def output_types(self):
return {
"z": NeuralType(('B', 'D', 'T'), NormalDistributionSamplesType()),
"y_m": NeuralType(('B', 'D', 'T'), NormalDistributionMeanType()),
"y_logs": NeuralType(('B', 'D', 'T'), NormalDistributionLogVarianceType()),
"logdet": NeuralType(('B'), LogDeterminantType()),
"log_durs_predicted": NeuralType(('B', 'T'), TokenLogDurationType()),
"log_durs_extracted": NeuralType(('B', 'T'), TokenLogDurationType()),
"spect_lengths": NeuralType(('B'), LengthsType()),
"attn": NeuralType(('B', 'T', 'T'), SequenceToSequenceAlignmentType()),
}
def _prepare_for_export(self, **kwargs):
super()._prepare_for_export(**kwargs)
# Define input_types and output_types as required by export()
self._input_types = {
"text": NeuralType(('B', 'T_text'), TokenIndex()),
"pitch": NeuralType(('B', 'T_text'), RegressionValuesType()),
"pace": NeuralType(('B', 'T_text'), optional=True),
"volume": NeuralType(('B', 'T_text')),
"speaker": NeuralType(('B'), Index()),
}
self._output_types = {
"spect":
NeuralType(('B', 'D', 'T_spec'), MelSpectrogramType()),
"num_frames":
NeuralType(('B'), TokenDurationType()),
"durs_predicted":
NeuralType(('B', 'T_text'), TokenDurationType()),
"log_durs_predicted":
NeuralType(('B', 'T_text'), TokenLogDurationType()),
"pitch_predicted":
NeuralType(('B', 'T_text'), RegressionValuesType()),
"volume_aligned":
NeuralType(('B', 'T_spec'), RegressionValuesType()),
}
def input_types(self):
return {
"log_duration_pred": NeuralType(('B', 'T'),
TokenLogDurationType()),
"duration_target": NeuralType(('B', 'T'), TokenDurationType()),
"mask": NeuralType(('B', 'T', 'D'), MaskType()),
}
def output_types(self):
return {
"x_m": NeuralType(('B', 'D', 'T'), NormalDistributionMeanType()),
"x_logs": NeuralType(('B', 'D', 'T'), NormalDistributionLogVarianceType()),
"logw": NeuralType(('B', 'T'), TokenLogDurationType()),
"x_mask": NeuralType(('B', 'D', 'T'), MaskType()),
}
def input_types(self):
return {
"log_durs_predicted": NeuralType(('B', 'T'),
TokenLogDurationType()),
"durs_tgt": NeuralType(('B', 'T'), TokenDurationType()),
"len": NeuralType(('B'), LengthsType()),
}
def input_types(self):
return {
"log_durs_predicted": NeuralType(('B', 'T'),
TokenLogDurationType()),
"pitch_predicted": NeuralType(('B', 'T'), RegressionValuesType()),
"durs_tgt": NeuralType(('B', 'T'), TokenDurationType()),
"dur_lens": NeuralType(('B'), LengthsType()),
"pitch_tgt": NeuralType(('B', 'T'), RegressionValuesType()),
}
def output_types(self):
return {
"spect": NeuralType(('B', 'D', 'T'), MelSpectrogramType()),
"spect_lens": NeuralType(('B'), SequenceToSequenceAlignmentType()),
"spect_mask": NeuralType(('B', 'D', 'T'), MaskType()),
"durs_predicted": NeuralType(('B', 'T'), TokenDurationType()),
"log_durs_predicted": NeuralType(('B', 'T'),
TokenLogDurationType()),
"pitch_predicted": NeuralType(('B', 'T'), RegressionValuesType()),
}
def output_types(self):
return {
"spect": NeuralType(('B', 'D', 'T_spec'), MelSpectrogramType()),
"num_frames": NeuralType(('B'), TokenDurationType()),
"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()),
"pitch": NeuralType(('B', 'T_audio'), RegressionValuesType()),
}
class FastSpeech2HifiGanE2EModel(TextToWaveform):
"""An end-to-end speech synthesis model based on FastSpeech2 and HiFiGan that converts strings to audio without
using the intermediate mel spectrogram representation."""
