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Zeed< d Zeed!< d"Zeed#< d$S )%HifiganConfiga  Defines parameters for FullBand MelGAN vocoder.

    Example:

        >>> from TTS.vocoder.configs import HifiganConfig
        >>> config = HifiganConfig()

    Args:
        model (str):
            Model name used for selecting the right model at initialization. Defaults to `hifigan`.
        discriminator_model (str): One of the discriminators from `TTS.vocoder.models.*_discriminator`. Defaults to
            'hifigan_discriminator`.
        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 `hifigan_generator`.
        generator_model_params (dict): Parameters of the generator model. Defaults to
            `
            {
                "upsample_factors": [8, 8, 2, 2],
                "upsample_kernel_sizes": [16, 16, 4, 4],
                "upsample_initial_channel": 512,
                "resblock_kernel_sizes": [3, 7, 11],
                "resblock_dilation_sizes": [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
                "resblock_type": "1",
            }
            `
        batch_size (int):
            Batch size used at training. Larger values use more memory. Defaults to 16.
        seq_len (int):
            Audio segment length used at training. Larger values use more memory. Defaults to 8192.
        pad_short (int):
            Additional padding applied to the audio samples shorter than `seq_len`. Defaults to 0.
        use_noise_augment (bool):
            enable / disable random noise added to the input waveform. The noise is added after computing the
            features. Defaults to True.
        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.
        use_stft_loss (bool):
            enable / disable use of STFT loss originally used by ParallelWaveGAN model. Defaults to True.
        use_subband_stft (bool):
            enable / disable use of subband loss computation originally used by MultiBandMelgan model. Defaults to True.
        use_mse_gan_loss (bool):
            enable / disable using Mean Squeare Error GAN loss. Defaults to True.
        use_hinge_gan_loss (bool):
            enable / disable using Hinge GAN loss. You should choose either Hinge or MSE loss for training GAN models.
            Defaults to False.
        use_feat_match_loss (bool):
            enable / disable using Feature Matching loss originally used by MelGAN model. Defaults to True.
        use_l1_spec_loss (bool):
            enable / disable using L1 spectrogram loss originally used by HifiGAN model. Defaults to False.
        stft_loss_params (dict):
            STFT loss parameters. Default to
            `{
                "n_ffts": [1024, 2048, 512],
                "hop_lengths": [120, 240, 50],
                "win_lengths": [600, 1200, 240]
            }`
        l1_spec_loss_params (dict):
            L1 spectrogram loss parameters. Default to
            `{
                "use_mel": True,
                "sample_rate": 22050,
                "n_fft": 1024,
                "hop_length": 256,
                "win_length": 1024,
                "n_mels": 80,
                "mel_fmin": 0.0,
                "mel_fmax": None,
            }`
        stft_loss_weight (float): STFT loss weight that multiplies the computed loss before summing up the total
            model loss. Defaults to 0.5.
        subband_stft_loss_weight (float):
            Subband STFT loss weight that multiplies the computed loss before summing up the total loss. Defaults to 0.
        mse_G_loss_weight (float):
            MSE generator loss weight that multiplies the computed loss before summing up the total loss. faults to 2.5.
        hinge_G_loss_weight (float):
            Hinge generator loss weight that multiplies the computed loss before summing up the total loss. Defaults to 0.
        feat_match_loss_weight (float):
            Feature matching loss weight that multiplies the computed loss before summing up the total loss. faults to 108.
        l1_spec_loss_weight (float):
            L1 spectrogram loss weight that multiplies the computed loss before summing up the total loss. Defaults to 0.
    hifiganmodelhifigan_discriminatordiscriminator_modelhifigan_generatorgenerator_modelc                   C   s0   g dg ddg dg dg dg dgddS )N)   r      r   )   r      r   i   )         )   r      1)upsample_factorsupsample_kernel_sizesupsample_initial_channelresblock_kernel_sizesresblock_dilation_sizesresblock_type r   r   r   U/home/kuhnn/.local/lib/python3.10/site-packages/TTS/vocoder/configs/hifigan_config.py<lambda>`   s   zHifiganConfig.<lambda>)default_factorygenerator_model_paramsFuse_stft_lossuse_subband_stft_lossTuse_mse_gan_lossuse_hinge_gan_lossuse_feat_match_lossuse_l1_spec_lossr   stft_loss_weightsubband_stft_loss_weightr   mse_G_loss_weighthinge_G_loss_weightl   feat_match_loss_weight-   l1_spec_loss_weightc                	   C   s   dddddddd dS )NTi"V  i      P   g        )use_melsample_raten_fft
hop_length
win_lengthn_melsmel_fminmel_fmaxr   r   r   r   r   r   z   s   l1_spec_loss_paramsg-C6?lrgư>wdN)__name__
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