args: o_en (torch.FloatTensor): Encoder output. dr (torch.IntTensor): Ground truth durations or alignment network durations. x_mask (torch.IntTensor): Input sequence mask. y_lengths (torch.IntTensor): Output sequence lengths. g (torch.FloatTensor): Conditioning vectors. In general speaker embeddings. Returns: Tuple[torch.FloatTensor, torch.FloatTensor]: Decoder output, attention map from durations. """ y_mask = torch.unsqueeze(sequence_mask(y_lengths, None), 1).to(o_en.dtype) # expand o_en with durations o_en_ex, attn = self.expand_encoder_outputs(o_en, dr, x_mask, y_mask) # positional encoding if hasattr(self, "pos_encoder"): o_en_ex = self.pos_encoder(o_en_ex, y_mask) # decoder pass o_de = self.decoder(o_en_ex, y_mask, g=g) return o_de.transpose(1, 2), attn.transpose(1, 2) def _forward_pitch_predictor( self, o_en: torch.FloatTensor, x_mask: torch.IntTensor, pitch: torch.FloatTensor = None, dr: torch.IntTensor = None, ) -> Tuple[torch.FloatTensor, torch.FloatTensor]: """Pitch predictor forward pass. 1. Predict pitch from encoder outputs. 2. In training - Compute average pitch values for each input character from the ground truth pitch values. 3. Embed average pitch values. Args: o_en (torch.FloatTensor): Encoder output. x_mask (torch.IntTensor): Input sequence mask. pitch (torch.FloatTensor, optional): Ground truth pitch values. Defaults to None. dr (torch.IntTensor, optional): Ground truth durations. Defaults to None. Returns: Tuple[torch.FloatTensor, torch.FloatTensor]: Pitch embedding, pitch prediction. Shapes: - o_en: :math:`(B, C, T_{en})` - x_mask: :math:`(B, 1, T_{en})` - pitch: :math:`(B, 1, T_{de})` - dr: :math:`(B, T_{en})` """ o_pitch = self.pitch_predictor(o_en, x_mask) if pitch is not None: avg_pitch = average_over_durations(pitch, dr) o_pitch_emb = self.pitch_emb(avg_pitch) return o_pitch_emb, o_pitch, avg_pitch o_pitch_emb = self.pitch_emb(o_pitch) return o_pitch_emb, o_pitch def _forward_aligner( self, x: torch.FloatTensor, y: torch.FloatTensor, x_mask: torch.IntTensor, y_mask: torch.IntTensor ) -> Tuple[torch.IntTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]: """Aligner forward pass. 1. Compute a mask to apply to the attention map. 2. Run the alignment network. 3. Apply MAS to compute the hard alignment map. 4. Compute the durations from the hard alignment map. Args: x (torch.FloatTensor): Input sequence. y (torch.FloatTensor): Output sequence. x_mask (torch.IntTensor): Input sequence mask. y_mask (torch.IntTensor): Output sequence mask. Returns: Tuple[torch.IntTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]: Durations from the hard alignment map, soft alignment potentials, log scale alignment potentials, hard alignment map. Shapes: - x: :math:`[B, T_en, C_en]` - y: :math:`[B, T_de, C_de]` - x_mask: :math:`[B, 1, T_en]` - y_mask: :math:`[B, 1, T_de]` - o_alignment_dur: :math:`[B, T_en]` - alignment_soft: :math:`[B, T_en, T_de]` - alignment_logprob: :math:`[B, 1, T_de, T_en]` - alignment_mas: :math:`[B, T_en, T_de]` """ attn_mask = torch.unsqueeze(x_mask, -1) * torch.unsqueeze(y_mask, 2) alignment_soft, alignment_logprob = self.aligner(y.transpose(1, 2), x.transpose(1, 2), x_mask, None) alignment_mas = maximum_path( alignment_soft.squeeze(1).transpose(1, 2).contiguous(), attn_mask.squeeze(1).contiguous() ) o_alignment_dur = torch.sum(alignment_mas, -1).int() alignment_soft = alignment_soft.squeeze(1).transpose(1, 2) return o_alignment_dur, alignment_soft, alignment_logprob, alignment_mas 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