def __init__(self, cfg: DictConfig, trainer: 'Trainer' = None):
if isinstance(cfg, dict):
cfg = OmegaConf.create(cfg)
super().__init__(cfg=cfg, trainer=trainer)
self.audio_to_melspec_precessor = instantiate(cfg.preprocessor)
self.encoder = instantiate(cfg.encoder)
self.variance_adapter = instantiate(cfg.variance_adaptor)
self.generator = instantiate(cfg.generator)
self.multiperioddisc = MultiPeriodDiscriminator()
self.multiscaledisc = MultiScaleDiscriminator()
self.melspec_fn = instantiate(cfg.preprocessor,
highfreq=None,
use_grads=True)
self.mel_val_loss = L1MelLoss()
self.durationloss = DurationLoss()
self.feat_matching_loss = FeatureMatchingLoss()
self.disc_loss = DiscriminatorLoss()
self.gen_loss = GeneratorLoss()
self.mseloss = torch.nn.MSELoss()
self.energy = cfg.add_energy_predictor
self.pitch = cfg.add_pitch_predictor
self.mel_loss_coeff = cfg.mel_loss_coeff
self.pitch_loss_coeff = cfg.pitch_loss_coeff
self.energy_loss_coeff = cfg.energy_loss_coeff
self.splice_length = cfg.splice_length
self.use_energy_pred = False
self.use_pitch_pred = False
self.log_train_images = False
self.logged_real_samples = False
self._tb_logger = None
self.sample_rate = cfg.sample_rate
self.hop_size = cfg.hop_size
# Parser and mappings are used for inference only.
self.parser = parsers.make_parser(name='en')
if 'mappings_filepath' in cfg:
mappings_filepath = cfg.get('mappings_filepath')
else:
logging.error(
"ERROR: You must specify a mappings.json file in the config file under model.mappings_filepath."
)
mappings_filepath = self.register_artifact('mappings_filepath',
mappings_filepath)
with open(mappings_filepath, 'r') as f:
mappings = json.load(f)
self.word2phones = mappings['word2phones']
self.phone2idx = mappings['phone2idx']
@property
def tb_logger(self):
if self._tb_logger is None:
if self.logger is None and self.logger.experiment is None:
return None
tb_logger = self.logger.experiment
if isinstance(self.logger, LoggerCollection):
for logger in self.logger:
if isinstance(logger, TensorBoardLogger):
tb_logger = logger.experiment
break
self._tb_logger = tb_logger
return self._tb_logger
def configure_optimizers(self):
gen_params = chain(
self.encoder.parameters(),
self.generator.parameters(),
self.variance_adapter.parameters(),
)
disc_params = chain(self.multiscaledisc.parameters(),
self.multiperioddisc.parameters())
opt1 = torch.optim.AdamW(disc_params, lr=self._cfg.lr)
opt2 = torch.optim.AdamW(gen_params, lr=self._cfg.lr)
num_procs = self._trainer.num_gpus * self._trainer.num_nodes
num_samples = len(self._train_dl.dataset)
batch_size = self._train_dl.batch_size
iter_per_epoch = np.ceil(num_samples / (num_procs * batch_size))
max_steps = iter_per_epoch * self._trainer.max_epochs
logging.info(f"MAX STEPS: {max_steps}")
sch1 = NoamAnnealing(opt1,
d_model=256,
warmup_steps=3000,
max_steps=max_steps,
min_lr=1e-5)
sch1_dict = {
'scheduler': sch1,
'interval': 'step',
}
sch2 = NoamAnnealing(opt2,
d_model=256,
warmup_steps=3000,
max_steps=max_steps,
min_lr=1e-5)
sch2_dict = {
'scheduler': sch2,
'interval': 'step',
}
return [opt1, opt2], [sch1_dict, sch2_dict]
@typecheck(
input_types={
"text":
NeuralType(('B', 'T'), TokenIndex()),
"text_length":
NeuralType(('B'), LengthsType()),
"splice":
NeuralType(optional=True),
"spec_len":
NeuralType(('B'), LengthsType(), optional=True),
"durations":
NeuralType(('B', 'T'), TokenDurationType(), optional=True),
"pitch":
NeuralType(('B', 'T'), RegressionValuesType(), optional=True),
"energies":
NeuralType(('B', 'T'), RegressionValuesType(), optional=True),
},
output_types={
"audio": NeuralType(('B', 'S', 'T'), MelSpectrogramType()),
"splices": NeuralType(),
"log_dur_preds": NeuralType(('B', 'T'), TokenLogDurationType()),
"pitch_preds": NeuralType(('B', 'T'), RegressionValuesType()),
"energy_preds": NeuralType(('B', 'T'), RegressionValuesType()),
"encoded_text_mask": NeuralType(('B', 'T', 'D'), MaskType()),
},
)
def forward(self,
*,
text,
text_length,
splice=True,
durations=None,
pitch=None,
energies=None,
spec_len=None):
encoded_text, encoded_text_mask = self.encoder(text=text,
text_length=text_length)
context, log_dur_preds, pitch_preds, energy_preds, spec_len = self.variance_adapter(
