mirror of https://github.com/coqui-ai/TTS.git
104 lines
4.3 KiB
Python
104 lines
4.3 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 GlowTTSConfig(BaseTTSConfig):
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"""Defines parameters for GlowTTS model.
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Example:
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>>> from TTS.tts.configs import GlowTTSConfig
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>>> config = GlowTTSConfig()
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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 `glow_tts`.
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encoder_type (str):
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Type of the encoder used by the model. Look at `TTS.tts.layers.glow_tts.encoder` for more details.
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Defaults to `rel_pos_transformers`.
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encoder_params (dict):
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Parameters used to define the encoder network. Look at `TTS.tts.layers.glow_tts.encoder` for more details.
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Defaults to `{"kernel_size": 3, "dropout_p": 0.1, "num_layers": 6, "num_heads": 2, "hidden_channels_ffn": 768}`
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use_encoder_prenet (bool):
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enable / disable the use of a prenet for the encoder. Defaults to True.
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hidden_channels_encoder (int):
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Number of base hidden channels used by the encoder network. It defines the input and the output channel sizes,
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and for some encoder types internal hidden channels sizes too. Defaults to 192.
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hidden_channels_decoder (int):
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Number of base hidden channels used by the decoder WaveNet network. Defaults to 192 as in the original work.
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hidden_channels_duration_predictor (int):
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Number of layer channels of the duration predictor network. Defaults to 256 as in the original work.
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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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style_wav_for_test (str):
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Path to the wav file used for changing the style of the speech. Defaults to None.
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inference_noise_scale (float):
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Variance used for sampling the random noise added to the decoder's input at inference. Defaults to 0.0.
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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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"""
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model: str = "glow_tts"
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# model params
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encoder_type: str = "rel_pos_transformer"
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encoder_params: dict = field(
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default_factory=lambda: {
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"kernel_size": 3,
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"dropout_p": 0.1,
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"num_layers": 6,
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"num_heads": 2,
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"hidden_channels_ffn": 768,
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}
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)
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use_encoder_prenet: bool = True
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hidden_channels_encoder: int = 192
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hidden_channels_decoder: int = 192
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hidden_channels_duration_predictor: int = 256
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# training params
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data_dep_init_steps: int = 10
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# inference params
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style_wav_for_test: str = None
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inference_noise_scale: float = 0.0
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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 params
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noam_schedule: bool = True
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warmup_steps: int = 4000
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grad_clip: float = 5.0
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lr: float = 1e-3
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wd: float = 0.000001
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# overrides
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min_seq_len: int = 3
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max_seq_len: int = 500
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r: int = 1 # DO NOT CHANGE - TODO: make this immutable once coqpit implements it.
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