[docs] def forward( self, x: torch.LongTensor, x_lengths: torch.LongTensor, y_lengths: torch.LongTensor, y: torch.FloatTensor = None, dr: torch.IntTensor = None, pitch: torch.FloatTensor = None, aux_input: Dict = {"d_vectors": None, "speaker_ids": None}, # pylint: disable=unused-argument ) -> Dict: """Model's forward pass. Args: x (torch.LongTensor): Input character sequences. x_lengths (torch.LongTensor): Input sequence lengths. y_lengths (torch.LongTensor): Output sequnce lengths. Defaults to None. y (torch.FloatTensor): Spectrogram frames. Only used when the alignment network is on. Defaults to None. dr (torch.IntTensor): Character durations over the spectrogram frames. Only used when the alignment network is off. Defaults to None. pitch (torch.FloatTensor): Pitch values for each spectrogram frame. Only used when the pitch predictor is on. Defaults to None. aux_input (Dict): Auxiliary model inputs for multi-speaker training. Defaults to `{"d_vectors": 0, "speaker_ids": None}`. Shapes: - x: :math:`[B, T_max]` - x_lengths: :math:`[B]` - y_lengths: :math:`[B]` - y: :math:`[B, T_max2]` - dr: :math:`[B, T_max]` - g: :math:`[B, C]` - pitch: :math:`[B, 1, T]` """ g = self._set_speaker_input(aux_input) # compute sequence masks y_mask = torch.unsqueeze(sequence_mask(y_lengths, None), 1).float() x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.shape[1]), 1).float() # encoder pass o_en, x_mask, g, x_emb = self._forward_encoder(x, x_mask, g) # duration predictor pass if self.args.detach_duration_predictor: o_dr_log = self.duration_predictor(o_en.detach(), x_mask) else: o_dr_log = self.duration_predictor(o_en, x_mask) o_dr = torch.clamp(torch.exp(o_dr_log) - 1, 0, self.max_duration) # generate attn mask from predicted durations o_attn = self.generate_attn(o_dr.squeeze(1), x_mask) # aligner o_alignment_dur = None alignment_soft = None alignment_logprob = None alignment_mas = None if self.use_aligner: o_alignment_dur, alignment_soft, alignment_logprob, alignment_mas = self._forward_aligner( x_emb, y, x_mask, y_mask ) alignment_soft = alignment_soft.transpose(1, 2) alignment_mas = alignment_mas.transpose(1, 2) dr = o_alignment_dur # pitch predictor pass o_pitch = None avg_pitch = None if self.args.use_pitch: o_pitch_emb, o_pitch, avg_pitch = self._forward_pitch_predictor(o_en, x_mask, pitch, dr) o_en = o_en + o_pitch_emb # decoder pass o_de, attn = self._forward_decoder( o_en, dr, x_mask, y_lengths, g=None ) # TODO: maybe pass speaker embedding (g) too outputs = { "model_outputs": o_de, # [B, T, C] "durations_log": o_dr_log.squeeze(1), # [B, T] "durations": o_dr.squeeze(1), # [B, T] "attn_durations": o_attn, # for visualization [B, T_en, T_de'] "pitch_avg": o_pitch, "pitch_avg_gt": avg_pitch, "alignments": attn, # [B, T_de, T_en] "alignment_soft": alignment_soft, "alignment_mas": alignment_mas, "o_alignment_dur": o_alignment_dur, "alignment_logprob": alignment_logprob, "x_mask": x_mask, "y_mask": y_mask, } return outputs
[docs] @torch.no_grad() def inference(self, x, aux_input={"d_vectors": None, "speaker_ids": None}): # pylint: disable=unused-argument """Model's inference pass. Args: x (torch.LongTensor): Input character sequence. aux_input (Dict): Auxiliary model inputs. Defaults to `{"d_vectors": None, "speaker_ids": None}`. Shapes: - x: [B, T_max] - x_lengths: [B] - g: [B, C] """ g = self._set_speaker_input(aux_input) x_lengths = torch.tensor(x.shape[1:2]).to(x.device) x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.shape[1]), 1).to(x.dtype).float() # encoder pass o_en, x_mask, g, _ = self._forward_encoder(x, x_mask, g) # duration predictor pass o_dr_log = self.duration_predictor(o_en, x_mask) o_dr = self.format_durations(o_dr_log, x_mask).squeeze(1) y_lengths = o_dr.sum(1) # pitch predictor pass o_pitch = None if self.args.use_pitch: o_pitch_emb, o_pitch = self._forward_pitch_predictor(o_en, x_mask) o_en = o_en + o_pitch_emb # decoder pass o_de, attn = self._forward_decoder(o_en, o_dr, x_mask, y_lengths, g=None) outputs = { "model_outputs": o_de, "alignments": attn, "pitch": o_pitch, "durations_log": o_dr_log, } return outputs
