program(1.2)
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "5.33.4"}, {"coremlc-version", "1877.0.10.505.1"}}), mldb_token = string("mldb-7nwypnpa9o")]
{
    func main<ios15>(tensor<fp32, [1, 1, 480]> audio, tensor<fp16, [1, 1, 40]> cast_1702_in_state, tensor<fp16, [1, 256, 1]> input1_3_cast_in_state, tensor<fp16, [1, 128, 32]> input_107_cast_in_state, tensor<fp16, [1, 128, 64]> input_117_cast_in_state, tensor<fp16, [1, 128, 2]> input_127_cast_in_state, tensor<fp16, [1, 128, 4]> input_137_cast_in_state, tensor<fp16, [1, 128, 2]> input_13_cast_elementwise_in_state, tensor<fp16, [1, 128, 8]> input_147_cast_in_state, tensor<fp16, [1, 128, 16]> input_157_cast_in_state, tensor<fp16, [1, 128, 32]> input_167_cast_in_state, tensor<fp16, [1, 128, 64]> input_177_cast_in_state, tensor<fp16, [1, 128, 4]> input_17_cast_in_state, tensor<fp16, [1, 128, 2]> input_187_cast_in_state, tensor<fp16, [1, 128, 4]> input_197_cast_in_state, tensor<fp16, [1, 128, 8]> input_207_cast_in_state, tensor<fp16, [1, 128, 16]> input_217_cast_in_state, tensor<fp16, [1, 128, 32]> input_227_cast_in_state, tensor<fp16, [1, 128, 64]> input_237_cast_in_state, tensor<fp16, [1, 128, 4]> input_23_cast_elementwise_in_state, tensor<fp16, [1, 128, 2]> input_247_cast_in_state, tensor<fp16, [1, 128, 4]> input_257_cast_in_state, tensor<fp16, [1, 128, 8]> input_267_cast_in_state, tensor<fp16, [1, 128, 16]> input_277_cast_in_state, tensor<fp16, [1, 128, 8]> input_27_cast_in_state, tensor<fp16, [1, 128, 32]> input_287_cast_in_state, tensor<fp16, [1, 128, 64]> input_297_cast_in_state, tensor<fp16, [1, 128, 2]> input_307_cast_in_state, tensor<fp16, [1, 128, 4]> input_317_cast_in_state, tensor<fp16, [1, 128, 8]> input_327_cast_in_state, tensor<fp16, [1, 128, 16]> input_337_cast_in_state, tensor<fp16, [1, 128, 32]> input_347_cast_in_state, tensor<fp16, [1, 128, 64]> input_357_cast_in_state, tensor<fp16, [1, 128, 2]> input_367_cast_in_state, tensor<fp16, [1, 128, 4]> input_377_cast_in_state, tensor<fp16, [1, 128, 16]> input_37_cast_in_state, tensor<fp16, [1, 128, 8]> input_387_cast_in_state, tensor<fp16, [1, 128, 16]> input_397_cast_in_state, tensor<fp16, [1, 128, 32]> input_407_cast_in_state, tensor<fp16, [1, 128, 64]> input_417_cast_in_state, tensor<fp16, [1, 128, 2]> input_427_cast_in_state, tensor<fp16, [1, 128, 4]> input_437_cast_in_state, tensor<fp16, [1, 128, 8]> input_447_cast_in_state, tensor<fp16, [1, 128, 16]> input_457_cast_in_state, tensor<fp16, [1, 128, 32]> input_467_cast_in_state, tensor<fp16, [1, 128, 64]> input_477_cast_in_state, tensor<fp16, [1, 128, 32]> input_47_cast_in_state, tensor<fp16, [1, 128, 2]> input_487_cast_in_state, tensor<fp16, [1, 128, 4]> input_497_cast_in_state, tensor<fp16, [1, 128, 64]> input_4_cast_in_state, tensor<fp16, [1, 128, 8]> input_507_cast_in_state, tensor<fp16, [1, 128, 16]> input_517_cast_in_state, tensor<fp16, [1, 128, 32]> input_527_cast_in_state, tensor<fp16, [1, 128, 64]> input_537_cast_in_state, tensor<fp16, [1, 128, 2]> input_547_cast_in_state, tensor<fp16, [1, 128, 4]> input_557_cast_in_state, tensor<fp16, [1, 128, 8]> input_567_cast_in_state, tensor<fp16, [1, 128, 16]> input_577_cast_in_state, tensor<fp16, [1, 128, 64]> input_57_cast_in_state, tensor<fp16, [1, 128, 32]> input_587_cast_in_state, tensor<fp16, [1, 128, 1]> input_597_cast_elementwise_in_state, tensor<fp16, [1, 128, 2]> input_67_cast_in_state, tensor<fp16, [1, 128, 4]> input_77_cast_in_state, tensor<fp16, [1, 128, 2]> input_7_cast_in_state, tensor<fp16, [1, 128, 8]> input_87_cast_in_state, tensor<fp16, [1, 128, 16]> input_97_cast_in_state, tensor<fp16, [1, 1, 256, 7]> var_3624_cast_elementwise_in_state) [FlexibleShapeInformation = tuple<tuple<tensor<string, []>, dict<tensor<string, []>, tensor<int32, [?]>>>, tuple<tensor<string, []>, dict<tensor<string, []>, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"audio", [1, 1, 480]}}), ("RangeDims", {{"audio", [[1, 1], [1, 1], [480, 480]]}}))), UserMetadata = dict<tensor<string, []>, tensor<string, []>>({{"iteration", "1037686"}, {"taskid", "fcd5bwx4px"}})] {
            tensor<int32, []> var_17 = const()[val = tensor<int32, []>(1)];
            tensor<int32, [1]> var_21 = const()[val = tensor<int32, [1]>([40])];
            tensor<int32, [1]> var_23 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input0_1_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input0_1_pad_0 = const()[val = tensor<int32, [2]>([40, 40])];
            tensor<string, []> audio_to_fp16_dtype_0 = const()[val = tensor<string, []>("fp16")];
            tensor<fp16, [256, 1, 80]> front_end_0_weight_to_fp16 = const()[val = tensor<fp16, [256, 1, 80]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(64)))];
            tensor<fp16, [1, 1, 480]> cast_1702 = cast(dtype = audio_to_fp16_dtype_0, x = audio);
            tensor<fp16, [1, 1, 520]> cast_1702_expanded = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (cast_1702_in_state, cast_1702));
            tensor<fp16, [1, 1, 40]> cast_1702_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -40]), size = tensor<int32, [3]>([1, 1, 40]), x = cast_1702_expanded);
            tensor<fp16, [1, 256, 12]> input0_1_cast = conv(dilations = tensor<int32, [1]>([1]), groups = tensor<int32, []>(1), pad = tensor<int32, [2]>([0, 0]), pad_type = tensor<string, []>("custom"), strides = tensor<int32, [1]>([40]), weight = front_end_0_weight_to_fp16, x = cast_1702_expanded);
            tensor<fp16, [1, 256, 12]> var_26_cast = relu(x = input0_1_cast);
            tensor<bool, []> var_29 = const()[val = tensor<bool, []>(true)];
            tensor<int32, [1]> var_34 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_4_cast = reduce_mean(axes = var_34, keep_dims = var_29, x = var_26_cast);
            tensor<fp16, [1, 256, 12]> var_36_cast = sub(x = var_26_cast, y = mean_y_4_cast);
            tensor<fp16, [1, 256, 12]> var_37_cast = square(x = var_36_cast);
            tensor<int32, [1]> var_38 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_39_cast = reduce_mean(axes = var_38, keep_dims = var_29, x = var_37_cast);
            tensor<fp16, []> var_40_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_41_cast = add(x = var_39_cast, y = var_40_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_4_cast = sqrt(x = var_41_cast);
            tensor<fp16, [1, 256, 1]> front_norm_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(41088)))];
            tensor<fp16, [1, 256, 12]> var_44_cast = mul(x = front_norm_norm_gamma_to_fp16, y = var_36_cast);
            tensor<fp16, [1, 256, 12]> var_45_cast = real_div(x = var_44_cast, y = std_y_4_cast);
            tensor<fp16, [1, 256, 1]> front_norm_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(41664)))];
            tensor<fp16, [1, 256, 12]> input_593_cast = add(x = var_45_cast, y = front_norm_norm_beta_to_fp16);
            tensor<int32, []> var_48 = const()[val = tensor<int32, []>(1)];
            tensor<int32, [1]> var_53 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_55 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_597_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_597_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 256, 1]> to_latent_weight_to_fp16 = const()[val = tensor<fp16, [128, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(42240)))];
            tensor<fp16, [1, 128, 12]> input_597_cast = conv(dilations = var_55, groups = var_48, pad = input_597_pad_0, pad_type = input_597_pad_type_0, strides = var_53, weight = to_latent_weight_to_fp16, x = input_593_cast);
            tensor<int32, []> var_64 = const()[val = tensor<int32, []>(1)];
            tensor<int32, []> var_65 = const()[val = tensor<int32, []>(128)];
            tensor<bool, []> var_66 = const()[val = tensor<bool, []>(true)];
            tensor<int32, [1]> var_138 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_140 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_5_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_5_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_0_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(107840)))];
            tensor<fp16, [1, 128, 12]> input_5_cast = conv(dilations = var_140, groups = var_64, pad = input_5_pad_0, pad_type = input_5_pad_type_0, strides = var_138, weight = sep_module_0_tcn_0_weight_to_fp16, x = input_597_cast);
            tensor<fp16, []> var_144_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.4d4p-2)];
            tensor<fp16, [1, 128, 12]> var_144_cast = leaky_relu(alpha = var_144_alpha_1_to_fp16, x = input_5_cast);
            tensor<int32, [1]> var_148 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_3_cast = reduce_mean(axes = var_148, keep_dims = var_66, x = var_144_cast);
            tensor<fp16, [1, 128, 12]> var_150_cast = sub(x = var_144_cast, y = mean_y_3_cast);
            tensor<fp16, [1, 128, 12]> var_151_cast = square(x = var_150_cast);
            tensor<int32, [1]> var_152 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_153_cast = reduce_mean(axes = var_152, keep_dims = var_66, x = var_151_cast);
            tensor<fp16, []> var_154_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_155_cast = add(x = var_153_cast, y = var_154_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_3_cast = sqrt(x = var_155_cast);
            tensor<fp16, [1, 128, 1]> sep_module_0_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(140672)))];
            tensor<fp16, [1, 128, 12]> var_158_cast = mul(x = sep_module_0_tcn_2_norm_gamma_to_fp16, y = var_150_cast);
            tensor<fp16, [1, 128, 12]> var_159_cast = real_div(x = var_158_cast, y = std_y_3_cast);
            tensor<fp16, [1, 128, 1]> sep_module_0_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(140992)))];
            tensor<fp16, [1, 128, 12]> input_7_cast = add(x = var_159_cast, y = sep_module_0_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_9_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 1, 1])];
            tensor<string, []> input_9_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_9_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 14]> input_9_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_7_cast_in_state, input_7_cast));
            tensor<fp16, [1, 128, 2]> input_7_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -2]), size = tensor<int32, [3]>([1, 128, 2]), x = input_9_cast);
            tensor<int32, [1]> var_164 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_166 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_11_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_11_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_0_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(141312)))];
            tensor<fp16, [1, 128, 12]> input_11_cast = conv(dilations = var_166, groups = var_65, pad = input_11_pad_0, pad_type = input_11_pad_type_0, strides = var_164, weight = sep_module_0_tcn_4_weight_to_fp16, x = input_9_cast);
            tensor<fp16, []> var_170_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.54p-4)];
            tensor<fp16, [1, 128, 12]> var_170_cast = leaky_relu(alpha = var_170_alpha_1_to_fp16, x = input_11_cast);
            tensor<int32, [1]> var_174 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_5_cast = reduce_mean(axes = var_174, keep_dims = var_66, x = var_170_cast);
            tensor<fp16, [1, 128, 12]> var_176_cast = sub(x = var_170_cast, y = mean_y_5_cast);
            tensor<fp16, [1, 128, 12]> var_177_cast = square(x = var_176_cast);
            tensor<int32, [1]> var_178 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_179_cast = reduce_mean(axes = var_178, keep_dims = var_66, x = var_177_cast);
            tensor<fp16, []> var_180_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_181_cast = add(x = var_179_cast, y = var_180_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_5_cast = sqrt(x = var_181_cast);
            tensor<fp16, [1, 128, 1]> sep_module_0_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(142144)))];
            tensor<fp16, [1, 128, 12]> var_184_cast = mul(x = sep_module_0_tcn_6_norm_gamma_to_fp16, y = var_176_cast);
            tensor<fp16, [1, 128, 12]> var_185_cast = real_div(x = var_184_cast, y = std_y_5_cast);
            tensor<fp16, [1, 128, 1]> sep_module_0_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(142464)))];
            tensor<fp16, [1, 128, 12]> y_2_cast = add(x = var_185_cast, y = sep_module_0_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 13]> input_597_cast_elementwise_expanded = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_597_cast_elementwise_in_state, input_597_cast));
            tensor<fp16, [1, 128, 12]> input_597_cast_elementwise_delayed = slice_by_size(begin = tensor<int32, [3]>([0, 0, 0]), size = tensor<int32, [3]>([1, 128, 12]), x = input_597_cast_elementwise_expanded);
            tensor<fp16, [1, 128, 1]> input_597_cast_elementwise_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -1]), size = tensor<int32, [3]>([1, 128, 1]), x = input_597_cast_elementwise_expanded);
            tensor<fp16, [1, 128, 12]> input_13_cast = add(x = input_597_cast_elementwise_delayed, y = y_2_cast);
            tensor<int32, [1]> var_196 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_198 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_15_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_15_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_1_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(142784)))];
            tensor<fp16, [1, 128, 12]> input_15_cast = conv(dilations = var_198, groups = var_64, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = var_196, weight = sep_module_1_tcn_0_weight_to_fp16, x = input_13_cast);
            tensor<fp16, []> var_202_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.0f4p-1)];
            tensor<fp16, [1, 128, 12]> var_202_cast = leaky_relu(alpha = var_202_alpha_1_to_fp16, x = input_15_cast);
            tensor<int32, [1]> var_206 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_7_cast = reduce_mean(axes = var_206, keep_dims = var_66, x = var_202_cast);
            tensor<fp16, [1, 128, 12]> var_208_cast = sub(x = var_202_cast, y = mean_y_7_cast);
            tensor<fp16, [1, 128, 12]> var_209_cast = square(x = var_208_cast);
            tensor<int32, [1]> var_210 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_211_cast = reduce_mean(axes = var_210, keep_dims = var_66, x = var_209_cast);
            tensor<fp16, []> var_212_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_213_cast = add(x = var_211_cast, y = var_212_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_7_cast = sqrt(x = var_213_cast);
            tensor<fp16, [1, 128, 1]> sep_module_1_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(175616)))];
            tensor<fp16, [1, 128, 12]> var_216_cast = mul(x = sep_module_1_tcn_2_norm_gamma_to_fp16, y = var_208_cast);
            tensor<fp16, [1, 128, 12]> var_217_cast = real_div(x = var_216_cast, y = std_y_7_cast);
            tensor<fp16, [1, 128, 1]> sep_module_1_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(175936)))];
            tensor<fp16, [1, 128, 12]> input_17_cast = add(x = var_217_cast, y = sep_module_1_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_19_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 2, 2])];
            tensor<string, []> input_19_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_19_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 16]> input_19_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_17_cast_in_state, input_17_cast));
            tensor<fp16, [1, 128, 4]> input_17_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -4]), size = tensor<int32, [3]>([1, 128, 4]), x = input_19_cast);
            tensor<int32, [1]> var_222 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_224 = const()[val = tensor<int32, [1]>([2])];
            tensor<string, []> input_21_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_21_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_1_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(176256)))];
            tensor<fp16, [1, 128, 12]> input_21_cast = conv(dilations = var_224, groups = var_65, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = var_222, weight = sep_module_1_tcn_4_weight_to_fp16, x = input_19_cast);
            tensor<fp16, []> var_228_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.54p-3)];
            tensor<fp16, [1, 128, 12]> var_228_cast = leaky_relu(alpha = var_228_alpha_1_to_fp16, x = input_21_cast);
            tensor<int32, [1]> var_232 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_9_cast = reduce_mean(axes = var_232, keep_dims = var_66, x = var_228_cast);
            tensor<fp16, [1, 128, 12]> var_234_cast = sub(x = var_228_cast, y = mean_y_9_cast);
            tensor<fp16, [1, 128, 12]> var_235_cast = square(x = var_234_cast);
            tensor<int32, [1]> var_236 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_237_cast = reduce_mean(axes = var_236, keep_dims = var_66, x = var_235_cast);
            tensor<fp16, []> var_238_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_239_cast = add(x = var_237_cast, y = var_238_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_9_cast = sqrt(x = var_239_cast);
            tensor<fp16, [1, 128, 1]> sep_module_1_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(177088)))];
            tensor<fp16, [1, 128, 12]> var_242_cast = mul(x = sep_module_1_tcn_6_norm_gamma_to_fp16, y = var_234_cast);
            tensor<fp16, [1, 128, 12]> var_243_cast = real_div(x = var_242_cast, y = std_y_9_cast);
            tensor<fp16, [1, 128, 1]> sep_module_1_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(177408)))];
            tensor<fp16, [1, 128, 12]> y_4_cast = add(x = var_243_cast, y = sep_module_1_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 14]> input_13_cast_elementwise_expanded = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_13_cast_elementwise_in_state, input_13_cast));
            tensor<fp16, [1, 128, 12]> input_13_cast_elementwise_delayed = slice_by_size(begin = tensor<int32, [3]>([0, 0, 0]), size = tensor<int32, [3]>([1, 128, 12]), x = input_13_cast_elementwise_expanded);
            tensor<fp16, [1, 128, 2]> input_13_cast_elementwise_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -2]), size = tensor<int32, [3]>([1, 128, 2]), x = input_13_cast_elementwise_expanded);
            tensor<fp16, [1, 128, 12]> input_23_cast = add(x = input_13_cast_elementwise_delayed, y = y_4_cast);
            tensor<int32, [1]> var_254 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_256 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_25_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_25_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_2_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(177728)))];
            tensor<fp16, [1, 128, 12]> input_25_cast = conv(dilations = var_256, groups = var_64, pad = input_25_pad_0, pad_type = input_25_pad_type_0, strides = var_254, weight = sep_module_2_tcn_0_weight_to_fp16, x = input_23_cast);
            tensor<fp16, []> var_260_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.e9cp-1)];
            tensor<fp16, [1, 128, 12]> var_260_cast = leaky_relu(alpha = var_260_alpha_1_to_fp16, x = input_25_cast);
            tensor<int32, [1]> var_264 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_11_cast = reduce_mean(axes = var_264, keep_dims = var_66, x = var_260_cast);
            tensor<fp16, [1, 128, 12]> var_266_cast = sub(x = var_260_cast, y = mean_y_11_cast);
            tensor<fp16, [1, 128, 12]> var_267_cast = square(x = var_266_cast);
            tensor<int32, [1]> var_268 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_269_cast = reduce_mean(axes = var_268, keep_dims = var_66, x = var_267_cast);
            tensor<fp16, []> var_270_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_271_cast = add(x = var_269_cast, y = var_270_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_11_cast = sqrt(x = var_271_cast);
            tensor<fp16, [1, 128, 1]> sep_module_2_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(210560)))];
            tensor<fp16, [1, 128, 12]> var_274_cast = mul(x = sep_module_2_tcn_2_norm_gamma_to_fp16, y = var_266_cast);
            tensor<fp16, [1, 128, 12]> var_275_cast = real_div(x = var_274_cast, y = std_y_11_cast);
            tensor<fp16, [1, 128, 1]> sep_module_2_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(210880)))];
            tensor<fp16, [1, 128, 12]> input_27_cast = add(x = var_275_cast, y = sep_module_2_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_29_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_29_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_29_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 20]> input_29_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_27_cast_in_state, input_27_cast));
            tensor<fp16, [1, 128, 8]> input_27_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -8]), size = tensor<int32, [3]>([1, 128, 8]), x = input_29_cast);
            tensor<int32, [1]> var_280 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_282 = const()[val = tensor<int32, [1]>([4])];
            tensor<string, []> input_31_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_31_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_2_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(211200)))];
            tensor<fp16, [1, 128, 12]> input_31_cast = conv(dilations = var_282, groups = var_65, pad = input_31_pad_0, pad_type = input_31_pad_type_0, strides = var_280, weight = sep_module_2_tcn_4_weight_to_fp16, x = input_29_cast);
            tensor<fp16, []> var_286_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.ebcp-1)];
            tensor<fp16, [1, 128, 12]> var_286_cast = leaky_relu(alpha = var_286_alpha_1_to_fp16, x = input_31_cast);
            tensor<int32, [1]> var_290 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_13_cast = reduce_mean(axes = var_290, keep_dims = var_66, x = var_286_cast);
            tensor<fp16, [1, 128, 12]> var_292_cast = sub(x = var_286_cast, y = mean_y_13_cast);
            tensor<fp16, [1, 128, 12]> var_293_cast = square(x = var_292_cast);
            tensor<int32, [1]> var_294 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_295_cast = reduce_mean(axes = var_294, keep_dims = var_66, x = var_293_cast);
            tensor<fp16, []> var_296_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_297_cast = add(x = var_295_cast, y = var_296_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_13_cast = sqrt(x = var_297_cast);
            tensor<fp16, [1, 128, 1]> sep_module_2_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(212032)))];
            tensor<fp16, [1, 128, 12]> var_300_cast = mul(x = sep_module_2_tcn_6_norm_gamma_to_fp16, y = var_292_cast);
            tensor<fp16, [1, 128, 12]> var_301_cast = real_div(x = var_300_cast, y = std_y_13_cast);
            tensor<fp16, [1, 128, 1]> sep_module_2_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(212352)))];
            tensor<fp16, [1, 128, 12]> y_6_cast = add(x = var_301_cast, y = sep_module_2_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 16]> input_23_cast_elementwise_expanded = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_23_cast_elementwise_in_state, input_23_cast));
            tensor<fp16, [1, 128, 12]> input_23_cast_elementwise_delayed = slice_by_size(begin = tensor<int32, [3]>([0, 0, 0]), size = tensor<int32, [3]>([1, 128, 12]), x = input_23_cast_elementwise_expanded);
            tensor<fp16, [1, 128, 4]> input_23_cast_elementwise_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -4]), size = tensor<int32, [3]>([1, 128, 4]), x = input_23_cast_elementwise_expanded);
            tensor<fp16, [1, 128, 12]> input_33_cast = add(x = input_23_cast_elementwise_delayed, y = y_6_cast);
            tensor<int32, [1]> var_312 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_314 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_35_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_35_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_3_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(212672)))];
            tensor<fp16, [1, 128, 12]> input_35_cast = conv(dilations = var_314, groups = var_64, pad = input_35_pad_0, pad_type = input_35_pad_type_0, strides = var_312, weight = sep_module_3_tcn_0_weight_to_fp16, x = input_33_cast);
            tensor<fp16, []> var_318_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.658p-1)];
            tensor<fp16, [1, 128, 12]> var_318_cast = leaky_relu(alpha = var_318_alpha_1_to_fp16, x = input_35_cast);
            tensor<int32, [1]> var_322 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_15_cast = reduce_mean(axes = var_322, keep_dims = var_66, x = var_318_cast);
            tensor<fp16, [1, 128, 12]> var_324_cast = sub(x = var_318_cast, y = mean_y_15_cast);
            tensor<fp16, [1, 128, 12]> var_325_cast = square(x = var_324_cast);
            tensor<int32, [1]> var_326 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_327_cast = reduce_mean(axes = var_326, keep_dims = var_66, x = var_325_cast);
            tensor<fp16, []> var_328_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_329_cast = add(x = var_327_cast, y = var_328_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_15_cast = sqrt(x = var_329_cast);
            tensor<fp16, [1, 128, 1]> sep_module_3_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(245504)))];
            tensor<fp16, [1, 128, 12]> var_332_cast = mul(x = sep_module_3_tcn_2_norm_gamma_to_fp16, y = var_324_cast);
            tensor<fp16, [1, 128, 12]> var_333_cast = real_div(x = var_332_cast, y = std_y_15_cast);
            tensor<fp16, [1, 128, 1]> sep_module_3_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(245824)))];
            tensor<fp16, [1, 128, 12]> input_37_cast = add(x = var_333_cast, y = sep_module_3_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_39_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 16, 0])];
            tensor<string, []> input_39_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_39_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 28]> input_39_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_37_cast_in_state, input_37_cast));
            tensor<fp16, [1, 128, 16]> input_37_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -16]), size = tensor<int32, [3]>([1, 128, 16]), x = input_39_cast);
            tensor<int32, [1]> var_338 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_340 = const()[val = tensor<int32, [1]>([8])];
            tensor<string, []> input_41_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_41_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_3_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(246144)))];
            tensor<fp16, [1, 128, 12]> input_41_cast = conv(dilations = var_340, groups = var_65, pad = input_41_pad_0, pad_type = input_41_pad_type_0, strides = var_338, weight = sep_module_3_tcn_4_weight_to_fp16, x = input_39_cast);
            tensor<fp16, []> var_344_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.1f4p-3)];
            tensor<fp16, [1, 128, 12]> var_344_cast = leaky_relu(alpha = var_344_alpha_1_to_fp16, x = input_41_cast);
            tensor<int32, [1]> var_348 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_17_cast = reduce_mean(axes = var_348, keep_dims = var_66, x = var_344_cast);
            tensor<fp16, [1, 128, 12]> var_350_cast = sub(x = var_344_cast, y = mean_y_17_cast);
            tensor<fp16, [1, 128, 12]> var_351_cast = square(x = var_350_cast);
            tensor<int32, [1]> var_352 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_353_cast = reduce_mean(axes = var_352, keep_dims = var_66, x = var_351_cast);
            tensor<fp16, []> var_354_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_355_cast = add(x = var_353_cast, y = var_354_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_17_cast = sqrt(x = var_355_cast);
            tensor<fp16, [1, 128, 1]> sep_module_3_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(246976)))];
            tensor<fp16, [1, 128, 12]> var_358_cast = mul(x = sep_module_3_tcn_6_norm_gamma_to_fp16, y = var_350_cast);
            tensor<fp16, [1, 128, 12]> var_359_cast = real_div(x = var_358_cast, y = std_y_17_cast);
            tensor<fp16, [1, 128, 1]> sep_module_3_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(247296)))];
            tensor<fp16, [1, 128, 12]> y_8_cast = add(x = var_359_cast, y = sep_module_3_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_43_cast = add(x = input_33_cast, y = y_8_cast);
            tensor<int32, [1]> var_370 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_372 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_45_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_45_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_4_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(247616)))];
            tensor<fp16, [1, 128, 12]> input_45_cast = conv(dilations = var_372, groups = var_64, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = var_370, weight = sep_module_4_tcn_0_weight_to_fp16, x = input_43_cast);
            tensor<fp16, []> var_376_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.5d8p-1)];
            tensor<fp16, [1, 128, 12]> var_376_cast = leaky_relu(alpha = var_376_alpha_1_to_fp16, x = input_45_cast);
            tensor<int32, [1]> var_380 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_19_cast = reduce_mean(axes = var_380, keep_dims = var_66, x = var_376_cast);
            tensor<fp16, [1, 128, 12]> var_382_cast = sub(x = var_376_cast, y = mean_y_19_cast);
            tensor<fp16, [1, 128, 12]> var_383_cast = square(x = var_382_cast);
            tensor<int32, [1]> var_384 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_385_cast = reduce_mean(axes = var_384, keep_dims = var_66, x = var_383_cast);
            tensor<fp16, []> var_386_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_387_cast = add(x = var_385_cast, y = var_386_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_19_cast = sqrt(x = var_387_cast);
            tensor<fp16, [1, 128, 1]> sep_module_4_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(280448)))];
            tensor<fp16, [1, 128, 12]> var_390_cast = mul(x = sep_module_4_tcn_2_norm_gamma_to_fp16, y = var_382_cast);
            tensor<fp16, [1, 128, 12]> var_391_cast = real_div(x = var_390_cast, y = std_y_19_cast);
            tensor<fp16, [1, 128, 1]> sep_module_4_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(280768)))];
            tensor<fp16, [1, 128, 12]> input_47_cast = add(x = var_391_cast, y = sep_module_4_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_49_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 32, 0])];
            tensor<string, []> input_49_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_49_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 44]> input_49_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_47_cast_in_state, input_47_cast));
            tensor<fp16, [1, 128, 32]> input_47_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -32]), size = tensor<int32, [3]>([1, 128, 32]), x = input_49_cast);
            tensor<int32, [1]> var_396 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_398 = const()[val = tensor<int32, [1]>([16])];
            tensor<string, []> input_51_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_51_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_4_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(281088)))];
            tensor<fp16, [1, 128, 12]> input_51_cast = conv(dilations = var_398, groups = var_65, pad = input_51_pad_0, pad_type = input_51_pad_type_0, strides = var_396, weight = sep_module_4_tcn_4_weight_to_fp16, x = input_49_cast);
            tensor<fp16, []> var_402_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.8d4p-3)];
            tensor<fp16, [1, 128, 12]> var_402_cast = leaky_relu(alpha = var_402_alpha_1_to_fp16, x = input_51_cast);
            tensor<int32, [1]> var_406 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_21_cast = reduce_mean(axes = var_406, keep_dims = var_66, x = var_402_cast);
            tensor<fp16, [1, 128, 12]> var_408_cast = sub(x = var_402_cast, y = mean_y_21_cast);
            tensor<fp16, [1, 128, 12]> var_409_cast = square(x = var_408_cast);
            tensor<int32, [1]> var_410 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_411_cast = reduce_mean(axes = var_410, keep_dims = var_66, x = var_409_cast);
            tensor<fp16, []> var_412_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_413_cast = add(x = var_411_cast, y = var_412_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_21_cast = sqrt(x = var_413_cast);
            tensor<fp16, [1, 128, 1]> sep_module_4_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(281920)))];
            tensor<fp16, [1, 128, 12]> var_416_cast = mul(x = sep_module_4_tcn_6_norm_gamma_to_fp16, y = var_408_cast);
            tensor<fp16, [1, 128, 12]> var_417_cast = real_div(x = var_416_cast, y = std_y_21_cast);
            tensor<fp16, [1, 128, 1]> sep_module_4_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(282240)))];
            tensor<fp16, [1, 128, 12]> y_10_cast = add(x = var_417_cast, y = sep_module_4_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_53_cast = add(x = input_43_cast, y = y_10_cast);
            tensor<int32, [1]> var_428 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_430 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_55_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_55_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_5_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(282560)))];
            tensor<fp16, [1, 128, 12]> input_55_cast = conv(dilations = var_430, groups = var_64, pad = input_55_pad_0, pad_type = input_55_pad_type_0, strides = var_428, weight = sep_module_5_tcn_0_weight_to_fp16, x = input_53_cast);
            tensor<fp16, []> var_434_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.668p-1)];
            tensor<fp16, [1, 128, 12]> var_434_cast = leaky_relu(alpha = var_434_alpha_1_to_fp16, x = input_55_cast);
            tensor<int32, [1]> var_438 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_23_cast = reduce_mean(axes = var_438, keep_dims = var_66, x = var_434_cast);
            tensor<fp16, [1, 128, 12]> var_440_cast = sub(x = var_434_cast, y = mean_y_23_cast);
            tensor<fp16, [1, 128, 12]> var_441_cast = square(x = var_440_cast);
            tensor<int32, [1]> var_442 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_443_cast = reduce_mean(axes = var_442, keep_dims = var_66, x = var_441_cast);
            tensor<fp16, []> var_444_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_445_cast = add(x = var_443_cast, y = var_444_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_23_cast = sqrt(x = var_445_cast);
            tensor<fp16, [1, 128, 1]> sep_module_5_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(315392)))];
            tensor<fp16, [1, 128, 12]> var_448_cast = mul(x = sep_module_5_tcn_2_norm_gamma_to_fp16, y = var_440_cast);
            tensor<fp16, [1, 128, 12]> var_449_cast = real_div(x = var_448_cast, y = std_y_23_cast);
            tensor<fp16, [1, 128, 1]> sep_module_5_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(315712)))];
            tensor<fp16, [1, 128, 12]> input_57_cast = add(x = var_449_cast, y = sep_module_5_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_59_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 64, 0])];
            tensor<string, []> input_59_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_59_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 76]> input_59_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_57_cast_in_state, input_57_cast));
            tensor<fp16, [1, 128, 64]> input_57_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -64]), size = tensor<int32, [3]>([1, 128, 64]), x = input_59_cast);
            tensor<int32, [1]> var_454 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_456 = const()[val = tensor<int32, [1]>([32])];
            tensor<string, []> input_61_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_61_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_5_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(316032)))];
            tensor<fp16, [1, 128, 12]> input_61_cast = conv(dilations = var_456, groups = var_65, pad = input_61_pad_0, pad_type = input_61_pad_type_0, strides = var_454, weight = sep_module_5_tcn_4_weight_to_fp16, x = input_59_cast);
            tensor<fp16, []> var_460_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.7d4p-7)];
            tensor<fp16, [1, 128, 12]> var_460_cast = leaky_relu(alpha = var_460_alpha_1_to_fp16, x = input_61_cast);
            tensor<int32, [1]> var_464 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_25_cast = reduce_mean(axes = var_464, keep_dims = var_66, x = var_460_cast);
            tensor<fp16, [1, 128, 12]> var_466_cast = sub(x = var_460_cast, y = mean_y_25_cast);
            tensor<fp16, [1, 128, 12]> var_467_cast = square(x = var_466_cast);
            tensor<int32, [1]> var_468 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_469_cast = reduce_mean(axes = var_468, keep_dims = var_66, x = var_467_cast);
            tensor<fp16, []> var_470_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_471_cast = add(x = var_469_cast, y = var_470_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_25_cast = sqrt(x = var_471_cast);
            tensor<fp16, [1, 128, 1]> sep_module_5_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(316864)))];
            tensor<fp16, [1, 128, 12]> var_474_cast = mul(x = sep_module_5_tcn_6_norm_gamma_to_fp16, y = var_466_cast);
            tensor<fp16, [1, 128, 12]> var_475_cast = real_div(x = var_474_cast, y = std_y_25_cast);
            tensor<fp16, [1, 128, 1]> sep_module_5_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(317184)))];