x=encoded_text,
x_len=text_length,
dur_target=durations,
pitch_target=pitch,
energy_target=energies,
spec_len=spec_len,
)
gen_in = context
splices = None
if splice:
# Splice generated spec
output = []
splices = []
for i, sample in enumerate(context):
start = np.random.randint(
low=0,
high=min(int(sample.size(0)), int(spec_len[i])) -
self.splice_length)
output.append(sample[start:start + self.splice_length, :])
splices.append(start)
gen_in = torch.stack(output)
output = self.generator(x=gen_in.transpose(1, 2))
return output, splices, log_dur_preds, pitch_preds, energy_preds, encoded_text_mask
def training_step(self, batch, batch_idx, optimizer_idx):
f, fl, t, tl, durations, pitch, energies = batch
spec, spec_len = self.audio_to_melspec_precessor(f, fl)
# train discriminator
if optimizer_idx == 0:
with torch.no_grad():
audio_pred, splices, _, _, _, _ = self(
spec=spec,
spec_len=spec_len,
text=t,
text_length=tl,
durations=durations,
pitch=pitch if not self.use_pitch_pred else None,
energies=energies if not self.use_energy_pred else None,
)
real_audio = []
for i, splice in enumerate(splices):
real_audio.append(
f[i, splice *
self.hop_size:(splice + self.splice_length) *
self.hop_size])
real_audio = torch.stack(real_audio).unsqueeze(1)
real_score_mp, gen_score_mp, _, _ = self.multiperioddisc(
real_audio, audio_pred)
real_score_ms, gen_score_ms, _, _ = self.multiscaledisc(
real_audio, audio_pred)
loss_mp, loss_mp_real, _ = self.disc_loss(real_score_mp,
gen_score_mp)
loss_ms, loss_ms_real, _ = self.disc_loss(real_score_ms,
gen_score_ms)
loss_mp /= len(loss_mp_real)
loss_ms /= len(loss_ms_real)
loss_disc = loss_mp + loss_ms
self.log("loss_discriminator", loss_disc, prog_bar=True)
self.log("loss_discriminator_ms", loss_ms)
self.log("loss_discriminator_mp", loss_mp)
return loss_disc
# train generator
elif optimizer_idx == 1:
audio_pred, splices, log_dur_preds, pitch_preds, energy_preds, encoded_text_mask = self(
spec=spec,
spec_len=spec_len,
text=t,
text_length=tl,
durations=durations,
pitch=pitch if not self.use_pitch_pred else None,
energies=energies if not self.use_energy_pred else None,
)
real_audio = []
for i, splice in enumerate(splices):
real_audio.append(
f[i, splice * self.hop_size:(splice + self.splice_length) *
self.hop_size])
real_audio = torch.stack(real_audio).unsqueeze(1)
# Do HiFiGAN generator loss
audio_length = torch.tensor([
self.splice_length * self.hop_size
for _ in range(real_audio.shape[0])
]).to(real_audio.device)
real_spliced_spec, _ = self.melspec_fn(real_audio.squeeze(),
seq_len=audio_length)
pred_spliced_spec, _ = self.melspec_fn(audio_pred.squeeze(),
seq_len=audio_length)
loss_mel = torch.nn.functional.l1_loss(real_spliced_spec,
pred_spliced_spec)
loss_mel *= self.mel_loss_coeff
_, gen_score_mp, real_feat_mp, gen_feat_mp = self.multiperioddisc(
real_audio, audio_pred)
_, gen_score_ms, real_feat_ms, gen_feat_ms = self.multiscaledisc(
real_audio, audio_pred)
loss_gen_mp, list_loss_gen_mp = self.gen_loss(gen_score_mp)
loss_gen_ms, list_loss_gen_ms = self.gen_loss(gen_score_ms)
loss_gen_mp /= len(list_loss_gen_mp)
loss_gen_ms /= len(list_loss_gen_ms)
total_loss = loss_gen_mp + loss_gen_ms + loss_mel
loss_feat_mp = self.feat_matching_loss(real_feat_mp, gen_feat_mp)
loss_feat_ms = self.feat_matching_loss(real_feat_ms, gen_feat_ms)
total_loss += loss_feat_mp + loss_feat_ms
self.log(name="loss_gen_disc_feat",
value=loss_feat_mp + loss_feat_ms)
self.log(name="loss_gen_disc_feat_ms", value=loss_feat_ms)
self.log(name="loss_gen_disc_feat_mp", value=loss_feat_mp)
self.log(name="loss_gen_mel", value=loss_mel)
self.log(name="loss_gen_disc", value=loss_gen_mp + loss_gen_ms)
self.log(name="loss_gen_disc_mp", value=loss_gen_mp)
self.log(name="loss_gen_disc_ms", value=loss_gen_ms)
dur_loss = self.durationloss(log_duration_pred=log_dur_preds,
duration_target=durations.float(),
mask=encoded_text_mask)
self.log(name="loss_gen_duration", value=dur_loss)
total_loss += dur_loss
if self.pitch:
pitch_loss = self.mseloss(
pitch_preds, pitch.float()) * self.pitch_loss_coeff
total_loss += pitch_loss
self.log(name="loss_gen_pitch", value=pitch_loss)
