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