Fastspeech2

TTS/tts/configs/fast_pitch_config.py

@@ -100,6 +100,13 @@ class FastPitchConfig(BaseTTSConfig):
 
  max_seq_len (int):
  Maximum input sequence length to be used at training. Larger values result in more VRAM usage.
+
+ # dataset configs
+ compute_f0(bool):
+ Compute pitch. defaults to True
+
+ f0_cache_path(str):
+ pith cache path. defaults to None
  """
 
  model: str = "fast_pitch"

TTS/tts/configs/fastspeech2_config.py

@@ -0,0 +1,198 @@
+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
+
+
+@dataclass
+class Fastspeech2Config(BaseTTSConfig):
+ """Configure `ForwardTTS` as FastPitch model.
+
+ Example:
+
+ >>> from TTS.tts.configs.fastspeech2_config import FastSpeech2Config
+ >>> config = FastSpeech2Config()
+
+ 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 `FastPitchArgs` for more details. Defaults to `FastPitchArgs()`.
+
+ 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.
+
+ energy_loss_alpha (float):
+ Weight for the energy predictor's loss. If set 0, disables the energy predictor. Defaults to 1.0.
+
+ binary_align_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.
+
+ # dataset configs
+ compute_f0(bool):
+ Compute pitch. defaults to True
+
+ f0_cache_path(str):
+ pith cache path. defaults to None
+
+ # dataset configs
+ compute_energy(bool):
+ Compute energy. defaults to True
+
+ energy_cache_path(str):
+ energy cache path. defaults to None
+ """
+
+ model: str = "fastspeech2"
+ base_model: str = "forward_tts"
+
+ # model specific params
+ model_args: ForwardTTSArgs = ForwardTTSArgs()
+
+ # 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
+ spec_loss_alpha: float = 1.0
+ aligner_loss_alpha: float = 1.0
+ pitch_loss_alpha: float = 0.1
+ energy_loss_alpha: float = 0.1
+ dur_loss_alpha: float = 0.1
+ binary_align_loss_alpha: float = 0.1
+ 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 = True
+ f0_cache_path: str = None
+
+ # dataset configs
+ compute_energy: bool = True
+ energy_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

TTS/tts/configs/shared_configs.py

@@ -217,6 +217,9 @@ class BaseTTSConfig(BaseTrainingConfig):
  compute_f0 (int):
  (Not in use yet).
 
+ compute_energy (int):
+ (Not in use yet).
+
  compute_linear_spec (bool):
  If True data loader computes and returns linear spectrograms alongside the other data.
 
@@ -305,6 +308,7 @@ class BaseTTSConfig(BaseTrainingConfig):
  min_text_len: int = 1
  max_text_len: int = float("inf")
  compute_f0: bool = False
+ compute_energy: bool = False
  compute_linear_spec: bool = False
  precompute_num_workers: int = 0
  use_noise_augment: bool = False