if self.energy:
energy_loss = self.mseloss(energy_preds,
energies) * self.energy_loss_coeff
total_loss += energy_loss
self.log(name="loss_gen_energy", value=energy_loss)
# Log images to tensorboard
if self.log_train_images:
self.log_train_images = False
if self.logger is not None and self.logger.experiment is not None:
self.tb_logger.add_image(
"train_mel_target",
plot_spectrogram_to_numpy(
real_spliced_spec[0].data.cpu().numpy()),
self.global_step,
dataformats="HWC",
)
spec_predict = pred_spliced_spec[0].data.cpu().numpy()
self.tb_logger.add_image(
"train_mel_predicted",
plot_spectrogram_to_numpy(spec_predict),
self.global_step,
dataformats="HWC",
)
self.log(name="loss_gen", prog_bar=True, value=total_loss)
return total_loss
def validation_step(self, batch, batch_idx):
f, fl, t, tl, _, _, _ = batch
spec, spec_len = self.audio_to_melspec_precessor(f, fl)
audio_pred, _, _, _, _, _ = self(spec=spec,
spec_len=spec_len,
text=t,
text_length=tl,
splice=False)
audio_pred.squeeze_()
pred_spec, _ = self.melspec_fn(audio_pred, seq_len=spec_len)
loss = self.mel_val_loss(spec_pred=pred_spec,
spec_target=spec,
spec_target_len=spec_len,
pad_value=-11.52)
return {
"val_loss": loss,
"audio_target": f.squeeze() if batch_idx == 0 else None,
"audio_pred": audio_pred if batch_idx == 0 else None,
}
def on_train_epoch_start(self):
# Switch to using energy predictions after 50% of training
if not self.use_energy_pred and self.current_epoch >= np.ceil(
0.5 * self._trainer.max_epochs):
logging.info(
f"Using energy predictions after epoch: {self.current_epoch}")
self.use_energy_pred = True
# Switch to using pitch predictions after 62.5% of training
if not self.use_pitch_pred and self.current_epoch >= np.ceil(
0.625 * self._trainer.max_epochs):
logging.info(
f"Using pitch predictions after epoch: {self.current_epoch}")
self.use_pitch_pred = True
def validation_epoch_end(self, outputs):
if self.tb_logger is not None:
_, audio_target, audio_predict = outputs[0].values()
if not self.logged_real_samples:
self.tb_logger.add_audio("val_target",
audio_target[0].data.cpu(),
self.global_step, self.sample_rate)
self.logged_real_samples = True
audio_predict = audio_predict[0].data.cpu()
self.tb_logger.add_audio("val_pred", audio_predict,
self.global_step, self.sample_rate)
avg_loss = torch.stack([
x['val_loss'] for x in outputs
]).mean() # This reduces across batches, not workers!
self.log('val_loss', avg_loss, sync_dist=True)
self.log_train_images = True
def __setup_dataloader_from_config(self,
cfg,
shuffle_should_be: bool = True,
name: str = "train"):
if "dataset" not in cfg or not isinstance(cfg.dataset, DictConfig):
raise ValueError(f"No dataset for {name}")
if "dataloader_params" not in cfg or not isinstance(
cfg.dataloader_params, DictConfig):
raise ValueError(f"No dataloder_params for {name}")
if shuffle_should_be:
if 'shuffle' not in cfg.dataloader_params:
logging.warning(
f"Shuffle should be set to True for {self}'s {name} dataloader but was not found in its "
"config. Manually setting to True")
with open_dict(cfg.dataloader_params):
cfg.dataloader_params.shuffle = True
elif not cfg.dataloader_params.shuffle:
logging.error(
f"The {name} dataloader for {self} has shuffle set to False!!!"
)
elif not shuffle_should_be and cfg.dataloader_params.shuffle:
logging.error(
f"The {name} dataloader for {self} has shuffle set to True!!!")
dataset = instantiate(cfg.dataset)
return torch.utils.data.DataLoader(dataset,
collate_fn=dataset.collate_fn,
**cfg.dataloader_params)
def setup_training_data(self, cfg):
self._train_dl = self.__setup_dataloader_from_config(cfg)
def setup_validation_data(self, cfg):
self._validation_dl = self.__setup_dataloader_from_config(
cfg, shuffle_should_be=False, name="validation")
def parse(self,
str_input: str,
additional_word2phones=None) -> torch.tensor:
"""
Parses text input and converts them to phoneme indices.
str_input (str): The input text to be converted.
additional_word2phones (dict): Optional dictionary mapping words to phonemes for updating the model's
word2phones. This will not overwrite the existing dictionary, just update it with OOV or new mappings.
