# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import contextlib from typing import List, Optional import numpy as np import omegaconf import torch import transformers import wandb from hydra.utils import instantiate from lightning.pytorch import Trainer from lightning.pytorch.loggers import WandbLogger from omegaconf import DictConfig from torch import nn from torch.nn import functional as F from transformers import AlbertTokenizer from nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers import ( EnglishCharsTokenizer, EnglishPhonemesTokenizer, ) from nemo.collections.tts.losses.aligner_loss import BinLoss, ForwardSumLoss from nemo.collections.tts.models.base import SpectrogramGenerator from nemo.collections.tts.modules.fastpitch import average_features, regulate_len from nemo.collections.tts.parts.utils.helpers import ( binarize_attention_parallel, g2p_backward_compatible_support, get_mask_from_lengths, plot_pitch_to_numpy, plot_spectrogram_to_numpy, ) from nemo.core import Exportable from nemo.core.classes.common import PretrainedModelInfo, typecheck from nemo.core.neural_types.elements import ( LengthsType, LogprobsType, MelSpectrogramType, ProbsType, RegressionValuesType, TokenDurationType, TokenIndex, TokenLogDurationType, ) from nemo.core.neural_types.neural_type import NeuralType from nemo.utils import logging, model_utils class MixerTTSModel(SpectrogramGenerator, Exportable): """Mixer-TTS and Mixer-TTS-X models (https://arxiv.org/abs/2110.03584) that is used to generate mel spectrogram from text.""" def __init__(self, cfg: DictConfig, trainer: 'Trainer' = None): # Convert to Hydra 1.0 compatible DictConfig cfg = model_utils.convert_model_config_to_dict_config(cfg) cfg = model_utils.maybe_update_config_version(cfg) # Setup normalizer self.normalizer = None self.text_normalizer_call = None self.text_normalizer_call_kwargs = {} self._setup_normalizer(cfg) # Setup tokenizer self.tokenizer = None self._setup_tokenizer(cfg) assert self.tokenizer is not None num_tokens = len(self.tokenizer.tokens) self.tokenizer_pad = self.tokenizer.pad self.tokenizer_unk = self.tokenizer.oov super().__init__(cfg=cfg, trainer=trainer) self.pitch_loss_scale = cfg.pitch_loss_scale self.durs_loss_scale = cfg.durs_loss_scale self.mel_loss_scale = cfg.mel_loss_scale self.aligner = instantiate(cfg.alignment_module) self.forward_sum_loss = ForwardSumLoss() self.bin_loss = BinLoss() self.add_bin_loss = False self.bin_loss_scale = 0.0 self.bin_loss_start_ratio = cfg.bin_loss_start_ratio self.bin_loss_warmup_epochs = cfg.bin_loss_warmup_epochs self.cond_on_lm_embeddings = cfg.get("cond_on_lm_embeddings", False) if self.cond_on_lm_embeddings: self.lm_padding_value = ( self._train_dl.dataset.lm_padding_value if self._train_dl is not None else self._get_lm_padding_value(cfg.lm_model) ) self.lm_embeddings = self._get_lm_embeddings(cfg.lm_model) self.lm_embeddings.weight.requires_grad = False self.self_attention_module = instantiate( cfg.self_attention_module, n_lm_tokens_channels=self.lm_embeddings.weight.shape[1] ) self.encoder = instantiate(cfg.encoder, num_tokens=num_tokens, padding_idx=self.tokenizer_pad) self.symbol_emb = self.encoder.to_embed self.duration_predictor = instantiate(cfg.duration_predictor) self.pitch_mean, self.pitch_std = float(cfg.pitch_mean), float(cfg.pitch_std) self.pitch_predictor = instantiate(cfg.pitch_predictor) self.pitch_emb = instantiate(cfg.pitch_emb) self.preprocessor = instantiate(cfg.preprocessor) self.decoder = instantiate(cfg.decoder) self.proj = nn.Linear(self.decoder.d_model, cfg.n_mel_channels) def _setup_tokenizer(self, cfg): text_tokenizer_kwargs = {} if "g2p" in cfg.text_tokenizer: # for backward compatibility if ( self._is_model_being_restored() and (cfg.text_tokenizer.g2p.get('_target_', None) is not None) and cfg.text_tokenizer.g2p["_target_"].startswith("nemo_text_processing.g2p") ): cfg.text_tokenizer.g2p["_target_"] = g2p_backward_compatible_support( cfg.text_tokenizer.g2p["_target_"] ) g2p_kwargs = {} if "phoneme_dict" in cfg.text_tokenizer.g2p: g2p_kwargs["phoneme_dict"] = self.register_artifact( 'text_tokenizer.g2p.phoneme_dict', cfg.text_tokenizer.g2p.phoneme_dict, ) if "heteronyms" in cfg.text_tokenizer.g2p: g2p_kwargs["heteronyms"] = self.register_artifact( 'text_tokenizer.g2p.heteronyms', cfg.text_tokenizer.g2p.heteronyms, ) text_tokenizer_kwargs["g2p"] = instantiate(cfg.text_tokenizer.g2p, **g2p_kwargs) self.tokenizer = instantiate(cfg.text_tokenizer, **text_tokenizer_kwargs) def _get_lm_model_tokenizer(self, lm_model="albert"): if getattr(self, "_lm_model_tokenizer", None) is not None: return self._lm_model_tokenizer if self._train_dl is not None and self._train_dl.dataset is not None: self._lm_model_tokenizer = self._train_dl.dataset.lm_model_tokenizer if lm_model == "albert": self._lm_model_tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2') else: raise NotImplementedError( f"{lm_model} lm model is not supported. Only albert is supported at this moment." ) return self._lm_model_tokenizer def _get_lm_embeddings(self, lm_model="albert"): if lm_model == "albert": return transformers.AlbertModel.from_pretrained('albert-base-v2').embeddings.word_embeddings else: raise NotImplementedError( f"{lm_model} lm model is not supported. Only albert is supported at this moment." ) def _get_lm_padding_value(self, lm_model="albert"): if lm_model == "albert": return transformers.AlbertTokenizer.from_pretrained('albert-base-v2')._convert_token_to_id('') else: raise NotImplementedError( f"{lm_model} lm model is not supported. Only albert is supported at this moment." ) def _metrics( self, true_durs, true_text_len, pred_durs, true_pitch, pred_pitch, true_spect=None, pred_spect=None, true_spect_len=None, attn_logprob=None, attn_soft=None, attn_hard=None, attn_hard_dur=None, ): text_mask = get_mask_from_lengths(true_text_len) mel_mask = get_mask_from_lengths(true_spect_len) loss = 0.0 # Dur loss and metrics durs_loss = F.mse_loss(pred_durs, (true_durs + 1).float().log(), reduction='none') durs_loss = durs_loss * text_mask.float() durs_loss = durs_loss.sum() / text_mask.sum() durs_pred = pred_durs.exp() - 1 durs_pred = torch.clamp_min(durs_pred, min=0) durs_pred = durs_pred.round().long() acc = ((true_durs == durs_pred) * text_mask).sum().float() / text_mask.sum() * 100 acc_dist_1 = (((true_durs - durs_pred).abs() <= 1) * text_mask).sum().float() / text_mask.sum() * 100 acc_dist_3 = (((true_durs - durs_pred).abs() <= 3) * text_mask).sum().float() / text_mask.sum() * 100 pred_spect = pred_spect.transpose(1, 2) # Mel loss mel_loss = F.mse_loss(pred_spect, true_spect, reduction='none').mean(dim=-2) mel_loss = mel_loss * mel_mask.float() mel_loss = mel_loss.sum() / mel_mask.sum() loss = loss + self.durs_loss_scale * durs_loss + self.mel_loss_scale * mel_loss # Aligner loss bin_loss, ctc_loss = None, None ctc_loss = self.forward_sum_loss(attn_logprob=attn_logprob, in_lens=true_text_len, out_lens=true_spect_len) loss = loss + ctc_loss if self.add_bin_loss: bin_loss = self.bin_loss(hard_attention=attn_hard, soft_attention=attn_soft) loss = loss + self.bin_loss_scale * bin_loss true_avg_pitch = average_features(true_pitch.unsqueeze(1), attn_hard_dur).squeeze(1) # Pitch loss pitch_loss = F.mse_loss(pred_pitch, true_avg_pitch, reduction='none') # noqa pitch_loss = (pitch_loss * text_mask).sum() / text_mask.sum() loss = loss + self.pitch_loss_scale * pitch_loss return loss, durs_loss, acc, acc_dist_1, acc_dist_3, pitch_loss, mel_loss, ctc_loss, bin_loss @torch.jit.unused def run_aligner(self, text, text_len, text_mask, spect, spect_len, attn_prior): text_emb = self.symbol_emb(text) attn_soft, attn_logprob = self.aligner( spect, text_emb.permute(0, 2, 1), mask=text_mask == 0, attn_prior=attn_prior, ) attn_hard = binarize_attention_parallel(attn_soft, text_len, spect_len) attn_hard_dur = attn_hard.sum(2)[:, 0, :] assert torch.all(torch.eq(attn_hard_dur.sum(dim=1), spect_len)) return attn_soft, attn_logprob, attn_hard, attn_hard_dur @typecheck( input_types={ "text": NeuralType(('B', 'T_text'), TokenIndex()), "text_len": NeuralType(('B',), LengthsType()), "pitch": NeuralType(('B', 'T_audio'), RegressionValuesType(), optional=True), "spect": NeuralType(('B', 'D', 'T_spec'), MelSpectrogramType(), optional=True), "spect_len": NeuralType(('B',), LengthsType(), optional=True), "attn_prior": NeuralType(('B', 'T_spec', 'T_text'), ProbsType(), optional=True), "lm_tokens": NeuralType(('B', 'T_lm_tokens'), TokenIndex(), optional=True), }, output_types={ "pred_spect": NeuralType(('B', 'D', 'T_spec'), MelSpectrogramType()), "durs_predicted": NeuralType(('B', 'T_text'), TokenDurationType()), "log_durs_predicted": NeuralType(('B', 'T_text'), TokenLogDurationType()), "pitch_predicted": NeuralType(('B', 'T_text'), RegressionValuesType()), "attn_soft": NeuralType(('B', 'S', 'T_spec', 'T_text'), ProbsType()), "attn_logprob": NeuralType(('B', 'S', 'T_spec', 'T_text'), LogprobsType()), "attn_hard": NeuralType(('B', 'S', 'T_spec', 'T_text'), ProbsType()), "attn_hard_dur": NeuralType(('B', 'T_text'), TokenDurationType()), }, ) def forward(self, text, text_len, pitch=None, spect=None, spect_len=None, attn_prior=None, lm_tokens=None): if self.training: assert pitch is not None text_mask = get_mask_from_lengths(text_len).unsqueeze(2) enc_out, enc_mask = self.encoder(text, text_mask) # Aligner attn_soft, attn_logprob, attn_hard, attn_hard_dur = None, None, None, None if spect is not None: attn_soft, attn_logprob, attn_hard, attn_hard_dur = self.run_aligner( text, text_len, text_mask, spect, spect_len, attn_prior ) if self.cond_on_lm_embeddings: lm_emb = self.lm_embeddings(lm_tokens) lm_features = self.self_attention_module( enc_out, lm_emb, lm_emb, q_mask=enc_mask.squeeze(2), kv_mask=lm_tokens != self.lm_padding_value ) # Duration predictor log_durs_predicted = self.duration_predictor(enc_out, enc_mask) durs_predicted = torch.clamp(log_durs_predicted.exp() - 1, 0) # Pitch predictor pitch_predicted = self.pitch_predictor(enc_out, enc_mask) # Avg pitch, add pitch_emb