103 lines
4.4 KiB
Python
103 lines
4.4 KiB
Python
from dataclasses import dataclass, field
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from TTS.vocoder.configs.shared_configs import BaseVocoderConfig
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from TTS.vocoder.models.wavernn import WavernnArgs
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@dataclass
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class WavernnConfig(BaseVocoderConfig):
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"""Defines parameters for Wavernn vocoder.
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Example:
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>>> from TTS.vocoder.configs import WavernnConfig
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>>> config = WavernnConfig()
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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 `wavernn`.
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mode (str):
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Output mode of the WaveRNN vocoder. `mold` for Mixture of Logistic Distribution, `gauss` for a single
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Gaussian Distribution and `bits` for quantized bits as the model's output.
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mulaw (bool):
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enable / disable the use of Mulaw quantization for training. Only applicable if `mode == 'bits'`. Defaults
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to `True`.
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generator_model (str):
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One of the generators from TTS.vocoder.models.*`. Every other non-GAN vocoder model is
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considered as a generator too. Defaults to `WaveRNN`.
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wavernn_model_params (dict):
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kwargs for the WaveRNN model. Defaults to
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`{
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"rnn_dims": 512,
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"fc_dims": 512,
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"compute_dims": 128,
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"res_out_dims": 128,
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"num_res_blocks": 10,
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"use_aux_net": True,
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"use_upsample_net": True,
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"upsample_factors": [4, 8, 8]
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}`
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batched (bool):
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enable / disable the batched inference. It speeds up the inference by splitting the input into segments and
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processing the segments in a batch. Then it merges the outputs with a certain overlap and smoothing. If
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you set it False, without CUDA, it is too slow to be practical. Defaults to True.
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target_samples (int):
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Size of the segments in batched mode. Defaults to 11000.
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overlap_sampels (int):
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Size of the overlap between consecutive segments. Defaults to 550.
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batch_size (int):
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Batch size used at training. Larger values use more memory. Defaults to 256.
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seq_len (int):
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Audio segment length used at training. Larger values use more memory. Defaults to 1280.
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use_noise_augment (bool):
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enable / disable random noise added to the input waveform. The noise is added after computing the
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features. Defaults to True.
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use_cache (bool):
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enable / disable in memory caching of the computed features. It can cause OOM error if the system RAM is
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not large enough. Defaults to True.
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mixed_precision (bool):
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enable / disable mixed precision training. Default is True.
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eval_split_size (int):
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Number of samples used for evalutaion. Defaults to 50.
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num_epochs_before_test (int):
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Number of epochs waited to run the next evalution. Since inference takes some time, it is better to
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wait some number of epochs not ot waste training time. Defaults to 10.
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grad_clip (float):
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Gradient clipping threshold. If <= 0.0, no clipping is applied. Defaults to 4.0
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lr (float):
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Initila leraning rate. Defaults to 1e-4.
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lr_scheduler (str):
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One of the learning rate schedulers from `torch.optim.scheduler.*`. Defaults to `MultiStepLR`.
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lr_scheduler_params (dict):
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kwargs for the scheduler. Defaults to `{"gamma": 0.5, "milestones": [200000, 400000, 600000]}`
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"""
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model: str = "wavernn"
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# Model specific params
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model_args: WavernnArgs = field(default_factory=WavernnArgs)
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target_loss: str = "loss"
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# Inference
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batched: bool = True
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target_samples: int = 11000
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overlap_samples: int = 550
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# Training - overrides
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epochs: int = 10000
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batch_size: int = 256
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seq_len: int = 1280
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use_noise_augment: bool = False
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use_cache: bool = True
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mixed_precision: bool = True
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eval_split_size: int = 50
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num_epochs_before_test: int = (
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10 # number of epochs to wait until the next test run (synthesizing a full audio clip).
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)
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# optimizer overrides
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grad_clip: float = 4.0
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lr: float = 1e-4 # Initial learning rate.
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lr_scheduler: str = "MultiStepLR" # one of the schedulers from https:#pytorch.org/docs/stable/optim.html
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lr_scheduler_params: dict = field(default_factory=lambda: {"gamma": 0.5, "milestones": [200000, 400000, 600000]})
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