Source code for TTS.tts.configs.fast_speech_config

from dataclasses import dataclass, field
from typing import List

from TTS.tts.configs.shared_configs import BaseTTSConfig
from TTS.tts.models.forward_tts import ForwardTTSArgs

[docs]@dataclass class FastSpeechConfig(BaseTTSConfig): """Configure `ForwardTTS` as FastSpeech model. Example: >>> from TTS.tts.configs.fast_speech_config import FastSpeechConfig >>> config = FastSpeechConfig() Args: model (str): Model name used for selecting the right model at initialization. Defaults to `fast_pitch`. base_model (str): Name of the base model being configured as this model so that 🐸 TTS knows it needs to initiate the base model rather than searching for the `model` implementation. Defaults to `forward_tts`. model_args (Coqpit): Model class arguments. Check `FastSpeechArgs` for more details. Defaults to `FastSpeechArgs()`. data_dep_init_steps (int): Number of steps used for computing normalization parameters at the beginning of the training. GlowTTS uses Activation Normalization that pre-computes normalization stats at the beginning and use the same values for the rest. Defaults to 10. speakers_file (str): Path to the file containing the list of speakers. Needed at inference for loading matching speaker ids to speaker names. Defaults to `None`. use_speaker_embedding (bool): enable / disable using speaker embeddings for multi-speaker models. If set True, the model is in the multi-speaker mode. Defaults to False. use_d_vector_file (bool): enable /disable using external speaker embeddings in place of the learned embeddings. Defaults to False. d_vector_file (str): Path to the file including pre-computed speaker embeddings. Defaults to None. d_vector_dim (int): Dimension of the external speaker embeddings. Defaults to 0. optimizer (str): Name of the model optimizer. Defaults to `Adam`. optimizer_params (dict): Arguments of the model optimizer. Defaults to `{"betas": [0.9, 0.998], "weight_decay": 1e-6}`. lr_scheduler (str): Name of the learning rate scheduler. Defaults to `Noam`. lr_scheduler_params (dict): Arguments of the learning rate scheduler. Defaults to `{"warmup_steps": 4000}`. lr (float): Initial learning rate. Defaults to `1e-3`. grad_clip (float): Gradient norm clipping value. Defaults to `5.0`. spec_loss_type (str): Type of the spectrogram loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`. duration_loss_type (str): Type of the duration loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`. use_ssim_loss (bool): Enable/disable the use of SSIM (Structural Similarity) loss. Defaults to True. wd (float): Weight decay coefficient. Defaults to `1e-7`. ssim_loss_alpha (float): Weight for the SSIM loss. If set 0, disables the SSIM loss. Defaults to 1.0. dur_loss_alpha (float): Weight for the duration predictor's loss. If set 0, disables the huber loss. Defaults to 1.0. spec_loss_alpha (float): Weight for the L1 spectrogram loss. If set 0, disables the L1 loss. Defaults to 1.0. pitch_loss_alpha (float): Weight for the pitch predictor's loss. If set 0, disables the pitch predictor. Defaults to 1.0. binary_loss_alpha (float): Weight for the binary loss. If set 0, disables the binary loss. Defaults to 1.0. binary_loss_warmup_epochs (float): Number of epochs to gradually increase the binary loss impact. Defaults to 150. min_seq_len (int): Minimum input sequence length to be used at training. max_seq_len (int): Maximum input sequence length to be used at training. Larger values result in more VRAM usage. """ model: str = "fast_speech" base_model: str = "forward_tts" # model specific params model_args: ForwardTTSArgs = ForwardTTSArgs(use_pitch=False) # multi-speaker settings num_speakers: int = 0 speakers_file: str = None use_speaker_embedding: bool = False use_d_vector_file: bool = False d_vector_file: str = False d_vector_dim: int = 0 # optimizer parameters optimizer: str = "Adam" optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6}) lr_scheduler: str = "NoamLR" lr_scheduler_params: dict = field(default_factory=lambda: {"warmup_steps": 4000}) lr: float = 1e-4 grad_clip: float = 5.0 # loss params spec_loss_type: str = "mse" duration_loss_type: str = "mse" use_ssim_loss: bool = True ssim_loss_alpha: float = 1.0 dur_loss_alpha: float = 1.0 spec_loss_alpha: float = 1.0 pitch_loss_alpha: float = 0.0 aligner_loss_alpha: float = 1.0 binary_align_loss_alpha: float = 1.0 binary_loss_warmup_epochs: int = 150 # overrides min_seq_len: int = 13 max_seq_len: int = 200 r: int = 1 # DO NOT CHANGE # dataset configs compute_f0: bool = False f0_cache_path: str = None # testing test_sentences: List[str] = field( default_factory=lambda: [ "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.", "Be a voice, not an echo.", "I'm sorry Dave. I'm afraid I can't do that.", "This cake is great. It's so delicious and moist.", "Prior to November 22, 1963.", ] ) def __post_init__(self): # Pass multi-speaker parameters to the model args as `model.init_multispeaker()` looks for it there. if self.num_speakers > 0: self.model_args.num_speakers = self.num_speakers # speaker embedding settings if self.use_speaker_embedding: self.model_args.use_speaker_embedding = True if self.speakers_file: self.model_args.speakers_file = self.speakers_file # d-vector settings if self.use_d_vector_file: self.model_args.use_d_vector_file = True if self.d_vector_dim is not None and self.d_vector_dim > 0: self.model_args.d_vector_dim = self.d_vector_dim if self.d_vector_file: self.model_args.d_vector_file = self.d_vector_file