if not self.training: if pitch is not None: pitch = average_features(pitch.unsqueeze(1), attn_hard_dur).squeeze(1) pitch_emb = self.pitch_emb(pitch.unsqueeze(1)) else: pitch_emb = self.pitch_emb(pitch_predicted.unsqueeze(1)) else: pitch = average_features(pitch.unsqueeze(1), attn_hard_dur).squeeze(1) pitch_emb = self.pitch_emb(pitch.unsqueeze(1)) enc_out = enc_out + pitch_emb.transpose(1, 2) if self.cond_on_lm_embeddings: enc_out = enc_out + lm_features # Regulate length len_regulated_enc_out, dec_lens = regulate_len(attn_hard_dur, enc_out) dec_out, dec_lens = self.decoder(len_regulated_enc_out, get_mask_from_lengths(dec_lens).unsqueeze(2)) pred_spect = self.proj(dec_out) return ( pred_spect, durs_predicted, log_durs_predicted, pitch_predicted, attn_soft, attn_logprob, attn_hard, attn_hard_dur, ) def infer( self, text, text_len=None, text_mask=None, spect=None, spect_len=None, attn_prior=None, use_gt_durs=False, lm_tokens=None, pitch=None, ): if text_mask is None: text_mask = get_mask_from_lengths(text_len).unsqueeze(2) enc_out, enc_mask = self.encoder(text, text_mask) # Aligner attn_hard_dur = None if use_gt_durs: attn_soft, attn_logprob, attn_hard, attn_hard_dur = self.run_aligner( text, text_len, text_mask, spect, spect_len, attn_prior ) if self.cond_on_lm_embeddings: lm_emb = self.lm_embeddings(lm_tokens) lm_features = self.self_attention_module( enc_out, lm_emb, lm_emb, q_mask=enc_mask.squeeze(2), kv_mask=lm_tokens != self.lm_padding_value ) # Duration predictor log_durs_predicted = self.duration_predictor(enc_out, enc_mask) durs_predicted = torch.clamp(log_durs_predicted.exp() - 1, 0) # Avg pitch, pitch predictor if use_gt_durs and pitch is not None: pitch = average_features(pitch.unsqueeze(1), attn_hard_dur).squeeze(1) pitch_emb = self.pitch_emb(pitch.unsqueeze(1)) else: pitch_predicted = self.pitch_predictor(enc_out, enc_mask) pitch_emb = self.pitch_emb(pitch_predicted.unsqueeze(1)) # Add pitch emb enc_out = enc_out + pitch_emb.transpose(1, 2) if self.cond_on_lm_embeddings: enc_out = enc_out + lm_features if use_gt_durs: if attn_hard_dur is not None: len_regulated_enc_out, dec_lens = regulate_len(attn_hard_dur, enc_out) else: raise NotImplementedError else: len_regulated_enc_out, dec_lens = regulate_len(durs_predicted, enc_out) dec_out, _ = self.decoder(len_regulated_enc_out, get_mask_from_lengths(dec_lens).unsqueeze(2)) pred_spect = self.proj(dec_out) return pred_spect def on_train_epoch_start(self): bin_loss_start_epoch = np.ceil(self.bin_loss_start_ratio * self._trainer.max_epochs) # Add bin loss when current_epoch >= bin_start_epoch if not self.add_bin_loss and self.current_epoch >= bin_loss_start_epoch: logging.info(f"Using hard attentions after epoch: {self.current_epoch}") self.add_bin_loss = True if self.add_bin_loss: self.bin_loss_scale = min((self.current_epoch - bin_loss_start_epoch) / self.bin_loss_warmup_epochs, 1.0) def training_step(self, batch, batch_idx): attn_prior, lm_tokens = None, None if self.cond_on_lm_embeddings: audio, audio_len, text, text_len, attn_prior, pitch, _, lm_tokens = batch else: audio, audio_len, text, text_len, attn_prior, pitch, _ = batch spect, spect_len = self.preprocessor(input_signal=audio, length=audio_len) # pitch normalization zero_pitch_idx = pitch == 0 pitch = (pitch - self.pitch_mean) / self.pitch_std pitch[zero_pitch_idx] = 