mirror of https://github.com/coqui-ai/TTS.git
104 lines
4.7 KiB
Python
104 lines
4.7 KiB
Python
from dataclasses import dataclass, field
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from TTS.tts.configs.shared_configs import BaseTTSConfig
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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 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_external_speaker_embedding_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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external_speaker_embedding_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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positional_encoding: bool = True
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hidden_channels_dp: int = 256
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hidden_channels: int = 256
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encoder_type: str = "fftransformer"
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encoder_params: dict = field(
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default_factory=lambda: {"hidden_channels_ffn": 1024, "num_heads": 2, "num_layers": 6, "dropout_p": 0.1}
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)
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decoder_type: str = "fftransformer"
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decoder_params: dict = field(
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default_factory=lambda: {"hidden_channels_ffn": 1024, "num_heads": 2, "num_layers": 6, "dropout_p": 0.1}
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)
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phase_start_steps: list = 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_external_speaker_embedding_file: bool = False
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external_speaker_embedding_file: str = False
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# optimizer parameters
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noam_schedule: bool = False
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warmup_steps: int = 4000
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lr: float = 1e-4
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wd: float = 1e-6
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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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