o j@s@ddlmZmZddlmZddlmZeGdddeZdS)) dataclassfield)BaseVocoderConfig) WavegradArgsc@seZdZUdZdZeed<dZeed<ee dZ e ed<dZ eed<d Z e ed <d Ze ed <d Ze ed<dZeed<dZeed<dZe ed<edddZeed<edddZeed<dZeed<dZeed<dZeed<edddZeed <d!S)"WavegradConfigap Defines parameters for WaveGrad vocoder. Example: >>> from TTS.vocoder.configs import WavegradConfig >>> config = WavegradConfig() Args: model (str): Model name used for selecting the right model at initialization. Defaults to `wavegrad`. generator_model (str): One of the generators from TTS.vocoder.models.*`. Every other non-GAN vocoder model is considered as a generator too. Defaults to `wavegrad`. model_params (WavegradArgs): Model parameters. Check `WavegradArgs` for default values. target_loss (str): Target loss name that defines the quality of the model. Defaults to `avg_wavegrad_loss`. epochs (int): Number of epochs to traing the model. Defaults to 10000. batch_size (int): Batch size used at training. Larger values use more memory. Defaults to 96. seq_len (int): Audio segment length used at training. Larger values use more memory. Defaults to 6144. use_cache (bool): enable / disable in memory caching of the computed features. It can cause OOM error if the system RAM is not large enough. Defaults to True. mixed_precision (bool): enable / disable mixed precision training. Default is True. eval_split_size (int): Number of samples used for evalutaion. Defaults to 50. train_noise_schedule (dict): Training noise schedule. Defaults to `{"min_val": 1e-6, "max_val": 1e-2, "num_steps": 1000}` test_noise_schedule (dict): Inference noise schedule. For a better performance, you may need to use `bin/tune_wavegrad.py` to find a better schedule. Defaults to ` { "min_val": 1e-6, "max_val": 1e-2, "num_steps": 50, } ` grad_clip (float): Gradient clipping threshold. If <= 0.0, no clipping is applied. Defaults to 1.0 lr (float): Initila leraning rate. Defaults to 1e-4. lr_scheduler (str): One of the learning rate schedulers from `torch.optim.scheduler.*`. Defaults to `MultiStepLR`. lr_scheduler_params (dict): kwargs for the scheduler. Defaults to `{"gamma": 0.5, "milestones": [100000, 200000, 300000, 400000, 500000, 600000]}` wavegradmodelgenerator_model)default_factory model_paramsloss target_lossi'epochs` batch_sizeiseq_lenT use_cachemixed_precision2eval_split_sizecC ddddS)Nư>{Gz?imin_valmax_val num_stepsrrrV/home/kuhnn/.local/lib/python3.10/site-packages/TTS/vocoder/configs/wavegrad_config.pyJs zWavegradConfig.train_noise_schedulecCr)NrrrrrrrrrrMstest_noise_scheduleg? grad_clipg-C6?lr MultiStepLR lr_schedulercCsdgddS)Ng?)ii@ iii i' )gamma milestonesrrrrrrYslr_scheduler_paramsN)__name__ __module__ __qualname____doc__rstr__annotations__r rrr r rintrrrboolrrr dictr!r"floatr#r%r(rrrrrs,  2          rN) dataclassesrr"TTS.vocoder.configs.shared_configsrTTS.vocoder.models.wavegradrrrrrrs