0.0 ( pred_spect, _, pred_log_durs, pred_pitch, attn_soft, attn_logprob, attn_hard, attn_hard_dur, ) = self( text=text, text_len=text_len, pitch=pitch, spect=spect, spect_len=spect_len, attn_prior=attn_prior, lm_tokens=lm_tokens, ) ( loss, durs_loss, acc, acc_dist_1, acc_dist_3, pitch_loss, mel_loss, ctc_loss, bin_loss, ) = self._metrics( pred_durs=pred_log_durs, pred_pitch=pred_pitch, true_durs=attn_hard_dur, true_text_len=text_len, true_pitch=pitch, true_spect=spect, pred_spect=pred_spect, true_spect_len=spect_len, attn_logprob=attn_logprob, attn_soft=attn_soft, attn_hard=attn_hard, attn_hard_dur=attn_hard_dur, ) train_log = { 'train_loss': loss, 'train_durs_loss': durs_loss, 'train_pitch_loss': torch.tensor(1.0).to(durs_loss.device) if pitch_loss is None else pitch_loss, 'train_mel_loss': mel_loss, 'train_durs_acc': acc, 'train_durs_acc_dist_3': acc_dist_3, 'train_ctc_loss': torch.tensor(1.0).to(durs_loss.device) if ctc_loss is None else ctc_loss, 'train_bin_loss': torch.tensor(1.0).to(durs_loss.device) if bin_loss is None else bin_loss, } return {'loss': loss, 'progress_bar': {k: v.detach() for k, v in train_log.items()}, 'log': train_log} def validation_step(self, batch, batch_idx): attn_prior, lm_tokens = None, None if self.cond_on_lm_embeddings: audio, audio_len, text, text_len, attn_prior, pitch, _, lm_tokens = batch else: audio, audio_len, text, text_len, attn_prior, pitch, _ = batch spect, spect_len = self.preprocessor(input_signal=audio, length=audio_len) # pitch normalization zero_pitch_idx = pitch == 0 pitch = (pitch - self.pitch_mean) / self.pitch_std pitch[zero_pitch_idx] = 0.0 ( pred_spect, _, pred_log_durs, pred_pitch, attn_soft, attn_logprob, attn_hard, attn_hard_dur, ) = self( text=text, text_len=text_len, pitch=pitch, spect=spect, spect_len=spect_len, attn_prior=attn_prior, lm_tokens=lm_tokens, ) ( loss, durs_loss, acc, acc_dist_1, acc_dist_3, pitch_loss, mel_loss, ctc_loss, bin_loss, ) = self._metrics( pred_durs=pred_log_durs, pred_pitch=pred_pitch, true_durs=attn_hard_dur, true_text_len=text_len, true_pitch=pitch, true_spect=spect, pred_spect=pred_spect, true_spect_len=spect_len, attn_logprob=attn_logprob, attn_soft=attn_soft, attn_hard=attn_hard, attn_hard_dur=attn_hard_dur, ) # without ground truth internal features except for durations pred_spect, _, pred_log_durs, pred_pitch, attn_soft, attn_logprob, attn_hard, attn_hard_dur = self( text=text, text_len=text_len, pitch=None, spect=spect, spect_len=spect_len, attn_prior=attn_prior, lm_tokens=lm_tokens, ) *_, with_pred_features_mel_loss, _, _ = self._metrics( pred_durs=pred_log_durs, pred_pitch=pred_pitch, true_durs=attn_hard_dur, true_text_len=text_len, true_pitch=pitch, true_spect=spect, pred_spect=pred_spect, true_spect_len=spect_len, attn_logprob=attn_logprob, attn_soft=attn_soft, attn_hard=attn_hard, attn_hard_dur=attn_hard_dur, ) val_log = { 'val_loss': loss, 'val_durs_loss': durs_loss, 'val_pitch_loss': torch.tensor(1.0).to(durs_loss.device) if pitch_loss is None else pitch_loss, 'val_mel_loss': mel_loss, 'val_with_pred_features_mel_loss': with_pred_features_mel_loss, 'val_durs_acc': acc, 'val_durs_acc_dist_3': acc_dist_3, 'val_ctc_loss': torch.tensor(1.0).to(durs_loss.device) if ctc_loss is None else ctc_loss, 'val_bin_loss': torch.tensor(1.0).to(durs_loss.device) if bin_loss is None else