mirror of https://github.com/coqui-ai/TTS.git
90 lines
3.5 KiB
Python
90 lines
3.5 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.speedy_speech import SpeedySpeechArgs
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@dataclass
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class SpeedySpeechConfig(BaseTTSConfig):
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"""Defines parameters for Speedy Speech (feed-forward encoder-decoder) based models.
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Example:
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>>> from TTS.tts.configs import SpeedySpeechConfig
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>>> config = SpeedySpeechConfig()
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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 `speedy_speech`.
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model_args (Coqpit):
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Model class arguments. Check `SpeedySpeechArgs` for more details. Defaults to `SpeedySpeechArgs()`.
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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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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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noam_schedule (bool):
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enable / disable the use of Noam LR scheduler. Defaults to False.
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warmup_steps (int):
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Number of warm-up steps for the Noam scheduler. Defaults 4000.
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lr (float):
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Initial learning rate. Defaults to `1e-3`.
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wd (float):
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Weight decay coefficient. Defaults to `1e-7`.
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ssim_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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huber_alpha (float):
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Weight for the duration predictor's loss. Defaults to 1.0.
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l1_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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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 = "speedy_speech"
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# model specific params
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model_args: SpeedySpeechArgs = field(default_factory=SpeedySpeechArgs)
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# multi-speaker settings
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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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# optimizer parameters
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optimizer: str = "RAdam"
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optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6})
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lr_scheduler: str = None
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lr_scheduler_params: dict = None
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lr: float = 1e-4
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grad_clip: float = 5.0
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# loss params
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ssim_alpha: float = 1.0
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huber_alpha: float = 1.0
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l1_alpha: float = 1.0
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# overrides
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min_seq_len: int = 13
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max_seq_len: int = 200
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r: int = 1 # DO NOT CHANGE
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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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