            tensor<fp16, [1, 128, 12]> y_12_cast = add(x = var_475_cast, y = sep_module_5_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_63_cast = add(x = input_53_cast, y = y_12_cast);
            tensor<int32, [1]> var_486 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_488 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_65_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_65_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_6_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(317504)))];
            tensor<fp16, [1, 128, 12]> input_65_cast = conv(dilations = var_488, groups = var_64, pad = input_65_pad_0, pad_type = input_65_pad_type_0, strides = var_486, weight = sep_module_6_tcn_0_weight_to_fp16, x = input_63_cast);
            tensor<fp16, []> var_492_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.48p-1)];
            tensor<fp16, [1, 128, 12]> var_492_cast = leaky_relu(alpha = var_492_alpha_1_to_fp16, x = input_65_cast);
            tensor<int32, [1]> var_496 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_27_cast = reduce_mean(axes = var_496, keep_dims = var_66, x = var_492_cast);
            tensor<fp16, [1, 128, 12]> var_498_cast = sub(x = var_492_cast, y = mean_y_27_cast);
            tensor<fp16, [1, 128, 12]> var_499_cast = square(x = var_498_cast);
            tensor<int32, [1]> var_500 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_501_cast = reduce_mean(axes = var_500, keep_dims = var_66, x = var_499_cast);
            tensor<fp16, []> var_502_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_503_cast = add(x = var_501_cast, y = var_502_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_27_cast = sqrt(x = var_503_cast);
            tensor<fp16, [1, 128, 1]> sep_module_6_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(350336)))];
            tensor<fp16, [1, 128, 12]> var_506_cast = mul(x = sep_module_6_tcn_2_norm_gamma_to_fp16, y = var_498_cast);
            tensor<fp16, [1, 128, 12]> var_507_cast = real_div(x = var_506_cast, y = std_y_27_cast);
            tensor<fp16, [1, 128, 1]> sep_module_6_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(350656)))];
            tensor<fp16, [1, 128, 12]> input_67_cast = add(x = var_507_cast, y = sep_module_6_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_69_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 2, 0])];
            tensor<string, []> input_69_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_69_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 14]> input_69_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_67_cast_in_state, input_67_cast));
            tensor<fp16, [1, 128, 2]> input_67_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -2]), size = tensor<int32, [3]>([1, 128, 2]), x = input_69_cast);
            tensor<int32, [1]> var_512 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_514 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_71_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_71_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_6_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(350976)))];
            tensor<fp16, [1, 128, 12]> input_71_cast = conv(dilations = var_514, groups = var_65, pad = input_71_pad_0, pad_type = input_71_pad_type_0, strides = var_512, weight = sep_module_6_tcn_4_weight_to_fp16, x = input_69_cast);
            tensor<fp16, []> var_518_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.d44p-1)];
            tensor<fp16, [1, 128, 12]> var_518_cast = leaky_relu(alpha = var_518_alpha_1_to_fp16, x = input_71_cast);
            tensor<int32, [1]> var_522 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_29_cast = reduce_mean(axes = var_522, keep_dims = var_66, x = var_518_cast);
            tensor<fp16, [1, 128, 12]> var_524_cast = sub(x = var_518_cast, y = mean_y_29_cast);
            tensor<fp16, [1, 128, 12]> var_525_cast = square(x = var_524_cast);
            tensor<int32, [1]> var_526 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_527_cast = reduce_mean(axes = var_526, keep_dims = var_66, x = var_525_cast);
            tensor<fp16, []> var_528_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_529_cast = add(x = var_527_cast, y = var_528_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_29_cast = sqrt(x = var_529_cast);
            tensor<fp16, [1, 128, 1]> sep_module_6_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(351808)))];
            tensor<fp16, [1, 128, 12]> var_532_cast = mul(x = sep_module_6_tcn_6_norm_gamma_to_fp16, y = var_524_cast);
            tensor<fp16, [1, 128, 12]> var_533_cast = real_div(x = var_532_cast, y = std_y_29_cast);
            tensor<fp16, [1, 128, 1]> sep_module_6_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(352128)))];
            tensor<fp16, [1, 128, 12]> y_14_cast = add(x = var_533_cast, y = sep_module_6_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_73_cast = add(x = input_63_cast, y = y_14_cast);
            tensor<int32, [1]> var_544 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_546 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_75_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_75_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_7_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(352448)))];
            tensor<fp16, [1, 128, 12]> input_75_cast = conv(dilations = var_546, groups = var_64, pad = input_75_pad_0, pad_type = input_75_pad_type_0, strides = var_544, weight = sep_module_7_tcn_0_weight_to_fp16, x = input_73_cast);
            tensor<fp16, []> var_550_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.74cp-4)];
            tensor<fp16, [1, 128, 12]> var_550_cast = leaky_relu(alpha = var_550_alpha_1_to_fp16, x = input_75_cast);
            tensor<int32, [1]> var_554 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_31_cast = reduce_mean(axes = var_554, keep_dims = var_66, x = var_550_cast);
            tensor<fp16, [1, 128, 12]> var_556_cast = sub(x = var_550_cast, y = mean_y_31_cast);
            tensor<fp16, [1, 128, 12]> var_557_cast = square(x = var_556_cast);
            tensor<int32, [1]> var_558 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_559_cast = reduce_mean(axes = var_558, keep_dims = var_66, x = var_557_cast);
            tensor<fp16, []> var_560_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_561_cast = add(x = var_559_cast, y = var_560_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_31_cast = sqrt(x = var_561_cast);
            tensor<fp16, [1, 128, 1]> sep_module_7_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(385280)))];
            tensor<fp16, [1, 128, 12]> var_564_cast = mul(x = sep_module_7_tcn_2_norm_gamma_to_fp16, y = var_556_cast);
            tensor<fp16, [1, 128, 12]> var_565_cast = real_div(x = var_564_cast, y = std_y_31_cast);
            tensor<fp16, [1, 128, 1]> sep_module_7_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(385600)))];
            tensor<fp16, [1, 128, 12]> input_77_cast = add(x = var_565_cast, y = sep_module_7_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_79_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 4, 0])];
            tensor<string, []> input_79_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_79_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 16]> input_79_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_77_cast_in_state, input_77_cast));
            tensor<fp16, [1, 128, 4]> input_77_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -4]), size = tensor<int32, [3]>([1, 128, 4]), x = input_79_cast);
            tensor<int32, [1]> var_570 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_572 = const()[val = tensor<int32, [1]>([2])];
            tensor<string, []> input_81_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_81_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_7_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(385920)))];
            tensor<fp16, [1, 128, 12]> input_81_cast = conv(dilations = var_572, groups = var_65, pad = input_81_pad_0, pad_type = input_81_pad_type_0, strides = var_570, weight = sep_module_7_tcn_4_weight_to_fp16, x = input_79_cast);
            tensor<fp16, []> var_576_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.b5p-1)];
            tensor<fp16, [1, 128, 12]> var_576_cast = leaky_relu(alpha = var_576_alpha_1_to_fp16, x = input_81_cast);
            tensor<int32, [1]> var_580 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_33_cast = reduce_mean(axes = var_580, keep_dims = var_66, x = var_576_cast);
            tensor<fp16, [1, 128, 12]> var_582_cast = sub(x = var_576_cast, y = mean_y_33_cast);
            tensor<fp16, [1, 128, 12]> var_583_cast = square(x = var_582_cast);
            tensor<int32, [1]> var_584 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_585_cast = reduce_mean(axes = var_584, keep_dims = var_66, x = var_583_cast);
            tensor<fp16, []> var_586_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_587_cast = add(x = var_585_cast, y = var_586_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_33_cast = sqrt(x = var_587_cast);
            tensor<fp16, [1, 128, 1]> sep_module_7_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(386752)))];
            tensor<fp16, [1, 128, 12]> var_590_cast = mul(x = sep_module_7_tcn_6_norm_gamma_to_fp16, y = var_582_cast);
            tensor<fp16, [1, 128, 12]> var_591_cast = real_div(x = var_590_cast, y = std_y_33_cast);
            tensor<fp16, [1, 128, 1]> sep_module_7_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(387072)))];
            tensor<fp16, [1, 128, 12]> y_16_cast = add(x = var_591_cast, y = sep_module_7_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_83_cast = add(x = input_73_cast, y = y_16_cast);
            tensor<int32, [1]> var_602 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_604 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_85_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_85_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_8_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(387392)))];
            tensor<fp16, [1, 128, 12]> input_85_cast = conv(dilations = var_604, groups = var_64, pad = input_85_pad_0, pad_type = input_85_pad_type_0, strides = var_602, weight = sep_module_8_tcn_0_weight_to_fp16, x = input_83_cast);
            tensor<fp16, []> var_608_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.284p-1)];
            tensor<fp16, [1, 128, 12]> var_608_cast = leaky_relu(alpha = var_608_alpha_1_to_fp16, x = input_85_cast);
            tensor<int32, [1]> var_612 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_35_cast = reduce_mean(axes = var_612, keep_dims = var_66, x = var_608_cast);
            tensor<fp16, [1, 128, 12]> var_614_cast = sub(x = var_608_cast, y = mean_y_35_cast);
            tensor<fp16, [1, 128, 12]> var_615_cast = square(x = var_614_cast);
            tensor<int32, [1]> var_616 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_617_cast = reduce_mean(axes = var_616, keep_dims = var_66, x = var_615_cast);
            tensor<fp16, []> var_618_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_619_cast = add(x = var_617_cast, y = var_618_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_35_cast = sqrt(x = var_619_cast);
            tensor<fp16, [1, 128, 1]> sep_module_8_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(420224)))];
            tensor<fp16, [1, 128, 12]> var_622_cast = mul(x = sep_module_8_tcn_2_norm_gamma_to_fp16, y = var_614_cast);
            tensor<fp16, [1, 128, 12]> var_623_cast = real_div(x = var_622_cast, y = std_y_35_cast);
            tensor<fp16, [1, 128, 1]> sep_module_8_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(420544)))];
            tensor<fp16, [1, 128, 12]> input_87_cast = add(x = var_623_cast, y = sep_module_8_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_89_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 8, 0])];
            tensor<string, []> input_89_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_89_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 20]> input_89_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_87_cast_in_state, input_87_cast));
            tensor<fp16, [1, 128, 8]> input_87_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -8]), size = tensor<int32, [3]>([1, 128, 8]), x = input_89_cast);
            tensor<int32, [1]> var_628 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_630 = const()[val = tensor<int32, [1]>([4])];
            tensor<string, []> input_91_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_91_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_8_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(420864)))];
            tensor<fp16, [1, 128, 12]> input_91_cast = conv(dilations = var_630, groups = var_65, pad = input_91_pad_0, pad_type = input_91_pad_type_0, strides = var_628, weight = sep_module_8_tcn_4_weight_to_fp16, x = input_89_cast);
            tensor<fp16, []> var_634_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.7dcp-3)];
            tensor<fp16, [1, 128, 12]> var_634_cast = leaky_relu(alpha = var_634_alpha_1_to_fp16, x = input_91_cast);
            tensor<int32, [1]> var_638 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_37_cast = reduce_mean(axes = var_638, keep_dims = var_66, x = var_634_cast);
            tensor<fp16, [1, 128, 12]> var_640_cast = sub(x = var_634_cast, y = mean_y_37_cast);
            tensor<fp16, [1, 128, 12]> var_641_cast = square(x = var_640_cast);
            tensor<int32, [1]> var_642 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_643_cast = reduce_mean(axes = var_642, keep_dims = var_66, x = var_641_cast);
            tensor<fp16, []> var_644_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_645_cast = add(x = var_643_cast, y = var_644_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_37_cast = sqrt(x = var_645_cast);
            tensor<fp16, [1, 128, 1]> sep_module_8_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(421696)))];
            tensor<fp16, [1, 128, 12]> var_648_cast = mul(x = sep_module_8_tcn_6_norm_gamma_to_fp16, y = var_640_cast);
            tensor<fp16, [1, 128, 12]> var_649_cast = real_div(x = var_648_cast, y = std_y_37_cast);
            tensor<fp16, [1, 128, 1]> sep_module_8_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(422016)))];
            tensor<fp16, [1, 128, 12]> y_18_cast = add(x = var_649_cast, y = sep_module_8_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_93_cast = add(x = input_83_cast, y = y_18_cast);
            tensor<int32, [1]> var_660 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_662 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_95_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_95_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_9_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(422336)))];
            tensor<fp16, [1, 128, 12]> input_95_cast = conv(dilations = var_662, groups = var_64, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = var_660, weight = sep_module_9_tcn_0_weight_to_fp16, x = input_93_cast);
            tensor<fp16, []> var_666_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.05p-1)];
            tensor<fp16, [1, 128, 12]> var_666_cast = leaky_relu(alpha = var_666_alpha_1_to_fp16, x = input_95_cast);
            tensor<int32, [1]> var_670 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_39_cast = reduce_mean(axes = var_670, keep_dims = var_66, x = var_666_cast);
            tensor<fp16, [1, 128, 12]> var_672_cast = sub(x = var_666_cast, y = mean_y_39_cast);
            tensor<fp16, [1, 128, 12]> var_673_cast = square(x = var_672_cast);
            tensor<int32, [1]> var_674 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_675_cast = reduce_mean(axes = var_674, keep_dims = var_66, x = var_673_cast);
            tensor<fp16, []> var_676_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_677_cast = add(x = var_675_cast, y = var_676_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_39_cast = sqrt(x = var_677_cast);
            tensor<fp16, [1, 128, 1]> sep_module_9_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(455168)))];
            tensor<fp16, [1, 128, 12]> var_680_cast = mul(x = sep_module_9_tcn_2_norm_gamma_to_fp16, y = var_672_cast);
            tensor<fp16, [1, 128, 12]> var_681_cast = real_div(x = var_680_cast, y = std_y_39_cast);
            tensor<fp16, [1, 128, 1]> sep_module_9_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(455488)))];
            tensor<fp16, [1, 128, 12]> input_97_cast = add(x = var_681_cast, y = sep_module_9_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_99_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 16, 0])];
            tensor<string, []> input_99_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_99_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 28]> input_99_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_97_cast_in_state, input_97_cast));
            tensor<fp16, [1, 128, 16]> input_97_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -16]), size = tensor<int32, [3]>([1, 128, 16]), x = input_99_cast);
            tensor<int32, [1]> var_686 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_688 = const()[val = tensor<int32, [1]>([8])];
            tensor<string, []> input_101_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_101_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_9_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(455808)))];
            tensor<fp16, [1, 128, 12]> input_101_cast = conv(dilations = var_688, groups = var_65, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = var_686, weight = sep_module_9_tcn_4_weight_to_fp16, x = input_99_cast);
            tensor<fp16, []> var_692_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.acp-4)];
            tensor<fp16, [1, 128, 12]> var_692_cast = leaky_relu(alpha = var_692_alpha_1_to_fp16, x = input_101_cast);
            tensor<int32, [1]> var_696 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_41_cast = reduce_mean(axes = var_696, keep_dims = var_66, x = var_692_cast);
            tensor<fp16, [1, 128, 12]> var_698_cast = sub(x = var_692_cast, y = mean_y_41_cast);
            tensor<fp16, [1, 128, 12]> var_699_cast = square(x = var_698_cast);
            tensor<int32, [1]> var_700 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_701_cast = reduce_mean(axes = var_700, keep_dims = var_66, x = var_699_cast);
            tensor<fp16, []> var_702_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_703_cast = add(x = var_701_cast, y = var_702_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_41_cast = sqrt(x = var_703_cast);
            tensor<fp16, [1, 128, 1]> sep_module_9_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(456640)))];
            tensor<fp16, [1, 128, 12]> var_706_cast = mul(x = sep_module_9_tcn_6_norm_gamma_to_fp16, y = var_698_cast);
            tensor<fp16, [1, 128, 12]> var_707_cast = real_div(x = var_706_cast, y = std_y_41_cast);
            tensor<fp16, [1, 128, 1]> sep_module_9_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(456960)))];
            tensor<fp16, [1, 128, 12]> y_20_cast = add(x = var_707_cast, y = sep_module_9_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_103_cast = add(x = input_93_cast, y = y_20_cast);
            tensor<int32, [1]> var_718 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_720 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_105_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_105_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_10_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(457280)))];
            tensor<fp16, [1, 128, 12]> input_105_cast = conv(dilations = var_720, groups = var_64, pad = input_105_pad_0, pad_type = input_105_pad_type_0, strides = var_718, weight = sep_module_10_tcn_0_weight_to_fp16, x = input_103_cast);
            tensor<fp16, []> var_724_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.384p-1)];
            tensor<fp16, [1, 128, 12]> var_724_cast = leaky_relu(alpha = var_724_alpha_1_to_fp16, x = input_105_cast);
            tensor<int32, [1]> var_728 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_43_cast = reduce_mean(axes = var_728, keep_dims = var_66, x = var_724_cast);
            tensor<fp16, [1, 128, 12]> var_730_cast = sub(x = var_724_cast, y = mean_y_43_cast);
            tensor<fp16, [1, 128, 12]> var_731_cast = square(x = var_730_cast);
            tensor<int32, [1]> var_732 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_733_cast = reduce_mean(axes = var_732, keep_dims = var_66, x = var_731_cast);
            tensor<fp16, []> var_734_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_735_cast = add(x = var_733_cast, y = var_734_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_43_cast = sqrt(x = var_735_cast);
            tensor<fp16, [1, 128, 1]> sep_module_10_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(490112)))];
            tensor<fp16, [1, 128, 12]> var_738_cast = mul(x = sep_module_10_tcn_2_norm_gamma_to_fp16, y = var_730_cast);
            tensor<fp16, [1, 128, 12]> var_739_cast = real_div(x = var_738_cast, y = std_y_43_cast);
            tensor<fp16, [1, 128, 1]> sep_module_10_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(490432)))];
            tensor<fp16, [1, 128, 12]> input_107_cast = add(x = var_739_cast, y = sep_module_10_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_109_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 32, 0])];
            tensor<string, []> input_109_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_109_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 44]> input_109_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_107_cast_in_state, input_107_cast));
            tensor<fp16, [1, 128, 32]> input_107_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -32]), size = tensor<int32, [3]>([1, 128, 32]), x = input_109_cast);
            tensor<int32, [1]> var_744 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_746 = const()[val = tensor<int32, [1]>([16])];
            tensor<string, []> input_111_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_111_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_10_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(490752)))];
            tensor<fp16, [1, 128, 12]> input_111_cast = conv(dilations = var_746, groups = var_65, pad = input_111_pad_0, pad_type = input_111_pad_type_0, strides = var_744, weight = sep_module_10_tcn_4_weight_to_fp16, x = input_109_cast);
            tensor<fp16, []> var_750_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.0e4p-4)];
            tensor<fp16, [1, 128, 12]> var_750_cast = leaky_relu(alpha = var_750_alpha_1_to_fp16, x = input_111_cast);
            tensor<int32, [1]> var_754 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_45_cast = reduce_mean(axes = var_754, keep_dims = var_66, x = var_750_cast);
            tensor<fp16, [1, 128, 12]> var_756_cast = sub(x = var_750_cast, y = mean_y_45_cast);
            tensor<fp16, [1, 128, 12]> var_757_cast = square(x = var_756_cast);
            tensor<int32, [1]> var_758 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_759_cast = reduce_mean(axes = var_758, keep_dims = var_66, x = var_757_cast);
            tensor<fp16, []> var_760_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_761_cast = add(x = var_759_cast, y = var_760_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_45_cast = sqrt(x = var_761_cast);
            tensor<fp16, [1, 128, 1]> sep_module_10_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(491584)))];
            tensor<fp16, [1, 128, 12]> var_764_cast = mul(x = sep_module_10_tcn_6_norm_gamma_to_fp16, y = var_756_cast);
            tensor<fp16, [1, 128, 12]> var_765_cast = real_div(x = var_764_cast, y = std_y_45_cast);
            tensor<fp16, [1, 128, 1]> sep_module_10_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(491904)))];
            tensor<fp16, [1, 128, 12]> y_22_cast = add(x = var_765_cast, y = sep_module_10_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_113_cast = add(x = input_103_cast, y = y_22_cast);
            tensor<int32, [1]> var_776 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_778 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_115_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_115_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_11_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(492224)))];
            tensor<fp16, [1, 128, 12]> input_115_cast = conv(dilations = var_778, groups = var_64, pad = input_115_pad_0, pad_type = input_115_pad_type_0, strides = var_776, weight = sep_module_11_tcn_0_weight_to_fp16, x = input_113_cast);
            tensor<fp16, []> var_782_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.d4p-2)];
            tensor<fp16, [1, 128, 12]> var_782_cast = leaky_relu(alpha = var_782_alpha_1_to_fp16, x = input_115_cast);
            tensor<int32, [1]> var_786 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_47_cast = reduce_mean(axes = var_786, keep_dims = var_66, x = var_782_cast);
            tensor<fp16, [1, 128, 12]> var_788_cast = sub(x = var_782_cast, y = mean_y_47_cast);
            tensor<fp16, [1, 128, 12]> var_789_cast = square(x = var_788_cast);
            tensor<int32, [1]> var_790 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_791_cast = reduce_mean(axes = var_790, keep_dims = var_66, x = var_789_cast);
            tensor<fp16, []> var_792_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_793_cast = add(x = var_791_cast, y = var_792_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_47_cast = sqrt(x = var_793_cast);
            tensor<fp16, [1, 128, 1]> sep_module_11_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(525056)))];
            tensor<fp16, [1, 128, 12]> var_796_cast = mul(x = sep_module_11_tcn_2_norm_gamma_to_fp16, y = var_788_cast);
            tensor<fp16, [1, 128, 12]> var_797_cast = real_div(x = var_796_cast, y = std_y_47_cast);
            tensor<fp16, [1, 128, 1]> sep_module_11_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(525376)))];
            tensor<fp16, [1, 128, 12]> input_117_cast = add(x = var_797_cast, y = sep_module_11_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_119_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 64, 0])];
            tensor<string, []> input_119_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_119_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 76]> input_119_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_117_cast_in_state, input_117_cast));
            tensor<fp16, [1, 128, 64]> input_117_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -64]), size = tensor<int32, [3]>([1, 128, 64]), x = input_119_cast);
            tensor<int32, [1]> var_802 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_804 = const()[val = tensor<int32, [1]>([32])];
            tensor<string, []> input_121_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_121_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_11_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(525696)))];
            tensor<fp16, [1, 128, 12]> input_121_cast = conv(dilations = var_804, groups = var_65, pad = input_121_pad_0, pad_type = input_121_pad_type_0, strides = var_802, weight = sep_module_11_tcn_4_weight_to_fp16, x = input_119_cast);
            tensor<fp16, []> var_808_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.3f8p-1)];
            tensor<fp16, [1, 128, 12]> var_808_cast = leaky_relu(alpha = var_808_alpha_1_to_fp16, x = input_121_cast);
            tensor<int32, [1]> var_812 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_49_cast = reduce_mean(axes = var_812, keep_dims = var_66, x = var_808_cast);
            tensor<fp16, [1, 128, 12]> var_814_cast = sub(x = var_808_cast, y = mean_y_49_cast);
            tensor<fp16, [1, 128, 12]> var_815_cast = square(x = var_814_cast);
            tensor<int32, [1]> var_816 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_817_cast = reduce_mean(axes = var_816, keep_dims = var_66, x = var_815_cast);
            tensor<fp16, []> var_818_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_819_cast = add(x = var_817_cast, y = var_818_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_49_cast = sqrt(x = var_819_cast);
            tensor<fp16, [1, 128, 1]> sep_module_11_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(526528)))];
            tensor<fp16, [1, 128, 12]> var_822_cast = mul(x = sep_module_11_tcn_6_norm_gamma_to_fp16, y = var_814_cast);
            tensor<fp16, [1, 128, 12]> var_823_cast = real_div(x = var_822_cast, y = std_y_49_cast);
            tensor<fp16, [1, 128, 1]> sep_module_11_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(526848)))];
            tensor<fp16, [1, 128, 12]> y_24_cast = add(x = var_823_cast, y = sep_module_11_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_123_cast = add(x = input_113_cast, y = y_24_cast);
            tensor<int32, [1]> var_834 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_836 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_125_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_125_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_12_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(527168)))];
            tensor<fp16, [1, 128, 12]> input_125_cast = conv(dilations = var_836, groups = var_64, pad = input_125_pad_0, pad_type = input_125_pad_type_0, strides = var_834, weight = sep_module_12_tcn_0_weight_to_fp16, x = input_123_cast);
            tensor<fp16, []> var_840_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.79p-2)];
            tensor<fp16, [1, 128, 12]> var_840_cast = leaky_relu(alpha = var_840_alpha_1_to_fp16, x = input_125_cast);
            tensor<int32, [1]> var_844 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_51_cast = reduce_mean(axes = var_844, keep_dims = var_66, x = var_840_cast);
            tensor<fp16, [1, 128, 12]> var_846_cast = sub(x = var_840_cast, y = mean_y_51_cast);
            tensor<fp16, [1, 128, 12]> var_847_cast = square(x = var_846_cast);
            tensor<int32, [1]> var_848 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_849_cast = reduce_mean(axes = var_848, keep_dims = var_66, x = var_847_cast);
            tensor<fp16, []> var_850_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_851_cast = add(x = var_849_cast, y = var_850_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_51_cast = sqrt(x = var_851_cast);
            tensor<fp16, [1, 128, 1]> sep_module_12_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(560000)))];
            tensor<fp16, [1, 128, 12]> var_854_cast = mul(x = sep_module_12_tcn_2_norm_gamma_to_fp16, y = var_846_cast);
            tensor<fp16, [1, 128, 12]> var_855_cast = real_div(x = var_854_cast, y = std_y_51_cast);
            tensor<fp16, [1, 128, 1]> sep_module_12_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(560320)))];
            tensor<fp16, [1, 128, 12]> input_127_cast = add(x = var_855_cast, y = sep_module_12_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_129_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 2, 0])];
            tensor<string, []> input_129_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_129_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 14]> input_129_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_127_cast_in_state, input_127_cast));
            tensor<fp16, [1, 128, 2]> input_127_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -2]), size = tensor<int32, [3]>([1, 128, 2]), x = input_129_cast);
            tensor<int32, [1]> var_860 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_862 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_131_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_131_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_12_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(560640)))];
            tensor<fp16, [1, 128, 12]> input_131_cast = conv(dilations = var_862, groups = var_65, pad = input_131_pad_0, pad_type = input_131_pad_type_0, strides = var_860, weight = sep_module_12_tcn_4_weight_to_fp16, x = input_129_cast);
            tensor<fp16, []> var_866_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.97cp-1)];
            tensor<fp16, [1, 128, 12]> var_866_cast = leaky_relu(alpha = var_866_alpha_1_to_fp16, x = input_131_cast);
            tensor<int32, [1]> var_870 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_53_cast = reduce_mean(axes = var_870, keep_dims = var_66, x = var_866_cast);
            tensor<fp16, [1, 128, 12]> var_872_cast = sub(x = var_866_cast, y = mean_y_53_cast);
            tensor<fp16, [1, 128, 12]> var_873_cast = square(x = var_872_cast);
            tensor<int32, [1]> var_874 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_875_cast = reduce_mean(axes = var_874, keep_dims = var_66, x = var_873_cast);
            tensor<fp16, []> var_876_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_877_cast = add(x = var_875_cast, y = var_876_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_53_cast = sqrt(x = var_877_cast);
            tensor<fp16, [1, 128, 1]> sep_module_12_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(561472)))];
            tensor<fp16, [1, 128, 12]> var_880_cast = mul(x = sep_module_12_tcn_6_norm_gamma_to_fp16, y = var_872_cast);
            tensor<fp16, [1, 128, 12]> var_881_cast = real_div(x = var_880_cast, y = std_y_53_cast);
            tensor<fp16, [1, 128, 1]> sep_module_12_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(561792)))];
            tensor<fp16, [1, 128, 12]> y_26_cast = add(x = var_881_cast, y = sep_module_12_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_133_cast = add(x = input_123_cast, y = y_26_cast);
            tensor<int32, [1]> var_892 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_894 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_135_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_135_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_13_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(562112)))];
            tensor<fp16, [1, 128, 12]> input_135_cast = conv(dilations = var_894, groups = var_64, pad = input_135_pad_0, pad_type = input_135_pad_type_0, strides = var_892, weight = sep_module_13_tcn_0_weight_to_fp16, x = input_133_cast);
            tensor<fp16, []> var_898_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.03p-1)];
            tensor<fp16, [1, 128, 12]> var_898_cast = leaky_relu(alpha = var_898_alpha_1_to_fp16, x = input_135_cast);
            tensor<int32, [1]> var_902 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_55_cast = reduce_mean(axes = var_902, keep_dims = var_66, x = var_898_cast);
            tensor<fp16, [1, 128, 12]> var_904_cast = sub(x = var_898_cast, y = mean_y_55_cast);
            tensor<fp16, [1, 128, 12]> var_905_cast = square(x = var_904_cast);
            tensor<int32, [1]> var_906 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_907_cast = reduce_mean(axes = var_906, keep_dims = var_66, x = var_905_cast);
            tensor<fp16, []> var_908_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_909_cast = add(x = var_907_cast, y = var_908_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_55_cast = sqrt(x = var_909_cast);
            tensor<fp16, [1, 128, 1]> sep_module_13_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(594944)))];
            tensor<fp16, [1, 128, 12]> var_912_cast = mul(x = sep_module_13_tcn_2_norm_gamma_to_fp16, y = var_904_cast);
            tensor<fp16, [1, 128, 12]> var_913_cast = real_div(x = var_912_cast, y = std_y_55_cast);
            tensor<fp16, [1, 128, 1]> sep_module_13_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(595264)))];
            tensor<fp16, [1, 128, 12]> input_137_cast = add(x = var_913_cast, y = sep_module_13_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_139_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 4, 0])];
            tensor<string, []> input_139_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_139_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 16]> input_139_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_137_cast_in_state, input_137_cast));
            tensor<fp16, [1, 128, 4]> input_137_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -4]), size = tensor<int32, [3]>([1, 128, 4]), x = input_139_cast);
            tensor<int32, [1]> var_918 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_920 = const()[val = tensor<int32, [1]>([2])];
            tensor<string, []> input_141_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_141_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_13_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(595584)))];
            tensor<fp16, [1, 128, 12]> input_141_cast = conv(dilations = var_920, groups = var_65, pad = input_141_pad_0, pad_type = input_141_pad_type_0, strides = var_918, weight = sep_module_13_tcn_4_weight_to_fp16, x = input_139_cast);
            tensor<fp16, []> var_924_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.d94p-5)];
            tensor<fp16, [1, 128, 12]> var_924_cast = leaky_relu(alpha = var_924_alpha_1_to_fp16, x = input_141_cast);
            tensor<int32, [1]> var_928 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_57_cast = reduce_mean(axes = var_928, keep_dims = var_66, x = var_924_cast);
            tensor<fp16, [1, 128, 12]> var_930_cast = sub(x = var_924_cast, y = mean_y_57_cast);
            tensor<fp16, [1, 128, 12]> var_931_cast = square(x = var_930_cast);
            tensor<int32, [1]> var_932 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_933_cast = reduce_mean(axes = var_932, keep_dims = var_66, x = var_931_cast);
            tensor<fp16, []> var_934_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_935_cast = add(x = var_933_cast, y = var_934_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_57_cast = sqrt(x = var_935_cast);
