108 lines
4.8 KiB
Python
108 lines
4.8 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.align_tts import AlignTTSArgs
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@dataclass
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class AlignTTSConfig(BaseTTSConfig):
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"""Defines parameters for AlignTTS model.
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Example:
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>>> from TTS.tts.configs.align_tts_config import AlignTTSConfig
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>>> config = AlignTTSConfig()
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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 `align_tts`.
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positional_encoding (bool):
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enable / disable positional encoding applied to the encoder output. Defaults to True.
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hidden_channels (int):
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Base number of hidden channels. Defines all the layers expect ones defined by the specific encoder or decoder
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parameters. Defaults to 256.
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hidden_channels_dp (int):
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Number of hidden channels of the duration predictor's layers. Defaults to 256.
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encoder_type (str):
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Type of the encoder used by the model. Look at `TTS.tts.layers.feed_forward.encoder` for more details.
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Defaults to `fftransformer`.
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encoder_params (dict):
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Parameters used to define the encoder network. Look at `TTS.tts.layers.feed_forward.encoder` for more details.
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Defaults to `{"hidden_channels_ffn": 1024, "num_heads": 2, "num_layers": 6, "dropout_p": 0.1}`.
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decoder_type (str):
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Type of the decoder used by the model. Look at `TTS.tts.layers.feed_forward.decoder` for more details.
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Defaults to `fftransformer`.
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decoder_params (dict):
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Parameters used to define the decoder network. Look at `TTS.tts.layers.feed_forward.decoder` for more details.
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Defaults to `{"hidden_channels_ffn": 1024, "num_heads": 2, "num_layers": 6, "dropout_p": 0.1}`.
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phase_start_steps (List[int]):
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A list of number of steps required to start the next training phase. AlignTTS has 4 different training
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phases. Thus you need to define 4 different values to enable phase based training. If None, it
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trains the whole model together. Defaults to None.
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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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duration_loss_alpha (float):
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Weight for the duration predictor's loss. Defaults to 1.0.
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mdn_alpha (float):
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Weight for the MDN loss. Defaults to 1.0.
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spec_loss_alpha (float):
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Weight for the MSE spectrogram loss. If set <= 0, disables the L1 loss. Defaults to 1.0.
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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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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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model: str = "align_tts"
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# model specific params
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model_args: AlignTTSArgs = field(default_factory=AlignTTSArgs)
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phase_start_steps: List[int] = None
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ssim_alpha: float = 1.0
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spec_loss_alpha: float = 1.0
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dur_loss_alpha: float = 1.0
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mdn_alpha: float = 1.0
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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 = "Adam"
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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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# 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
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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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