mirror of https://github.com/coqui-ai/TTS.git
179 lines
6.6 KiB
Python
179 lines
6.6 KiB
Python
from dataclasses import dataclass, field
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from typing import List
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from TTS.tts.configs.shared_configs import BaseTTSConfig
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from TTS.tts.models.forward_tts_e2e import ForwardTTSE2eArgs
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@dataclass
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class FastPitchE2eConfig(BaseTTSConfig):
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"""Configure `ForwardTTSE2e` as FastPitchE2e model.
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Example:
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>>> from TTS.tts.configs.fast_pitch_e2e_config import FastPitchE2EConfig
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>>> config = FastPitchE2EConfig()
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Args:
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model (str):
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Model name used for selecting the right model at initialization. Defaults to `fast_pitch`.
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base_model (str):
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Name of the base model being configured as this model so that 🐸 TTS knows it needs to initiate
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the base model rather than searching for the `model` implementation. Defaults to `forward_tts`.
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model_args (Coqpit):
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Model class arguments. Check `FastPitchArgs` for more details. Defaults to `FastPitchArgs()`.
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data_dep_init_steps (int):
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Number of steps used for computing normalization parameters at the beginning of the training. GlowTTS uses
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Activation Normalization that pre-computes normalization stats at the beginning and use the same values
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for the rest. Defaults to 10.
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speakers_file (str):
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Path to the file containing the list of speakers. Needed at inference for loading matching speaker ids to
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speaker names. Defaults to `None`.
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use_speaker_embedding (bool):
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enable / disable using speaker embeddings for multi-speaker models. If set True, the model is
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in the multi-speaker mode. Defaults to False.
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use_d_vector_file (bool):
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enable /disable using external speaker embeddings in place of the learned embeddings. Defaults to False.
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d_vector_file (str):
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Path to the file including pre-computed speaker embeddings. Defaults to None.
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d_vector_dim (int):
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Dimension of the external speaker embeddings. Defaults to 0.
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optimizer (str):
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Name of the model optimizer. Defaults to `Adam`.
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optimizer_params (dict):
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Arguments of the model optimizer. Defaults to `{"betas": [0.9, 0.998], "weight_decay": 1e-6}`.
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lr_scheduler (str):
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Name of the learning rate scheduler. Defaults to `Noam`.
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lr_scheduler_params (dict):
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Arguments of the learning rate scheduler. Defaults to `{"warmup_steps": 4000}`.
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lr (float):
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Initial learning rate. Defaults to `1e-3`.
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grad_clip (float):
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Gradient norm clipping value. Defaults to `5.0`.
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spec_loss_type (str):
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Type of the spectrogram loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`.
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duration_loss_type (str):
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Type of the duration loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`.
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use_ssim_loss (bool):
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Enable/disable the use of SSIM (Structural Similarity) loss. Defaults to True.
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wd (float):
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Weight decay coefficient. Defaults to `1e-7`.
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ssim_loss_alpha (float):
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Weight for the SSIM loss. If set 0, disables the SSIM loss. Defaults to 1.0.
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dur_loss_alpha (float):
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Weight for the duration predictor's loss. If set 0, disables the huber loss. Defaults to 1.0.
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spec_loss_alpha (float):
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Weight for the L1 spectrogram loss. If set 0, disables the L1 loss. Defaults to 1.0.
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pitch_loss_alpha (float):
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Weight for the pitch predictor's loss. If set 0, disables the pitch predictor. Defaults to 1.0.
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binary_align_loss_alpha (float):
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Weight for the binary loss. If set 0, disables the binary loss. Defaults to 1.0.
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binary_loss_warmup_epochs (float):
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Number of epochs to gradually increase the binary loss impact. Defaults to 150.
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min_seq_len (int):
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Minimum input sequence length to be used at training.
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max_seq_len (int):
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Maximum input sequence length to be used at training. Larger values result in more VRAM usage.
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"""
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model: str = "fast_pitch_e2e_hifigan"
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base_model: str = "forward_tts_e2e"
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# model specific params
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# model_args: ForwardTTSE2eArgs = ForwardTTSE2eArgs(vocoder_config=HifiganConfig())
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model_args: ForwardTTSE2eArgs = ForwardTTSE2eArgs()
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# multi-speaker settings
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# num_speakers: int = 0
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# speakers_file: str = None
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# use_speaker_embedding: bool = False
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# use_d_vector_file: bool = False
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# d_vector_file: str = False
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# d_vector_dim: int = 0
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spec_segment_size: int = 30
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# optimizer
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grad_clip: List[float] = field(default_factory=lambda: [1000, 1000])
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lr_gen: float = 0.0002
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lr_disc: float = 0.0002
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lr_scheduler_gen: str = "ExponentialLR"
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lr_scheduler_gen_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1})
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lr_scheduler_disc: str = "ExponentialLR"
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lr_scheduler_disc_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1})
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scheduler_after_epoch: bool = True
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optimizer: str = "AdamW"
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optimizer_params: dict = field(default_factory=lambda: {"betas": [0.8, 0.99], "eps": 1e-9, "weight_decay": 0.01})
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# encoder loss params
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spec_loss_type: str = "mse"
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duration_loss_type: str = "mse"
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use_ssim_loss: bool = False
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ssim_loss_alpha: float = 0.0
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spec_loss_alpha: float = 1.0
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aligner_loss_alpha: float = 1.0
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pitch_loss_alpha: float = 1.0
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dur_loss_alpha: float = 1.0
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binary_align_loss_alpha: float = 0.1
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binary_loss_warmup_epochs: int = 150
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# dvocoder loss params
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disc_loss_alpha: float = 1.0
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gen_loss_alpha: float = 1.0
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feat_loss_alpha: float = 1.0
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mel_loss_alpha: float = 10.0
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multi_scale_stft_loss_alpha: float = 2.5
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multi_scale_stft_loss_params: dict = field(
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default_factory=lambda: {
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"n_ffts": [1024, 2048, 512],
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"hop_lengths": [120, 240, 50],
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"win_lengths": [600, 1200, 240],
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}
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)
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# data loader params
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return_wav: bool = True
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# overrides
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r: int = 1
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# dataset configs
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compute_f0: bool = True
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f0_cache_path: str = None
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# testing
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test_sentences: List[str] = field(
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default_factory=lambda: [
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"It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
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"Be a voice, not an echo.",
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"I'm sorry Dave. I'm afraid I can't do that.",
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"This cake is great. It's so delicious and moist.",
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"Prior to November 22, 1963.",
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]
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)
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