            tensor<fp16, [1, 128, 1]> sep_module_13_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(596416)))];
            tensor<fp16, [1, 128, 12]> var_938_cast = mul(x = sep_module_13_tcn_6_norm_gamma_to_fp16, y = var_930_cast);
            tensor<fp16, [1, 128, 12]> var_939_cast = real_div(x = var_938_cast, y = std_y_57_cast);
            tensor<fp16, [1, 128, 1]> sep_module_13_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(596736)))];
            tensor<fp16, [1, 128, 12]> y_28_cast = add(x = var_939_cast, y = sep_module_13_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_143_cast = add(x = input_133_cast, y = y_28_cast);
            tensor<int32, [1]> var_950 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_952 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_145_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_145_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_14_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(597056)))];
            tensor<fp16, [1, 128, 12]> input_145_cast = conv(dilations = var_952, groups = var_64, pad = input_145_pad_0, pad_type = input_145_pad_type_0, strides = var_950, weight = sep_module_14_tcn_0_weight_to_fp16, x = input_143_cast);
            tensor<fp16, []> var_956_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.c9p-2)];
            tensor<fp16, [1, 128, 12]> var_956_cast = leaky_relu(alpha = var_956_alpha_1_to_fp16, x = input_145_cast);
            tensor<int32, [1]> var_960 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_59_cast = reduce_mean(axes = var_960, keep_dims = var_66, x = var_956_cast);
            tensor<fp16, [1, 128, 12]> var_962_cast = sub(x = var_956_cast, y = mean_y_59_cast);
            tensor<fp16, [1, 128, 12]> var_963_cast = square(x = var_962_cast);
            tensor<int32, [1]> var_964 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_965_cast = reduce_mean(axes = var_964, keep_dims = var_66, x = var_963_cast);
            tensor<fp16, []> var_966_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_967_cast = add(x = var_965_cast, y = var_966_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_59_cast = sqrt(x = var_967_cast);
            tensor<fp16, [1, 128, 1]> sep_module_14_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(629888)))];
            tensor<fp16, [1, 128, 12]> var_970_cast = mul(x = sep_module_14_tcn_2_norm_gamma_to_fp16, y = var_962_cast);
            tensor<fp16, [1, 128, 12]> var_971_cast = real_div(x = var_970_cast, y = std_y_59_cast);
            tensor<fp16, [1, 128, 1]> sep_module_14_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(630208)))];
            tensor<fp16, [1, 128, 12]> input_147_cast = add(x = var_971_cast, y = sep_module_14_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_149_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 8, 0])];
            tensor<string, []> input_149_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_149_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 20]> input_149_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_147_cast_in_state, input_147_cast));
            tensor<fp16, [1, 128, 8]> input_147_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -8]), size = tensor<int32, [3]>([1, 128, 8]), x = input_149_cast);
            tensor<int32, [1]> var_976 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_978 = const()[val = tensor<int32, [1]>([4])];
            tensor<string, []> input_151_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_151_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_14_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(630528)))];
            tensor<fp16, [1, 128, 12]> input_151_cast = conv(dilations = var_978, groups = var_65, pad = input_151_pad_0, pad_type = input_151_pad_type_0, strides = var_976, weight = sep_module_14_tcn_4_weight_to_fp16, x = input_149_cast);
            tensor<fp16, []> var_982_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.60cp-3)];
            tensor<fp16, [1, 128, 12]> var_982_cast = leaky_relu(alpha = var_982_alpha_1_to_fp16, x = input_151_cast);
            tensor<int32, [1]> var_986 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_61_cast = reduce_mean(axes = var_986, keep_dims = var_66, x = var_982_cast);
            tensor<fp16, [1, 128, 12]> var_988_cast = sub(x = var_982_cast, y = mean_y_61_cast);
            tensor<fp16, [1, 128, 12]> var_989_cast = square(x = var_988_cast);
            tensor<int32, [1]> var_990 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_991_cast = reduce_mean(axes = var_990, keep_dims = var_66, x = var_989_cast);
            tensor<fp16, []> var_992_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_993_cast = add(x = var_991_cast, y = var_992_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_61_cast = sqrt(x = var_993_cast);
            tensor<fp16, [1, 128, 1]> sep_module_14_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(631360)))];
            tensor<fp16, [1, 128, 12]> var_996_cast = mul(x = sep_module_14_tcn_6_norm_gamma_to_fp16, y = var_988_cast);
            tensor<fp16, [1, 128, 12]> var_997_cast = real_div(x = var_996_cast, y = std_y_61_cast);
            tensor<fp16, [1, 128, 1]> sep_module_14_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(631680)))];
            tensor<fp16, [1, 128, 12]> y_30_cast = add(x = var_997_cast, y = sep_module_14_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_153_cast = add(x = input_143_cast, y = y_30_cast);
            tensor<int32, [1]> var_1008 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1010 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_155_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_155_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_15_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(632000)))];
            tensor<fp16, [1, 128, 12]> input_155_cast = conv(dilations = var_1010, groups = var_64, pad = input_155_pad_0, pad_type = input_155_pad_type_0, strides = var_1008, weight = sep_module_15_tcn_0_weight_to_fp16, x = input_153_cast);
            tensor<fp16, []> var_1014_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.d2cp-7)];
            tensor<fp16, [1, 128, 12]> var_1014_cast = leaky_relu(alpha = var_1014_alpha_1_to_fp16, x = input_155_cast);
            tensor<int32, [1]> var_1018 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_63_cast = reduce_mean(axes = var_1018, keep_dims = var_66, x = var_1014_cast);
            tensor<fp16, [1, 128, 12]> var_1020_cast = sub(x = var_1014_cast, y = mean_y_63_cast);
            tensor<fp16, [1, 128, 12]> var_1021_cast = square(x = var_1020_cast);
            tensor<int32, [1]> var_1022 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1023_cast = reduce_mean(axes = var_1022, keep_dims = var_66, x = var_1021_cast);
            tensor<fp16, []> var_1024_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1025_cast = add(x = var_1023_cast, y = var_1024_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_63_cast = sqrt(x = var_1025_cast);
            tensor<fp16, [1, 128, 1]> sep_module_15_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(664832)))];
            tensor<fp16, [1, 128, 12]> var_1028_cast = mul(x = sep_module_15_tcn_2_norm_gamma_to_fp16, y = var_1020_cast);
            tensor<fp16, [1, 128, 12]> var_1029_cast = real_div(x = var_1028_cast, y = std_y_63_cast);
            tensor<fp16, [1, 128, 1]> sep_module_15_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(665152)))];
            tensor<fp16, [1, 128, 12]> input_157_cast = add(x = var_1029_cast, y = sep_module_15_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_159_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 16, 0])];
            tensor<string, []> input_159_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_159_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 28]> input_159_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_157_cast_in_state, input_157_cast));
            tensor<fp16, [1, 128, 16]> input_157_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -16]), size = tensor<int32, [3]>([1, 128, 16]), x = input_159_cast);
            tensor<int32, [1]> var_1034 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1036 = const()[val = tensor<int32, [1]>([8])];
            tensor<string, []> input_161_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_161_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_15_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(665472)))];
            tensor<fp16, [1, 128, 12]> input_161_cast = conv(dilations = var_1036, groups = var_65, pad = input_161_pad_0, pad_type = input_161_pad_type_0, strides = var_1034, weight = sep_module_15_tcn_4_weight_to_fp16, x = input_159_cast);
            tensor<fp16, []> var_1040_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.1ep-1)];
            tensor<fp16, [1, 128, 12]> var_1040_cast = leaky_relu(alpha = var_1040_alpha_1_to_fp16, x = input_161_cast);
            tensor<int32, [1]> var_1044 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_65_cast = reduce_mean(axes = var_1044, keep_dims = var_66, x = var_1040_cast);
            tensor<fp16, [1, 128, 12]> var_1046_cast = sub(x = var_1040_cast, y = mean_y_65_cast);
            tensor<fp16, [1, 128, 12]> var_1047_cast = square(x = var_1046_cast);
            tensor<int32, [1]> var_1048 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1049_cast = reduce_mean(axes = var_1048, keep_dims = var_66, x = var_1047_cast);
            tensor<fp16, []> var_1050_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1051_cast = add(x = var_1049_cast, y = var_1050_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_65_cast = sqrt(x = var_1051_cast);
            tensor<fp16, [1, 128, 1]> sep_module_15_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(666304)))];
            tensor<fp16, [1, 128, 12]> var_1054_cast = mul(x = sep_module_15_tcn_6_norm_gamma_to_fp16, y = var_1046_cast);
            tensor<fp16, [1, 128, 12]> var_1055_cast = real_div(x = var_1054_cast, y = std_y_65_cast);
            tensor<fp16, [1, 128, 1]> sep_module_15_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(666624)))];
            tensor<fp16, [1, 128, 12]> y_32_cast = add(x = var_1055_cast, y = sep_module_15_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_163_cast = add(x = input_153_cast, y = y_32_cast);
            tensor<int32, [1]> var_1066 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1068 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_165_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_165_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_16_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(666944)))];
            tensor<fp16, [1, 128, 12]> input_165_cast = conv(dilations = var_1068, groups = var_64, pad = input_165_pad_0, pad_type = input_165_pad_type_0, strides = var_1066, weight = sep_module_16_tcn_0_weight_to_fp16, x = input_163_cast);
            tensor<fp16, []> var_1072_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.e3p-2)];
            tensor<fp16, [1, 128, 12]> var_1072_cast = leaky_relu(alpha = var_1072_alpha_1_to_fp16, x = input_165_cast);
            tensor<int32, [1]> var_1076 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_67_cast = reduce_mean(axes = var_1076, keep_dims = var_66, x = var_1072_cast);
            tensor<fp16, [1, 128, 12]> var_1078_cast = sub(x = var_1072_cast, y = mean_y_67_cast);
            tensor<fp16, [1, 128, 12]> var_1079_cast = square(x = var_1078_cast);
            tensor<int32, [1]> var_1080 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1081_cast = reduce_mean(axes = var_1080, keep_dims = var_66, x = var_1079_cast);
            tensor<fp16, []> var_1082_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1083_cast = add(x = var_1081_cast, y = var_1082_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_67_cast = sqrt(x = var_1083_cast);
            tensor<fp16, [1, 128, 1]> sep_module_16_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(699776)))];
            tensor<fp16, [1, 128, 12]> var_1086_cast = mul(x = sep_module_16_tcn_2_norm_gamma_to_fp16, y = var_1078_cast);
            tensor<fp16, [1, 128, 12]> var_1087_cast = real_div(x = var_1086_cast, y = std_y_67_cast);
            tensor<fp16, [1, 128, 1]> sep_module_16_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(700096)))];
            tensor<fp16, [1, 128, 12]> input_167_cast = add(x = var_1087_cast, y = sep_module_16_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_169_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 32, 0])];
            tensor<string, []> input_169_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_169_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 44]> input_169_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_167_cast_in_state, input_167_cast));
            tensor<fp16, [1, 128, 32]> input_167_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -32]), size = tensor<int32, [3]>([1, 128, 32]), x = input_169_cast);
            tensor<int32, [1]> var_1092 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1094 = const()[val = tensor<int32, [1]>([16])];
            tensor<string, []> input_171_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_171_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_16_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(700416)))];
            tensor<fp16, [1, 128, 12]> input_171_cast = conv(dilations = var_1094, groups = var_65, pad = input_171_pad_0, pad_type = input_171_pad_type_0, strides = var_1092, weight = sep_module_16_tcn_4_weight_to_fp16, x = input_169_cast);
            tensor<fp16, []> var_1098_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.564p-1)];
            tensor<fp16, [1, 128, 12]> var_1098_cast = leaky_relu(alpha = var_1098_alpha_1_to_fp16, x = input_171_cast);
            tensor<int32, [1]> var_1102 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_69_cast = reduce_mean(axes = var_1102, keep_dims = var_66, x = var_1098_cast);
            tensor<fp16, [1, 128, 12]> var_1104_cast = sub(x = var_1098_cast, y = mean_y_69_cast);
            tensor<fp16, [1, 128, 12]> var_1105_cast = square(x = var_1104_cast);
            tensor<int32, [1]> var_1106 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1107_cast = reduce_mean(axes = var_1106, keep_dims = var_66, x = var_1105_cast);
            tensor<fp16, []> var_1108_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1109_cast = add(x = var_1107_cast, y = var_1108_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_69_cast = sqrt(x = var_1109_cast);
            tensor<fp16, [1, 128, 1]> sep_module_16_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(701248)))];
            tensor<fp16, [1, 128, 12]> var_1112_cast = mul(x = sep_module_16_tcn_6_norm_gamma_to_fp16, y = var_1104_cast);
            tensor<fp16, [1, 128, 12]> var_1113_cast = real_div(x = var_1112_cast, y = std_y_69_cast);
            tensor<fp16, [1, 128, 1]> sep_module_16_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(701568)))];
            tensor<fp16, [1, 128, 12]> y_34_cast = add(x = var_1113_cast, y = sep_module_16_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_173_cast = add(x = input_163_cast, y = y_34_cast);
            tensor<int32, [1]> var_1124 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1126 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_175_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_175_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_17_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(701888)))];
            tensor<fp16, [1, 128, 12]> input_175_cast = conv(dilations = var_1126, groups = var_64, pad = input_175_pad_0, pad_type = input_175_pad_type_0, strides = var_1124, weight = sep_module_17_tcn_0_weight_to_fp16, x = input_173_cast);
            tensor<fp16, []> var_1130_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.58p-1)];
            tensor<fp16, [1, 128, 12]> var_1130_cast = leaky_relu(alpha = var_1130_alpha_1_to_fp16, x = input_175_cast);
            tensor<int32, [1]> var_1134 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_71_cast = reduce_mean(axes = var_1134, keep_dims = var_66, x = var_1130_cast);
            tensor<fp16, [1, 128, 12]> var_1136_cast = sub(x = var_1130_cast, y = mean_y_71_cast);
            tensor<fp16, [1, 128, 12]> var_1137_cast = square(x = var_1136_cast);
            tensor<int32, [1]> var_1138 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1139_cast = reduce_mean(axes = var_1138, keep_dims = var_66, x = var_1137_cast);
            tensor<fp16, []> var_1140_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1141_cast = add(x = var_1139_cast, y = var_1140_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_71_cast = sqrt(x = var_1141_cast);
            tensor<fp16, [1, 128, 1]> sep_module_17_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(734720)))];
            tensor<fp16, [1, 128, 12]> var_1144_cast = mul(x = sep_module_17_tcn_2_norm_gamma_to_fp16, y = var_1136_cast);
            tensor<fp16, [1, 128, 12]> var_1145_cast = real_div(x = var_1144_cast, y = std_y_71_cast);
            tensor<fp16, [1, 128, 1]> sep_module_17_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(735040)))];
            tensor<fp16, [1, 128, 12]> input_177_cast = add(x = var_1145_cast, y = sep_module_17_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_179_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 64, 0])];
            tensor<string, []> input_179_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_179_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 76]> input_179_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_177_cast_in_state, input_177_cast));
            tensor<fp16, [1, 128, 64]> input_177_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -64]), size = tensor<int32, [3]>([1, 128, 64]), x = input_179_cast);
            tensor<int32, [1]> var_1150 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1152 = const()[val = tensor<int32, [1]>([32])];
            tensor<string, []> input_181_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_181_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_17_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(735360)))];
            tensor<fp16, [1, 128, 12]> input_181_cast = conv(dilations = var_1152, groups = var_65, pad = input_181_pad_0, pad_type = input_181_pad_type_0, strides = var_1150, weight = sep_module_17_tcn_4_weight_to_fp16, x = input_179_cast);
            tensor<fp16, []> var_1156_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.01cp-1)];
            tensor<fp16, [1, 128, 12]> var_1156_cast = leaky_relu(alpha = var_1156_alpha_1_to_fp16, x = input_181_cast);
            tensor<int32, [1]> var_1160 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_73_cast = reduce_mean(axes = var_1160, keep_dims = var_66, x = var_1156_cast);
            tensor<fp16, [1, 128, 12]> var_1162_cast = sub(x = var_1156_cast, y = mean_y_73_cast);
            tensor<fp16, [1, 128, 12]> var_1163_cast = square(x = var_1162_cast);
            tensor<int32, [1]> var_1164 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1165_cast = reduce_mean(axes = var_1164, keep_dims = var_66, x = var_1163_cast);
            tensor<fp16, []> var_1166_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1167_cast = add(x = var_1165_cast, y = var_1166_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_73_cast = sqrt(x = var_1167_cast);
            tensor<fp16, [1, 128, 1]> sep_module_17_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(736192)))];
            tensor<fp16, [1, 128, 12]> var_1170_cast = mul(x = sep_module_17_tcn_6_norm_gamma_to_fp16, y = var_1162_cast);
            tensor<fp16, [1, 128, 12]> var_1171_cast = real_div(x = var_1170_cast, y = std_y_73_cast);
            tensor<fp16, [1, 128, 1]> sep_module_17_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(736512)))];
            tensor<fp16, [1, 128, 12]> y_36_cast = add(x = var_1171_cast, y = sep_module_17_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_183_cast = add(x = input_173_cast, y = y_36_cast);
            tensor<int32, [1]> var_1182 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1184 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_185_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_185_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_18_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(736832)))];
            tensor<fp16, [1, 128, 12]> input_185_cast = conv(dilations = var_1184, groups = var_64, pad = input_185_pad_0, pad_type = input_185_pad_type_0, strides = var_1182, weight = sep_module_18_tcn_0_weight_to_fp16, x = input_183_cast);
            tensor<fp16, []> var_1188_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.e24p-3)];
            tensor<fp16, [1, 128, 12]> var_1188_cast = leaky_relu(alpha = var_1188_alpha_1_to_fp16, x = input_185_cast);
            tensor<int32, [1]> var_1192 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_75_cast = reduce_mean(axes = var_1192, keep_dims = var_66, x = var_1188_cast);
            tensor<fp16, [1, 128, 12]> var_1194_cast = sub(x = var_1188_cast, y = mean_y_75_cast);
            tensor<fp16, [1, 128, 12]> var_1195_cast = square(x = var_1194_cast);
            tensor<int32, [1]> var_1196 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1197_cast = reduce_mean(axes = var_1196, keep_dims = var_66, x = var_1195_cast);
            tensor<fp16, []> var_1198_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1199_cast = add(x = var_1197_cast, y = var_1198_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_75_cast = sqrt(x = var_1199_cast);
            tensor<fp16, [1, 128, 1]> sep_module_18_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(769664)))];
            tensor<fp16, [1, 128, 12]> var_1202_cast = mul(x = sep_module_18_tcn_2_norm_gamma_to_fp16, y = var_1194_cast);
            tensor<fp16, [1, 128, 12]> var_1203_cast = real_div(x = var_1202_cast, y = std_y_75_cast);
            tensor<fp16, [1, 128, 1]> sep_module_18_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(769984)))];
            tensor<fp16, [1, 128, 12]> input_187_cast = add(x = var_1203_cast, y = sep_module_18_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_189_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 2, 0])];
            tensor<string, []> input_189_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_189_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 14]> input_189_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_187_cast_in_state, input_187_cast));
            tensor<fp16, [1, 128, 2]> input_187_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -2]), size = tensor<int32, [3]>([1, 128, 2]), x = input_189_cast);
            tensor<int32, [1]> var_1208 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1210 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_191_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_191_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_18_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(770304)))];
            tensor<fp16, [1, 128, 12]> input_191_cast = conv(dilations = var_1210, groups = var_65, pad = input_191_pad_0, pad_type = input_191_pad_type_0, strides = var_1208, weight = sep_module_18_tcn_4_weight_to_fp16, x = input_189_cast);
            tensor<fp16, []> var_1214_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.b34p-1)];
            tensor<fp16, [1, 128, 12]> var_1214_cast = leaky_relu(alpha = var_1214_alpha_1_to_fp16, x = input_191_cast);
            tensor<int32, [1]> var_1218 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_77_cast = reduce_mean(axes = var_1218, keep_dims = var_66, x = var_1214_cast);
            tensor<fp16, [1, 128, 12]> var_1220_cast = sub(x = var_1214_cast, y = mean_y_77_cast);
            tensor<fp16, [1, 128, 12]> var_1221_cast = square(x = var_1220_cast);
            tensor<int32, [1]> var_1222 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1223_cast = reduce_mean(axes = var_1222, keep_dims = var_66, x = var_1221_cast);
            tensor<fp16, []> var_1224_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1225_cast = add(x = var_1223_cast, y = var_1224_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_77_cast = sqrt(x = var_1225_cast);
            tensor<fp16, [1, 128, 1]> sep_module_18_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(771136)))];
            tensor<fp16, [1, 128, 12]> var_1228_cast = mul(x = sep_module_18_tcn_6_norm_gamma_to_fp16, y = var_1220_cast);
            tensor<fp16, [1, 128, 12]> var_1229_cast = real_div(x = var_1228_cast, y = std_y_77_cast);
            tensor<fp16, [1, 128, 1]> sep_module_18_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(771456)))];
            tensor<fp16, [1, 128, 12]> y_38_cast = add(x = var_1229_cast, y = sep_module_18_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_193_cast = add(x = input_183_cast, y = y_38_cast);
            tensor<int32, [1]> var_1240 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1242 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_195_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_195_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_19_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(771776)))];
            tensor<fp16, [1, 128, 12]> input_195_cast = conv(dilations = var_1242, groups = var_64, pad = input_195_pad_0, pad_type = input_195_pad_type_0, strides = var_1240, weight = sep_module_19_tcn_0_weight_to_fp16, x = input_193_cast);
            tensor<fp16, []> var_1246_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.d2p-2)];
            tensor<fp16, [1, 128, 12]> var_1246_cast = leaky_relu(alpha = var_1246_alpha_1_to_fp16, x = input_195_cast);
            tensor<int32, [1]> var_1250 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_79_cast = reduce_mean(axes = var_1250, keep_dims = var_66, x = var_1246_cast);
            tensor<fp16, [1, 128, 12]> var_1252_cast = sub(x = var_1246_cast, y = mean_y_79_cast);
            tensor<fp16, [1, 128, 12]> var_1253_cast = square(x = var_1252_cast);
            tensor<int32, [1]> var_1254 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1255_cast = reduce_mean(axes = var_1254, keep_dims = var_66, x = var_1253_cast);
            tensor<fp16, []> var_1256_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1257_cast = add(x = var_1255_cast, y = var_1256_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_79_cast = sqrt(x = var_1257_cast);
            tensor<fp16, [1, 128, 1]> sep_module_19_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(804608)))];
            tensor<fp16, [1, 128, 12]> var_1260_cast = mul(x = sep_module_19_tcn_2_norm_gamma_to_fp16, y = var_1252_cast);
            tensor<fp16, [1, 128, 12]> var_1261_cast = real_div(x = var_1260_cast, y = std_y_79_cast);
            tensor<fp16, [1, 128, 1]> sep_module_19_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(804928)))];
            tensor<fp16, [1, 128, 12]> input_197_cast = add(x = var_1261_cast, y = sep_module_19_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_199_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 4, 0])];
            tensor<string, []> input_199_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_199_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 16]> input_199_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_197_cast_in_state, input_197_cast));
            tensor<fp16, [1, 128, 4]> input_197_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -4]), size = tensor<int32, [3]>([1, 128, 4]), x = input_199_cast);
            tensor<int32, [1]> var_1266 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1268 = const()[val = tensor<int32, [1]>([2])];
            tensor<string, []> input_201_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_201_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_19_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(805248)))];
            tensor<fp16, [1, 128, 12]> input_201_cast = conv(dilations = var_1268, groups = var_65, pad = input_201_pad_0, pad_type = input_201_pad_type_0, strides = var_1266, weight = sep_module_19_tcn_4_weight_to_fp16, x = input_199_cast);
            tensor<fp16, []> var_1272_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.7e8p-2)];
            tensor<fp16, [1, 128, 12]> var_1272_cast = leaky_relu(alpha = var_1272_alpha_1_to_fp16, x = input_201_cast);
            tensor<int32, [1]> var_1276 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_81_cast = reduce_mean(axes = var_1276, keep_dims = var_66, x = var_1272_cast);
            tensor<fp16, [1, 128, 12]> var_1278_cast = sub(x = var_1272_cast, y = mean_y_81_cast);
            tensor<fp16, [1, 128, 12]> var_1279_cast = square(x = var_1278_cast);
            tensor<int32, [1]> var_1280 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1281_cast = reduce_mean(axes = var_1280, keep_dims = var_66, x = var_1279_cast);
            tensor<fp16, []> var_1282_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1283_cast = add(x = var_1281_cast, y = var_1282_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_81_cast = sqrt(x = var_1283_cast);
            tensor<fp16, [1, 128, 1]> sep_module_19_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(806080)))];
            tensor<fp16, [1, 128, 12]> var_1286_cast = mul(x = sep_module_19_tcn_6_norm_gamma_to_fp16, y = var_1278_cast);
            tensor<fp16, [1, 128, 12]> var_1287_cast = real_div(x = var_1286_cast, y = std_y_81_cast);
            tensor<fp16, [1, 128, 1]> sep_module_19_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(806400)))];
            tensor<fp16, [1, 128, 12]> y_40_cast = add(x = var_1287_cast, y = sep_module_19_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_203_cast = add(x = input_193_cast, y = y_40_cast);
            tensor<int32, [1]> var_1298 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1300 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_205_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_205_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_20_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(806720)))];
            tensor<fp16, [1, 128, 12]> input_205_cast = conv(dilations = var_1300, groups = var_64, pad = input_205_pad_0, pad_type = input_205_pad_type_0, strides = var_1298, weight = sep_module_20_tcn_0_weight_to_fp16, x = input_203_cast);
            tensor<fp16, []> var_1304_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.35cp-1)];
            tensor<fp16, [1, 128, 12]> var_1304_cast = leaky_relu(alpha = var_1304_alpha_1_to_fp16, x = input_205_cast);
            tensor<int32, [1]> var_1308 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_83_cast = reduce_mean(axes = var_1308, keep_dims = var_66, x = var_1304_cast);
            tensor<fp16, [1, 128, 12]> var_1310_cast = sub(x = var_1304_cast, y = mean_y_83_cast);
            tensor<fp16, [1, 128, 12]> var_1311_cast = square(x = var_1310_cast);
            tensor<int32, [1]> var_1312 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1313_cast = reduce_mean(axes = var_1312, keep_dims = var_66, x = var_1311_cast);
            tensor<fp16, []> var_1314_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1315_cast = add(x = var_1313_cast, y = var_1314_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_83_cast = sqrt(x = var_1315_cast);
            tensor<fp16, [1, 128, 1]> sep_module_20_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(839552)))];
            tensor<fp16, [1, 128, 12]> var_1318_cast = mul(x = sep_module_20_tcn_2_norm_gamma_to_fp16, y = var_1310_cast);
            tensor<fp16, [1, 128, 12]> var_1319_cast = real_div(x = var_1318_cast, y = std_y_83_cast);
            tensor<fp16, [1, 128, 1]> sep_module_20_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(839872)))];
            tensor<fp16, [1, 128, 12]> input_207_cast = add(x = var_1319_cast, y = sep_module_20_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_209_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 8, 0])];
            tensor<string, []> input_209_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_209_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 20]> input_209_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_207_cast_in_state, input_207_cast));
            tensor<fp16, [1, 128, 8]> input_207_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -8]), size = tensor<int32, [3]>([1, 128, 8]), x = input_209_cast);
            tensor<int32, [1]> var_1324 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1326 = const()[val = tensor<int32, [1]>([4])];
            tensor<string, []> input_211_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_211_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_20_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(840192)))];
            tensor<fp16, [1, 128, 12]> input_211_cast = conv(dilations = var_1326, groups = var_65, pad = input_211_pad_0, pad_type = input_211_pad_type_0, strides = var_1324, weight = sep_module_20_tcn_4_weight_to_fp16, x = input_209_cast);
            tensor<fp16, []> var_1330_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.ef8p-2)];
            tensor<fp16, [1, 128, 12]> var_1330_cast = leaky_relu(alpha = var_1330_alpha_1_to_fp16, x = input_211_cast);
            tensor<int32, [1]> var_1334 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_85_cast = reduce_mean(axes = var_1334, keep_dims = var_66, x = var_1330_cast);
            tensor<fp16, [1, 128, 12]> var_1336_cast = sub(x = var_1330_cast, y = mean_y_85_cast);
            tensor<fp16, [1, 128, 12]> var_1337_cast = square(x = var_1336_cast);
            tensor<int32, [1]> var_1338 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1339_cast = reduce_mean(axes = var_1338, keep_dims = var_66, x = var_1337_cast);
            tensor<fp16, []> var_1340_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1341_cast = add(x = var_1339_cast, y = var_1340_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_85_cast = sqrt(x = var_1341_cast);
            tensor<fp16, [1, 128, 1]> sep_module_20_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(841024)))];
            tensor<fp16, [1, 128, 12]> var_1344_cast = mul(x = sep_module_20_tcn_6_norm_gamma_to_fp16, y = var_1336_cast);
            tensor<fp16, [1, 128, 12]> var_1345_cast = real_div(x = var_1344_cast, y = std_y_85_cast);
            tensor<fp16, [1, 128, 1]> sep_module_20_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(841344)))];
            tensor<fp16, [1, 128, 12]> y_42_cast = add(x = var_1345_cast, y = sep_module_20_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_213_cast = add(x = input_203_cast, y = y_42_cast);
            tensor<int32, [1]> var_1356 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1358 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_215_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_215_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_21_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(841664)))];
            tensor<fp16, [1, 128, 12]> input_215_cast = conv(dilations = var_1358, groups = var_64, pad = input_215_pad_0, pad_type = input_215_pad_type_0, strides = var_1356, weight = sep_module_21_tcn_0_weight_to_fp16, x = input_213_cast);
            tensor<fp16, []> var_1362_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.58cp-1)];
            tensor<fp16, [1, 128, 12]> var_1362_cast = leaky_relu(alpha = var_1362_alpha_1_to_fp16, x = input_215_cast);
            tensor<int32, [1]> var_1366 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_87_cast = reduce_mean(axes = var_1366, keep_dims = var_66, x = var_1362_cast);
            tensor<fp16, [1, 128, 12]> var_1368_cast = sub(x = var_1362_cast, y = mean_y_87_cast);
            tensor<fp16, [1, 128, 12]> var_1369_cast = square(x = var_1368_cast);
            tensor<int32, [1]> var_1370 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1371_cast = reduce_mean(axes = var_1370, keep_dims = var_66, x = var_1369_cast);
            tensor<fp16, []> var_1372_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1373_cast = add(x = var_1371_cast, y = var_1372_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_87_cast = sqrt(x = var_1373_cast);
            tensor<fp16, [1, 128, 1]> sep_module_21_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(874496)))];
            tensor<fp16, [1, 128, 12]> var_1376_cast = mul(x = sep_module_21_tcn_2_norm_gamma_to_fp16, y = var_1368_cast);
            tensor<fp16, [1, 128, 12]> var_1377_cast = real_div(x = var_1376_cast, y = std_y_87_cast);
            tensor<fp16, [1, 128, 1]> sep_module_21_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(874816)))];
            tensor<fp16, [1, 128, 12]> input_217_cast = add(x = var_1377_cast, y = sep_module_21_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_219_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 16, 0])];
            tensor<string, []> input_219_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_219_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 28]> input_219_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_217_cast_in_state, input_217_cast));
            tensor<fp16, [1, 128, 16]> input_217_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -16]), size = tensor<int32, [3]>([1, 128, 16]), x = input_219_cast);
            tensor<int32, [1]> var_1382 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1384 = const()[val = tensor<int32, [1]>([8])];
            tensor<string, []> input_221_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_221_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_21_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(875136)))];
            tensor<fp16, [1, 128, 12]> input_221_cast = conv(dilations = var_1384, groups = var_65, pad = input_221_pad_0, pad_type = input_221_pad_type_0, strides = var_1382, weight = sep_module_21_tcn_4_weight_to_fp16, x = input_219_cast);
            tensor<fp16, []> var_1388_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.25p-1)];
