Move FreeVCConfig to TTS.vc.configs (like all other config classes)

TTS/vc/configs/freevc_config.py

@@ -1,5 +1,278 @@
 from dataclasses import dataclass, field
-from typing import List
+from typing import List, Optional
+
+from coqpit import Coqpit
 
 from TTS.vc.configs.shared_configs import BaseVCConfig
-from TTS.vc.models.freevc import FreeVCArgs, FreeVCAudioConfig, FreeVCConfig
+
+
+@dataclass
+class FreeVCAudioConfig(Coqpit):
+ """Audio configuration
+
+ Args:
+ max_wav_value (float):
+ The maximum value of the waveform.
+
+ input_sample_rate (int):
+ The sampling rate of the input waveform.
+
+ output_sample_rate (int):
+ The sampling rate of the output waveform.
+
+ filter_length (int):
+ The length of the filter.
+
+ hop_length (int):
+ The hop length.
+
+ win_length (int):
+ The window length.
+
+ n_mel_channels (int):
+ The number of mel channels.
+
+ mel_fmin (float):
+ The minimum frequency of the mel filterbank.
+
+ mel_fmax (Optional[float]):
+ The maximum frequency of the mel filterbank.
+ """
+
+ max_wav_value: float = field(default=32768.0)
+ input_sample_rate: int = field(default=16000)
+ output_sample_rate: int = field(default=24000)
+ filter_length: int = field(default=1280)
+ hop_length: int = field(default=320)
+ win_length: int = field(default=1280)
+ n_mel_channels: int = field(default=80)
+ mel_fmin: float = field(default=0.0)
+ mel_fmax: Optional[float] = field(default=None)
+
+
+@dataclass
+class FreeVCArgs(Coqpit):
+ """FreeVC model arguments
+
+ Args:
+ spec_channels (int):
+ The number of channels in the spectrogram.
+
+ inter_channels (int):
+ The number of channels in the intermediate layers.
+
+ hidden_channels (int):
+ The number of channels in the hidden layers.
+
+ filter_channels (int):
+ The number of channels in the filter layers.
+
+ n_heads (int):
+ The number of attention heads.
+
+ n_layers (int):
+ The number of layers.
+
+ kernel_size (int):
+ The size of the kernel.
+
+ p_dropout (float):
+ The dropout probability.
+
+ resblock (str):
+ The type of residual block.
+
+ resblock_kernel_sizes (List[int]):
+ The kernel sizes for the residual blocks.
+
+ resblock_dilation_sizes (List[List[int]]):
+ The dilation sizes for the residual blocks.
+
+ upsample_rates (List[int]):
+ The upsample rates.
+
+ upsample_initial_channel (int):
+ The number of channels in the initial upsample layer.
+
+ upsample_kernel_sizes (List[int]):
+ The kernel sizes for the upsample layers.
+
+ n_layers_q (int):
+ The number of layers in the quantization network.
+
+ use_spectral_norm (bool):
+ Whether to use spectral normalization.
+
+ gin_channels (int):
+ The number of channels in the global conditioning vector.
+
+ ssl_dim (int):
+ The dimension of the self-supervised learning embedding.
+
+ use_spk (bool):
+ Whether to use external speaker encoder.
+ """
+
+ spec_channels: int = field(default=641)
+ inter_channels: int = field(default=192)
+ hidden_channels: int = field(default=192)
+ filter_channels: int = field(default=768)
+ n_heads: int = field(default=2)
+ n_layers: int = field(default=6)
+ kernel_size: int = field(default=3)
+ p_dropout: float = field(default=0.1)
+ resblock: str = field(default="1")
+ resblock_kernel_sizes: List[int] = field(default_factory=lambda: [3, 7, 11])
+ resblock_dilation_sizes: List[List[int]] = field(default_factory=lambda: [[1, 3, 5], [1, 3, 5], [1, 3, 5]])
+ upsample_rates: List[int] = field(default_factory=lambda: [10, 8, 2, 2])
+ upsample_initial_channel: int = field(default=512)
+ upsample_kernel_sizes: List[int] = field(default_factory=lambda: [16, 16, 4, 4])
+ n_layers_q: int = field(default=3)
+ use_spectral_norm: bool = field(default=False)
+ gin_channels: int = field(default=256)
+ ssl_dim: int = field(default=1024)
+ use_spk: bool = field(default=False)
+ num_spks: int = field(default=0)
+ segment_size: int = field(default=8960)
+
+
+@dataclass
+class FreeVCConfig(BaseVCConfig):
+ """Defines parameters for FreeVC End2End TTS model.
+
+ Args:
+ model (str):
+ Model name. Do not change unless you know what you are doing.
+
+ model_args (FreeVCArgs):
+ Model architecture arguments. Defaults to `FreeVCArgs()`.
+
+ audio (FreeVCAudioConfig):
+ Audio processing configuration. Defaults to `FreeVCAudioConfig()`.
+
+ grad_clip (List):
+ Gradient clipping thresholds for each optimizer. Defaults to `[1000.0, 1000.0]`.