Defaults to None, which will keep the existing mapping.
"""
# Update model's word2phones if applicable
if additional_word2phones is not None:
self.word2phones.update(additional_word2phones)
# Convert text -> normalized text -> list of phones per word -> indices
if str_input[-1] not in [".", "!", "?"]:
str_input = str_input + "."
norm_text = re.findall(r"""[\w']+|[.,!?;"]""",
self.parser._normalize(str_input))
try:
phones = [self.word2phones[t] for t in norm_text]
except KeyError as error:
logging.error(
f"ERROR: The following word in the input is not in the model's dictionary and could not be converted"
f" to phonemes: ({error}).\n"
f"You can pass in an `additional_word2phones` dictionary with a conversion for"
f" this word, e.g. {{'{error}': \['phone1', 'phone2', ...\]}} to update the model's mapping."
)
raise
tokens = []
for phone_list in phones:
inds = [self.phone2idx[p] for p in phone_list]
tokens += inds
x = torch.tensor(tokens).unsqueeze_(0).long().to(self.device)
return x
def convert_text_to_waveform(self, *, tokens):
"""
Accepts tokens returned from self.parse() and returns a list of tensors. Note: The tensors in the list can have
different lengths.
"""
self.eval()
token_len = torch.tensor([len(i) for i in tokens]).to(self.device)
audio, _, log_dur_pred, _, _, _ = self(text=tokens,
text_length=token_len,
splice=False)
audio = audio.squeeze(1)
durations = torch.sum(torch.exp(log_dur_pred) - 1, 1).to(torch.int)
audio_list = []
for i, sample in enumerate(audio):
audio_list.append(sample[:durations[i] * self.hop_size])
return audio_list
@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_e2e_fastspeech2hifigan",
location=
"https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_en_e2e_fastspeech2hifigan/versions/1.0.0/files/tts_en_e2e_fastspeech2hifigan.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,
)
list_of_models.append(model)
return list_of_models
class MixerTTSModel(SpectrogramGenerator, Exportable):
"""MixerTTS pipeline."""
def __init__(self, cfg: DictConfig, trainer: 'Trainer' = None):
super().__init__(cfg=cfg, trainer=trainer)
cfg = self._cfg
if "text_normalizer" in cfg.train_ds.dataset:
self.normalizer = instantiate(cfg.train_ds.dataset.text_normalizer)
self.text_normalizer_call = self.normalizer.normalize
self.text_normalizer_call_args = {}
if cfg.train_ds.dataset.get("text_normalizer_call_args",
None) is not None:
self.text_normalizer_call_args = cfg.train_ds.dataset.text_normalizer_call_args
self.tokenizer = instantiate(cfg.train_ds.dataset.text_tokenizer)
num_tokens = len(self.tokenizer.tokens)
self.tokenizer_pad = self.tokenizer.pad
self.tokenizer_unk = self.tokenizer.oov
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.train_ds.dataset.lm_model))
self.lm_embeddings = self._get_lm_embeddings(
cfg.train_ds.dataset.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 _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('<pad>')
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_pitch(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_pitch(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_pitch(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_pitch(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': train_log, '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_pitch(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,
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:
logging.error("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:
logging.error(
"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('<pad>')
lm_space_value = lm_model_tokenizer._convert_token_to_id('▁')
assert isinstance(self.tokenizer,
EnglishCharsTokenizer) or isinstance(
self.tokenizer, EnglishPhonemesTokenizer)
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 normalize and getattr(self, "text_normalizer_call",
None) is not None:
text = self.text_normalizer_call(text,
**self.text_normalizer_call_args)
return torch.tensor(
self.tokenizer.encode(text)).long().unsqueeze(0).to(self.device)
@staticmethod
def _loader(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)
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):
"""Empty."""
pass
@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 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)
class FastPitchHifiGanE2EModel(TextToWaveform):
"""An end-to-end speech synthesis model based on FastPitch and HiFiGan that converts strings to audio without using
the intermediate mel spectrogram representation.