            tensor<fp16, [1, 128, 12]> var_1388_cast = leaky_relu(alpha = var_1388_alpha_1_to_fp16, x = input_221_cast);
            tensor<int32, [1]> var_1392 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_89_cast = reduce_mean(axes = var_1392, keep_dims = var_66, x = var_1388_cast);
            tensor<fp16, [1, 128, 12]> var_1394_cast = sub(x = var_1388_cast, y = mean_y_89_cast);
            tensor<fp16, [1, 128, 12]> var_1395_cast = square(x = var_1394_cast);
            tensor<int32, [1]> var_1396 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1397_cast = reduce_mean(axes = var_1396, keep_dims = var_66, x = var_1395_cast);
            tensor<fp16, []> var_1398_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1399_cast = add(x = var_1397_cast, y = var_1398_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_89_cast = sqrt(x = var_1399_cast);
            tensor<fp16, [1, 128, 1]> sep_module_21_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(875968)))];
            tensor<fp16, [1, 128, 12]> var_1402_cast = mul(x = sep_module_21_tcn_6_norm_gamma_to_fp16, y = var_1394_cast);
            tensor<fp16, [1, 128, 12]> var_1403_cast = real_div(x = var_1402_cast, y = std_y_89_cast);
            tensor<fp16, [1, 128, 1]> sep_module_21_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(876288)))];
            tensor<fp16, [1, 128, 12]> y_44_cast = add(x = var_1403_cast, y = sep_module_21_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_223_cast = add(x = input_213_cast, y = y_44_cast);
            tensor<int32, [1]> var_1414 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1416 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_225_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_225_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_22_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(876608)))];
            tensor<fp16, [1, 128, 12]> input_225_cast = conv(dilations = var_1416, groups = var_64, pad = input_225_pad_0, pad_type = input_225_pad_type_0, strides = var_1414, weight = sep_module_22_tcn_0_weight_to_fp16, x = input_223_cast);
            tensor<fp16, []> var_1420_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.2acp-1)];
            tensor<fp16, [1, 128, 12]> var_1420_cast = leaky_relu(alpha = var_1420_alpha_1_to_fp16, x = input_225_cast);
            tensor<int32, [1]> var_1424 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_91_cast = reduce_mean(axes = var_1424, keep_dims = var_66, x = var_1420_cast);
            tensor<fp16, [1, 128, 12]> var_1426_cast = sub(x = var_1420_cast, y = mean_y_91_cast);
            tensor<fp16, [1, 128, 12]> var_1427_cast = square(x = var_1426_cast);
            tensor<int32, [1]> var_1428 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1429_cast = reduce_mean(axes = var_1428, keep_dims = var_66, x = var_1427_cast);
            tensor<fp16, []> var_1430_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1431_cast = add(x = var_1429_cast, y = var_1430_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_91_cast = sqrt(x = var_1431_cast);
            tensor<fp16, [1, 128, 1]> sep_module_22_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(909440)))];
            tensor<fp16, [1, 128, 12]> var_1434_cast = mul(x = sep_module_22_tcn_2_norm_gamma_to_fp16, y = var_1426_cast);
            tensor<fp16, [1, 128, 12]> var_1435_cast = real_div(x = var_1434_cast, y = std_y_91_cast);
            tensor<fp16, [1, 128, 1]> sep_module_22_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(909760)))];
            tensor<fp16, [1, 128, 12]> input_227_cast = add(x = var_1435_cast, y = sep_module_22_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_229_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 32, 0])];
            tensor<string, []> input_229_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_229_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 44]> input_229_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_227_cast_in_state, input_227_cast));
            tensor<fp16, [1, 128, 32]> input_227_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -32]), size = tensor<int32, [3]>([1, 128, 32]), x = input_229_cast);
            tensor<int32, [1]> var_1440 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1442 = const()[val = tensor<int32, [1]>([16])];
            tensor<string, []> input_231_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_231_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_22_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(910080)))];
            tensor<fp16, [1, 128, 12]> input_231_cast = conv(dilations = var_1442, groups = var_65, pad = input_231_pad_0, pad_type = input_231_pad_type_0, strides = var_1440, weight = sep_module_22_tcn_4_weight_to_fp16, x = input_229_cast);
            tensor<fp16, []> var_1446_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.64cp-6)];
            tensor<fp16, [1, 128, 12]> var_1446_cast = leaky_relu(alpha = var_1446_alpha_1_to_fp16, x = input_231_cast);
            tensor<int32, [1]> var_1450 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_93_cast = reduce_mean(axes = var_1450, keep_dims = var_66, x = var_1446_cast);
            tensor<fp16, [1, 128, 12]> var_1452_cast = sub(x = var_1446_cast, y = mean_y_93_cast);
            tensor<fp16, [1, 128, 12]> var_1453_cast = square(x = var_1452_cast);
            tensor<int32, [1]> var_1454 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1455_cast = reduce_mean(axes = var_1454, keep_dims = var_66, x = var_1453_cast);
            tensor<fp16, []> var_1456_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1457_cast = add(x = var_1455_cast, y = var_1456_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_93_cast = sqrt(x = var_1457_cast);
            tensor<fp16, [1, 128, 1]> sep_module_22_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(910912)))];
            tensor<fp16, [1, 128, 12]> var_1460_cast = mul(x = sep_module_22_tcn_6_norm_gamma_to_fp16, y = var_1452_cast);
            tensor<fp16, [1, 128, 12]> var_1461_cast = real_div(x = var_1460_cast, y = std_y_93_cast);
            tensor<fp16, [1, 128, 1]> sep_module_22_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(911232)))];
            tensor<fp16, [1, 128, 12]> y_46_cast = add(x = var_1461_cast, y = sep_module_22_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_233_cast = add(x = input_223_cast, y = y_46_cast);
            tensor<int32, [1]> var_1472 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1474 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_235_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_235_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_23_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(911552)))];
            tensor<fp16, [1, 128, 12]> input_235_cast = conv(dilations = var_1474, groups = var_64, pad = input_235_pad_0, pad_type = input_235_pad_type_0, strides = var_1472, weight = sep_module_23_tcn_0_weight_to_fp16, x = input_233_cast);
            tensor<fp16, []> var_1478_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.9bp-1)];
            tensor<fp16, [1, 128, 12]> var_1478_cast = leaky_relu(alpha = var_1478_alpha_1_to_fp16, x = input_235_cast);
            tensor<int32, [1]> var_1482 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_95_cast = reduce_mean(axes = var_1482, keep_dims = var_66, x = var_1478_cast);
            tensor<fp16, [1, 128, 12]> var_1484_cast = sub(x = var_1478_cast, y = mean_y_95_cast);
            tensor<fp16, [1, 128, 12]> var_1485_cast = square(x = var_1484_cast);
            tensor<int32, [1]> var_1486 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1487_cast = reduce_mean(axes = var_1486, keep_dims = var_66, x = var_1485_cast);
            tensor<fp16, []> var_1488_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1489_cast = add(x = var_1487_cast, y = var_1488_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_95_cast = sqrt(x = var_1489_cast);
            tensor<fp16, [1, 128, 1]> sep_module_23_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(944384)))];
            tensor<fp16, [1, 128, 12]> var_1492_cast = mul(x = sep_module_23_tcn_2_norm_gamma_to_fp16, y = var_1484_cast);
            tensor<fp16, [1, 128, 12]> var_1493_cast = real_div(x = var_1492_cast, y = std_y_95_cast);
            tensor<fp16, [1, 128, 1]> sep_module_23_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(944704)))];
            tensor<fp16, [1, 128, 12]> input_237_cast = add(x = var_1493_cast, y = sep_module_23_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_239_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 64, 0])];
            tensor<string, []> input_239_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_239_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 76]> input_239_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_237_cast_in_state, input_237_cast));
            tensor<fp16, [1, 128, 64]> input_237_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -64]), size = tensor<int32, [3]>([1, 128, 64]), x = input_239_cast);
            tensor<int32, [1]> var_1498 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1500 = const()[val = tensor<int32, [1]>([32])];
            tensor<string, []> input_241_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_241_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_23_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(945024)))];
            tensor<fp16, [1, 128, 12]> input_241_cast = conv(dilations = var_1500, groups = var_65, pad = input_241_pad_0, pad_type = input_241_pad_type_0, strides = var_1498, weight = sep_module_23_tcn_4_weight_to_fp16, x = input_239_cast);
            tensor<fp16, []> var_1504_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.928p-2)];
            tensor<fp16, [1, 128, 12]> var_1504_cast = leaky_relu(alpha = var_1504_alpha_1_to_fp16, x = input_241_cast);
            tensor<int32, [1]> var_1508 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_97_cast = reduce_mean(axes = var_1508, keep_dims = var_66, x = var_1504_cast);
            tensor<fp16, [1, 128, 12]> var_1510_cast = sub(x = var_1504_cast, y = mean_y_97_cast);
            tensor<fp16, [1, 128, 12]> var_1511_cast = square(x = var_1510_cast);
            tensor<int32, [1]> var_1512 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1513_cast = reduce_mean(axes = var_1512, keep_dims = var_66, x = var_1511_cast);
            tensor<fp16, []> var_1514_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1515_cast = add(x = var_1513_cast, y = var_1514_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_97_cast = sqrt(x = var_1515_cast);
            tensor<fp16, [1, 128, 1]> sep_module_23_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(945856)))];
            tensor<fp16, [1, 128, 12]> var_1518_cast = mul(x = sep_module_23_tcn_6_norm_gamma_to_fp16, y = var_1510_cast);
            tensor<fp16, [1, 128, 12]> var_1519_cast = real_div(x = var_1518_cast, y = std_y_97_cast);
            tensor<fp16, [1, 128, 1]> sep_module_23_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(946176)))];
            tensor<fp16, [1, 128, 12]> y_48_cast = add(x = var_1519_cast, y = sep_module_23_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_243_cast = add(x = input_233_cast, y = y_48_cast);
            tensor<int32, [1]> var_1530 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1532 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_245_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_245_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_24_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(946496)))];
            tensor<fp16, [1, 128, 12]> input_245_cast = conv(dilations = var_1532, groups = var_64, pad = input_245_pad_0, pad_type = input_245_pad_type_0, strides = var_1530, weight = sep_module_24_tcn_0_weight_to_fp16, x = input_243_cast);
            tensor<fp16, []> var_1536_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.eecp-2)];
            tensor<fp16, [1, 128, 12]> var_1536_cast = leaky_relu(alpha = var_1536_alpha_1_to_fp16, x = input_245_cast);
            tensor<int32, [1]> var_1540 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_99_cast = reduce_mean(axes = var_1540, keep_dims = var_66, x = var_1536_cast);
            tensor<fp16, [1, 128, 12]> var_1542_cast = sub(x = var_1536_cast, y = mean_y_99_cast);
            tensor<fp16, [1, 128, 12]> var_1543_cast = square(x = var_1542_cast);
            tensor<int32, [1]> var_1544 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1545_cast = reduce_mean(axes = var_1544, keep_dims = var_66, x = var_1543_cast);
            tensor<fp16, []> var_1546_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1547_cast = add(x = var_1545_cast, y = var_1546_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_99_cast = sqrt(x = var_1547_cast);
            tensor<fp16, [1, 128, 1]> sep_module_24_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(979328)))];
            tensor<fp16, [1, 128, 12]> var_1550_cast = mul(x = sep_module_24_tcn_2_norm_gamma_to_fp16, y = var_1542_cast);
            tensor<fp16, [1, 128, 12]> var_1551_cast = real_div(x = var_1550_cast, y = std_y_99_cast);
            tensor<fp16, [1, 128, 1]> sep_module_24_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(979648)))];
            tensor<fp16, [1, 128, 12]> input_247_cast = add(x = var_1551_cast, y = sep_module_24_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_249_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 2, 0])];
            tensor<string, []> input_249_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_249_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 14]> input_249_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_247_cast_in_state, input_247_cast));
            tensor<fp16, [1, 128, 2]> input_247_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -2]), size = tensor<int32, [3]>([1, 128, 2]), x = input_249_cast);
            tensor<int32, [1]> var_1556 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1558 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_251_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_251_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_24_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(979968)))];
            tensor<fp16, [1, 128, 12]> input_251_cast = conv(dilations = var_1558, groups = var_65, pad = input_251_pad_0, pad_type = input_251_pad_type_0, strides = var_1556, weight = sep_module_24_tcn_4_weight_to_fp16, x = input_249_cast);
            tensor<fp16, []> var_1562_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.e78p-1)];
            tensor<fp16, [1, 128, 12]> var_1562_cast = leaky_relu(alpha = var_1562_alpha_1_to_fp16, x = input_251_cast);
            tensor<int32, [1]> var_1566 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_101_cast = reduce_mean(axes = var_1566, keep_dims = var_66, x = var_1562_cast);
            tensor<fp16, [1, 128, 12]> var_1568_cast = sub(x = var_1562_cast, y = mean_y_101_cast);
            tensor<fp16, [1, 128, 12]> var_1569_cast = square(x = var_1568_cast);
            tensor<int32, [1]> var_1570 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1571_cast = reduce_mean(axes = var_1570, keep_dims = var_66, x = var_1569_cast);
            tensor<fp16, []> var_1572_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1573_cast = add(x = var_1571_cast, y = var_1572_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_101_cast = sqrt(x = var_1573_cast);
            tensor<fp16, [1, 128, 1]> sep_module_24_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(980800)))];
            tensor<fp16, [1, 128, 12]> var_1576_cast = mul(x = sep_module_24_tcn_6_norm_gamma_to_fp16, y = var_1568_cast);
            tensor<fp16, [1, 128, 12]> var_1577_cast = real_div(x = var_1576_cast, y = std_y_101_cast);
            tensor<fp16, [1, 128, 1]> sep_module_24_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(981120)))];
            tensor<fp16, [1, 128, 12]> y_50_cast = add(x = var_1577_cast, y = sep_module_24_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_253_cast = add(x = input_243_cast, y = y_50_cast);
            tensor<int32, [1]> var_1588 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1590 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_255_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_255_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_25_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(981440)))];
            tensor<fp16, [1, 128, 12]> input_255_cast = conv(dilations = var_1590, groups = var_64, pad = input_255_pad_0, pad_type = input_255_pad_type_0, strides = var_1588, weight = sep_module_25_tcn_0_weight_to_fp16, x = input_253_cast);
            tensor<fp16, []> var_1594_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.31p-1)];
            tensor<fp16, [1, 128, 12]> var_1594_cast = leaky_relu(alpha = var_1594_alpha_1_to_fp16, x = input_255_cast);
            tensor<int32, [1]> var_1598 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_103_cast = reduce_mean(axes = var_1598, keep_dims = var_66, x = var_1594_cast);
            tensor<fp16, [1, 128, 12]> var_1600_cast = sub(x = var_1594_cast, y = mean_y_103_cast);
            tensor<fp16, [1, 128, 12]> var_1601_cast = square(x = var_1600_cast);
            tensor<int32, [1]> var_1602 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1603_cast = reduce_mean(axes = var_1602, keep_dims = var_66, x = var_1601_cast);
            tensor<fp16, []> var_1604_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1605_cast = add(x = var_1603_cast, y = var_1604_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_103_cast = sqrt(x = var_1605_cast);
            tensor<fp16, [1, 128, 1]> sep_module_25_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1014272)))];
            tensor<fp16, [1, 128, 12]> var_1608_cast = mul(x = sep_module_25_tcn_2_norm_gamma_to_fp16, y = var_1600_cast);
            tensor<fp16, [1, 128, 12]> var_1609_cast = real_div(x = var_1608_cast, y = std_y_103_cast);
            tensor<fp16, [1, 128, 1]> sep_module_25_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1014592)))];
            tensor<fp16, [1, 128, 12]> input_257_cast = add(x = var_1609_cast, y = sep_module_25_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_259_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 4, 0])];
            tensor<string, []> input_259_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_259_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 16]> input_259_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_257_cast_in_state, input_257_cast));
            tensor<fp16, [1, 128, 4]> input_257_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -4]), size = tensor<int32, [3]>([1, 128, 4]), x = input_259_cast);
            tensor<int32, [1]> var_1614 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1616 = const()[val = tensor<int32, [1]>([2])];
            tensor<string, []> input_261_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_261_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_25_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1014912)))];
            tensor<fp16, [1, 128, 12]> input_261_cast = conv(dilations = var_1616, groups = var_65, pad = input_261_pad_0, pad_type = input_261_pad_type_0, strides = var_1614, weight = sep_module_25_tcn_4_weight_to_fp16, x = input_259_cast);
            tensor<fp16, []> var_1620_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.d8cp-1)];
            tensor<fp16, [1, 128, 12]> var_1620_cast = leaky_relu(alpha = var_1620_alpha_1_to_fp16, x = input_261_cast);
            tensor<int32, [1]> var_1624 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_105_cast = reduce_mean(axes = var_1624, keep_dims = var_66, x = var_1620_cast);
            tensor<fp16, [1, 128, 12]> var_1626_cast = sub(x = var_1620_cast, y = mean_y_105_cast);
            tensor<fp16, [1, 128, 12]> var_1627_cast = square(x = var_1626_cast);
            tensor<int32, [1]> var_1628 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1629_cast = reduce_mean(axes = var_1628, keep_dims = var_66, x = var_1627_cast);
            tensor<fp16, []> var_1630_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1631_cast = add(x = var_1629_cast, y = var_1630_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_105_cast = sqrt(x = var_1631_cast);
            tensor<fp16, [1, 128, 1]> sep_module_25_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1015744)))];
            tensor<fp16, [1, 128, 12]> var_1634_cast = mul(x = sep_module_25_tcn_6_norm_gamma_to_fp16, y = var_1626_cast);
            tensor<fp16, [1, 128, 12]> var_1635_cast = real_div(x = var_1634_cast, y = std_y_105_cast);
            tensor<fp16, [1, 128, 1]> sep_module_25_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1016064)))];
            tensor<fp16, [1, 128, 12]> y_52_cast = add(x = var_1635_cast, y = sep_module_25_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_263_cast = add(x = input_253_cast, y = y_52_cast);
            tensor<int32, [1]> var_1646 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1648 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_265_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_265_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_26_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1016384)))];
            tensor<fp16, [1, 128, 12]> input_265_cast = conv(dilations = var_1648, groups = var_64, pad = input_265_pad_0, pad_type = input_265_pad_type_0, strides = var_1646, weight = sep_module_26_tcn_0_weight_to_fp16, x = input_263_cast);
            tensor<fp16, []> var_1652_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.478p-2)];
            tensor<fp16, [1, 128, 12]> var_1652_cast = leaky_relu(alpha = var_1652_alpha_1_to_fp16, x = input_265_cast);
            tensor<int32, [1]> var_1656 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_107_cast = reduce_mean(axes = var_1656, keep_dims = var_66, x = var_1652_cast);
            tensor<fp16, [1, 128, 12]> var_1658_cast = sub(x = var_1652_cast, y = mean_y_107_cast);
            tensor<fp16, [1, 128, 12]> var_1659_cast = square(x = var_1658_cast);
            tensor<int32, [1]> var_1660 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1661_cast = reduce_mean(axes = var_1660, keep_dims = var_66, x = var_1659_cast);
            tensor<fp16, []> var_1662_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1663_cast = add(x = var_1661_cast, y = var_1662_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_107_cast = sqrt(x = var_1663_cast);
            tensor<fp16, [1, 128, 1]> sep_module_26_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1049216)))];
            tensor<fp16, [1, 128, 12]> var_1666_cast = mul(x = sep_module_26_tcn_2_norm_gamma_to_fp16, y = var_1658_cast);
            tensor<fp16, [1, 128, 12]> var_1667_cast = real_div(x = var_1666_cast, y = std_y_107_cast);
            tensor<fp16, [1, 128, 1]> sep_module_26_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1049536)))];
            tensor<fp16, [1, 128, 12]> input_267_cast = add(x = var_1667_cast, y = sep_module_26_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_269_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 8, 0])];
            tensor<string, []> input_269_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_269_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 20]> input_269_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_267_cast_in_state, input_267_cast));
            tensor<fp16, [1, 128, 8]> input_267_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -8]), size = tensor<int32, [3]>([1, 128, 8]), x = input_269_cast);
            tensor<int32, [1]> var_1672 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1674 = const()[val = tensor<int32, [1]>([4])];
            tensor<string, []> input_271_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_271_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_26_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1049856)))];
            tensor<fp16, [1, 128, 12]> input_271_cast = conv(dilations = var_1674, groups = var_65, pad = input_271_pad_0, pad_type = input_271_pad_type_0, strides = var_1672, weight = sep_module_26_tcn_4_weight_to_fp16, x = input_269_cast);
            tensor<fp16, []> var_1678_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.7ccp-1)];
            tensor<fp16, [1, 128, 12]> var_1678_cast = leaky_relu(alpha = var_1678_alpha_1_to_fp16, x = input_271_cast);
            tensor<int32, [1]> var_1682 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_109_cast = reduce_mean(axes = var_1682, keep_dims = var_66, x = var_1678_cast);
            tensor<fp16, [1, 128, 12]> var_1684_cast = sub(x = var_1678_cast, y = mean_y_109_cast);
            tensor<fp16, [1, 128, 12]> var_1685_cast = square(x = var_1684_cast);
            tensor<int32, [1]> var_1686 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1687_cast = reduce_mean(axes = var_1686, keep_dims = var_66, x = var_1685_cast);
            tensor<fp16, []> var_1688_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1689_cast = add(x = var_1687_cast, y = var_1688_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_109_cast = sqrt(x = var_1689_cast);
            tensor<fp16, [1, 128, 1]> sep_module_26_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1050688)))];
            tensor<fp16, [1, 128, 12]> var_1692_cast = mul(x = sep_module_26_tcn_6_norm_gamma_to_fp16, y = var_1684_cast);
            tensor<fp16, [1, 128, 12]> var_1693_cast = real_div(x = var_1692_cast, y = std_y_109_cast);
            tensor<fp16, [1, 128, 1]> sep_module_26_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1051008)))];
            tensor<fp16, [1, 128, 12]> y_54_cast = add(x = var_1693_cast, y = sep_module_26_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_273_cast = add(x = input_263_cast, y = y_54_cast);
            tensor<int32, [1]> var_1704 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1706 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_275_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_275_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_27_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1051328)))];
            tensor<fp16, [1, 128, 12]> input_275_cast = conv(dilations = var_1706, groups = var_64, pad = input_275_pad_0, pad_type = input_275_pad_type_0, strides = var_1704, weight = sep_module_27_tcn_0_weight_to_fp16, x = input_273_cast);
            tensor<fp16, []> var_1710_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.48p-1)];
            tensor<fp16, [1, 128, 12]> var_1710_cast = leaky_relu(alpha = var_1710_alpha_1_to_fp16, x = input_275_cast);
            tensor<int32, [1]> var_1714 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_111_cast = reduce_mean(axes = var_1714, keep_dims = var_66, x = var_1710_cast);
            tensor<fp16, [1, 128, 12]> var_1716_cast = sub(x = var_1710_cast, y = mean_y_111_cast);
            tensor<fp16, [1, 128, 12]> var_1717_cast = square(x = var_1716_cast);
            tensor<int32, [1]> var_1718 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1719_cast = reduce_mean(axes = var_1718, keep_dims = var_66, x = var_1717_cast);
            tensor<fp16, []> var_1720_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1721_cast = add(x = var_1719_cast, y = var_1720_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_111_cast = sqrt(x = var_1721_cast);
            tensor<fp16, [1, 128, 1]> sep_module_27_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1084160)))];
            tensor<fp16, [1, 128, 12]> var_1724_cast = mul(x = sep_module_27_tcn_2_norm_gamma_to_fp16, y = var_1716_cast);
            tensor<fp16, [1, 128, 12]> var_1725_cast = real_div(x = var_1724_cast, y = std_y_111_cast);
            tensor<fp16, [1, 128, 1]> sep_module_27_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1084480)))];
            tensor<fp16, [1, 128, 12]> input_277_cast = add(x = var_1725_cast, y = sep_module_27_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_279_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 16, 0])];
            tensor<string, []> input_279_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_279_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 28]> input_279_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_277_cast_in_state, input_277_cast));
            tensor<fp16, [1, 128, 16]> input_277_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -16]), size = tensor<int32, [3]>([1, 128, 16]), x = input_279_cast);
            tensor<int32, [1]> var_1730 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1732 = const()[val = tensor<int32, [1]>([8])];
            tensor<string, []> input_281_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_281_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_27_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1084800)))];
            tensor<fp16, [1, 128, 12]> input_281_cast = conv(dilations = var_1732, groups = var_65, pad = input_281_pad_0, pad_type = input_281_pad_type_0, strides = var_1730, weight = sep_module_27_tcn_4_weight_to_fp16, x = input_279_cast);
            tensor<fp16, []> var_1736_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.214p-1)];
            tensor<fp16, [1, 128, 12]> var_1736_cast = leaky_relu(alpha = var_1736_alpha_1_to_fp16, x = input_281_cast);
            tensor<int32, [1]> var_1740 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_113_cast = reduce_mean(axes = var_1740, keep_dims = var_66, x = var_1736_cast);
            tensor<fp16, [1, 128, 12]> var_1742_cast = sub(x = var_1736_cast, y = mean_y_113_cast);
            tensor<fp16, [1, 128, 12]> var_1743_cast = square(x = var_1742_cast);
            tensor<int32, [1]> var_1744 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1745_cast = reduce_mean(axes = var_1744, keep_dims = var_66, x = var_1743_cast);
            tensor<fp16, []> var_1746_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1747_cast = add(x = var_1745_cast, y = var_1746_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_113_cast = sqrt(x = var_1747_cast);
            tensor<fp16, [1, 128, 1]> sep_module_27_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1085632)))];
            tensor<fp16, [1, 128, 12]> var_1750_cast = mul(x = sep_module_27_tcn_6_norm_gamma_to_fp16, y = var_1742_cast);
            tensor<fp16, [1, 128, 12]> var_1751_cast = real_div(x = var_1750_cast, y = std_y_113_cast);
            tensor<fp16, [1, 128, 1]> sep_module_27_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1085952)))];
            tensor<fp16, [1, 128, 12]> y_56_cast = add(x = var_1751_cast, y = sep_module_27_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_283_cast = add(x = input_273_cast, y = y_56_cast);
            tensor<int32, [1]> var_1762 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1764 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_285_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_285_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_28_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1086272)))];
            tensor<fp16, [1, 128, 12]> input_285_cast = conv(dilations = var_1764, groups = var_64, pad = input_285_pad_0, pad_type = input_285_pad_type_0, strides = var_1762, weight = sep_module_28_tcn_0_weight_to_fp16, x = input_283_cast);
            tensor<fp16, []> var_1768_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.548p-2)];
            tensor<fp16, [1, 128, 12]> var_1768_cast = leaky_relu(alpha = var_1768_alpha_1_to_fp16, x = input_285_cast);
            tensor<int32, [1]> var_1772 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_115_cast = reduce_mean(axes = var_1772, keep_dims = var_66, x = var_1768_cast);
            tensor<fp16, [1, 128, 12]> var_1774_cast = sub(x = var_1768_cast, y = mean_y_115_cast);
            tensor<fp16, [1, 128, 12]> var_1775_cast = square(x = var_1774_cast);
            tensor<int32, [1]> var_1776 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1777_cast = reduce_mean(axes = var_1776, keep_dims = var_66, x = var_1775_cast);
            tensor<fp16, []> var_1778_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1779_cast = add(x = var_1777_cast, y = var_1778_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_115_cast = sqrt(x = var_1779_cast);
            tensor<fp16, [1, 128, 1]> sep_module_28_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1119104)))];
            tensor<fp16, [1, 128, 12]> var_1782_cast = mul(x = sep_module_28_tcn_2_norm_gamma_to_fp16, y = var_1774_cast);
            tensor<fp16, [1, 128, 12]> var_1783_cast = real_div(x = var_1782_cast, y = std_y_115_cast);
            tensor<fp16, [1, 128, 1]> sep_module_28_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1119424)))];
            tensor<fp16, [1, 128, 12]> input_287_cast = add(x = var_1783_cast, y = sep_module_28_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_289_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 32, 0])];
            tensor<string, []> input_289_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_289_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 44]> input_289_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_287_cast_in_state, input_287_cast));
            tensor<fp16, [1, 128, 32]> input_287_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -32]), size = tensor<int32, [3]>([1, 128, 32]), x = input_289_cast);
            tensor<int32, [1]> var_1788 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1790 = const()[val = tensor<int32, [1]>([16])];
            tensor<string, []> input_291_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_291_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_28_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1119744)))];
            tensor<fp16, [1, 128, 12]> input_291_cast = conv(dilations = var_1790, groups = var_65, pad = input_291_pad_0, pad_type = input_291_pad_type_0, strides = var_1788, weight = sep_module_28_tcn_4_weight_to_fp16, x = input_289_cast);
            tensor<fp16, []> var_1794_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.68p-1)];
            tensor<fp16, [1, 128, 12]> var_1794_cast = leaky_relu(alpha = var_1794_alpha_1_to_fp16, x = input_291_cast);
            tensor<int32, [1]> var_1798 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_117_cast = reduce_mean(axes = var_1798, keep_dims = var_66, x = var_1794_cast);
            tensor<fp16, [1, 128, 12]> var_1800_cast = sub(x = var_1794_cast, y = mean_y_117_cast);
            tensor<fp16, [1, 128, 12]> var_1801_cast = square(x = var_1800_cast);
            tensor<int32, [1]> var_1802 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1803_cast = reduce_mean(axes = var_1802, keep_dims = var_66, x = var_1801_cast);
            tensor<fp16, []> var_1804_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1805_cast = add(x = var_1803_cast, y = var_1804_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_117_cast = sqrt(x = var_1805_cast);
            tensor<fp16, [1, 128, 1]> sep_module_28_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1120576)))];
            tensor<fp16, [1, 128, 12]> var_1808_cast = mul(x = sep_module_28_tcn_6_norm_gamma_to_fp16, y = var_1800_cast);
            tensor<fp16, [1, 128, 12]> var_1809_cast = real_div(x = var_1808_cast, y = std_y_117_cast);
            tensor<fp16, [1, 128, 1]> sep_module_28_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1120896)))];
            tensor<fp16, [1, 128, 12]> y_58_cast = add(x = var_1809_cast, y = sep_module_28_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_293_cast = add(x = input_283_cast, y = y_58_cast);
            tensor<int32, [1]> var_1820 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1822 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_295_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_295_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_29_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1121216)))];
            tensor<fp16, [1, 128, 12]> input_295_cast = conv(dilations = var_1822, groups = var_64, pad = input_295_pad_0, pad_type = input_295_pad_type_0, strides = var_1820, weight = sep_module_29_tcn_0_weight_to_fp16, x = input_293_cast);
            tensor<fp16, []> var_1826_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.7e8p-1)];
            tensor<fp16, [1, 128, 12]> var_1826_cast = leaky_relu(alpha = var_1826_alpha_1_to_fp16, x = input_295_cast);
            tensor<int32, [1]> var_1830 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_119_cast = reduce_mean(axes = var_1830, keep_dims = var_66, x = var_1826_cast);
            tensor<fp16, [1, 128, 12]> var_1832_cast = sub(x = var_1826_cast, y = mean_y_119_cast);
            tensor<fp16, [1, 128, 12]> var_1833_cast = square(x = var_1832_cast);
            tensor<int32, [1]> var_1834 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1835_cast = reduce_mean(axes = var_1834, keep_dims = var_66, x = var_1833_cast);
            tensor<fp16, []> var_1836_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1837_cast = add(x = var_1835_cast, y = var_1836_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_119_cast = sqrt(x = var_1837_cast);
            tensor<fp16, [1, 128, 1]> sep_module_29_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1154048)))];
            tensor<fp16, [1, 128, 12]> var_1840_cast = mul(x = sep_module_29_tcn_2_norm_gamma_to_fp16, y = var_1832_cast);
            tensor<fp16, [1, 128, 12]> var_1841_cast = real_div(x = var_1840_cast, y = std_y_119_cast);
            tensor<fp16, [1, 128, 1]> sep_module_29_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1154368)))];
            tensor<fp16, [1, 128, 12]> input_297_cast = add(x = var_1841_cast, y = sep_module_29_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_299_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 64, 0])];