+
+ lr_gen (float):
+ Initial learning rate for the generator. Defaults to 0.0002.
+
+ lr_disc (float):
+ Initial learning rate for the discriminator. Defaults to 0.0002.
+
+ lr_scheduler_gen (str):
+ Name of the learning rate scheduler for the generator. One of the `torch.optim.lr_scheduler.*`. Defaults to
+ `ExponentialLR`.
+
+ lr_scheduler_gen_params (dict):
+ Parameters for the learning rate scheduler of the generator. Defaults to `{'gamma': 0.999875, "last_epoch":-1}`.
+
+ lr_scheduler_disc (str):
+ Name of the learning rate scheduler for the discriminator. One of the `torch.optim.lr_scheduler.*`. Defaults to
+ `ExponentialLR`.
+
+ lr_scheduler_disc_params (dict):
+ Parameters for the learning rate scheduler of the discriminator. Defaults to `{'gamma': 0.999875, "last_epoch":-1}`.
+
+ scheduler_after_epoch (bool):
+ If true, step the schedulers after each epoch else after each step. Defaults to `False`.
+
+ optimizer (str):
+ Name of the optimizer to use with both the generator and the discriminator networks. One of the
+ `torch.optim.*`. Defaults to `AdamW`.
+
+ kl_loss_alpha (float):
+ Loss weight for KL loss. Defaults to 1.0.
+
+ disc_loss_alpha (float):
+ Loss weight for the discriminator loss. Defaults to 1.0.
+
+ gen_loss_alpha (float):
+ Loss weight for the generator loss. Defaults to 1.0.
+
+ feat_loss_alpha (float):
+ Loss weight for the feature matching loss. Defaults to 1.0.
+
+ mel_loss_alpha (float):
+ Loss weight for the mel loss. Defaults to 45.0.
+
+ return_wav (bool):
+ If true, data loader returns the waveform as well as the other outputs. Do not change. Defaults to `True`.
+
+ compute_linear_spec (bool):
+ If true, the linear spectrogram is computed and returned alongside the mel output. Do not change. Defaults to `True`.
+
+ use_weighted_sampler (bool):
+ If true, use weighted sampler with bucketing for balancing samples between datasets used in training. Defaults to `False`.
+
+ weighted_sampler_attrs (dict):
+ Key retuned by the formatter to be used for weighted sampler. For example `{"root_path": 2.0, "speaker_name": 1.0}` sets sample probabilities
+ by overweighting `root_path` by 2.0. Defaults to `{}`.
+
+ weighted_sampler_multipliers (dict):
+ Weight each unique value of a key returned by the formatter for weighted sampling.
+ For example `{"root_path":{"/raid/datasets/libritts-clean-16khz-bwe-coqui_44khz/LibriTTS/train-clean-100/":1.0, "/raid/datasets/libritts-clean-16khz-bwe-coqui_44khz/LibriTTS/train-clean-360/": 0.5}`.
+ It will sample instances from `train-clean-100` 2 times more than `train-clean-360`. Defaults to `{}`.
+
+ r (int):
+ Number of spectrogram frames to be generated at a time. Do not change. Defaults to `1`.
+
+ add_blank (bool):
+ If true, a blank token is added in between every character. Defaults to `True`.
+
+ test_sentences (List[List]):
+ List of sentences with speaker and language information to be used for testing.
+
+ language_ids_file (str):
+ Path to the language ids file.
+
+ use_language_embedding (bool):
+ If true, language embedding is used. Defaults to `False`.
+
+ Note:
+ Check :class:`TTS.tts.configs.shared_configs.BaseTTSConfig` for the inherited parameters.
+
+ Example:
+
+ >>> from TTS.vc.configs.freevc_config import FreeVCConfig
+ >>> config = FreeVCConfig()
+ """
+
+ model: str = "freevc"
+ # model specific params
+ model_args: FreeVCArgs = field(default_factory=FreeVCArgs)
+ audio: FreeVCAudioConfig = field(default_factory=FreeVCAudioConfig)
+
+ # optimizer
+ # TODO with training support
+
+ # loss params
+ # TODO with training support
+
+ # data loader params
+ return_wav: bool = True
+ compute_linear_spec: bool = True
+
+ # sampler params
+ use_weighted_sampler: bool = False # TODO: move it to the base config
+ weighted_sampler_attrs: dict = field(default_factory=lambda: {})
+ weighted_sampler_multipliers: dict = field(default_factory=lambda: {})
+
+ # overrides
+ r: int = 1 # DO NOT CHANGE
+ add_blank: bool = True
+
+ # multi-speaker settings
+ # use speaker embedding layer
+ num_speakers: int = 0
+ speakers_file: str = None
+ speaker_embedding_channels: int = 256
+
+ # use d-vectors
+ use_d_vector_file: bool = False
+ d_vector_file: List[str] = None
+ d_vector_dim: int = None
+
+ def __post_init__(self):
+ for key, val in self.model_args.items():
+ if hasattr(self, key):
+ self[key] = val