"""
def __init__(self, cfg: DictConfig, trainer: Trainer = None):
if isinstance(cfg, dict):
cfg = OmegaConf.create(cfg)
self._parser = parsers.make_parser(
labels=cfg.labels,
name='en',
unk_id=-1,
blank_id=-1,
do_normalize=True,
abbreviation_version="fastpitch",
make_table=False,
)
super().__init__(cfg=cfg, trainer=trainer)
schema = OmegaConf.structured(FastPitchHifiGanE2EConfig)
# ModelPT ensures that cfg is a DictConfig, but do this second check in case ModelPT changes
if isinstance(cfg, dict):
cfg = OmegaConf.create(cfg)
elif not isinstance(cfg, DictConfig):
raise ValueError(
f"cfg was type: {type(cfg)}. Expected either a dict or a DictConfig"
)
# Ensure passed cfg is compliant with schema
OmegaConf.merge(cfg, schema)
self.preprocessor = instantiate(cfg.preprocessor)
self.melspec_fn = instantiate(cfg.preprocessor,
highfreq=None,
use_grads=True)
self.encoder = instantiate(cfg.input_fft)
self.duration_predictor = instantiate(cfg.duration_predictor)
self.pitch_predictor = instantiate(cfg.pitch_predictor)
self.generator = instantiate(cfg.generator)
self.multiperioddisc = MultiPeriodDiscriminator()
self.multiscaledisc = MultiScaleDiscriminator()
self.mel_val_loss = L1MelLoss()
self.feat_matching_loss = FeatureMatchingLoss()
self.disc_loss = DiscriminatorLoss()
self.gen_loss = GeneratorLoss()
self.max_token_duration = cfg.max_token_duration
self.pitch_emb = torch.nn.Conv1d(
1,
cfg.symbols_embedding_dim,
kernel_size=cfg.pitch_embedding_kernel_size,
padding=int((cfg.pitch_embedding_kernel_size - 1) / 2),
)
# Store values precomputed from training data for convenience
self.register_buffer('pitch_mean', torch.zeros(1))
self.register_buffer('pitch_std', torch.zeros(1))
self.pitchloss = PitchLoss()
self.durationloss = DurationLoss()
self.mel_loss_coeff = cfg.mel_loss_coeff
self.log_train_images = False
self.logged_real_samples = False
self._tb_logger = None
self.hann_window = None
self.splice_length = cfg.splice_length
self.sample_rate = cfg.sample_rate
self.hop_size = cfg.hop_size
@property
def tb_logger(self):
if self._tb_logger is None:
if self.logger is None and self.logger.experiment is None:
return None
tb_logger = self.logger.experiment
if isinstance(self.logger, LoggerCollection):
for logger in self.logger:
if isinstance(logger, TensorBoardLogger):
tb_logger = logger.experiment
break
self._tb_logger = tb_logger
return self._tb_logger
@property
def parser(self):
if self._parser is not None:
return self._parser
self._parser = parsers.make_parser(
labels=self._cfg.labels,
name='en',
unk_id=-1,
blank_id=-1,
do_normalize=True,
abbreviation_version="fastpitch",
make_table=False,
)
return self._parser
def parse(self, str_input: str) -> torch.tensor:
if str_input[-1] not in [".", "!", "?"]:
str_input = str_input + "."
tokens = self.parser(str_input)
x = torch.tensor(tokens).unsqueeze_(0).long().to(self.device)
return x
def configure_optimizers(self):
gen_params = chain(
self.pitch_emb.parameters(),
self.encoder.parameters(),
self.duration_predictor.parameters(),
self.pitch_predictor.parameters(),
self.generator.parameters(),
)
disc_params = chain(self.multiscaledisc.parameters(),
self.multiperioddisc.parameters())
opt1 = torch.optim.AdamW(disc_params, lr=self._cfg.lr)
opt2 = torch.optim.AdamW(gen_params, lr=self._cfg.lr)
num_procs = self._trainer.num_gpus * self._trainer.num_nodes
num_samples = len(self._train_dl.dataset)
batch_size = self._train_dl.batch_size
iter_per_epoch = np.ceil(num_samples / (num_procs * batch_size))
max_steps = iter_per_epoch * self._trainer.max_epochs
logging.info(f"MAX STEPS: {max_steps}")
sch1 = NoamAnnealing(opt1,
d_model=1,
warmup_steps=1000,
max_steps=max_steps,
last_epoch=-1)
sch1_dict = {
'scheduler': sch1,
'interval': 'step',
}
sch2 = NoamAnnealing(opt2,
d_model=1,
warmup_steps=1000,
max_steps=max_steps,
last_epoch=-1)
sch2_dict = {
'scheduler': sch2,
'interval': 'step',
}
return [opt1, opt2], [sch1_dict, sch2_dict]
@typecheck(
input_types={