            tensor<string, []> input_299_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_299_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 76]> input_299_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_297_cast_in_state, input_297_cast));
            tensor<fp16, [1, 128, 64]> input_297_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -64]), size = tensor<int32, [3]>([1, 128, 64]), x = input_299_cast);
            tensor<int32, [1]> var_1846 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1848 = const()[val = tensor<int32, [1]>([32])];
            tensor<string, []> input_301_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_301_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_29_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1154688)))];
            tensor<fp16, [1, 128, 12]> input_301_cast = conv(dilations = var_1848, groups = var_65, pad = input_301_pad_0, pad_type = input_301_pad_type_0, strides = var_1846, weight = sep_module_29_tcn_4_weight_to_fp16, x = input_299_cast);
            tensor<fp16, []> var_1852_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.85p-1)];
            tensor<fp16, [1, 128, 12]> var_1852_cast = leaky_relu(alpha = var_1852_alpha_1_to_fp16, x = input_301_cast);
            tensor<int32, [1]> var_1856 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_121_cast = reduce_mean(axes = var_1856, keep_dims = var_66, x = var_1852_cast);
            tensor<fp16, [1, 128, 12]> var_1858_cast = sub(x = var_1852_cast, y = mean_y_121_cast);
            tensor<fp16, [1, 128, 12]> var_1859_cast = square(x = var_1858_cast);
            tensor<int32, [1]> var_1860 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1861_cast = reduce_mean(axes = var_1860, keep_dims = var_66, x = var_1859_cast);
            tensor<fp16, []> var_1862_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1863_cast = add(x = var_1861_cast, y = var_1862_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_121_cast = sqrt(x = var_1863_cast);
            tensor<fp16, [1, 128, 1]> sep_module_29_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1155520)))];
            tensor<fp16, [1, 128, 12]> var_1866_cast = mul(x = sep_module_29_tcn_6_norm_gamma_to_fp16, y = var_1858_cast);
            tensor<fp16, [1, 128, 12]> var_1867_cast = real_div(x = var_1866_cast, y = std_y_121_cast);
            tensor<fp16, [1, 128, 1]> sep_module_29_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1155840)))];
            tensor<fp16, [1, 128, 12]> y_60_cast = add(x = var_1867_cast, y = sep_module_29_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_303_cast = add(x = input_293_cast, y = y_60_cast);
            tensor<int32, [1]> var_1878 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1880 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_305_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_305_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_30_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1156160)))];
            tensor<fp16, [1, 128, 12]> input_305_cast = conv(dilations = var_1880, groups = var_64, pad = input_305_pad_0, pad_type = input_305_pad_type_0, strides = var_1878, weight = sep_module_30_tcn_0_weight_to_fp16, x = input_303_cast);
            tensor<fp16, []> var_1884_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.e74p-2)];
            tensor<fp16, [1, 128, 12]> var_1884_cast = leaky_relu(alpha = var_1884_alpha_1_to_fp16, x = input_305_cast);
            tensor<int32, [1]> var_1888 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_123_cast = reduce_mean(axes = var_1888, keep_dims = var_66, x = var_1884_cast);
            tensor<fp16, [1, 128, 12]> var_1890_cast = sub(x = var_1884_cast, y = mean_y_123_cast);
            tensor<fp16, [1, 128, 12]> var_1891_cast = square(x = var_1890_cast);
            tensor<int32, [1]> var_1892 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1893_cast = reduce_mean(axes = var_1892, keep_dims = var_66, x = var_1891_cast);
            tensor<fp16, []> var_1894_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1895_cast = add(x = var_1893_cast, y = var_1894_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_123_cast = sqrt(x = var_1895_cast);
            tensor<fp16, [1, 128, 1]> sep_module_30_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1188992)))];
            tensor<fp16, [1, 128, 12]> var_1898_cast = mul(x = sep_module_30_tcn_2_norm_gamma_to_fp16, y = var_1890_cast);
            tensor<fp16, [1, 128, 12]> var_1899_cast = real_div(x = var_1898_cast, y = std_y_123_cast);
            tensor<fp16, [1, 128, 1]> sep_module_30_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1189312)))];
            tensor<fp16, [1, 128, 12]> input_307_cast = add(x = var_1899_cast, y = sep_module_30_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_309_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 2, 0])];
            tensor<string, []> input_309_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_309_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 14]> input_309_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_307_cast_in_state, input_307_cast));
            tensor<fp16, [1, 128, 2]> input_307_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -2]), size = tensor<int32, [3]>([1, 128, 2]), x = input_309_cast);
            tensor<int32, [1]> var_1904 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1906 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_311_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_311_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_30_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1189632)))];
            tensor<fp16, [1, 128, 12]> input_311_cast = conv(dilations = var_1906, groups = var_65, pad = input_311_pad_0, pad_type = input_311_pad_type_0, strides = var_1904, weight = sep_module_30_tcn_4_weight_to_fp16, x = input_309_cast);
            tensor<fp16, []> var_1910_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.c1cp-1)];
            tensor<fp16, [1, 128, 12]> var_1910_cast = leaky_relu(alpha = var_1910_alpha_1_to_fp16, x = input_311_cast);
            tensor<int32, [1]> var_1914 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_125_cast = reduce_mean(axes = var_1914, keep_dims = var_66, x = var_1910_cast);
            tensor<fp16, [1, 128, 12]> var_1916_cast = sub(x = var_1910_cast, y = mean_y_125_cast);
            tensor<fp16, [1, 128, 12]> var_1917_cast = square(x = var_1916_cast);
            tensor<int32, [1]> var_1918 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1919_cast = reduce_mean(axes = var_1918, keep_dims = var_66, x = var_1917_cast);
            tensor<fp16, []> var_1920_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1921_cast = add(x = var_1919_cast, y = var_1920_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_125_cast = sqrt(x = var_1921_cast);
            tensor<fp16, [1, 128, 1]> sep_module_30_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1190464)))];
            tensor<fp16, [1, 128, 12]> var_1924_cast = mul(x = sep_module_30_tcn_6_norm_gamma_to_fp16, y = var_1916_cast);
            tensor<fp16, [1, 128, 12]> var_1925_cast = real_div(x = var_1924_cast, y = std_y_125_cast);
            tensor<fp16, [1, 128, 1]> sep_module_30_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1190784)))];
            tensor<fp16, [1, 128, 12]> y_62_cast = add(x = var_1925_cast, y = sep_module_30_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_313_cast = add(x = input_303_cast, y = y_62_cast);
            tensor<int32, [1]> var_1936 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1938 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_315_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_315_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_31_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1191104)))];
            tensor<fp16, [1, 128, 12]> input_315_cast = conv(dilations = var_1938, groups = var_64, pad = input_315_pad_0, pad_type = input_315_pad_type_0, strides = var_1936, weight = sep_module_31_tcn_0_weight_to_fp16, x = input_313_cast);
            tensor<fp16, []> var_1942_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.384p-1)];
            tensor<fp16, [1, 128, 12]> var_1942_cast = leaky_relu(alpha = var_1942_alpha_1_to_fp16, x = input_315_cast);
            tensor<int32, [1]> var_1946 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_127_cast = reduce_mean(axes = var_1946, keep_dims = var_66, x = var_1942_cast);
            tensor<fp16, [1, 128, 12]> var_1948_cast = sub(x = var_1942_cast, y = mean_y_127_cast);
            tensor<fp16, [1, 128, 12]> var_1949_cast = square(x = var_1948_cast);
            tensor<int32, [1]> var_1950 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1951_cast = reduce_mean(axes = var_1950, keep_dims = var_66, x = var_1949_cast);
            tensor<fp16, []> var_1952_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1953_cast = add(x = var_1951_cast, y = var_1952_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_127_cast = sqrt(x = var_1953_cast);
            tensor<fp16, [1, 128, 1]> sep_module_31_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1223936)))];
            tensor<fp16, [1, 128, 12]> var_1956_cast = mul(x = sep_module_31_tcn_2_norm_gamma_to_fp16, y = var_1948_cast);
            tensor<fp16, [1, 128, 12]> var_1957_cast = real_div(x = var_1956_cast, y = std_y_127_cast);
            tensor<fp16, [1, 128, 1]> sep_module_31_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1224256)))];
            tensor<fp16, [1, 128, 12]> input_317_cast = add(x = var_1957_cast, y = sep_module_31_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_319_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 4, 0])];
            tensor<string, []> input_319_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_319_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 16]> input_319_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_317_cast_in_state, input_317_cast));
            tensor<fp16, [1, 128, 4]> input_317_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -4]), size = tensor<int32, [3]>([1, 128, 4]), x = input_319_cast);
            tensor<int32, [1]> var_1962 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1964 = const()[val = tensor<int32, [1]>([2])];
            tensor<string, []> input_321_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_321_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_31_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1224576)))];
            tensor<fp16, [1, 128, 12]> input_321_cast = conv(dilations = var_1964, groups = var_65, pad = input_321_pad_0, pad_type = input_321_pad_type_0, strides = var_1962, weight = sep_module_31_tcn_4_weight_to_fp16, x = input_319_cast);
            tensor<fp16, []> var_1968_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.a9cp-1)];
            tensor<fp16, [1, 128, 12]> var_1968_cast = leaky_relu(alpha = var_1968_alpha_1_to_fp16, x = input_321_cast);
            tensor<int32, [1]> var_1972 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_129_cast = reduce_mean(axes = var_1972, keep_dims = var_66, x = var_1968_cast);
            tensor<fp16, [1, 128, 12]> var_1974_cast = sub(x = var_1968_cast, y = mean_y_129_cast);
            tensor<fp16, [1, 128, 12]> var_1975_cast = square(x = var_1974_cast);
            tensor<int32, [1]> var_1976 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1977_cast = reduce_mean(axes = var_1976, keep_dims = var_66, x = var_1975_cast);
            tensor<fp16, []> var_1978_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1979_cast = add(x = var_1977_cast, y = var_1978_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_129_cast = sqrt(x = var_1979_cast);
            tensor<fp16, [1, 128, 1]> sep_module_31_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1225408)))];
            tensor<fp16, [1, 128, 12]> var_1982_cast = mul(x = sep_module_31_tcn_6_norm_gamma_to_fp16, y = var_1974_cast);
            tensor<fp16, [1, 128, 12]> var_1983_cast = real_div(x = var_1982_cast, y = std_y_129_cast);
            tensor<fp16, [1, 128, 1]> sep_module_31_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1225728)))];
            tensor<fp16, [1, 128, 12]> y_64_cast = add(x = var_1983_cast, y = sep_module_31_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_323_cast = add(x = input_313_cast, y = y_64_cast);
            tensor<int32, [1]> var_1994 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1996 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_325_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_325_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_32_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1226048)))];
            tensor<fp16, [1, 128, 12]> input_325_cast = conv(dilations = var_1996, groups = var_64, pad = input_325_pad_0, pad_type = input_325_pad_type_0, strides = var_1994, weight = sep_module_32_tcn_0_weight_to_fp16, x = input_323_cast);
            tensor<fp16, []> var_2000_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.418p-1)];
            tensor<fp16, [1, 128, 12]> var_2000_cast = leaky_relu(alpha = var_2000_alpha_1_to_fp16, x = input_325_cast);
            tensor<int32, [1]> var_2004 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_131_cast = reduce_mean(axes = var_2004, keep_dims = var_66, x = var_2000_cast);
            tensor<fp16, [1, 128, 12]> var_2006_cast = sub(x = var_2000_cast, y = mean_y_131_cast);
            tensor<fp16, [1, 128, 12]> var_2007_cast = square(x = var_2006_cast);
            tensor<int32, [1]> var_2008 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2009_cast = reduce_mean(axes = var_2008, keep_dims = var_66, x = var_2007_cast);
            tensor<fp16, []> var_2010_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2011_cast = add(x = var_2009_cast, y = var_2010_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_131_cast = sqrt(x = var_2011_cast);
            tensor<fp16, [1, 128, 1]> sep_module_32_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1258880)))];
            tensor<fp16, [1, 128, 12]> var_2014_cast = mul(x = sep_module_32_tcn_2_norm_gamma_to_fp16, y = var_2006_cast);
            tensor<fp16, [1, 128, 12]> var_2015_cast = real_div(x = var_2014_cast, y = std_y_131_cast);
            tensor<fp16, [1, 128, 1]> sep_module_32_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1259200)))];
            tensor<fp16, [1, 128, 12]> input_327_cast = add(x = var_2015_cast, y = sep_module_32_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_329_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 8, 0])];
            tensor<string, []> input_329_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_329_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 20]> input_329_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_327_cast_in_state, input_327_cast));
            tensor<fp16, [1, 128, 8]> input_327_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -8]), size = tensor<int32, [3]>([1, 128, 8]), x = input_329_cast);
            tensor<int32, [1]> var_2020 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2022 = const()[val = tensor<int32, [1]>([4])];
            tensor<string, []> input_331_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_331_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_32_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1259520)))];
            tensor<fp16, [1, 128, 12]> input_331_cast = conv(dilations = var_2022, groups = var_65, pad = input_331_pad_0, pad_type = input_331_pad_type_0, strides = var_2020, weight = sep_module_32_tcn_4_weight_to_fp16, x = input_329_cast);
            tensor<fp16, []> var_2026_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.c14p-1)];
            tensor<fp16, [1, 128, 12]> var_2026_cast = leaky_relu(alpha = var_2026_alpha_1_to_fp16, x = input_331_cast);
            tensor<int32, [1]> var_2030 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_133_cast = reduce_mean(axes = var_2030, keep_dims = var_66, x = var_2026_cast);
            tensor<fp16, [1, 128, 12]> var_2032_cast = sub(x = var_2026_cast, y = mean_y_133_cast);
            tensor<fp16, [1, 128, 12]> var_2033_cast = square(x = var_2032_cast);
            tensor<int32, [1]> var_2034 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2035_cast = reduce_mean(axes = var_2034, keep_dims = var_66, x = var_2033_cast);
            tensor<fp16, []> var_2036_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2037_cast = add(x = var_2035_cast, y = var_2036_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_133_cast = sqrt(x = var_2037_cast);
            tensor<fp16, [1, 128, 1]> sep_module_32_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1260352)))];
            tensor<fp16, [1, 128, 12]> var_2040_cast = mul(x = sep_module_32_tcn_6_norm_gamma_to_fp16, y = var_2032_cast);
            tensor<fp16, [1, 128, 12]> var_2041_cast = real_div(x = var_2040_cast, y = std_y_133_cast);
            tensor<fp16, [1, 128, 1]> sep_module_32_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1260672)))];
            tensor<fp16, [1, 128, 12]> y_66_cast = add(x = var_2041_cast, y = sep_module_32_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_333_cast = add(x = input_323_cast, y = y_66_cast);
            tensor<int32, [1]> var_2052 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2054 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_335_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_335_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_33_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1260992)))];
            tensor<fp16, [1, 128, 12]> input_335_cast = conv(dilations = var_2054, groups = var_64, pad = input_335_pad_0, pad_type = input_335_pad_type_0, strides = var_2052, weight = sep_module_33_tcn_0_weight_to_fp16, x = input_333_cast);
            tensor<fp16, []> var_2058_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.b3cp-2)];
            tensor<fp16, [1, 128, 12]> var_2058_cast = leaky_relu(alpha = var_2058_alpha_1_to_fp16, x = input_335_cast);
            tensor<int32, [1]> var_2062 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_135_cast = reduce_mean(axes = var_2062, keep_dims = var_66, x = var_2058_cast);
            tensor<fp16, [1, 128, 12]> var_2064_cast = sub(x = var_2058_cast, y = mean_y_135_cast);
            tensor<fp16, [1, 128, 12]> var_2065_cast = square(x = var_2064_cast);
            tensor<int32, [1]> var_2066 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2067_cast = reduce_mean(axes = var_2066, keep_dims = var_66, x = var_2065_cast);
            tensor<fp16, []> var_2068_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2069_cast = add(x = var_2067_cast, y = var_2068_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_135_cast = sqrt(x = var_2069_cast);
            tensor<fp16, [1, 128, 1]> sep_module_33_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1293824)))];
            tensor<fp16, [1, 128, 12]> var_2072_cast = mul(x = sep_module_33_tcn_2_norm_gamma_to_fp16, y = var_2064_cast);
            tensor<fp16, [1, 128, 12]> var_2073_cast = real_div(x = var_2072_cast, y = std_y_135_cast);
            tensor<fp16, [1, 128, 1]> sep_module_33_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1294144)))];
            tensor<fp16, [1, 128, 12]> input_337_cast = add(x = var_2073_cast, y = sep_module_33_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_339_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 16, 0])];
            tensor<string, []> input_339_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_339_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 28]> input_339_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_337_cast_in_state, input_337_cast));
            tensor<fp16, [1, 128, 16]> input_337_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -16]), size = tensor<int32, [3]>([1, 128, 16]), x = input_339_cast);
            tensor<int32, [1]> var_2078 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2080 = const()[val = tensor<int32, [1]>([8])];
            tensor<string, []> input_341_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_341_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_33_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1294464)))];
            tensor<fp16, [1, 128, 12]> input_341_cast = conv(dilations = var_2080, groups = var_65, pad = input_341_pad_0, pad_type = input_341_pad_type_0, strides = var_2078, weight = sep_module_33_tcn_4_weight_to_fp16, x = input_339_cast);
            tensor<fp16, []> var_2084_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.a84p-2)];
            tensor<fp16, [1, 128, 12]> var_2084_cast = leaky_relu(alpha = var_2084_alpha_1_to_fp16, x = input_341_cast);
            tensor<int32, [1]> var_2088 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_137_cast = reduce_mean(axes = var_2088, keep_dims = var_66, x = var_2084_cast);
            tensor<fp16, [1, 128, 12]> var_2090_cast = sub(x = var_2084_cast, y = mean_y_137_cast);
            tensor<fp16, [1, 128, 12]> var_2091_cast = square(x = var_2090_cast);
            tensor<int32, [1]> var_2092 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2093_cast = reduce_mean(axes = var_2092, keep_dims = var_66, x = var_2091_cast);
            tensor<fp16, []> var_2094_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2095_cast = add(x = var_2093_cast, y = var_2094_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_137_cast = sqrt(x = var_2095_cast);
            tensor<fp16, [1, 128, 1]> sep_module_33_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1295296)))];
            tensor<fp16, [1, 128, 12]> var_2098_cast = mul(x = sep_module_33_tcn_6_norm_gamma_to_fp16, y = var_2090_cast);
            tensor<fp16, [1, 128, 12]> var_2099_cast = real_div(x = var_2098_cast, y = std_y_137_cast);
            tensor<fp16, [1, 128, 1]> sep_module_33_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1295616)))];
            tensor<fp16, [1, 128, 12]> y_68_cast = add(x = var_2099_cast, y = sep_module_33_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_343_cast = add(x = input_333_cast, y = y_68_cast);
            tensor<int32, [1]> var_2110 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2112 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_345_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_345_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_34_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1295936)))];
            tensor<fp16, [1, 128, 12]> input_345_cast = conv(dilations = var_2112, groups = var_64, pad = input_345_pad_0, pad_type = input_345_pad_type_0, strides = var_2110, weight = sep_module_34_tcn_0_weight_to_fp16, x = input_343_cast);
            tensor<fp16, []> var_2116_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.77p-2)];
            tensor<fp16, [1, 128, 12]> var_2116_cast = leaky_relu(alpha = var_2116_alpha_1_to_fp16, x = input_345_cast);
            tensor<int32, [1]> var_2120 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_139_cast = reduce_mean(axes = var_2120, keep_dims = var_66, x = var_2116_cast);
            tensor<fp16, [1, 128, 12]> var_2122_cast = sub(x = var_2116_cast, y = mean_y_139_cast);
            tensor<fp16, [1, 128, 12]> var_2123_cast = square(x = var_2122_cast);
            tensor<int32, [1]> var_2124 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2125_cast = reduce_mean(axes = var_2124, keep_dims = var_66, x = var_2123_cast);
            tensor<fp16, []> var_2126_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2127_cast = add(x = var_2125_cast, y = var_2126_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_139_cast = sqrt(x = var_2127_cast);
            tensor<fp16, [1, 128, 1]> sep_module_34_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1328768)))];
            tensor<fp16, [1, 128, 12]> var_2130_cast = mul(x = sep_module_34_tcn_2_norm_gamma_to_fp16, y = var_2122_cast);
            tensor<fp16, [1, 128, 12]> var_2131_cast = real_div(x = var_2130_cast, y = std_y_139_cast);
            tensor<fp16, [1, 128, 1]> sep_module_34_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1329088)))];
            tensor<fp16, [1, 128, 12]> input_347_cast = add(x = var_2131_cast, y = sep_module_34_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_349_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 32, 0])];
            tensor<string, []> input_349_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_349_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 44]> input_349_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_347_cast_in_state, input_347_cast));
            tensor<fp16, [1, 128, 32]> input_347_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -32]), size = tensor<int32, [3]>([1, 128, 32]), x = input_349_cast);
            tensor<int32, [1]> var_2136 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2138 = const()[val = tensor<int32, [1]>([16])];
            tensor<string, []> input_351_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_351_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_34_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1329408)))];
            tensor<fp16, [1, 128, 12]> input_351_cast = conv(dilations = var_2138, groups = var_65, pad = input_351_pad_0, pad_type = input_351_pad_type_0, strides = var_2136, weight = sep_module_34_tcn_4_weight_to_fp16, x = input_349_cast);
            tensor<fp16, []> var_2142_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.4a8p-1)];
            tensor<fp16, [1, 128, 12]> var_2142_cast = leaky_relu(alpha = var_2142_alpha_1_to_fp16, x = input_351_cast);
            tensor<int32, [1]> var_2146 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_141_cast = reduce_mean(axes = var_2146, keep_dims = var_66, x = var_2142_cast);
            tensor<fp16, [1, 128, 12]> var_2148_cast = sub(x = var_2142_cast, y = mean_y_141_cast);
            tensor<fp16, [1, 128, 12]> var_2149_cast = square(x = var_2148_cast);
            tensor<int32, [1]> var_2150 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2151_cast = reduce_mean(axes = var_2150, keep_dims = var_66, x = var_2149_cast);
            tensor<fp16, []> var_2152_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2153_cast = add(x = var_2151_cast, y = var_2152_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_141_cast = sqrt(x = var_2153_cast);
            tensor<fp16, [1, 128, 1]> sep_module_34_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1330240)))];
            tensor<fp16, [1, 128, 12]> var_2156_cast = mul(x = sep_module_34_tcn_6_norm_gamma_to_fp16, y = var_2148_cast);
            tensor<fp16, [1, 128, 12]> var_2157_cast = real_div(x = var_2156_cast, y = std_y_141_cast);
            tensor<fp16, [1, 128, 1]> sep_module_34_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1330560)))];
            tensor<fp16, [1, 128, 12]> y_70_cast = add(x = var_2157_cast, y = sep_module_34_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_353_cast = add(x = input_343_cast, y = y_70_cast);
            tensor<int32, [1]> var_2168 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2170 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_355_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_355_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_35_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1330880)))];
            tensor<fp16, [1, 128, 12]> input_355_cast = conv(dilations = var_2170, groups = var_64, pad = input_355_pad_0, pad_type = input_355_pad_type_0, strides = var_2168, weight = sep_module_35_tcn_0_weight_to_fp16, x = input_353_cast);
            tensor<fp16, []> var_2174_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.ef8p-1)];
            tensor<fp16, [1, 128, 12]> var_2174_cast = leaky_relu(alpha = var_2174_alpha_1_to_fp16, x = input_355_cast);
            tensor<int32, [1]> var_2178 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_143_cast = reduce_mean(axes = var_2178, keep_dims = var_66, x = var_2174_cast);
            tensor<fp16, [1, 128, 12]> var_2180_cast = sub(x = var_2174_cast, y = mean_y_143_cast);
            tensor<fp16, [1, 128, 12]> var_2181_cast = square(x = var_2180_cast);
            tensor<int32, [1]> var_2182 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2183_cast = reduce_mean(axes = var_2182, keep_dims = var_66, x = var_2181_cast);
            tensor<fp16, []> var_2184_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2185_cast = add(x = var_2183_cast, y = var_2184_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_143_cast = sqrt(x = var_2185_cast);
            tensor<fp16, [1, 128, 1]> sep_module_35_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1363712)))];
            tensor<fp16, [1, 128, 12]> var_2188_cast = mul(x = sep_module_35_tcn_2_norm_gamma_to_fp16, y = var_2180_cast);
            tensor<fp16, [1, 128, 12]> var_2189_cast = real_div(x = var_2188_cast, y = std_y_143_cast);
            tensor<fp16, [1, 128, 1]> sep_module_35_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1364032)))];
            tensor<fp16, [1, 128, 12]> input_357_cast = add(x = var_2189_cast, y = sep_module_35_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_359_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 64, 0])];
            tensor<string, []> input_359_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_359_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 76]> input_359_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_357_cast_in_state, input_357_cast));
            tensor<fp16, [1, 128, 64]> input_357_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -64]), size = tensor<int32, [3]>([1, 128, 64]), x = input_359_cast);
            tensor<int32, [1]> var_2194 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2196 = const()[val = tensor<int32, [1]>([32])];
            tensor<string, []> input_361_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_361_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_35_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1364352)))];
            tensor<fp16, [1, 128, 12]> input_361_cast = conv(dilations = var_2196, groups = var_65, pad = input_361_pad_0, pad_type = input_361_pad_type_0, strides = var_2194, weight = sep_module_35_tcn_4_weight_to_fp16, x = input_359_cast);
            tensor<fp16, []> var_2200_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.ebcp-1)];
            tensor<fp16, [1, 128, 12]> var_2200_cast = leaky_relu(alpha = var_2200_alpha_1_to_fp16, x = input_361_cast);
            tensor<int32, [1]> var_2204 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_145_cast = reduce_mean(axes = var_2204, keep_dims = var_66, x = var_2200_cast);
            tensor<fp16, [1, 128, 12]> var_2206_cast = sub(x = var_2200_cast, y = mean_y_145_cast);
            tensor<fp16, [1, 128, 12]> var_2207_cast = square(x = var_2206_cast);
            tensor<int32, [1]> var_2208 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2209_cast = reduce_mean(axes = var_2208, keep_dims = var_66, x = var_2207_cast);
            tensor<fp16, []> var_2210_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2211_cast = add(x = var_2209_cast, y = var_2210_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_145_cast = sqrt(x = var_2211_cast);
            tensor<fp16, [1, 128, 1]> sep_module_35_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1365184)))];
            tensor<fp16, [1, 128, 12]> var_2214_cast = mul(x = sep_module_35_tcn_6_norm_gamma_to_fp16, y = var_2206_cast);
            tensor<fp16, [1, 128, 12]> var_2215_cast = real_div(x = var_2214_cast, y = std_y_145_cast);
            tensor<fp16, [1, 128, 1]> sep_module_35_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1365504)))];
            tensor<fp16, [1, 128, 12]> y_72_cast = add(x = var_2215_cast, y = sep_module_35_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_363_cast = add(x = input_353_cast, y = y_72_cast);
            tensor<int32, [1]> var_2226 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2228 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_365_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_365_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_36_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1365824)))];
            tensor<fp16, [1, 128, 12]> input_365_cast = conv(dilations = var_2228, groups = var_64, pad = input_365_pad_0, pad_type = input_365_pad_type_0, strides = var_2226, weight = sep_module_36_tcn_0_weight_to_fp16, x = input_363_cast);
            tensor<fp16, []> var_2232_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.214p-2)];
            tensor<fp16, [1, 128, 12]> var_2232_cast = leaky_relu(alpha = var_2232_alpha_1_to_fp16, x = input_365_cast);
            tensor<int32, [1]> var_2236 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_147_cast = reduce_mean(axes = var_2236, keep_dims = var_66, x = var_2232_cast);
            tensor<fp16, [1, 128, 12]> var_2238_cast = sub(x = var_2232_cast, y = mean_y_147_cast);
            tensor<fp16, [1, 128, 12]> var_2239_cast = square(x = var_2238_cast);
            tensor<int32, [1]> var_2240 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2241_cast = reduce_mean(axes = var_2240, keep_dims = var_66, x = var_2239_cast);
            tensor<fp16, []> var_2242_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2243_cast = add(x = var_2241_cast, y = var_2242_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_147_cast = sqrt(x = var_2243_cast);
            tensor<fp16, [1, 128, 1]> sep_module_36_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1398656)))];
            tensor<fp16, [1, 128, 12]> var_2246_cast = mul(x = sep_module_36_tcn_2_norm_gamma_to_fp16, y = var_2238_cast);
            tensor<fp16, [1, 128, 12]> var_2247_cast = real_div(x = var_2246_cast, y = std_y_147_cast);
            tensor<fp16, [1, 128, 1]> sep_module_36_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1398976)))];
            tensor<fp16, [1, 128, 12]> input_367_cast = add(x = var_2247_cast, y = sep_module_36_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_369_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 2, 0])];
            tensor<string, []> input_369_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_369_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 14]> input_369_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_367_cast_in_state, input_367_cast));
            tensor<fp16, [1, 128, 2]> input_367_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -2]), size = tensor<int32, [3]>([1, 128, 2]), x = input_369_cast);
            tensor<int32, [1]> var_2252 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2254 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_371_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_371_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_36_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1399296)))];
            tensor<fp16, [1, 128, 12]> input_371_cast = conv(dilations = var_2254, groups = var_65, pad = input_371_pad_0, pad_type = input_371_pad_type_0, strides = var_2252, weight = sep_module_36_tcn_4_weight_to_fp16, x = input_369_cast);
            tensor<fp16, []> var_2258_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.fd4p-1)];
            tensor<fp16, [1, 128, 12]> var_2258_cast = leaky_relu(alpha = var_2258_alpha_1_to_fp16, x = input_371_cast);
            tensor<int32, [1]> var_2262 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_149_cast = reduce_mean(axes = var_2262, keep_dims = var_66, x = var_2258_cast);
            tensor<fp16, [1, 128, 12]> var_2264_cast = sub(x = var_2258_cast, y = mean_y_149_cast);
            tensor<fp16, [1, 128, 12]> var_2265_cast = square(x = var_2264_cast);
            tensor<int32, [1]> var_2266 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2267_cast = reduce_mean(axes = var_2266, keep_dims = var_66, x = var_2265_cast);
            tensor<fp16, []> var_2268_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2269_cast = add(x = var_2267_cast, y = var_2268_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_149_cast = sqrt(x = var_2269_cast);
            tensor<fp16, [1, 128, 1]> sep_module_36_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1400128)))];
            tensor<fp16, [1, 128, 12]> var_2272_cast = mul(x = sep_module_36_tcn_6_norm_gamma_to_fp16, y = var_2264_cast);
            tensor<fp16, [1, 128, 12]> var_2273_cast = real_div(x = var_2272_cast, y = std_y_149_cast);
            tensor<fp16, [1, 128, 1]> sep_module_36_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1400448)))];
            tensor<fp16, [1, 128, 12]> y_74_cast = add(x = var_2273_cast, y = sep_module_36_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_373_cast = add(x = input_363_cast, y = y_74_cast);
            tensor<int32, [1]> var_2284 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2286 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_375_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_375_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_37_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1400768)))];
            tensor<fp16, [1, 128, 12]> input_375_cast = conv(dilations = var_2286, groups = var_64, pad = input_375_pad_0, pad_type = input_375_pad_type_0, strides = var_2284, weight = sep_module_37_tcn_0_weight_to_fp16, x = input_373_cast);
            tensor<fp16, []> var_2290_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.31p-1)];
            tensor<fp16, [1, 128, 12]> var_2290_cast = leaky_relu(alpha = var_2290_alpha_1_to_fp16, x = input_375_cast);
            tensor<int32, [1]> var_2294 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_151_cast = reduce_mean(axes = var_2294, keep_dims = var_66, x = var_2290_cast);
            tensor<fp16, [1, 128, 12]> var_2296_cast = sub(x = var_2290_cast, y = mean_y_151_cast);
            tensor<fp16, [1, 128, 12]> var_2297_cast = square(x = var_2296_cast);
