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)
# use a different melspec extractor because:
# 1. we need to pass grads
# 2. we need remove fmax limitation
self.trg_melspec_fn = instantiate(cfg.preprocessor,
highfreq=None,
use_grads=True)
self.generator = instantiate(cfg.generator)
self.mpd = MultiPeriodDiscriminator()
self.msd = MultiScaleDiscriminator()
self.feature_loss = FeatureMatchingLoss()
self.discriminator_loss = DiscriminatorLoss()
self.generator_loss = GeneratorLoss()
self.sample_rate = self._cfg.preprocessor.sample_rate
self.stft_bias = None
if isinstance(self._train_dl.dataset, MelAudioDataset):
self.finetune = True
logging.info("fine-tuning on pre-computed mels")
else:
self.finetune = False
logging.info("training on ground-truth mels")
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)
# use a different melspec extractor because:
# 1. we need to pass grads
# 2. we need remove fmax limitation
self.trg_melspec_fn = instantiate(cfg.preprocessor,
highfreq=None,
use_grads=True)
self.generator = instantiate(cfg.generator)
self.mpd = MultiPeriodDiscriminator()
self.msd = MultiScaleDiscriminator()
self.feature_loss = FeatureMatchingLoss()
self.discriminator_loss = DiscriminatorLoss()
self.generator_loss = GeneratorLoss()
self.l1_factor = cfg.get("l1_loss_factor", 45)
self.sample_rate = self._cfg.preprocessor.sample_rate
self.stft_bias = None
if self._train_dl and isinstance(self._train_dl.dataset,
MelAudioDataset):
self.input_as_mel = True
else:
self.input_as_mel = False
self.automatic_optimization = False
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)
super().__init__(cfg=cfg, trainer=trainer)
self.audio_to_melspec_precessor = instantiate(cfg.preprocessor)
# We use separate preprocessor for training, because we need to pass grads and remove pitch fmax limitation
self.trg_melspec_fn = instantiate(cfg.preprocessor, highfreq=None, use_grads=True)
self.generator = instantiate(cfg.generator)
self.mpd = MultiPeriodDiscriminator(debug=cfg.debug if "debug" in cfg else False)
self.msd = MultiScaleDiscriminator(debug=cfg.debug if "debug" in cfg else False)
self.feature_loss = FeatureMatchingLoss()
self.discriminator_loss = DiscriminatorLoss()
self.generator_loss = GeneratorLoss()
self.l1_factor = cfg.get("l1_loss_factor", 45)
self.sample_rate = self._cfg.preprocessor.sample_rate
self.stft_bias = None
self.input_as_mel = False
if self._train_dl:
# TODO(Oktai15): remove it in 1.8.0 version
if isinstance(self._train_dl.dataset, MelAudioDataset):
self.input_as_mel = True
elif isinstance(self._train_dl.dataset, VocoderDataset):
self.input_as_mel = self._train_dl.dataset.load_precomputed_mel
self.automatic_optimization = False
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']
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)
# use a different melspec extractor because:
# 1. we need to pass grads
# 2. we need remove fmax limitation
self.trg_melspec_fn = instantiate(cfg.preprocessor,
highfreq=None,
use_grads=True)
self.generator = instantiate(cfg.generator)
self.mpd = MultiPeriodDiscriminator()
self.msd = MultiScaleDiscriminator()
self.feature_loss = FeatureMatchingLoss()
self.discriminator_loss = DiscriminatorLoss()
self.generator_loss = GeneratorLoss()
self.sample_rate = self._cfg.preprocessor.sample_rate
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