bin_loss, } self.log_dict(val_log, prog_bar=False, on_epoch=True, logger=True, sync_dist=True) if batch_idx == 0 and self.current_epoch % 5 == 0 and isinstance(self.logger, WandbLogger): specs = [] pitches = [] for i in range(min(3, spect.shape[0])): specs += [ wandb.Image( plot_spectrogram_to_numpy(spect[i, :, : spect_len[i]].data.cpu().numpy()), caption=f"gt mel {i}", ), wandb.Image( plot_spectrogram_to_numpy(pred_spect.transpose(1, 2)[i, :, : spect_len[i]].data.cpu().numpy()), caption=f"pred mel {i}", ), ] pitches += [ wandb.Image( plot_pitch_to_numpy( average_features(pitch.unsqueeze(1), attn_hard_dur) .squeeze(1)[i, : text_len[i]] .data.cpu() .numpy(), ylim_range=[-2.5, 2.5], ), caption=f"gt pitch {i}", ), ] pitches += [ wandb.Image( plot_pitch_to_numpy(pred_pitch[i, : text_len[i]].data.cpu().numpy(), ylim_range=[-2.5, 2.5]), caption=f"pred pitch {i}", ), ] self.logger.experiment.log({"specs": specs, "pitches": pitches}) @typecheck( input_types={ "tokens": NeuralType(('B', 'T_text'), TokenIndex(), optional=True), "tokens_len": NeuralType(('B'), LengthsType(), optional=True), "lm_tokens": NeuralType(('B', 'T_lm_tokens'), TokenIndex(), optional=True), "raw_texts": [NeuralType(optional=True)], "lm_model": NeuralType(optional=True), }, output_types={ "spect": NeuralType(('B', 'D', 'T_spec'), MelSpectrogramType()), }, ) def generate_spectrogram( self, tokens: Optional[torch.Tensor] = None, tokens_len: Optional[torch.Tensor] = None, lm_tokens: Optional[torch.Tensor] = None, raw_texts: Optional[List[str]] = None, norm_text_for_lm_model: bool = True, lm_model: str = "albert", ): if tokens is not None: if tokens_len is None: # It is assumed that padding is consecutive and only at the end tokens_len = (tokens != self.tokenizer.pad).sum(dim=-1) else: if raw_texts is None: raise ValueError("raw_texts must be specified if tokens is None") t_seqs = [self.tokenizer(t) for t in raw_texts] tokens = torch.nn.utils.rnn.pad_sequence( sequences=[torch.tensor(t, dtype=torch.long, device=self.device) for t in t_seqs], batch_first=True, padding_value=self.tokenizer.pad, ) tokens_len = torch.tensor([len(t) for t in t_seqs], dtype=torch.long, device=tokens.device) if self.cond_on_lm_embeddings and lm_tokens is None: if raw_texts is None: raise ValueError("raw_texts must be specified if lm_tokens is None") lm_model_tokenizer = self._get_lm_model_tokenizer(lm_model) lm_padding_value = lm_model_tokenizer._convert_token_to_id('') lm_space_value = lm_model_tokenizer._convert_token_to_id('▁') assert isinstance(self.tokenizer, EnglishCharsTokenizer) or isinstance( self.tokenizer, EnglishPhonemesTokenizer ) if norm_text_for_lm_model and self.text_normalizer_call is not None: raw_texts = [self.text_normalizer_call(t, **self.text_normalizer_call_kwargs) for t in raw_texts] preprocess_texts_as_tts_input = [self.tokenizer.text_preprocessing_func(t) for t in raw_texts] lm_tokens_as_ids_list = [ lm_model_tokenizer.encode(t, add_special_tokens=False) for t in preprocess_texts_as_tts_input ] if self.tokenizer.pad_with_space: lm_tokens_as_ids_list = [[lm_space_value] + t + [lm_space_value] for t in lm_tokens_as_ids_list] lm_tokens = torch.full( (len(lm_tokens_as_ids_list), max([len(t) for t in lm_tokens_as_ids_list])), fill_value=lm_padding_value, device=tokens.device, ) for i, lm_tokens_i in