            tensor<int32, [1]> var_2298 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2299_cast = reduce_mean(axes = var_2298, keep_dims = var_66, x = var_2297_cast);
            tensor<fp16, []> var_2300_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2301_cast = add(x = var_2299_cast, y = var_2300_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_151_cast = sqrt(x = var_2301_cast);
            tensor<fp16, [1, 128, 1]> sep_module_37_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1433600)))];
            tensor<fp16, [1, 128, 12]> var_2304_cast = mul(x = sep_module_37_tcn_2_norm_gamma_to_fp16, y = var_2296_cast);
            tensor<fp16, [1, 128, 12]> var_2305_cast = real_div(x = var_2304_cast, y = std_y_151_cast);
            tensor<fp16, [1, 128, 1]> sep_module_37_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1433920)))];
            tensor<fp16, [1, 128, 12]> input_377_cast = add(x = var_2305_cast, y = sep_module_37_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_379_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 4, 0])];
            tensor<string, []> input_379_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_379_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 16]> input_379_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_377_cast_in_state, input_377_cast));
            tensor<fp16, [1, 128, 4]> input_377_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -4]), size = tensor<int32, [3]>([1, 128, 4]), x = input_379_cast);
            tensor<int32, [1]> var_2310 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2312 = const()[val = tensor<int32, [1]>([2])];
            tensor<string, []> input_381_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_381_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_37_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1434240)))];
            tensor<fp16, [1, 128, 12]> input_381_cast = conv(dilations = var_2312, groups = var_65, pad = input_381_pad_0, pad_type = input_381_pad_type_0, strides = var_2310, weight = sep_module_37_tcn_4_weight_to_fp16, x = input_379_cast);
            tensor<fp16, []> var_2316_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.f64p-1)];
            tensor<fp16, [1, 128, 12]> var_2316_cast = leaky_relu(alpha = var_2316_alpha_1_to_fp16, x = input_381_cast);
            tensor<int32, [1]> var_2320 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_153_cast = reduce_mean(axes = var_2320, keep_dims = var_66, x = var_2316_cast);
            tensor<fp16, [1, 128, 12]> var_2322_cast = sub(x = var_2316_cast, y = mean_y_153_cast);
            tensor<fp16, [1, 128, 12]> var_2323_cast = square(x = var_2322_cast);
            tensor<int32, [1]> var_2324 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2325_cast = reduce_mean(axes = var_2324, keep_dims = var_66, x = var_2323_cast);
            tensor<fp16, []> var_2326_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2327_cast = add(x = var_2325_cast, y = var_2326_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_153_cast = sqrt(x = var_2327_cast);
            tensor<fp16, [1, 128, 1]> sep_module_37_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1435072)))];
            tensor<fp16, [1, 128, 12]> var_2330_cast = mul(x = sep_module_37_tcn_6_norm_gamma_to_fp16, y = var_2322_cast);
            tensor<fp16, [1, 128, 12]> var_2331_cast = real_div(x = var_2330_cast, y = std_y_153_cast);
            tensor<fp16, [1, 128, 1]> sep_module_37_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1435392)))];
            tensor<fp16, [1, 128, 12]> y_76_cast = add(x = var_2331_cast, y = sep_module_37_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_383_cast = add(x = input_373_cast, y = y_76_cast);
            tensor<int32, [1]> var_2342 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2344 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_385_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_385_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_38_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1435712)))];
            tensor<fp16, [1, 128, 12]> input_385_cast = conv(dilations = var_2344, groups = var_64, pad = input_385_pad_0, pad_type = input_385_pad_type_0, strides = var_2342, weight = sep_module_38_tcn_0_weight_to_fp16, x = input_383_cast);
            tensor<fp16, []> var_2348_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.214p-2)];
            tensor<fp16, [1, 128, 12]> var_2348_cast = leaky_relu(alpha = var_2348_alpha_1_to_fp16, x = input_385_cast);
            tensor<int32, [1]> var_2352 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_155_cast = reduce_mean(axes = var_2352, keep_dims = var_66, x = var_2348_cast);
            tensor<fp16, [1, 128, 12]> var_2354_cast = sub(x = var_2348_cast, y = mean_y_155_cast);
            tensor<fp16, [1, 128, 12]> var_2355_cast = square(x = var_2354_cast);
            tensor<int32, [1]> var_2356 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2357_cast = reduce_mean(axes = var_2356, keep_dims = var_66, x = var_2355_cast);
            tensor<fp16, []> var_2358_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2359_cast = add(x = var_2357_cast, y = var_2358_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_155_cast = sqrt(x = var_2359_cast);
            tensor<fp16, [1, 128, 1]> sep_module_38_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1468544)))];
            tensor<fp16, [1, 128, 12]> var_2362_cast = mul(x = sep_module_38_tcn_2_norm_gamma_to_fp16, y = var_2354_cast);
            tensor<fp16, [1, 128, 12]> var_2363_cast = real_div(x = var_2362_cast, y = std_y_155_cast);
            tensor<fp16, [1, 128, 1]> sep_module_38_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1468864)))];
            tensor<fp16, [1, 128, 12]> input_387_cast = add(x = var_2363_cast, y = sep_module_38_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_389_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 8, 0])];
            tensor<string, []> input_389_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_389_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 20]> input_389_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_387_cast_in_state, input_387_cast));
            tensor<fp16, [1, 128, 8]> input_387_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -8]), size = tensor<int32, [3]>([1, 128, 8]), x = input_389_cast);
            tensor<int32, [1]> var_2368 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2370 = const()[val = tensor<int32, [1]>([4])];
            tensor<string, []> input_391_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_391_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_38_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1469184)))];
            tensor<fp16, [1, 128, 12]> input_391_cast = conv(dilations = var_2370, groups = var_65, pad = input_391_pad_0, pad_type = input_391_pad_type_0, strides = var_2368, weight = sep_module_38_tcn_4_weight_to_fp16, x = input_389_cast);
            tensor<fp16, []> var_2374_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.e38p-1)];
            tensor<fp16, [1, 128, 12]> var_2374_cast = leaky_relu(alpha = var_2374_alpha_1_to_fp16, x = input_391_cast);
            tensor<int32, [1]> var_2378 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_157_cast = reduce_mean(axes = var_2378, keep_dims = var_66, x = var_2374_cast);
            tensor<fp16, [1, 128, 12]> var_2380_cast = sub(x = var_2374_cast, y = mean_y_157_cast);
            tensor<fp16, [1, 128, 12]> var_2381_cast = square(x = var_2380_cast);
            tensor<int32, [1]> var_2382 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2383_cast = reduce_mean(axes = var_2382, keep_dims = var_66, x = var_2381_cast);
            tensor<fp16, []> var_2384_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2385_cast = add(x = var_2383_cast, y = var_2384_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_157_cast = sqrt(x = var_2385_cast);
            tensor<fp16, [1, 128, 1]> sep_module_38_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1470016)))];
            tensor<fp16, [1, 128, 12]> var_2388_cast = mul(x = sep_module_38_tcn_6_norm_gamma_to_fp16, y = var_2380_cast);
            tensor<fp16, [1, 128, 12]> var_2389_cast = real_div(x = var_2388_cast, y = std_y_157_cast);
            tensor<fp16, [1, 128, 1]> sep_module_38_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1470336)))];
            tensor<fp16, [1, 128, 12]> y_78_cast = add(x = var_2389_cast, y = sep_module_38_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_393_cast = add(x = input_383_cast, y = y_78_cast);
            tensor<int32, [1]> var_2400 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2402 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_395_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_395_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_39_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1470656)))];
            tensor<fp16, [1, 128, 12]> input_395_cast = conv(dilations = var_2402, groups = var_64, pad = input_395_pad_0, pad_type = input_395_pad_type_0, strides = var_2400, weight = sep_module_39_tcn_0_weight_to_fp16, x = input_393_cast);
            tensor<fp16, []> var_2406_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.72cp-2)];
            tensor<fp16, [1, 128, 12]> var_2406_cast = leaky_relu(alpha = var_2406_alpha_1_to_fp16, x = input_395_cast);
            tensor<int32, [1]> var_2410 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_159_cast = reduce_mean(axes = var_2410, keep_dims = var_66, x = var_2406_cast);
            tensor<fp16, [1, 128, 12]> var_2412_cast = sub(x = var_2406_cast, y = mean_y_159_cast);
            tensor<fp16, [1, 128, 12]> var_2413_cast = square(x = var_2412_cast);
            tensor<int32, [1]> var_2414 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2415_cast = reduce_mean(axes = var_2414, keep_dims = var_66, x = var_2413_cast);
            tensor<fp16, []> var_2416_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2417_cast = add(x = var_2415_cast, y = var_2416_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_159_cast = sqrt(x = var_2417_cast);
            tensor<fp16, [1, 128, 1]> sep_module_39_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1503488)))];
            tensor<fp16, [1, 128, 12]> var_2420_cast = mul(x = sep_module_39_tcn_2_norm_gamma_to_fp16, y = var_2412_cast);
            tensor<fp16, [1, 128, 12]> var_2421_cast = real_div(x = var_2420_cast, y = std_y_159_cast);
            tensor<fp16, [1, 128, 1]> sep_module_39_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1503808)))];
            tensor<fp16, [1, 128, 12]> input_397_cast = add(x = var_2421_cast, y = sep_module_39_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_399_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 16, 0])];
            tensor<string, []> input_399_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_399_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 28]> input_399_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_397_cast_in_state, input_397_cast));
            tensor<fp16, [1, 128, 16]> input_397_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -16]), size = tensor<int32, [3]>([1, 128, 16]), x = input_399_cast);
            tensor<int32, [1]> var_2426 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2428 = const()[val = tensor<int32, [1]>([8])];
            tensor<string, []> input_401_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_401_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_39_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1504128)))];
            tensor<fp16, [1, 128, 12]> input_401_cast = conv(dilations = var_2428, groups = var_65, pad = input_401_pad_0, pad_type = input_401_pad_type_0, strides = var_2426, weight = sep_module_39_tcn_4_weight_to_fp16, x = input_399_cast);
            tensor<fp16, []> var_2432_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.f94p-1)];
            tensor<fp16, [1, 128, 12]> var_2432_cast = leaky_relu(alpha = var_2432_alpha_1_to_fp16, x = input_401_cast);
            tensor<int32, [1]> var_2436 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_161_cast = reduce_mean(axes = var_2436, keep_dims = var_66, x = var_2432_cast);
            tensor<fp16, [1, 128, 12]> var_2438_cast = sub(x = var_2432_cast, y = mean_y_161_cast);
            tensor<fp16, [1, 128, 12]> var_2439_cast = square(x = var_2438_cast);
            tensor<int32, [1]> var_2440 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2441_cast = reduce_mean(axes = var_2440, keep_dims = var_66, x = var_2439_cast);
            tensor<fp16, []> var_2442_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2443_cast = add(x = var_2441_cast, y = var_2442_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_161_cast = sqrt(x = var_2443_cast);
            tensor<fp16, [1, 128, 1]> sep_module_39_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1504960)))];
            tensor<fp16, [1, 128, 12]> var_2446_cast = mul(x = sep_module_39_tcn_6_norm_gamma_to_fp16, y = var_2438_cast);
            tensor<fp16, [1, 128, 12]> var_2447_cast = real_div(x = var_2446_cast, y = std_y_161_cast);
            tensor<fp16, [1, 128, 1]> sep_module_39_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1505280)))];
            tensor<fp16, [1, 128, 12]> y_80_cast = add(x = var_2447_cast, y = sep_module_39_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_403_cast = add(x = input_393_cast, y = y_80_cast);
            tensor<int32, [1]> var_2458 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2460 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_405_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_405_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_40_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1505600)))];
            tensor<fp16, [1, 128, 12]> input_405_cast = conv(dilations = var_2460, groups = var_64, pad = input_405_pad_0, pad_type = input_405_pad_type_0, strides = var_2458, weight = sep_module_40_tcn_0_weight_to_fp16, x = input_403_cast);
            tensor<fp16, []> var_2464_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.164p-1)];
            tensor<fp16, [1, 128, 12]> var_2464_cast = leaky_relu(alpha = var_2464_alpha_1_to_fp16, x = input_405_cast);
            tensor<int32, [1]> var_2468 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_163_cast = reduce_mean(axes = var_2468, keep_dims = var_66, x = var_2464_cast);
            tensor<fp16, [1, 128, 12]> var_2470_cast = sub(x = var_2464_cast, y = mean_y_163_cast);
            tensor<fp16, [1, 128, 12]> var_2471_cast = square(x = var_2470_cast);
            tensor<int32, [1]> var_2472 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2473_cast = reduce_mean(axes = var_2472, keep_dims = var_66, x = var_2471_cast);
            tensor<fp16, []> var_2474_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2475_cast = add(x = var_2473_cast, y = var_2474_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_163_cast = sqrt(x = var_2475_cast);
            tensor<fp16, [1, 128, 1]> sep_module_40_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1538432)))];
            tensor<fp16, [1, 128, 12]> var_2478_cast = mul(x = sep_module_40_tcn_2_norm_gamma_to_fp16, y = var_2470_cast);
            tensor<fp16, [1, 128, 12]> var_2479_cast = real_div(x = var_2478_cast, y = std_y_163_cast);
            tensor<fp16, [1, 128, 1]> sep_module_40_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1538752)))];
            tensor<fp16, [1, 128, 12]> input_407_cast = add(x = var_2479_cast, y = sep_module_40_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_409_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 32, 0])];
            tensor<string, []> input_409_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_409_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 44]> input_409_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_407_cast_in_state, input_407_cast));
            tensor<fp16, [1, 128, 32]> input_407_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -32]), size = tensor<int32, [3]>([1, 128, 32]), x = input_409_cast);
            tensor<int32, [1]> var_2484 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2486 = const()[val = tensor<int32, [1]>([16])];
            tensor<string, []> input_411_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_411_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_40_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1539072)))];
            tensor<fp16, [1, 128, 12]> input_411_cast = conv(dilations = var_2486, groups = var_65, pad = input_411_pad_0, pad_type = input_411_pad_type_0, strides = var_2484, weight = sep_module_40_tcn_4_weight_to_fp16, x = input_409_cast);
            tensor<fp16, []> var_2490_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.41p-1)];
            tensor<fp16, [1, 128, 12]> var_2490_cast = leaky_relu(alpha = var_2490_alpha_1_to_fp16, x = input_411_cast);
            tensor<int32, [1]> var_2494 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_165_cast = reduce_mean(axes = var_2494, keep_dims = var_66, x = var_2490_cast);
            tensor<fp16, [1, 128, 12]> var_2496_cast = sub(x = var_2490_cast, y = mean_y_165_cast);
            tensor<fp16, [1, 128, 12]> var_2497_cast = square(x = var_2496_cast);
            tensor<int32, [1]> var_2498 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2499_cast = reduce_mean(axes = var_2498, keep_dims = var_66, x = var_2497_cast);
            tensor<fp16, []> var_2500_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2501_cast = add(x = var_2499_cast, y = var_2500_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_165_cast = sqrt(x = var_2501_cast);
            tensor<fp16, [1, 128, 1]> sep_module_40_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1539904)))];
            tensor<fp16, [1, 128, 12]> var_2504_cast = mul(x = sep_module_40_tcn_6_norm_gamma_to_fp16, y = var_2496_cast);
            tensor<fp16, [1, 128, 12]> var_2505_cast = real_div(x = var_2504_cast, y = std_y_165_cast);
            tensor<fp16, [1, 128, 1]> sep_module_40_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1540224)))];
            tensor<fp16, [1, 128, 12]> y_82_cast = add(x = var_2505_cast, y = sep_module_40_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_413_cast = add(x = input_403_cast, y = y_82_cast);
            tensor<int32, [1]> var_2516 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2518 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_415_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_415_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_41_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1540544)))];
            tensor<fp16, [1, 128, 12]> input_415_cast = conv(dilations = var_2518, groups = var_64, pad = input_415_pad_0, pad_type = input_415_pad_type_0, strides = var_2516, weight = sep_module_41_tcn_0_weight_to_fp16, x = input_413_cast);
            tensor<fp16, []> var_2522_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.fccp-4)];
            tensor<fp16, [1, 128, 12]> var_2522_cast = leaky_relu(alpha = var_2522_alpha_1_to_fp16, x = input_415_cast);
            tensor<int32, [1]> var_2526 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_167_cast = reduce_mean(axes = var_2526, keep_dims = var_66, x = var_2522_cast);
            tensor<fp16, [1, 128, 12]> var_2528_cast = sub(x = var_2522_cast, y = mean_y_167_cast);
            tensor<fp16, [1, 128, 12]> var_2529_cast = square(x = var_2528_cast);
            tensor<int32, [1]> var_2530 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2531_cast = reduce_mean(axes = var_2530, keep_dims = var_66, x = var_2529_cast);
            tensor<fp16, []> var_2532_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2533_cast = add(x = var_2531_cast, y = var_2532_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_167_cast = sqrt(x = var_2533_cast);
            tensor<fp16, [1, 128, 1]> sep_module_41_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1573376)))];
            tensor<fp16, [1, 128, 12]> var_2536_cast = mul(x = sep_module_41_tcn_2_norm_gamma_to_fp16, y = var_2528_cast);
            tensor<fp16, [1, 128, 12]> var_2537_cast = real_div(x = var_2536_cast, y = std_y_167_cast);
            tensor<fp16, [1, 128, 1]> sep_module_41_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1573696)))];
            tensor<fp16, [1, 128, 12]> input_417_cast = add(x = var_2537_cast, y = sep_module_41_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_419_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 64, 0])];
            tensor<string, []> input_419_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_419_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 76]> input_419_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_417_cast_in_state, input_417_cast));
            tensor<fp16, [1, 128, 64]> input_417_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -64]), size = tensor<int32, [3]>([1, 128, 64]), x = input_419_cast);
            tensor<int32, [1]> var_2542 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2544 = const()[val = tensor<int32, [1]>([32])];
            tensor<string, []> input_421_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_421_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_41_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1574016)))];
            tensor<fp16, [1, 128, 12]> input_421_cast = conv(dilations = var_2544, groups = var_65, pad = input_421_pad_0, pad_type = input_421_pad_type_0, strides = var_2542, weight = sep_module_41_tcn_4_weight_to_fp16, x = input_419_cast);
            tensor<fp16, []> var_2548_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.4b4p-1)];
            tensor<fp16, [1, 128, 12]> var_2548_cast = leaky_relu(alpha = var_2548_alpha_1_to_fp16, x = input_421_cast);
            tensor<int32, [1]> var_2552 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_169_cast = reduce_mean(axes = var_2552, keep_dims = var_66, x = var_2548_cast);
            tensor<fp16, [1, 128, 12]> var_2554_cast = sub(x = var_2548_cast, y = mean_y_169_cast);
            tensor<fp16, [1, 128, 12]> var_2555_cast = square(x = var_2554_cast);
            tensor<int32, [1]> var_2556 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2557_cast = reduce_mean(axes = var_2556, keep_dims = var_66, x = var_2555_cast);
            tensor<fp16, []> var_2558_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2559_cast = add(x = var_2557_cast, y = var_2558_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_169_cast = sqrt(x = var_2559_cast);
            tensor<fp16, [1, 128, 1]> sep_module_41_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1574848)))];
            tensor<fp16, [1, 128, 12]> var_2562_cast = mul(x = sep_module_41_tcn_6_norm_gamma_to_fp16, y = var_2554_cast);
            tensor<fp16, [1, 128, 12]> var_2563_cast = real_div(x = var_2562_cast, y = std_y_169_cast);
            tensor<fp16, [1, 128, 1]> sep_module_41_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1575168)))];
            tensor<fp16, [1, 128, 12]> y_84_cast = add(x = var_2563_cast, y = sep_module_41_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_423_cast = add(x = input_413_cast, y = y_84_cast);
            tensor<int32, [1]> var_2574 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2576 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_425_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_425_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_42_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1575488)))];
            tensor<fp16, [1, 128, 12]> input_425_cast = conv(dilations = var_2576, groups = var_64, pad = input_425_pad_0, pad_type = input_425_pad_type_0, strides = var_2574, weight = sep_module_42_tcn_0_weight_to_fp16, x = input_423_cast);
            tensor<fp16, []> var_2580_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.26p-1)];
            tensor<fp16, [1, 128, 12]> var_2580_cast = leaky_relu(alpha = var_2580_alpha_1_to_fp16, x = input_425_cast);
            tensor<int32, [1]> var_2584 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_171_cast = reduce_mean(axes = var_2584, keep_dims = var_66, x = var_2580_cast);
            tensor<fp16, [1, 128, 12]> var_2586_cast = sub(x = var_2580_cast, y = mean_y_171_cast);
            tensor<fp16, [1, 128, 12]> var_2587_cast = square(x = var_2586_cast);
            tensor<int32, [1]> var_2588 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2589_cast = reduce_mean(axes = var_2588, keep_dims = var_66, x = var_2587_cast);
            tensor<fp16, []> var_2590_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2591_cast = add(x = var_2589_cast, y = var_2590_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_171_cast = sqrt(x = var_2591_cast);
            tensor<fp16, [1, 128, 1]> sep_module_42_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1608320)))];
            tensor<fp16, [1, 128, 12]> var_2594_cast = mul(x = sep_module_42_tcn_2_norm_gamma_to_fp16, y = var_2586_cast);
            tensor<fp16, [1, 128, 12]> var_2595_cast = real_div(x = var_2594_cast, y = std_y_171_cast);
            tensor<fp16, [1, 128, 1]> sep_module_42_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1608640)))];
            tensor<fp16, [1, 128, 12]> input_427_cast = add(x = var_2595_cast, y = sep_module_42_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_429_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 2, 0])];
            tensor<string, []> input_429_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_429_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 14]> input_429_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_427_cast_in_state, input_427_cast));
            tensor<fp16, [1, 128, 2]> input_427_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -2]), size = tensor<int32, [3]>([1, 128, 2]), x = input_429_cast);
            tensor<int32, [1]> var_2600 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2602 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_431_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_431_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_42_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1608960)))];
            tensor<fp16, [1, 128, 12]> input_431_cast = conv(dilations = var_2602, groups = var_65, pad = input_431_pad_0, pad_type = input_431_pad_type_0, strides = var_2600, weight = sep_module_42_tcn_4_weight_to_fp16, x = input_429_cast);
            tensor<fp16, []> var_2606_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.9fcp-1)];
            tensor<fp16, [1, 128, 12]> var_2606_cast = leaky_relu(alpha = var_2606_alpha_1_to_fp16, x = input_431_cast);
            tensor<int32, [1]> var_2610 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_173_cast = reduce_mean(axes = var_2610, keep_dims = var_66, x = var_2606_cast);
            tensor<fp16, [1, 128, 12]> var_2612_cast = sub(x = var_2606_cast, y = mean_y_173_cast);
            tensor<fp16, [1, 128, 12]> var_2613_cast = square(x = var_2612_cast);
            tensor<int32, [1]> var_2614 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2615_cast = reduce_mean(axes = var_2614, keep_dims = var_66, x = var_2613_cast);
            tensor<fp16, []> var_2616_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2617_cast = add(x = var_2615_cast, y = var_2616_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_173_cast = sqrt(x = var_2617_cast);
            tensor<fp16, [1, 128, 1]> sep_module_42_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1609792)))];
            tensor<fp16, [1, 128, 12]> var_2620_cast = mul(x = sep_module_42_tcn_6_norm_gamma_to_fp16, y = var_2612_cast);
            tensor<fp16, [1, 128, 12]> var_2621_cast = real_div(x = var_2620_cast, y = std_y_173_cast);
            tensor<fp16, [1, 128, 1]> sep_module_42_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1610112)))];
            tensor<fp16, [1, 128, 12]> y_86_cast = add(x = var_2621_cast, y = sep_module_42_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_433_cast = add(x = input_423_cast, y = y_86_cast);
            tensor<int32, [1]> var_2632 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2634 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_435_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_435_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_43_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1610432)))];
            tensor<fp16, [1, 128, 12]> input_435_cast = conv(dilations = var_2634, groups = var_64, pad = input_435_pad_0, pad_type = input_435_pad_type_0, strides = var_2632, weight = sep_module_43_tcn_0_weight_to_fp16, x = input_433_cast);
            tensor<fp16, []> var_2638_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.12cp-1)];
            tensor<fp16, [1, 128, 12]> var_2638_cast = leaky_relu(alpha = var_2638_alpha_1_to_fp16, x = input_435_cast);
            tensor<int32, [1]> var_2642 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_175_cast = reduce_mean(axes = var_2642, keep_dims = var_66, x = var_2638_cast);
            tensor<fp16, [1, 128, 12]> var_2644_cast = sub(x = var_2638_cast, y = mean_y_175_cast);
            tensor<fp16, [1, 128, 12]> var_2645_cast = square(x = var_2644_cast);
            tensor<int32, [1]> var_2646 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2647_cast = reduce_mean(axes = var_2646, keep_dims = var_66, x = var_2645_cast);
            tensor<fp16, []> var_2648_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2649_cast = add(x = var_2647_cast, y = var_2648_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_175_cast = sqrt(x = var_2649_cast);
            tensor<fp16, [1, 128, 1]> sep_module_43_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1643264)))];
            tensor<fp16, [1, 128, 12]> var_2652_cast = mul(x = sep_module_43_tcn_2_norm_gamma_to_fp16, y = var_2644_cast);
            tensor<fp16, [1, 128, 12]> var_2653_cast = real_div(x = var_2652_cast, y = std_y_175_cast);
            tensor<fp16, [1, 128, 1]> sep_module_43_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1643584)))];
            tensor<fp16, [1, 128, 12]> input_437_cast = add(x = var_2653_cast, y = sep_module_43_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_439_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 4, 0])];
            tensor<string, []> input_439_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_439_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 16]> input_439_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_437_cast_in_state, input_437_cast));
            tensor<fp16, [1, 128, 4]> input_437_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -4]), size = tensor<int32, [3]>([1, 128, 4]), x = input_439_cast);
            tensor<int32, [1]> var_2658 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2660 = const()[val = tensor<int32, [1]>([2])];
            tensor<string, []> input_441_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_441_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_43_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1643904)))];
            tensor<fp16, [1, 128, 12]> input_441_cast = conv(dilations = var_2660, groups = var_65, pad = input_441_pad_0, pad_type = input_441_pad_type_0, strides = var_2658, weight = sep_module_43_tcn_4_weight_to_fp16, x = input_439_cast);
            tensor<fp16, []> var_2664_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.a1p-1)];
            tensor<fp16, [1, 128, 12]> var_2664_cast = leaky_relu(alpha = var_2664_alpha_1_to_fp16, x = input_441_cast);
            tensor<int32, [1]> var_2668 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_177_cast = reduce_mean(axes = var_2668, keep_dims = var_66, x = var_2664_cast);
            tensor<fp16, [1, 128, 12]> var_2670_cast = sub(x = var_2664_cast, y = mean_y_177_cast);
            tensor<fp16, [1, 128, 12]> var_2671_cast = square(x = var_2670_cast);
            tensor<int32, [1]> var_2672 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2673_cast = reduce_mean(axes = var_2672, keep_dims = var_66, x = var_2671_cast);
            tensor<fp16, []> var_2674_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2675_cast = add(x = var_2673_cast, y = var_2674_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_177_cast = sqrt(x = var_2675_cast);
            tensor<fp16, [1, 128, 1]> sep_module_43_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1644736)))];
            tensor<fp16, [1, 128, 12]> var_2678_cast = mul(x = sep_module_43_tcn_6_norm_gamma_to_fp16, y = var_2670_cast);
            tensor<fp16, [1, 128, 12]> var_2679_cast = real_div(x = var_2678_cast, y = std_y_177_cast);
            tensor<fp16, [1, 128, 1]> sep_module_43_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1645056)))];
            tensor<fp16, [1, 128, 12]> y_88_cast = add(x = var_2679_cast, y = sep_module_43_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_443_cast = add(x = input_433_cast, y = y_88_cast);
            tensor<int32, [1]> var_2690 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2692 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_445_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_445_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_44_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1645376)))];
            tensor<fp16, [1, 128, 12]> input_445_cast = conv(dilations = var_2692, groups = var_64, pad = input_445_pad_0, pad_type = input_445_pad_type_0, strides = var_2690, weight = sep_module_44_tcn_0_weight_to_fp16, x = input_443_cast);
            tensor<fp16, []> var_2696_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.9p-1)];
            tensor<fp16, [1, 128, 12]> var_2696_cast = leaky_relu(alpha = var_2696_alpha_1_to_fp16, x = input_445_cast);
            tensor<int32, [1]> var_2700 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_179_cast = reduce_mean(axes = var_2700, keep_dims = var_66, x = var_2696_cast);
            tensor<fp16, [1, 128, 12]> var_2702_cast = sub(x = var_2696_cast, y = mean_y_179_cast);
            tensor<fp16, [1, 128, 12]> var_2703_cast = square(x = var_2702_cast);
            tensor<int32, [1]> var_2704 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2705_cast = reduce_mean(axes = var_2704, keep_dims = var_66, x = var_2703_cast);
            tensor<fp16, []> var_2706_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2707_cast = add(x = var_2705_cast, y = var_2706_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_179_cast = sqrt(x = var_2707_cast);
            tensor<fp16, [1, 128, 1]> sep_module_44_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1678208)))];
            tensor<fp16, [1, 128, 12]> var_2710_cast = mul(x = sep_module_44_tcn_2_norm_gamma_to_fp16, y = var_2702_cast);
            tensor<fp16, [1, 128, 12]> var_2711_cast = real_div(x = var_2710_cast, y = std_y_179_cast);
            tensor<fp16, [1, 128, 1]> sep_module_44_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1678528)))];
            tensor<fp16, [1, 128, 12]> input_447_cast = add(x = var_2711_cast, y = sep_module_44_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_449_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 8, 0])];
            tensor<string, []> input_449_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_449_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 20]> input_449_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_447_cast_in_state, input_447_cast));
            tensor<fp16, [1, 128, 8]> input_447_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -8]), size = tensor<int32, [3]>([1, 128, 8]), x = input_449_cast);
            tensor<int32, [1]> var_2716 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2718 = const()[val = tensor<int32, [1]>([4])];
            tensor<string, []> input_451_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_451_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_44_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1678848)))];
            tensor<fp16, [1, 128, 12]> input_451_cast = conv(dilations = var_2718, groups = var_65, pad = input_451_pad_0, pad_type = input_451_pad_type_0, strides = var_2716, weight = sep_module_44_tcn_4_weight_to_fp16, x = input_449_cast);
            tensor<fp16, []> var_2722_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.274p-1)];
            tensor<fp16, [1, 128, 12]> var_2722_cast = leaky_relu(alpha = var_2722_alpha_1_to_fp16, x = input_451_cast);
            tensor<int32, [1]> var_2726 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_181_cast = reduce_mean(axes = var_2726, keep_dims = var_66, x = var_2722_cast);
            tensor<fp16, [1, 128, 12]> var_2728_cast = sub(x = var_2722_cast, y = mean_y_181_cast);
            tensor<fp16, [1, 128, 12]> var_2729_cast = square(x = var_2728_cast);
            tensor<int32, [1]> var_2730 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2731_cast = reduce_mean(axes = var_2730, keep_dims = var_66, x = var_2729_cast);
            tensor<fp16, []> var_2732_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2733_cast = add(x = var_2731_cast, y = var_2732_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_181_cast = sqrt(x = var_2733_cast);
            tensor<fp16, [1, 128, 1]> sep_module_44_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1679680)))];
            tensor<fp16, [1, 128, 12]> var_2736_cast = mul(x = sep_module_44_tcn_6_norm_gamma_to_fp16, y = var_2728_cast);
            tensor<fp16, [1, 128, 12]> var_2737_cast = real_div(x = var_2736_cast, y = std_y_181_cast);
            tensor<fp16, [1, 128, 1]> sep_module_44_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1680000)))];
            tensor<fp16, [1, 128, 12]> y_90_cast = add(x = var_2737_cast, y = sep_module_44_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_453_cast = add(x = input_443_cast, y = y_90_cast);
