__set_seed1: !apply:random.seed [1986]
__set_seed2: !apply:numpy.random.seed [1986]
__set_seed3: !apply:torch.manual_seed [1986]
__set_seed4: !apply:torch.cuda.manual_seed_all [1986]

sample_rate: 22050
text_encoder_input_size: 512
llm_input_size: 1024
llm_output_size: 1024
spk_embed_dim: 192

llm: !new:viettts.llm.llm.TransformerLM
    text_encoder_input_size: !ref <text_encoder_input_size>
    llm_input_size: !ref <llm_input_size>
    llm_output_size: !ref <llm_output_size>
    text_token_size: 60515
    speech_token_size: 4096
    length_normalized_loss: True
    lsm_weight: 0
    spk_embed_dim: !ref <spk_embed_dim>
    text_encoder: !new:viettts.transformer.encoder.ConformerEncoder
        input_size: !ref <text_encoder_input_size>
        output_size: 1024
        attention_heads: 16
        linear_units: 4096
        num_blocks: 6
        dropout_rate: 0.1
        positional_dropout_rate: 0.1
        attention_dropout_rate: 0.0
        normalize_before: True
        input_layer: 'linear'
        pos_enc_layer_type: 'rel_pos_espnet'
        selfattention_layer_type: 'rel_selfattn'
        use_cnn_module: False
        macaron_style: False
        use_dynamic_chunk: False
        use_dynamic_left_chunk: False
        static_chunk_size: 1
    llm: !new:viettts.transformer.encoder.TransformerEncoder
        input_size: !ref <llm_input_size>
        output_size: !ref <llm_output_size>
        attention_heads: 16
        linear_units: 4096
        num_blocks: 14
        dropout_rate: 0.1
        positional_dropout_rate: 0.1
        attention_dropout_rate: 0.0
        input_layer: 'linear_legacy'
        pos_enc_layer_type: 'rel_pos_espnet'
        selfattention_layer_type: 'rel_selfattn'
        static_chunk_size: 1
    sampling: !name:viettts.utils.common.ras_sampling
        top_p: 0.8
        top_k: 25
        win_size: 10
        tau_r: 0.1

flow: !new:viettts.flow.flow.MaskedDiffWithXvec
    input_size: 512
    output_size: 80
    spk_embed_dim: !ref <spk_embed_dim>
    output_type: 'mel'
    vocab_size: 4096
    input_frame_rate: 25
    only_mask_loss: True
    encoder: !new:viettts.transformer.encoder.ConformerEncoder
        output_size: 512
        attention_heads: 8
        linear_units: 2048
        num_blocks: 6
        dropout_rate: 0.1
        positional_dropout_rate: 0.1
        attention_dropout_rate: 0.1
        normalize_before: True
        input_layer: 'linear'
        pos_enc_layer_type: 'rel_pos_espnet'
        selfattention_layer_type: 'rel_selfattn'
        input_size: 512
        use_cnn_module: False
        macaron_style: False
    length_regulator: !new:viettts.flow.length_regulator.InterpolateRegulator
        channels: 80
        sampling_ratios: [1, 1, 1, 1]
    decoder: !new:viettts.flow.flow_matching.ConditionalCFM
        in_channels: 240
        n_spks: 1
        spk_emb_dim: 80
        cfm_params: !new:omegaconf.DictConfig
            content:
                sigma_min: 1e-06
                solver: 'euler'
                t_scheduler: 'cosine'
                training_cfg_rate: 0.2
                inference_cfg_rate: 0.7
                reg_loss_type: 'l1'
        estimator: !new:viettts.flow.decoder.ConditionalDecoder
            in_channels: 320
            out_channels: 80
            channels: [256, 256]
            dropout: 0.0
            attention_head_dim: 64
            n_blocks: 4
            num_mid_blocks: 12
            num_heads: 8
            act_fn: 'gelu'

hift: !new:viettts.hifigan.generator.HiFTGenerator
    in_channels: 80
    base_channels: 512
    nb_harmonics: 8
    sampling_rate: !ref <sample_rate>
    nsf_alpha: 0.1
    nsf_sigma: 0.003
    nsf_voiced_threshold: 10
    upsample_rates: [8, 8]
    upsample_kernel_sizes: [16, 16]
    istft_params:
        n_fft: 16
        hop_len: 4
    resblock_kernel_sizes: [3, 7, 11]
    resblock_dilation_sizes: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]
    source_resblock_kernel_sizes: [7, 11]
    source_resblock_dilation_sizes: [[1, 3, 5], [1, 3, 5]]
    lrelu_slope: 0.1
    audio_limit: 0.99
    f0_predictor: !new:viettts.hifigan.f0_predictor.ConvRNNF0Predictor
        num_class: 1
        in_channels: 80
        cond_channels: 512