def train_step(self, batch: dict, criterion: nn.Module): text_input = batch["text_input"] text_lengths = batch["text_lengths"] mel_input = batch["mel_input"] mel_lengths = batch["mel_lengths"] pitch = batch["pitch"] if self.args.use_pitch else None d_vectors = batch["d_vectors"] speaker_ids = batch["speaker_ids"] durations = batch["durations"] aux_input = {"d_vectors": d_vectors, "speaker_ids": speaker_ids} # forward pass outputs = self.forward( text_input, text_lengths, mel_lengths, y=mel_input, dr=durations, pitch=pitch, aux_input=aux_input ) # use aligner's output as the duration target if self.use_aligner: durations = outputs["o_alignment_dur"] # use float32 in AMP with autocast(enabled=False): # compute loss loss_dict = criterion( decoder_output=outputs["model_outputs"], decoder_target=mel_input, decoder_output_lens=mel_lengths, dur_output=outputs["durations_log"], dur_target=durations, pitch_output=outputs["pitch_avg"] if self.use_pitch else None, pitch_target=outputs["pitch_avg_gt"] if self.use_pitch else None, input_lens=text_lengths, alignment_logprob=outputs["alignment_logprob"] if self.use_aligner else None, alignment_soft=outputs["alignment_soft"], alignment_hard=outputs["alignment_mas"], binary_loss_weight=self.binary_loss_weight, ) # compute duration error durations_pred = outputs["durations"] duration_error = torch.abs(durations - durations_pred).sum() / text_lengths.sum() loss_dict["duration_error"] = duration_error return outputs, loss_dict def _create_logs(self, batch, outputs, ap): """Create common logger outputs.""" model_outputs = outputs["model_outputs"] alignments = outputs["alignments"] mel_input = batch["mel_input"] pred_spec = model_outputs[0].data.cpu().numpy() gt_spec = mel_input[0].data.cpu().numpy() align_img = alignments[0].data.cpu().numpy() figures = { "prediction": plot_spectrogram(pred_spec, ap, output_fig=False), "ground_truth": plot_spectrogram(gt_spec, ap, output_fig=False), "alignment": plot_alignment(align_img, output_fig=False), } # plot pitch figures if self.args.use_pitch: pitch_avg = abs(outputs["pitch_avg_gt"][0, 0].data.cpu().numpy()) pitch_avg_hat = abs(outputs["pitch_avg"][0, 0].data.cpu().numpy()) chars = self.tokenizer.decode(batch["text_input"][0].data.cpu().numpy()) pitch_figures = { "pitch_ground_truth": plot_avg_pitch(pitch_avg, chars, output_fig=False), "pitch_avg_predicted": plot_avg_pitch(pitch_avg_hat, chars, output_fig=False), } figures.update(pitch_figures) # plot the attention mask computed from the predicted durations if "attn_durations" in outputs: alignments_hat = outputs["attn_durations"][0].data.cpu().numpy() figures["alignment_hat"] = plot_alignment(alignments_hat.T, output_fig=False) # Sample audio train_audio = ap.inv_melspectrogram(pred_spec.T) return figures, {"audio": train_audio} def train_log( self, batch: dict, outputs: dict, logger: "Logger", assets: dict, steps: int ) -> None: # pylint: disable=no-self-use figures, audios = self._create_logs(batch, outputs, self.ap) logger.train_figures(steps, figures) logger.train_audios(steps, audios, self.ap.sample_rate) def eval_step(self, batch: dict, criterion: nn.Module): return self.train_step(batch, criterion) def eval_log(self, batch: dict, outputs: dict, logger: "Logger", assets: dict, steps: int) -> None: figures, audios = self._create_logs(batch, outputs, self.ap) logger.eval_figures(steps, figures) logger.eval_audios(steps, audios, self.ap.sample_rate) def load_checkpoint( self, config, checkpoint_path, eval=False ): # pylint: disable=unused-argument, redefined-builtin state = torch.load(checkpoint_path, map_location=torch.device("cpu")) self.load_state_dict(state["model"]) if eval: self.eval() assert not self.training def get_criterion(self): from TTS.tts.layers.losses import ForwardTTSLoss # pylint: disable=import-outside-toplevel return ForwardTTSLoss(self.config)
[docs] def on_train_step_start(self, trainer): """Schedule binary loss weight.""" self.binary_loss_weight = min(trainer.epochs_done / self.config.binary_loss_warmup_epochs, 1.0) * 1.0
[docs] @staticmethod def init_from_config(config: "ForwardTTSConfig", samples: Union[List[List], List[Dict]] = None): """Initiate model from config Args: config (ForwardTTSConfig): 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 ap = AudioProcessor.init_from_config(config) tokenizer, new_config = TTSTokenizer.init_from_config(config) speaker_manager = SpeakerManager.init_from_config(config, samples) return ForwardTTS(new_config, ap, tokenizer, speaker_manager)