            tensor<int32, [1]> var_2748 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2750 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_455_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_455_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_45_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1680320)))];
            tensor<fp16, [1, 128, 12]> input_455_cast = conv(dilations = var_2750, groups = var_64, pad = input_455_pad_0, pad_type = input_455_pad_type_0, strides = var_2748, weight = sep_module_45_tcn_0_weight_to_fp16, x = input_453_cast);
            tensor<fp16, []> var_2754_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.c1p-2)];
            tensor<fp16, [1, 128, 12]> var_2754_cast = leaky_relu(alpha = var_2754_alpha_1_to_fp16, x = input_455_cast);
            tensor<int32, [1]> var_2758 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_183_cast = reduce_mean(axes = var_2758, keep_dims = var_66, x = var_2754_cast);
            tensor<fp16, [1, 128, 12]> var_2760_cast = sub(x = var_2754_cast, y = mean_y_183_cast);
            tensor<fp16, [1, 128, 12]> var_2761_cast = square(x = var_2760_cast);
            tensor<int32, [1]> var_2762 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2763_cast = reduce_mean(axes = var_2762, keep_dims = var_66, x = var_2761_cast);
            tensor<fp16, []> var_2764_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2765_cast = add(x = var_2763_cast, y = var_2764_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_183_cast = sqrt(x = var_2765_cast);
            tensor<fp16, [1, 128, 1]> sep_module_45_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1713152)))];
            tensor<fp16, [1, 128, 12]> var_2768_cast = mul(x = sep_module_45_tcn_2_norm_gamma_to_fp16, y = var_2760_cast);
            tensor<fp16, [1, 128, 12]> var_2769_cast = real_div(x = var_2768_cast, y = std_y_183_cast);
            tensor<fp16, [1, 128, 1]> sep_module_45_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1713472)))];
            tensor<fp16, [1, 128, 12]> input_457_cast = add(x = var_2769_cast, y = sep_module_45_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_459_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 16, 0])];
            tensor<string, []> input_459_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_459_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 28]> input_459_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_457_cast_in_state, input_457_cast));
            tensor<fp16, [1, 128, 16]> input_457_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -16]), size = tensor<int32, [3]>([1, 128, 16]), x = input_459_cast);
            tensor<int32, [1]> var_2774 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2776 = const()[val = tensor<int32, [1]>([8])];
            tensor<string, []> input_461_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_461_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_45_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1713792)))];
            tensor<fp16, [1, 128, 12]> input_461_cast = conv(dilations = var_2776, groups = var_65, pad = input_461_pad_0, pad_type = input_461_pad_type_0, strides = var_2774, weight = sep_module_45_tcn_4_weight_to_fp16, x = input_459_cast);
            tensor<fp16, []> var_2780_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.c8cp-1)];
            tensor<fp16, [1, 128, 12]> var_2780_cast = leaky_relu(alpha = var_2780_alpha_1_to_fp16, x = input_461_cast);
            tensor<int32, [1]> var_2784 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_185_cast = reduce_mean(axes = var_2784, keep_dims = var_66, x = var_2780_cast);
            tensor<fp16, [1, 128, 12]> var_2786_cast = sub(x = var_2780_cast, y = mean_y_185_cast);
            tensor<fp16, [1, 128, 12]> var_2787_cast = square(x = var_2786_cast);
            tensor<int32, [1]> var_2788 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2789_cast = reduce_mean(axes = var_2788, keep_dims = var_66, x = var_2787_cast);
            tensor<fp16, []> var_2790_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2791_cast = add(x = var_2789_cast, y = var_2790_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_185_cast = sqrt(x = var_2791_cast);
            tensor<fp16, [1, 128, 1]> sep_module_45_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1714624)))];
            tensor<fp16, [1, 128, 12]> var_2794_cast = mul(x = sep_module_45_tcn_6_norm_gamma_to_fp16, y = var_2786_cast);
            tensor<fp16, [1, 128, 12]> var_2795_cast = real_div(x = var_2794_cast, y = std_y_185_cast);
            tensor<fp16, [1, 128, 1]> sep_module_45_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1714944)))];
            tensor<fp16, [1, 128, 12]> y_92_cast = add(x = var_2795_cast, y = sep_module_45_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_463_cast = add(x = input_453_cast, y = y_92_cast);
            tensor<int32, [1]> var_2806 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2808 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_465_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_465_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_46_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1715264)))];
            tensor<fp16, [1, 128, 12]> input_465_cast = conv(dilations = var_2808, groups = var_64, pad = input_465_pad_0, pad_type = input_465_pad_type_0, strides = var_2806, weight = sep_module_46_tcn_0_weight_to_fp16, x = input_463_cast);
            tensor<fp16, []> var_2812_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.a1p-1)];
            tensor<fp16, [1, 128, 12]> var_2812_cast = leaky_relu(alpha = var_2812_alpha_1_to_fp16, x = input_465_cast);
            tensor<int32, [1]> var_2816 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_187_cast = reduce_mean(axes = var_2816, keep_dims = var_66, x = var_2812_cast);
            tensor<fp16, [1, 128, 12]> var_2818_cast = sub(x = var_2812_cast, y = mean_y_187_cast);
            tensor<fp16, [1, 128, 12]> var_2819_cast = square(x = var_2818_cast);
            tensor<int32, [1]> var_2820 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2821_cast = reduce_mean(axes = var_2820, keep_dims = var_66, x = var_2819_cast);
            tensor<fp16, []> var_2822_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2823_cast = add(x = var_2821_cast, y = var_2822_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_187_cast = sqrt(x = var_2823_cast);
            tensor<fp16, [1, 128, 1]> sep_module_46_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1748096)))];
            tensor<fp16, [1, 128, 12]> var_2826_cast = mul(x = sep_module_46_tcn_2_norm_gamma_to_fp16, y = var_2818_cast);
            tensor<fp16, [1, 128, 12]> var_2827_cast = real_div(x = var_2826_cast, y = std_y_187_cast);
            tensor<fp16, [1, 128, 1]> sep_module_46_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1748416)))];
            tensor<fp16, [1, 128, 12]> input_467_cast = add(x = var_2827_cast, y = sep_module_46_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_469_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 32, 0])];
            tensor<string, []> input_469_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_469_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 44]> input_469_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_467_cast_in_state, input_467_cast));
            tensor<fp16, [1, 128, 32]> input_467_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -32]), size = tensor<int32, [3]>([1, 128, 32]), x = input_469_cast);
            tensor<int32, [1]> var_2832 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2834 = const()[val = tensor<int32, [1]>([16])];
            tensor<string, []> input_471_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_471_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_46_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1748736)))];
            tensor<fp16, [1, 128, 12]> input_471_cast = conv(dilations = var_2834, groups = var_65, pad = input_471_pad_0, pad_type = input_471_pad_type_0, strides = var_2832, weight = sep_module_46_tcn_4_weight_to_fp16, x = input_469_cast);
            tensor<fp16, []> var_2838_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.b78p-2)];
            tensor<fp16, [1, 128, 12]> var_2838_cast = leaky_relu(alpha = var_2838_alpha_1_to_fp16, x = input_471_cast);
            tensor<int32, [1]> var_2842 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_189_cast = reduce_mean(axes = var_2842, keep_dims = var_66, x = var_2838_cast);
            tensor<fp16, [1, 128, 12]> var_2844_cast = sub(x = var_2838_cast, y = mean_y_189_cast);
            tensor<fp16, [1, 128, 12]> var_2845_cast = square(x = var_2844_cast);
            tensor<int32, [1]> var_2846 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2847_cast = reduce_mean(axes = var_2846, keep_dims = var_66, x = var_2845_cast);
            tensor<fp16, []> var_2848_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2849_cast = add(x = var_2847_cast, y = var_2848_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_189_cast = sqrt(x = var_2849_cast);
            tensor<fp16, [1, 128, 1]> sep_module_46_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1749568)))];
            tensor<fp16, [1, 128, 12]> var_2852_cast = mul(x = sep_module_46_tcn_6_norm_gamma_to_fp16, y = var_2844_cast);
            tensor<fp16, [1, 128, 12]> var_2853_cast = real_div(x = var_2852_cast, y = std_y_189_cast);
            tensor<fp16, [1, 128, 1]> sep_module_46_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1749888)))];
            tensor<fp16, [1, 128, 12]> y_94_cast = add(x = var_2853_cast, y = sep_module_46_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_473_cast = add(x = input_463_cast, y = y_94_cast);
            tensor<int32, [1]> var_2864 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2866 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_475_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_475_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_47_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1750208)))];
            tensor<fp16, [1, 128, 12]> input_475_cast = conv(dilations = var_2866, groups = var_64, pad = input_475_pad_0, pad_type = input_475_pad_type_0, strides = var_2864, weight = sep_module_47_tcn_0_weight_to_fp16, x = input_473_cast);
            tensor<fp16, []> var_2870_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.fdcp-1)];
            tensor<fp16, [1, 128, 12]> var_2870_cast = leaky_relu(alpha = var_2870_alpha_1_to_fp16, x = input_475_cast);
            tensor<int32, [1]> var_2874 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_191_cast = reduce_mean(axes = var_2874, keep_dims = var_66, x = var_2870_cast);
            tensor<fp16, [1, 128, 12]> var_2876_cast = sub(x = var_2870_cast, y = mean_y_191_cast);
            tensor<fp16, [1, 128, 12]> var_2877_cast = square(x = var_2876_cast);
            tensor<int32, [1]> var_2878 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2879_cast = reduce_mean(axes = var_2878, keep_dims = var_66, x = var_2877_cast);
            tensor<fp16, []> var_2880_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2881_cast = add(x = var_2879_cast, y = var_2880_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_191_cast = sqrt(x = var_2881_cast);
            tensor<fp16, [1, 128, 1]> sep_module_47_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1783040)))];
            tensor<fp16, [1, 128, 12]> var_2884_cast = mul(x = sep_module_47_tcn_2_norm_gamma_to_fp16, y = var_2876_cast);
            tensor<fp16, [1, 128, 12]> var_2885_cast = real_div(x = var_2884_cast, y = std_y_191_cast);
            tensor<fp16, [1, 128, 1]> sep_module_47_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1783360)))];
            tensor<fp16, [1, 128, 12]> input_477_cast = add(x = var_2885_cast, y = sep_module_47_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_479_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 64, 0])];
            tensor<string, []> input_479_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_479_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 76]> input_479_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_477_cast_in_state, input_477_cast));
            tensor<fp16, [1, 128, 64]> input_477_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -64]), size = tensor<int32, [3]>([1, 128, 64]), x = input_479_cast);
            tensor<int32, [1]> var_2890 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2892 = const()[val = tensor<int32, [1]>([32])];
            tensor<string, []> input_481_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_481_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_47_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1783680)))];
            tensor<fp16, [1, 128, 12]> input_481_cast = conv(dilations = var_2892, groups = var_65, pad = input_481_pad_0, pad_type = input_481_pad_type_0, strides = var_2890, weight = sep_module_47_tcn_4_weight_to_fp16, x = input_479_cast);
            tensor<fp16, []> var_2896_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.fecp-1)];
            tensor<fp16, [1, 128, 12]> var_2896_cast = leaky_relu(alpha = var_2896_alpha_1_to_fp16, x = input_481_cast);
            tensor<int32, [1]> var_2900 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_193_cast = reduce_mean(axes = var_2900, keep_dims = var_66, x = var_2896_cast);
            tensor<fp16, [1, 128, 12]> var_2902_cast = sub(x = var_2896_cast, y = mean_y_193_cast);
            tensor<fp16, [1, 128, 12]> var_2903_cast = square(x = var_2902_cast);
            tensor<int32, [1]> var_2904 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2905_cast = reduce_mean(axes = var_2904, keep_dims = var_66, x = var_2903_cast);
            tensor<fp16, []> var_2906_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2907_cast = add(x = var_2905_cast, y = var_2906_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_193_cast = sqrt(x = var_2907_cast);
            tensor<fp16, [1, 128, 1]> sep_module_47_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1784512)))];
            tensor<fp16, [1, 128, 12]> var_2910_cast = mul(x = sep_module_47_tcn_6_norm_gamma_to_fp16, y = var_2902_cast);
            tensor<fp16, [1, 128, 12]> var_2911_cast = real_div(x = var_2910_cast, y = std_y_193_cast);
            tensor<fp16, [1, 128, 1]> sep_module_47_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1784832)))];
            tensor<fp16, [1, 128, 12]> y_96_cast = add(x = var_2911_cast, y = sep_module_47_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_483_cast = add(x = input_473_cast, y = y_96_cast);
            tensor<int32, [1]> var_2922 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2924 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_485_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_485_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_48_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1785152)))];
            tensor<fp16, [1, 128, 12]> input_485_cast = conv(dilations = var_2924, groups = var_64, pad = input_485_pad_0, pad_type = input_485_pad_type_0, strides = var_2922, weight = sep_module_48_tcn_0_weight_to_fp16, x = input_483_cast);
            tensor<fp16, []> var_2928_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.718p-4)];
            tensor<fp16, [1, 128, 12]> var_2928_cast = leaky_relu(alpha = var_2928_alpha_1_to_fp16, x = input_485_cast);
            tensor<int32, [1]> var_2932 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_195_cast = reduce_mean(axes = var_2932, keep_dims = var_66, x = var_2928_cast);
            tensor<fp16, [1, 128, 12]> var_2934_cast = sub(x = var_2928_cast, y = mean_y_195_cast);
            tensor<fp16, [1, 128, 12]> var_2935_cast = square(x = var_2934_cast);
            tensor<int32, [1]> var_2936 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2937_cast = reduce_mean(axes = var_2936, keep_dims = var_66, x = var_2935_cast);
            tensor<fp16, []> var_2938_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2939_cast = add(x = var_2937_cast, y = var_2938_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_195_cast = sqrt(x = var_2939_cast);
            tensor<fp16, [1, 128, 1]> sep_module_48_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1817984)))];
            tensor<fp16, [1, 128, 12]> var_2942_cast = mul(x = sep_module_48_tcn_2_norm_gamma_to_fp16, y = var_2934_cast);
            tensor<fp16, [1, 128, 12]> var_2943_cast = real_div(x = var_2942_cast, y = std_y_195_cast);
            tensor<fp16, [1, 128, 1]> sep_module_48_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1818304)))];
            tensor<fp16, [1, 128, 12]> input_487_cast = add(x = var_2943_cast, y = sep_module_48_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_489_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 2, 0])];
            tensor<string, []> input_489_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_489_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 14]> input_489_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_487_cast_in_state, input_487_cast));
            tensor<fp16, [1, 128, 2]> input_487_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -2]), size = tensor<int32, [3]>([1, 128, 2]), x = input_489_cast);
            tensor<int32, [1]> var_2948 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2950 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_491_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_491_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_48_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1818624)))];
            tensor<fp16, [1, 128, 12]> input_491_cast = conv(dilations = var_2950, groups = var_65, pad = input_491_pad_0, pad_type = input_491_pad_type_0, strides = var_2948, weight = sep_module_48_tcn_4_weight_to_fp16, x = input_489_cast);
            tensor<fp16, []> var_2954_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.f94p-1)];
            tensor<fp16, [1, 128, 12]> var_2954_cast = leaky_relu(alpha = var_2954_alpha_1_to_fp16, x = input_491_cast);
            tensor<int32, [1]> var_2958 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_197_cast = reduce_mean(axes = var_2958, keep_dims = var_66, x = var_2954_cast);
            tensor<fp16, [1, 128, 12]> var_2960_cast = sub(x = var_2954_cast, y = mean_y_197_cast);
            tensor<fp16, [1, 128, 12]> var_2961_cast = square(x = var_2960_cast);
            tensor<int32, [1]> var_2962 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2963_cast = reduce_mean(axes = var_2962, keep_dims = var_66, x = var_2961_cast);
            tensor<fp16, []> var_2964_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2965_cast = add(x = var_2963_cast, y = var_2964_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_197_cast = sqrt(x = var_2965_cast);
            tensor<fp16, [1, 128, 1]> sep_module_48_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1819456)))];
            tensor<fp16, [1, 128, 12]> var_2968_cast = mul(x = sep_module_48_tcn_6_norm_gamma_to_fp16, y = var_2960_cast);
            tensor<fp16, [1, 128, 12]> var_2969_cast = real_div(x = var_2968_cast, y = std_y_197_cast);
            tensor<fp16, [1, 128, 1]> sep_module_48_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1819776)))];
            tensor<fp16, [1, 128, 12]> y_98_cast = add(x = var_2969_cast, y = sep_module_48_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_493_cast = add(x = input_483_cast, y = y_98_cast);
            tensor<int32, [1]> var_2980 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2982 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_495_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_495_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_49_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1820096)))];
            tensor<fp16, [1, 128, 12]> input_495_cast = conv(dilations = var_2982, groups = var_64, pad = input_495_pad_0, pad_type = input_495_pad_type_0, strides = var_2980, weight = sep_module_49_tcn_0_weight_to_fp16, x = input_493_cast);
            tensor<fp16, []> var_2986_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.708p-2)];
            tensor<fp16, [1, 128, 12]> var_2986_cast = leaky_relu(alpha = var_2986_alpha_1_to_fp16, x = input_495_cast);
            tensor<int32, [1]> var_2990 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_199_cast = reduce_mean(axes = var_2990, keep_dims = var_66, x = var_2986_cast);
            tensor<fp16, [1, 128, 12]> var_2992_cast = sub(x = var_2986_cast, y = mean_y_199_cast);
            tensor<fp16, [1, 128, 12]> var_2993_cast = square(x = var_2992_cast);
            tensor<int32, [1]> var_2994 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_2995_cast = reduce_mean(axes = var_2994, keep_dims = var_66, x = var_2993_cast);
            tensor<fp16, []> var_2996_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_2997_cast = add(x = var_2995_cast, y = var_2996_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_199_cast = sqrt(x = var_2997_cast);
            tensor<fp16, [1, 128, 1]> sep_module_49_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1852928)))];
            tensor<fp16, [1, 128, 12]> var_3000_cast = mul(x = sep_module_49_tcn_2_norm_gamma_to_fp16, y = var_2992_cast);
            tensor<fp16, [1, 128, 12]> var_3001_cast = real_div(x = var_3000_cast, y = std_y_199_cast);
            tensor<fp16, [1, 128, 1]> sep_module_49_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1853248)))];
            tensor<fp16, [1, 128, 12]> input_497_cast = add(x = var_3001_cast, y = sep_module_49_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_499_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 4, 0])];
            tensor<string, []> input_499_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_499_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 16]> input_499_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_497_cast_in_state, input_497_cast));
            tensor<fp16, [1, 128, 4]> input_497_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -4]), size = tensor<int32, [3]>([1, 128, 4]), x = input_499_cast);
            tensor<int32, [1]> var_3006 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_3008 = const()[val = tensor<int32, [1]>([2])];
            tensor<string, []> input_501_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_501_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_49_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1853568)))];
            tensor<fp16, [1, 128, 12]> input_501_cast = conv(dilations = var_3008, groups = var_65, pad = input_501_pad_0, pad_type = input_501_pad_type_0, strides = var_3006, weight = sep_module_49_tcn_4_weight_to_fp16, x = input_499_cast);
            tensor<fp16, []> var_3012_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.d64p-1)];
            tensor<fp16, [1, 128, 12]> var_3012_cast = leaky_relu(alpha = var_3012_alpha_1_to_fp16, x = input_501_cast);
            tensor<int32, [1]> var_3016 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_201_cast = reduce_mean(axes = var_3016, keep_dims = var_66, x = var_3012_cast);
            tensor<fp16, [1, 128, 12]> var_3018_cast = sub(x = var_3012_cast, y = mean_y_201_cast);
            tensor<fp16, [1, 128, 12]> var_3019_cast = square(x = var_3018_cast);
            tensor<int32, [1]> var_3020 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_3021_cast = reduce_mean(axes = var_3020, keep_dims = var_66, x = var_3019_cast);
            tensor<fp16, []> var_3022_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_3023_cast = add(x = var_3021_cast, y = var_3022_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_201_cast = sqrt(x = var_3023_cast);
            tensor<fp16, [1, 128, 1]> sep_module_49_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1854400)))];
            tensor<fp16, [1, 128, 12]> var_3026_cast = mul(x = sep_module_49_tcn_6_norm_gamma_to_fp16, y = var_3018_cast);
            tensor<fp16, [1, 128, 12]> var_3027_cast = real_div(x = var_3026_cast, y = std_y_201_cast);
            tensor<fp16, [1, 128, 1]> sep_module_49_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1854720)))];
            tensor<fp16, [1, 128, 12]> y_100_cast = add(x = var_3027_cast, y = sep_module_49_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_503_cast = add(x = input_493_cast, y = y_100_cast);
            tensor<int32, [1]> var_3038 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_3040 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_505_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_505_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_50_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1855040)))];
            tensor<fp16, [1, 128, 12]> input_505_cast = conv(dilations = var_3040, groups = var_64, pad = input_505_pad_0, pad_type = input_505_pad_type_0, strides = var_3038, weight = sep_module_50_tcn_0_weight_to_fp16, x = input_503_cast);
            tensor<fp16, []> var_3044_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.638p-1)];
            tensor<fp16, [1, 128, 12]> var_3044_cast = leaky_relu(alpha = var_3044_alpha_1_to_fp16, x = input_505_cast);
            tensor<int32, [1]> var_3048 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_203_cast = reduce_mean(axes = var_3048, keep_dims = var_66, x = var_3044_cast);
            tensor<fp16, [1, 128, 12]> var_3050_cast = sub(x = var_3044_cast, y = mean_y_203_cast);
            tensor<fp16, [1, 128, 12]> var_3051_cast = square(x = var_3050_cast);
            tensor<int32, [1]> var_3052 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_3053_cast = reduce_mean(axes = var_3052, keep_dims = var_66, x = var_3051_cast);
            tensor<fp16, []> var_3054_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_3055_cast = add(x = var_3053_cast, y = var_3054_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_203_cast = sqrt(x = var_3055_cast);
            tensor<fp16, [1, 128, 1]> sep_module_50_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1887872)))];
            tensor<fp16, [1, 128, 12]> var_3058_cast = mul(x = sep_module_50_tcn_2_norm_gamma_to_fp16, y = var_3050_cast);
            tensor<fp16, [1, 128, 12]> var_3059_cast = real_div(x = var_3058_cast, y = std_y_203_cast);
            tensor<fp16, [1, 128, 1]> sep_module_50_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1888192)))];
            tensor<fp16, [1, 128, 12]> input_507_cast = add(x = var_3059_cast, y = sep_module_50_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_509_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 8, 0])];
            tensor<string, []> input_509_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_509_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 20]> input_509_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_507_cast_in_state, input_507_cast));
            tensor<fp16, [1, 128, 8]> input_507_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -8]), size = tensor<int32, [3]>([1, 128, 8]), x = input_509_cast);
            tensor<int32, [1]> var_3064 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_3066 = const()[val = tensor<int32, [1]>([4])];
            tensor<string, []> input_511_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_511_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_50_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1888512)))];
            tensor<fp16, [1, 128, 12]> input_511_cast = conv(dilations = var_3066, groups = var_65, pad = input_511_pad_0, pad_type = input_511_pad_type_0, strides = var_3064, weight = sep_module_50_tcn_4_weight_to_fp16, x = input_509_cast);
            tensor<fp16, []> var_3070_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.c8p-2)];
            tensor<fp16, [1, 128, 12]> var_3070_cast = leaky_relu(alpha = var_3070_alpha_1_to_fp16, x = input_511_cast);
            tensor<int32, [1]> var_3074 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_205_cast = reduce_mean(axes = var_3074, keep_dims = var_66, x = var_3070_cast);
            tensor<fp16, [1, 128, 12]> var_3076_cast = sub(x = var_3070_cast, y = mean_y_205_cast);
            tensor<fp16, [1, 128, 12]> var_3077_cast = square(x = var_3076_cast);
            tensor<int32, [1]> var_3078 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_3079_cast = reduce_mean(axes = var_3078, keep_dims = var_66, x = var_3077_cast);
            tensor<fp16, []> var_3080_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_3081_cast = add(x = var_3079_cast, y = var_3080_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_205_cast = sqrt(x = var_3081_cast);
            tensor<fp16, [1, 128, 1]> sep_module_50_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1889344)))];
            tensor<fp16, [1, 128, 12]> var_3084_cast = mul(x = sep_module_50_tcn_6_norm_gamma_to_fp16, y = var_3076_cast);
            tensor<fp16, [1, 128, 12]> var_3085_cast = real_div(x = var_3084_cast, y = std_y_205_cast);
            tensor<fp16, [1, 128, 1]> sep_module_50_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1889664)))];
            tensor<fp16, [1, 128, 12]> y_102_cast = add(x = var_3085_cast, y = sep_module_50_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_513_cast = add(x = input_503_cast, y = y_102_cast);
            tensor<int32, [1]> var_3096 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_3098 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_515_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_515_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_51_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1889984)))];
            tensor<fp16, [1, 128, 12]> input_515_cast = conv(dilations = var_3098, groups = var_64, pad = input_515_pad_0, pad_type = input_515_pad_type_0, strides = var_3096, weight = sep_module_51_tcn_0_weight_to_fp16, x = input_513_cast);
            tensor<fp16, []> var_3102_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.b5p-1)];
            tensor<fp16, [1, 128, 12]> var_3102_cast = leaky_relu(alpha = var_3102_alpha_1_to_fp16, x = input_515_cast);
            tensor<int32, [1]> var_3106 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_207_cast = reduce_mean(axes = var_3106, keep_dims = var_66, x = var_3102_cast);
            tensor<fp16, [1, 128, 12]> var_3108_cast = sub(x = var_3102_cast, y = mean_y_207_cast);
            tensor<fp16, [1, 128, 12]> var_3109_cast = square(x = var_3108_cast);
            tensor<int32, [1]> var_3110 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_3111_cast = reduce_mean(axes = var_3110, keep_dims = var_66, x = var_3109_cast);
            tensor<fp16, []> var_3112_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_3113_cast = add(x = var_3111_cast, y = var_3112_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_207_cast = sqrt(x = var_3113_cast);
            tensor<fp16, [1, 128, 1]> sep_module_51_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1922816)))];
            tensor<fp16, [1, 128, 12]> var_3116_cast = mul(x = sep_module_51_tcn_2_norm_gamma_to_fp16, y = var_3108_cast);
            tensor<fp16, [1, 128, 12]> var_3117_cast = real_div(x = var_3116_cast, y = std_y_207_cast);
            tensor<fp16, [1, 128, 1]> sep_module_51_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1923136)))];
            tensor<fp16, [1, 128, 12]> input_517_cast = add(x = var_3117_cast, y = sep_module_51_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_519_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 16, 0])];
            tensor<string, []> input_519_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_519_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 28]> input_519_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_517_cast_in_state, input_517_cast));
            tensor<fp16, [1, 128, 16]> input_517_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -16]), size = tensor<int32, [3]>([1, 128, 16]), x = input_519_cast);
            tensor<int32, [1]> var_3122 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_3124 = const()[val = tensor<int32, [1]>([8])];
            tensor<string, []> input_521_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_521_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_51_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1923456)))];
            tensor<fp16, [1, 128, 12]> input_521_cast = conv(dilations = var_3124, groups = var_65, pad = input_521_pad_0, pad_type = input_521_pad_type_0, strides = var_3122, weight = sep_module_51_tcn_4_weight_to_fp16, x = input_519_cast);
            tensor<fp16, []> var_3128_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.978p-2)];
            tensor<fp16, [1, 128, 12]> var_3128_cast = leaky_relu(alpha = var_3128_alpha_1_to_fp16, x = input_521_cast);
            tensor<int32, [1]> var_3132 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_209_cast = reduce_mean(axes = var_3132, keep_dims = var_66, x = var_3128_cast);
            tensor<fp16, [1, 128, 12]> var_3134_cast = sub(x = var_3128_cast, y = mean_y_209_cast);
            tensor<fp16, [1, 128, 12]> var_3135_cast = square(x = var_3134_cast);
            tensor<int32, [1]> var_3136 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_3137_cast = reduce_mean(axes = var_3136, keep_dims = var_66, x = var_3135_cast);
            tensor<fp16, []> var_3138_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_3139_cast = add(x = var_3137_cast, y = var_3138_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_209_cast = sqrt(x = var_3139_cast);
            tensor<fp16, [1, 128, 1]> sep_module_51_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1924288)))];
            tensor<fp16, [1, 128, 12]> var_3142_cast = mul(x = sep_module_51_tcn_6_norm_gamma_to_fp16, y = var_3134_cast);
            tensor<fp16, [1, 128, 12]> var_3143_cast = real_div(x = var_3142_cast, y = std_y_209_cast);
            tensor<fp16, [1, 128, 1]> sep_module_51_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1924608)))];
            tensor<fp16, [1, 128, 12]> y_104_cast = add(x = var_3143_cast, y = sep_module_51_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_523_cast = add(x = input_513_cast, y = y_104_cast);
            tensor<int32, [1]> var_3154 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_3156 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_525_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_525_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_52_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1924928)))];
            tensor<fp16, [1, 128, 12]> input_525_cast = conv(dilations = var_3156, groups = var_64, pad = input_525_pad_0, pad_type = input_525_pad_type_0, strides = var_3154, weight = sep_module_52_tcn_0_weight_to_fp16, x = input_523_cast);
            tensor<fp16, []> var_3160_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.a4cp-7)];
            tensor<fp16, [1, 128, 12]> var_3160_cast = leaky_relu(alpha = var_3160_alpha_1_to_fp16, x = input_525_cast);
            tensor<int32, [1]> var_3164 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_211_cast = reduce_mean(axes = var_3164, keep_dims = var_66, x = var_3160_cast);
            tensor<fp16, [1, 128, 12]> var_3166_cast = sub(x = var_3160_cast, y = mean_y_211_cast);
            tensor<fp16, [1, 128, 12]> var_3167_cast = square(x = var_3166_cast);
            tensor<int32, [1]> var_3168 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_3169_cast = reduce_mean(axes = var_3168, keep_dims = var_66, x = var_3167_cast);
            tensor<fp16, []> var_3170_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_3171_cast = add(x = var_3169_cast, y = var_3170_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_211_cast = sqrt(x = var_3171_cast);
            tensor<fp16, [1, 128, 1]> sep_module_52_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1957760)))];
            tensor<fp16, [1, 128, 12]> var_3174_cast = mul(x = sep_module_52_tcn_2_norm_gamma_to_fp16, y = var_3166_cast);
            tensor<fp16, [1, 128, 12]> var_3175_cast = real_div(x = var_3174_cast, y = std_y_211_cast);
            tensor<fp16, [1, 128, 1]> sep_module_52_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1958080)))];
            tensor<fp16, [1, 128, 12]> input_527_cast = add(x = var_3175_cast, y = sep_module_52_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_529_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 32, 0])];
            tensor<string, []> input_529_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_529_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 44]> input_529_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_527_cast_in_state, input_527_cast));
            tensor<fp16, [1, 128, 32]> input_527_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -32]), size = tensor<int32, [3]>([1, 128, 32]), x = input_529_cast);
            tensor<int32, [1]> var_3180 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_3182 = const()[val = tensor<int32, [1]>([16])];
            tensor<string, []> input_531_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_531_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_52_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1958400)))];
            tensor<fp16, [1, 128, 12]> input_531_cast = conv(dilations = var_3182, groups = var_65, pad = input_531_pad_0, pad_type = input_531_pad_type_0, strides = var_3180, weight = sep_module_52_tcn_4_weight_to_fp16, x = input_529_cast);
            tensor<fp16, []> var_3186_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.02cp-1)];
            tensor<fp16, [1, 128, 12]> var_3186_cast = leaky_relu(alpha = var_3186_alpha_1_to_fp16, x = input_531_cast);
            tensor<int32, [1]> var_3190 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_213_cast = reduce_mean(axes = var_3190, keep_dims = var_66, x = var_3186_cast);
            tensor<fp16, [1, 128, 12]> var_3192_cast = sub(x = var_3186_cast, y = mean_y_213_cast);
            tensor<fp16, [1, 128, 12]> var_3193_cast = square(x = var_3192_cast);
            tensor<int32, [1]> var_3194 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_3195_cast = reduce_mean(axes = var_3194, keep_dims = var_66, x = var_3193_cast);