"text": NeuralType(('B', 'T'), TokenIndex()),
"durs": NeuralType(('B', 'T'), TokenDurationType(), optional=True),
"pitch": NeuralType(('B', 'T'),
RegressionValuesType(),
optional=True),
"pace": NeuralType(optional=True),
"splice": NeuralType(optional=True),
},
output_types={
"audio": NeuralType(('B', 'S', 'T'), AudioSignal()),
"splices": NeuralType(),
"log_dur_preds": NeuralType(('B', 'T'), TokenLogDurationType()),
"pitch_preds": NeuralType(('B', 'T'), RegressionValuesType()),
},
)
def forward(self, *, text, durs=None, pitch=None, pace=1.0, splice=True):
if self.training:
assert durs is not None
assert pitch is not None
# Input FFT
enc_out, enc_mask = self.encoder(input=text, conditioning=0)
# Embedded for predictors
pred_enc_out, pred_enc_mask = enc_out, enc_mask
# Predict durations
log_durs_predicted = self.duration_predictor(pred_enc_out,
pred_enc_mask)
durs_predicted = torch.clamp(
torch.exp(log_durs_predicted) - 1, 0, self.max_token_duration)
# Predict pitch
pitch_predicted = self.pitch_predictor(enc_out, enc_mask)
if pitch is None:
pitch_emb = self.pitch_emb(pitch_predicted.unsqueeze(1))
else:
pitch_emb = self.pitch_emb(pitch.unsqueeze(1))
enc_out = enc_out + pitch_emb.transpose(1, 2)
if durs is None:
len_regulated, dec_lens = regulate_len(durs_predicted, enc_out,
pace)
else:
len_regulated, dec_lens = regulate_len(durs, enc_out, pace)
gen_in = len_regulated
splices = []
if splice:
output = []
for i, sample in enumerate(len_regulated):
start = np.random.randint(
low=0,
high=min(int(sample.size(0)), int(dec_lens[i])) -
self.splice_length)
# Splice generated spec
output.append(sample[start:start + self.splice_length, :])
splices.append(start)
gen_in = torch.stack(output)
output = self.generator(x=gen_in.transpose(1, 2))
return output, torch.tensor(
splices), log_durs_predicted, pitch_predicted
def training_step(self, batch, batch_idx, optimizer_idx):
audio, _, text, text_lens, durs, pitch, _ = batch
# train discriminator
if optimizer_idx == 0:
with torch.no_grad():
audio_pred, splices, _, _ = self(text=text,
durs=durs,
pitch=pitch)
real_audio = []
for i, splice in enumerate(splices):
real_audio.append(
audio[i, splice *
self.hop_size:(splice + self.splice_length) *
self.hop_size])
real_audio = torch.stack(real_audio).unsqueeze(1)
real_score_mp, gen_score_mp, _, _ = self.multiperioddisc(
real_audio, audio_pred)
real_score_ms, gen_score_ms, _, _ = self.multiscaledisc(
real_audio, audio_pred)
loss_mp, loss_mp_real, _ = self.disc_loss(
disc_real_outputs=real_score_mp,
disc_generated_outputs=gen_score_mp)
loss_ms, loss_ms_real, _ = self.disc_loss(
disc_real_outputs=real_score_ms,
disc_generated_outputs=gen_score_ms)
loss_mp /= len(loss_mp_real)
loss_ms /= len(loss_ms_real)
loss_disc = loss_mp + loss_ms
self.log("loss_discriminator", loss_disc, prog_bar=True)
self.log("loss_discriminator_ms", loss_ms)
self.log("loss_discriminator_mp", loss_mp)
return loss_disc
# train generator
elif optimizer_idx == 1:
audio_pred, splices, log_dur_preds, pitch_preds = self(text=text,
durs=durs,
pitch=pitch)
real_audio = []
for i, splice in enumerate(splices):
real_audio.append(
audio[i, splice *
self.hop_size:(splice + self.splice_length) *
self.hop_size])
real_audio = torch.stack(real_audio).unsqueeze(1)
dur_loss = self.durationloss(log_durs_predicted=log_dur_preds,
durs_tgt=durs,
len=text_lens)
pitch_loss = self.pitchloss(
pitch_predicted=pitch_preds,
pitch_tgt=pitch,
)
# Do HiFiGAN generator loss
audio_length = torch.tensor([
self.splice_length * self.hop_size
for _ in range(real_audio.shape[0])
]).to(real_audio.device)
real_spliced_spec, _ = self.melspec_fn(real_audio.squeeze(),
audio_length)
pred_spliced_spec, _ = self.melspec_fn(audio_pred.squeeze(),
audio_length)
loss_mel = torch.nn.functional.l1_loss(real_spliced_spec,
pred_spliced_spec)
loss_mel *= self.mel_loss_coeff
_, gen_score_mp, _, _ = self.multiperioddisc(
real_audio, audio_pred)
_, gen_score_ms, _, _ = self.multiscaledisc(y=real_audio,