enumerate(lm_tokens_as_ids_list): lm_tokens[i, : len(lm_tokens_i)] = torch.tensor(lm_tokens_i, device=tokens.device) pred_spect = self.infer(tokens, tokens_len, lm_tokens=lm_tokens).transpose(1, 2) return pred_spect def parse(self, text: str, normalize=True) -> torch.Tensor: if self.training: logging.warning("parse() is meant to be called in eval mode.") if normalize and self.text_normalizer_call is not None: text = self.text_normalizer_call(text, **self.text_normalizer_call_kwargs) eval_phon_mode = contextlib.nullcontext() if hasattr(self.tokenizer, "set_phone_prob"): eval_phon_mode = self.tokenizer.set_phone_prob(prob=1.0) with eval_phon_mode: tokens = self.tokenizer.encode(text) return torch.tensor(tokens).long().unsqueeze(0).to(self.device) def _loader(self, cfg): try: _ = cfg.dataset.manifest_filepath except omegaconf.errors.MissingMandatoryValue: logging.warning("manifest_filepath was skipped. No dataset for this model.") return None dataset = instantiate( cfg.dataset, text_normalizer=self.normalizer, text_normalizer_call_kwargs=self.text_normalizer_call_kwargs, text_tokenizer=self.tokenizer, ) return torch.utils.data.DataLoader( # noqa dataset=dataset, collate_fn=dataset.collate_fn, **cfg.dataloader_params, ) def setup_training_data(self, cfg): self._train_dl = self._loader(cfg) def setup_validation_data(self, cfg): self._validation_dl = self._loader(cfg) def setup_test_data(self, cfg): """Omitted.""" pass @classmethod def list_available_models(cls) -> 'List[PretrainedModelInfo]': """ This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. Returns: List of available pre-trained models. """ list_of_models = [] model = PretrainedModelInfo( pretrained_model_name="tts_en_lj_mixertts", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_en_lj_mixertts/versions/1.6.0/files/tts_en_lj_mixertts.nemo", description="This model is trained on LJSpeech sampled at 22050Hz with and can be used to generate female English voices with an American accent.", class_=cls, # noqa ) list_of_models.append(model) model = PretrainedModelInfo( pretrained_model_name="tts_en_lj_mixerttsx", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_en_lj_mixerttsx/versions/1.6.0/files/tts_en_lj_mixerttsx.nemo", description="This model is trained on LJSpeech sampled at 22050Hz with and can be used to generate female English voices with an American accent.", class_=cls, # noqa ) list_of_models.append(model) return list_of_models # Methods for model exportability @property def input_types(self): return { "text": NeuralType(('B', 'T_text'), TokenIndex()), "lm_tokens": NeuralType(('B', 'T_lm_tokens'), TokenIndex(), optional=True), } @property def output_types(self): return { "spect": NeuralType(('B', 'D', 'T_spec'), MelSpectrogramType()), } def input_example(self, max_text_len=10, max_lm_tokens_len=10): text = torch.randint( low=0, high=len(self.tokenizer.tokens), size=(1, max_text_len), device=self.device, dtype=torch.long, ) inputs = {'text': text} if self.cond_on_lm_embeddings: inputs['lm_tokens'] = torch.randint( low=0, high=self.lm_embeddings.weight.shape[0], size=(1, max_lm_tokens_len), device=self.device, dtype=torch.long, ) return (inputs,) def forward_for_export(self, text, lm_tokens=None): text_mask = (text != self.tokenizer_pad).unsqueeze(2) spect = self.infer(text=text, text_mask=text_mask, lm_tokens=lm_tokens).transpose(1, 2) return spect.to(torch.float)