y_hat=audio_pred)
loss_gen_mp, list_loss_gen_mp = self.gen_loss(
disc_outputs=gen_score_mp)
loss_gen_ms, list_loss_gen_ms = self.gen_loss(
disc_outputs=gen_score_ms)
loss_gen_mp /= len(list_loss_gen_mp)
loss_gen_ms /= len(list_loss_gen_ms)
total_loss = loss_gen_mp + loss_gen_ms + loss_mel
total_loss += dur_loss
total_loss += pitch_loss
self.log(name="loss_gen_mel", value=loss_mel)
self.log(name="loss_gen_disc", value=loss_gen_mp + loss_gen_ms)
self.log(name="loss_gen_disc_mp", value=loss_gen_mp)
self.log(name="loss_gen_disc_ms", value=loss_gen_ms)
self.log(name="loss_gen_duration", value=dur_loss)
self.log(name="loss_gen_pitch", value=pitch_loss)
# Log images to tensorboard
if self.log_train_images:
self.log_train_images = False
if self.logger is not None and self.logger.experiment is not None:
self.tb_logger.add_image(
"train_mel_target",
plot_spectrogram_to_numpy(
real_spliced_spec[0].data.cpu().numpy()),
self.global_step,
dataformats="HWC",
)
spec_predict = pred_spliced_spec[0].data.cpu().numpy()
self.tb_logger.add_image(
"train_mel_predicted",
plot_spectrogram_to_numpy(spec_predict),
self.global_step,
dataformats="HWC",
)
self.log(name="loss_gen", prog_bar=True, value=total_loss)
return total_loss
def validation_step(self, batch, batch_idx):
audio, audio_lens, text, _, _, _, _ = batch
mels, mel_lens = self.preprocessor(audio, audio_lens)
audio_pred, _, log_durs_predicted, _ = self(text=text,
durs=None,
pitch=None,
splice=False)
audio_length = torch.sum(torch.clamp(torch.exp(log_durs_predicted - 1),
0),
axis=1)
audio_pred.squeeze_()
pred_spec, _ = self.melspec_fn(audio_pred, audio_length)
loss = self.mel_val_loss(spec_pred=pred_spec,
spec_target=mels,
spec_target_len=mel_lens,
pad_value=-11.52,
transpose=False)
return {
"val_loss": loss,
"audio_target": audio if batch_idx == 0 else None,
"audio_pred": audio_pred.squeeze() if batch_idx == 0 else None,
}
def validation_epoch_end(self, outputs):
if self.tb_logger is not None:
_, audio_target, audio_predict = outputs[0].values()
if not self.logged_real_samples:
self.tb_logger.add_audio("val_target",
audio_target[0].data.cpu(),
self.global_step, self.sample_rate)
self.logged_real_samples = True
audio_predict = audio_predict[0].data.cpu()
self.tb_logger.add_audio("val_pred", audio_predict,
self.global_step, self.sample_rate)
avg_loss = torch.stack([
x['val_loss'] for x in outputs
]).mean() # This reduces across batches, not workers!
self.log('val_loss', avg_loss, sync_dist=True)
self.log_train_images = True
def _loader(self, cfg):
dataset = FastPitchDataset(
manifest_filepath=cfg['manifest_filepath'],
parser=self.parser,
sample_rate=cfg['sample_rate'],
int_values=cfg.get('int_values', False),
max_duration=cfg.get('max_duration', None),
min_duration=cfg.get('min_duration', None),
max_utts=cfg.get('max_utts', 0),
trim=cfg.get('trim_silence', True),
)
return torch.utils.data.DataLoader(
dataset=dataset,
batch_size=cfg['batch_size'],
collate_fn=dataset.collate_fn,
drop_last=cfg.get('drop_last', True),
shuffle=cfg['shuffle'],
num_workers=cfg.get('num_workers', 16),
)
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_e2e_fastpitchhifigan",
location=
"https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_en_e2e_fastpitchhifigan/versions/1.0.0/files/tts_en_e2e_fastpitchhifigan.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,
)
list_of_models.append(model)
return list_of_models
def convert_text_to_waveform(self, *, tokens):
"""
Accepts tokens returned from self.parse() and returns a list of tensors. Note: The tensors in the list can have
different lengths.
"""
self.eval()
audio, _, log_dur_pred, _ = self(text=tokens, splice=False)
audio = audio.squeeze(1)
durations = torch.sum(
torch.clamp(
torch.exp(log_dur_pred) - 1, 0, self.max_token_duration),
1).to(torch.int)
audio_list = []
for i, sample in enumerate(audio):
audio_list.append(sample[:durations[i] * self.hop_size])
return audio_list