            tensor<fp16, []> var_3196_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_3197_cast = add(x = var_3195_cast, y = var_3196_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_213_cast = sqrt(x = var_3197_cast);
            tensor<fp16, [1, 128, 1]> sep_module_52_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1959232)))];
            tensor<fp16, [1, 128, 12]> var_3200_cast = mul(x = sep_module_52_tcn_6_norm_gamma_to_fp16, y = var_3192_cast);
            tensor<fp16, [1, 128, 12]> var_3201_cast = real_div(x = var_3200_cast, y = std_y_213_cast);
            tensor<fp16, [1, 128, 1]> sep_module_52_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1959552)))];
            tensor<fp16, [1, 128, 12]> y_106_cast = add(x = var_3201_cast, y = sep_module_52_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_533_cast = add(x = input_523_cast, y = y_106_cast);
            tensor<int32, [1]> var_3212 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_3214 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_535_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_535_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_53_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1959872)))];
            tensor<fp16, [1, 128, 12]> input_535_cast = conv(dilations = var_3214, groups = var_64, pad = input_535_pad_0, pad_type = input_535_pad_type_0, strides = var_3212, weight = sep_module_53_tcn_0_weight_to_fp16, x = input_533_cast);
            tensor<fp16, []> var_3218_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.fdp-1)];
            tensor<fp16, [1, 128, 12]> var_3218_cast = leaky_relu(alpha = var_3218_alpha_1_to_fp16, x = input_535_cast);
            tensor<int32, [1]> var_3222 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_215_cast = reduce_mean(axes = var_3222, keep_dims = var_66, x = var_3218_cast);
            tensor<fp16, [1, 128, 12]> var_3224_cast = sub(x = var_3218_cast, y = mean_y_215_cast);
            tensor<fp16, [1, 128, 12]> var_3225_cast = square(x = var_3224_cast);
            tensor<int32, [1]> var_3226 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_3227_cast = reduce_mean(axes = var_3226, keep_dims = var_66, x = var_3225_cast);
            tensor<fp16, []> var_3228_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_3229_cast = add(x = var_3227_cast, y = var_3228_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_215_cast = sqrt(x = var_3229_cast);
            tensor<fp16, [1, 128, 1]> sep_module_53_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1992704)))];
            tensor<fp16, [1, 128, 12]> var_3232_cast = mul(x = sep_module_53_tcn_2_norm_gamma_to_fp16, y = var_3224_cast);
            tensor<fp16, [1, 128, 12]> var_3233_cast = real_div(x = var_3232_cast, y = std_y_215_cast);
            tensor<fp16, [1, 128, 1]> sep_module_53_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1993024)))];
            tensor<fp16, [1, 128, 12]> input_537_cast = add(x = var_3233_cast, y = sep_module_53_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_539_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 64, 0])];
            tensor<string, []> input_539_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_539_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 76]> input_539_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_537_cast_in_state, input_537_cast));
            tensor<fp16, [1, 128, 64]> input_537_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -64]), size = tensor<int32, [3]>([1, 128, 64]), x = input_539_cast);
            tensor<int32, [1]> var_3238 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_3240 = const()[val = tensor<int32, [1]>([32])];
            tensor<string, []> input_541_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_541_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_53_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1993344)))];
            tensor<fp16, [1, 128, 12]> input_541_cast = conv(dilations = var_3240, groups = var_65, pad = input_541_pad_0, pad_type = input_541_pad_type_0, strides = var_3238, weight = sep_module_53_tcn_4_weight_to_fp16, x = input_539_cast);
            tensor<fp16, []> var_3244_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.004p+0)];
            tensor<fp16, [1, 128, 12]> var_3244_cast = leaky_relu(alpha = var_3244_alpha_1_to_fp16, x = input_541_cast);
            tensor<int32, [1]> var_3248 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_217_cast = reduce_mean(axes = var_3248, keep_dims = var_66, x = var_3244_cast);
            tensor<fp16, [1, 128, 12]> var_3250_cast = sub(x = var_3244_cast, y = mean_y_217_cast);
            tensor<fp16, [1, 128, 12]> var_3251_cast = square(x = var_3250_cast);
            tensor<int32, [1]> var_3252 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_3253_cast = reduce_mean(axes = var_3252, keep_dims = var_66, x = var_3251_cast);
            tensor<fp16, []> var_3254_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_3255_cast = add(x = var_3253_cast, y = var_3254_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_217_cast = sqrt(x = var_3255_cast);
            tensor<fp16, [1, 128, 1]> sep_module_53_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1994176)))];
            tensor<fp16, [1, 128, 12]> var_3258_cast = mul(x = sep_module_53_tcn_6_norm_gamma_to_fp16, y = var_3250_cast);
            tensor<fp16, [1, 128, 12]> var_3259_cast = real_div(x = var_3258_cast, y = std_y_217_cast);
            tensor<fp16, [1, 128, 1]> sep_module_53_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1994496)))];
            tensor<fp16, [1, 128, 12]> y_108_cast = add(x = var_3259_cast, y = sep_module_53_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_543_cast = add(x = input_533_cast, y = y_108_cast);
            tensor<int32, [1]> var_3270 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_3272 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_545_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_545_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_54_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(1994816)))];
            tensor<fp16, [1, 128, 12]> input_545_cast = conv(dilations = var_3272, groups = var_64, pad = input_545_pad_0, pad_type = input_545_pad_type_0, strides = var_3270, weight = sep_module_54_tcn_0_weight_to_fp16, x = input_543_cast);
            tensor<fp16, []> var_3276_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.d8cp-1)];
            tensor<fp16, [1, 128, 12]> var_3276_cast = leaky_relu(alpha = var_3276_alpha_1_to_fp16, x = input_545_cast);
            tensor<int32, [1]> var_3280 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_219_cast = reduce_mean(axes = var_3280, keep_dims = var_66, x = var_3276_cast);
            tensor<fp16, [1, 128, 12]> var_3282_cast = sub(x = var_3276_cast, y = mean_y_219_cast);
            tensor<fp16, [1, 128, 12]> var_3283_cast = square(x = var_3282_cast);
            tensor<int32, [1]> var_3284 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_3285_cast = reduce_mean(axes = var_3284, keep_dims = var_66, x = var_3283_cast);
            tensor<fp16, []> var_3286_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_3287_cast = add(x = var_3285_cast, y = var_3286_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_219_cast = sqrt(x = var_3287_cast);
            tensor<fp16, [1, 128, 1]> sep_module_54_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2027648)))];
            tensor<fp16, [1, 128, 12]> var_3290_cast = mul(x = sep_module_54_tcn_2_norm_gamma_to_fp16, y = var_3282_cast);
            tensor<fp16, [1, 128, 12]> var_3291_cast = real_div(x = var_3290_cast, y = std_y_219_cast);
            tensor<fp16, [1, 128, 1]> sep_module_54_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2027968)))];
            tensor<fp16, [1, 128, 12]> input_547_cast = add(x = var_3291_cast, y = sep_module_54_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_549_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 2, 0])];
            tensor<string, []> input_549_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_549_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 14]> input_549_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_547_cast_in_state, input_547_cast));
            tensor<fp16, [1, 128, 2]> input_547_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -2]), size = tensor<int32, [3]>([1, 128, 2]), x = input_549_cast);
            tensor<int32, [1]> var_3296 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_3298 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_551_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_551_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_54_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2028288)))];
            tensor<fp16, [1, 128, 12]> input_551_cast = conv(dilations = var_3298, groups = var_65, pad = input_551_pad_0, pad_type = input_551_pad_type_0, strides = var_3296, weight = sep_module_54_tcn_4_weight_to_fp16, x = input_549_cast);
            tensor<fp16, []> var_3302_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.e6p-1)];
            tensor<fp16, [1, 128, 12]> var_3302_cast = leaky_relu(alpha = var_3302_alpha_1_to_fp16, x = input_551_cast);
            tensor<int32, [1]> var_3306 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_221_cast = reduce_mean(axes = var_3306, keep_dims = var_66, x = var_3302_cast);
            tensor<fp16, [1, 128, 12]> var_3308_cast = sub(x = var_3302_cast, y = mean_y_221_cast);
            tensor<fp16, [1, 128, 12]> var_3309_cast = square(x = var_3308_cast);
            tensor<int32, [1]> var_3310 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_3311_cast = reduce_mean(axes = var_3310, keep_dims = var_66, x = var_3309_cast);
            tensor<fp16, []> var_3312_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_3313_cast = add(x = var_3311_cast, y = var_3312_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_221_cast = sqrt(x = var_3313_cast);
            tensor<fp16, [1, 128, 1]> sep_module_54_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2029120)))];
            tensor<fp16, [1, 128, 12]> var_3316_cast = mul(x = sep_module_54_tcn_6_norm_gamma_to_fp16, y = var_3308_cast);
            tensor<fp16, [1, 128, 12]> var_3317_cast = real_div(x = var_3316_cast, y = std_y_221_cast);
            tensor<fp16, [1, 128, 1]> sep_module_54_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2029440)))];
            tensor<fp16, [1, 128, 12]> y_110_cast = add(x = var_3317_cast, y = sep_module_54_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_553_cast = add(x = input_543_cast, y = y_110_cast);
            tensor<int32, [1]> var_3328 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_3330 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_555_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_555_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_55_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2029760)))];
            tensor<fp16, [1, 128, 12]> input_555_cast = conv(dilations = var_3330, groups = var_64, pad = input_555_pad_0, pad_type = input_555_pad_type_0, strides = var_3328, weight = sep_module_55_tcn_0_weight_to_fp16, x = input_553_cast);
            tensor<fp16, []> var_3334_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.d68p-1)];
            tensor<fp16, [1, 128, 12]> var_3334_cast = leaky_relu(alpha = var_3334_alpha_1_to_fp16, x = input_555_cast);
            tensor<int32, [1]> var_3338 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_223_cast = reduce_mean(axes = var_3338, keep_dims = var_66, x = var_3334_cast);
            tensor<fp16, [1, 128, 12]> var_3340_cast = sub(x = var_3334_cast, y = mean_y_223_cast);
            tensor<fp16, [1, 128, 12]> var_3341_cast = square(x = var_3340_cast);
            tensor<int32, [1]> var_3342 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_3343_cast = reduce_mean(axes = var_3342, keep_dims = var_66, x = var_3341_cast);
            tensor<fp16, []> var_3344_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_3345_cast = add(x = var_3343_cast, y = var_3344_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_223_cast = sqrt(x = var_3345_cast);
            tensor<fp16, [1, 128, 1]> sep_module_55_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2062592)))];
            tensor<fp16, [1, 128, 12]> var_3348_cast = mul(x = sep_module_55_tcn_2_norm_gamma_to_fp16, y = var_3340_cast);
            tensor<fp16, [1, 128, 12]> var_3349_cast = real_div(x = var_3348_cast, y = std_y_223_cast);
            tensor<fp16, [1, 128, 1]> sep_module_55_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2062912)))];
            tensor<fp16, [1, 128, 12]> input_557_cast = add(x = var_3349_cast, y = sep_module_55_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_559_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 4, 0])];
            tensor<string, []> input_559_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_559_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 16]> input_559_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_557_cast_in_state, input_557_cast));
            tensor<fp16, [1, 128, 4]> input_557_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -4]), size = tensor<int32, [3]>([1, 128, 4]), x = input_559_cast);
            tensor<int32, [1]> var_3354 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_3356 = const()[val = tensor<int32, [1]>([2])];
            tensor<string, []> input_561_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_561_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_55_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2063232)))];
            tensor<fp16, [1, 128, 12]> input_561_cast = conv(dilations = var_3356, groups = var_65, pad = input_561_pad_0, pad_type = input_561_pad_type_0, strides = var_3354, weight = sep_module_55_tcn_4_weight_to_fp16, x = input_559_cast);
            tensor<fp16, []> var_3360_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.744p-2)];
            tensor<fp16, [1, 128, 12]> var_3360_cast = leaky_relu(alpha = var_3360_alpha_1_to_fp16, x = input_561_cast);
            tensor<int32, [1]> var_3364 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_225_cast = reduce_mean(axes = var_3364, keep_dims = var_66, x = var_3360_cast);
            tensor<fp16, [1, 128, 12]> var_3366_cast = sub(x = var_3360_cast, y = mean_y_225_cast);
            tensor<fp16, [1, 128, 12]> var_3367_cast = square(x = var_3366_cast);
            tensor<int32, [1]> var_3368 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_3369_cast = reduce_mean(axes = var_3368, keep_dims = var_66, x = var_3367_cast);
            tensor<fp16, []> var_3370_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_3371_cast = add(x = var_3369_cast, y = var_3370_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_225_cast = sqrt(x = var_3371_cast);
            tensor<fp16, [1, 128, 1]> sep_module_55_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2064064)))];
            tensor<fp16, [1, 128, 12]> var_3374_cast = mul(x = sep_module_55_tcn_6_norm_gamma_to_fp16, y = var_3366_cast);
            tensor<fp16, [1, 128, 12]> var_3375_cast = real_div(x = var_3374_cast, y = std_y_225_cast);
            tensor<fp16, [1, 128, 1]> sep_module_55_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2064384)))];
            tensor<fp16, [1, 128, 12]> y_112_cast = add(x = var_3375_cast, y = sep_module_55_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_563_cast = add(x = input_553_cast, y = y_112_cast);
            tensor<int32, [1]> var_3386 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_3388 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_565_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_565_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_56_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2064704)))];
            tensor<fp16, [1, 128, 12]> input_565_cast = conv(dilations = var_3388, groups = var_64, pad = input_565_pad_0, pad_type = input_565_pad_type_0, strides = var_3386, weight = sep_module_56_tcn_0_weight_to_fp16, x = input_563_cast);
            tensor<fp16, []> var_3392_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.fd4p-1)];
            tensor<fp16, [1, 128, 12]> var_3392_cast = leaky_relu(alpha = var_3392_alpha_1_to_fp16, x = input_565_cast);
            tensor<int32, [1]> var_3396 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_227_cast = reduce_mean(axes = var_3396, keep_dims = var_66, x = var_3392_cast);
            tensor<fp16, [1, 128, 12]> var_3398_cast = sub(x = var_3392_cast, y = mean_y_227_cast);
            tensor<fp16, [1, 128, 12]> var_3399_cast = square(x = var_3398_cast);
            tensor<int32, [1]> var_3400 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_3401_cast = reduce_mean(axes = var_3400, keep_dims = var_66, x = var_3399_cast);
            tensor<fp16, []> var_3402_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_3403_cast = add(x = var_3401_cast, y = var_3402_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_227_cast = sqrt(x = var_3403_cast);
            tensor<fp16, [1, 128, 1]> sep_module_56_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2097536)))];
            tensor<fp16, [1, 128, 12]> var_3406_cast = mul(x = sep_module_56_tcn_2_norm_gamma_to_fp16, y = var_3398_cast);
            tensor<fp16, [1, 128, 12]> var_3407_cast = real_div(x = var_3406_cast, y = std_y_227_cast);
            tensor<fp16, [1, 128, 1]> sep_module_56_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2097856)))];
            tensor<fp16, [1, 128, 12]> input_567_cast = add(x = var_3407_cast, y = sep_module_56_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_569_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 8, 0])];
            tensor<string, []> input_569_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_569_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 20]> input_569_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_567_cast_in_state, input_567_cast));
            tensor<fp16, [1, 128, 8]> input_567_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -8]), size = tensor<int32, [3]>([1, 128, 8]), x = input_569_cast);
            tensor<int32, [1]> var_3412 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_3414 = const()[val = tensor<int32, [1]>([4])];
            tensor<string, []> input_571_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_571_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_56_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2098176)))];
            tensor<fp16, [1, 128, 12]> input_571_cast = conv(dilations = var_3414, groups = var_65, pad = input_571_pad_0, pad_type = input_571_pad_type_0, strides = var_3412, weight = sep_module_56_tcn_4_weight_to_fp16, x = input_569_cast);
            tensor<fp16, []> var_3418_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.ff8p-1)];
            tensor<fp16, [1, 128, 12]> var_3418_cast = leaky_relu(alpha = var_3418_alpha_1_to_fp16, x = input_571_cast);
            tensor<int32, [1]> var_3422 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_229_cast = reduce_mean(axes = var_3422, keep_dims = var_66, x = var_3418_cast);
            tensor<fp16, [1, 128, 12]> var_3424_cast = sub(x = var_3418_cast, y = mean_y_229_cast);
            tensor<fp16, [1, 128, 12]> var_3425_cast = square(x = var_3424_cast);
            tensor<int32, [1]> var_3426 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_3427_cast = reduce_mean(axes = var_3426, keep_dims = var_66, x = var_3425_cast);
            tensor<fp16, []> var_3428_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_3429_cast = add(x = var_3427_cast, y = var_3428_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_229_cast = sqrt(x = var_3429_cast);
            tensor<fp16, [1, 128, 1]> sep_module_56_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2099008)))];
            tensor<fp16, [1, 128, 12]> var_3432_cast = mul(x = sep_module_56_tcn_6_norm_gamma_to_fp16, y = var_3424_cast);
            tensor<fp16, [1, 128, 12]> var_3433_cast = real_div(x = var_3432_cast, y = std_y_229_cast);
            tensor<fp16, [1, 128, 1]> sep_module_56_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2099328)))];
            tensor<fp16, [1, 128, 12]> y_114_cast = add(x = var_3433_cast, y = sep_module_56_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_573_cast = add(x = input_563_cast, y = y_114_cast);
            tensor<int32, [1]> var_3444 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_3446 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_575_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_575_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_57_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2099648)))];
            tensor<fp16, [1, 128, 12]> input_575_cast = conv(dilations = var_3446, groups = var_64, pad = input_575_pad_0, pad_type = input_575_pad_type_0, strides = var_3444, weight = sep_module_57_tcn_0_weight_to_fp16, x = input_573_cast);
            tensor<fp16, []> var_3450_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(-0x1.5fp-3)];
            tensor<fp16, [1, 128, 12]> var_3450_cast = leaky_relu(alpha = var_3450_alpha_1_to_fp16, x = input_575_cast);
            tensor<int32, [1]> var_3454 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_231_cast = reduce_mean(axes = var_3454, keep_dims = var_66, x = var_3450_cast);
            tensor<fp16, [1, 128, 12]> var_3456_cast = sub(x = var_3450_cast, y = mean_y_231_cast);
            tensor<fp16, [1, 128, 12]> var_3457_cast = square(x = var_3456_cast);
            tensor<int32, [1]> var_3458 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_3459_cast = reduce_mean(axes = var_3458, keep_dims = var_66, x = var_3457_cast);
            tensor<fp16, []> var_3460_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_3461_cast = add(x = var_3459_cast, y = var_3460_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_231_cast = sqrt(x = var_3461_cast);
            tensor<fp16, [1, 128, 1]> sep_module_57_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2132480)))];
            tensor<fp16, [1, 128, 12]> var_3464_cast = mul(x = sep_module_57_tcn_2_norm_gamma_to_fp16, y = var_3456_cast);
            tensor<fp16, [1, 128, 12]> var_3465_cast = real_div(x = var_3464_cast, y = std_y_231_cast);
            tensor<fp16, [1, 128, 1]> sep_module_57_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2132800)))];
            tensor<fp16, [1, 128, 12]> input_577_cast = add(x = var_3465_cast, y = sep_module_57_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_579_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 16, 0])];
            tensor<string, []> input_579_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_579_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 28]> input_579_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_577_cast_in_state, input_577_cast));
            tensor<fp16, [1, 128, 16]> input_577_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -16]), size = tensor<int32, [3]>([1, 128, 16]), x = input_579_cast);
            tensor<int32, [1]> var_3470 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_3472 = const()[val = tensor<int32, [1]>([8])];
            tensor<string, []> input_581_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_581_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_57_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2133120)))];
            tensor<fp16, [1, 128, 12]> input_581_cast = conv(dilations = var_3472, groups = var_65, pad = input_581_pad_0, pad_type = input_581_pad_type_0, strides = var_3470, weight = sep_module_57_tcn_4_weight_to_fp16, x = input_579_cast);
            tensor<fp16, []> var_3476_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.13p-1)];
            tensor<fp16, [1, 128, 12]> var_3476_cast = leaky_relu(alpha = var_3476_alpha_1_to_fp16, x = input_581_cast);
            tensor<int32, [1]> var_3480 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_233_cast = reduce_mean(axes = var_3480, keep_dims = var_66, x = var_3476_cast);
            tensor<fp16, [1, 128, 12]> var_3482_cast = sub(x = var_3476_cast, y = mean_y_233_cast);
            tensor<fp16, [1, 128, 12]> var_3483_cast = square(x = var_3482_cast);
            tensor<int32, [1]> var_3484 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_3485_cast = reduce_mean(axes = var_3484, keep_dims = var_66, x = var_3483_cast);
            tensor<fp16, []> var_3486_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_3487_cast = add(x = var_3485_cast, y = var_3486_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_233_cast = sqrt(x = var_3487_cast);
            tensor<fp16, [1, 128, 1]> sep_module_57_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2133952)))];
            tensor<fp16, [1, 128, 12]> var_3490_cast = mul(x = sep_module_57_tcn_6_norm_gamma_to_fp16, y = var_3482_cast);
            tensor<fp16, [1, 128, 12]> var_3491_cast = real_div(x = var_3490_cast, y = std_y_233_cast);
            tensor<fp16, [1, 128, 1]> sep_module_57_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2134272)))];
            tensor<fp16, [1, 128, 12]> y_116_cast = add(x = var_3491_cast, y = sep_module_57_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_583_cast = add(x = input_573_cast, y = y_116_cast);
            tensor<int32, [1]> var_3502 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_3504 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_585_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_585_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_58_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2134592)))];
            tensor<fp16, [1, 128, 12]> input_585_cast = conv(dilations = var_3504, groups = var_64, pad = input_585_pad_0, pad_type = input_585_pad_type_0, strides = var_3502, weight = sep_module_58_tcn_0_weight_to_fp16, x = input_583_cast);
            tensor<fp16, []> var_3508_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.fap-1)];
            tensor<fp16, [1, 128, 12]> var_3508_cast = leaky_relu(alpha = var_3508_alpha_1_to_fp16, x = input_585_cast);
            tensor<int32, [1]> var_3512 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_235_cast = reduce_mean(axes = var_3512, keep_dims = var_66, x = var_3508_cast);
            tensor<fp16, [1, 128, 12]> var_3514_cast = sub(x = var_3508_cast, y = mean_y_235_cast);
            tensor<fp16, [1, 128, 12]> var_3515_cast = square(x = var_3514_cast);
            tensor<int32, [1]> var_3516 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_3517_cast = reduce_mean(axes = var_3516, keep_dims = var_66, x = var_3515_cast);
            tensor<fp16, []> var_3518_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_3519_cast = add(x = var_3517_cast, y = var_3518_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_235_cast = sqrt(x = var_3519_cast);
            tensor<fp16, [1, 128, 1]> sep_module_58_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2167424)))];
            tensor<fp16, [1, 128, 12]> var_3522_cast = mul(x = sep_module_58_tcn_2_norm_gamma_to_fp16, y = var_3514_cast);
            tensor<fp16, [1, 128, 12]> var_3523_cast = real_div(x = var_3522_cast, y = std_y_235_cast);
            tensor<fp16, [1, 128, 1]> sep_module_58_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2167744)))];
            tensor<fp16, [1, 128, 12]> input_587_cast = add(x = var_3523_cast, y = sep_module_58_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_589_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 32, 0])];
            tensor<string, []> input_589_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_589_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 44]> input_589_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_587_cast_in_state, input_587_cast));
            tensor<fp16, [1, 128, 32]> input_587_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -32]), size = tensor<int32, [3]>([1, 128, 32]), x = input_589_cast);
            tensor<int32, [1]> var_3528 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_3530 = const()[val = tensor<int32, [1]>([16])];
            tensor<string, []> input_591_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_591_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_58_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2168064)))];
            tensor<fp16, [1, 128, 12]> input_591_cast = conv(dilations = var_3530, groups = var_65, pad = input_591_pad_0, pad_type = input_591_pad_type_0, strides = var_3528, weight = sep_module_58_tcn_4_weight_to_fp16, x = input_589_cast);
            tensor<fp16, []> var_3534_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.ff4p-1)];
            tensor<fp16, [1, 128, 12]> var_3534_cast = leaky_relu(alpha = var_3534_alpha_1_to_fp16, x = input_591_cast);
            tensor<int32, [1]> var_3538 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_237_cast = reduce_mean(axes = var_3538, keep_dims = var_66, x = var_3534_cast);
            tensor<fp16, [1, 128, 12]> var_3540_cast = sub(x = var_3534_cast, y = mean_y_237_cast);
            tensor<fp16, [1, 128, 12]> var_3541_cast = square(x = var_3540_cast);
            tensor<int32, [1]> var_3542 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_3543_cast = reduce_mean(axes = var_3542, keep_dims = var_66, x = var_3541_cast);
            tensor<fp16, []> var_3544_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_3545_cast = add(x = var_3543_cast, y = var_3544_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_237_cast = sqrt(x = var_3545_cast);
            tensor<fp16, [1, 128, 1]> sep_module_58_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2168896)))];
            tensor<fp16, [1, 128, 12]> var_3548_cast = mul(x = sep_module_58_tcn_6_norm_gamma_to_fp16, y = var_3540_cast);
            tensor<fp16, [1, 128, 12]> var_3549_cast = real_div(x = var_3548_cast, y = std_y_237_cast);
            tensor<fp16, [1, 128, 1]> sep_module_58_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2169216)))];
            tensor<fp16, [1, 128, 12]> y_118_cast = add(x = var_3549_cast, y = sep_module_58_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> input_3_cast = add(x = input_583_cast, y = y_118_cast);
            tensor<int32, [1]> var_3560 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_3562 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> input_2_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_2_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 128, 1]> sep_module_59_tcn_0_weight_to_fp16 = const()[val = tensor<fp16, [128, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2169536)))];
            tensor<fp16, [1, 128, 12]> input_2_cast = conv(dilations = var_3562, groups = var_64, pad = input_2_pad_0, pad_type = input_2_pad_type_0, strides = var_3560, weight = sep_module_59_tcn_0_weight_to_fp16, x = input_3_cast);
            tensor<fp16, []> var_3566_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.fd4p-1)];
            tensor<fp16, [1, 128, 12]> var_3566_cast = leaky_relu(alpha = var_3566_alpha_1_to_fp16, x = input_2_cast);
            tensor<int32, [1]> var_3570 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_2_cast = reduce_mean(axes = var_3570, keep_dims = var_66, x = var_3566_cast);
            tensor<fp16, [1, 128, 12]> var_3572_cast = sub(x = var_3566_cast, y = mean_y_2_cast);
            tensor<fp16, [1, 128, 12]> var_3573_cast = square(x = var_3572_cast);
            tensor<int32, [1]> var_3574 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_3575_cast = reduce_mean(axes = var_3574, keep_dims = var_66, x = var_3573_cast);
            tensor<fp16, []> var_3576_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_3577_cast = add(x = var_3575_cast, y = var_3576_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_2_cast = sqrt(x = var_3577_cast);
            tensor<fp16, [1, 128, 1]> sep_module_59_tcn_2_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2202368)))];
            tensor<fp16, [1, 128, 12]> var_3580_cast = mul(x = sep_module_59_tcn_2_norm_gamma_to_fp16, y = var_3572_cast);
            tensor<fp16, [1, 128, 12]> var_3581_cast = real_div(x = var_3580_cast, y = std_y_2_cast);
            tensor<fp16, [1, 128, 1]> sep_module_59_tcn_2_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2202688)))];
            tensor<fp16, [1, 128, 12]> input_4_cast = add(x = var_3581_cast, y = sep_module_59_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_6_pad_0 = const()[val = tensor<int32, [6]>([0, 0, 0, 0, 64, 0])];
            tensor<string, []> input_6_mode_0 = const()[val = tensor<string, []>("constant")];
            tensor<fp16, []> input_6_constant_val_0_to_fp16 = const()[val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 76]> input_6_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_4_cast_in_state, input_4_cast));
            tensor<fp16, [1, 128, 64]> input_4_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -64]), size = tensor<int32, [3]>([1, 128, 64]), x = input_6_cast);
            tensor<int32, [1]> var_3586 = const()[val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_3588 = const()[val = tensor<int32, [1]>([32])];
            tensor<string, []> input_1_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_1_pad_0 = const()[val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 1, 3]> sep_module_59_tcn_4_weight_to_fp16 = const()[val = tensor<fp16, [128, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2203008)))];
            tensor<fp16, [1, 128, 12]> input_1_cast = conv(dilations = var_3588, groups = var_65, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = var_3586, weight = sep_module_59_tcn_4_weight_to_fp16, x = input_6_cast);
            tensor<fp16, []> var_3592_alpha_1_to_fp16 = const()[val = tensor<fp16, []>(0x1.ffcp-1)];
            tensor<fp16, [1, 128, 12]> var_3592_cast = leaky_relu(alpha = var_3592_alpha_1_to_fp16, x = input_1_cast);
            tensor<int32, [1]> var_3596 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_1_cast = reduce_mean(axes = var_3596, keep_dims = var_66, x = var_3592_cast);
            tensor<fp16, [1, 128, 12]> var_3598_cast = sub(x = var_3592_cast, y = mean_y_1_cast);
            tensor<fp16, [1, 128, 12]> var_3599_cast = square(x = var_3598_cast);
            tensor<int32, [1]> var_3600 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_3601_cast = reduce_mean(axes = var_3600, keep_dims = var_66, x = var_3599_cast);
            tensor<fp16, []> var_3602_to_fp16 = const()[val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_3603_cast = add(x = var_3601_cast, y = var_3602_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_1_cast = sqrt(x = var_3603_cast);
            tensor<fp16, [1, 128, 1]> sep_module_59_tcn_6_norm_gamma_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2203840)))];
            tensor<fp16, [1, 128, 12]> var_3606_cast = mul(x = sep_module_59_tcn_6_norm_gamma_to_fp16, y = var_3598_cast);
            tensor<fp16, [1, 128, 12]> var_3607_cast = real_div(x = var_3606_cast, y = std_y_1_cast);
            tensor<fp16, [1, 128, 1]> sep_module_59_tcn_6_norm_beta_to_fp16 = const()[val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2204160)))];
            tensor<fp16, [1, 128, 12]> y_1_cast = add(x = var_3607_cast, y = sep_module_59_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 128, 12]> x_1_cast = add(x = input_3_cast, y = y_1_cast);
            tensor<int32, [1]> input0_3_axes_0 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 128, 12]> input0_3_cast = expand_dims(axes = input0_3_axes_0, x = x_1_cast);
            tensor<int32, []> var_3613 = const()[val = tensor<int32, []>(1)];
            tensor<int32, [2]> var_3618 = const()[val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3620 = const()[val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> input1_1_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [4]> input1_1_pad_0 = const()[val = tensor<int32, [4]>([192, 192, 0, 0])];
            tensor<fp16, [1, 1, 257, 1]> mask_layer_weight_to_fp16 = const()[val = tensor<fp16, [1, 1, 257, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2204480)))];
            tensor<fp16, [1, 1, 256, 12]> input1_1_cast = conv(dilations = var_3620, groups = var_3613, pad = input1_1_pad_0, pad_type = input1_1_pad_type_0, strides = var_3618, weight = mask_layer_weight_to_fp16, x = input0_3_cast);
            tensor<fp16, [1, 1, 256, 12]> var_3623_cast = sigmoid(x = input1_1_cast);
            tensor<int32, [1]> var_3624_axes_0 = const()[val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 12]> var_3624_cast = expand_dims(axes = var_3624_axes_0, x = var_26_cast);
            tensor<fp16, [1, 1, 256, 19]> var_3624_cast_elementwise_expanded = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (var_3624_cast_elementwise_in_state, var_3624_cast));
            tensor<fp16, [1, 1, 256, 12]> var_3624_cast_elementwise_delayed = slice_by_size(begin = tensor<int32, [4]>([0, 0, 0, 0]), size = tensor<int32, [4]>([1, 1, 256, 12]), x = var_3624_cast_elementwise_expanded);
            tensor<fp16, [1, 1, 256, 7]> var_3624_cast_elementwise_out_state = slice_by_size(begin = tensor<int32, [4]>([0, 0, 0, -7]), size = tensor<int32, [4]>([1, 1, 256, 7]), x = var_3624_cast_elementwise_expanded);
            tensor<fp16, [1, 1, 256, 12]> x_11_cast = mul(x = var_3623_cast, y = var_3624_cast_elementwise_delayed);
            tensor<int32, [3]> concat_0x = const()[val = tensor<int32, [3]>([1, 256, -1])];
            tensor<fp16, [1, 256, 12]> input1_3_cast = reshape(shape = concat_0x, x = x_11_cast);
            tensor<int32, []> var_3641 = const()[val = tensor<int32, []>(1)];
            tensor<int32, [1]> var_3647 = const()[val = tensor<int32, [1]>([40])];
            tensor<int32, [1]> var_3649 = const()[val = tensor<int32, [1]>([1])];
            tensor<string, []> var_3651_pad_type_0 = const()[val = tensor<string, []>("custom")];
            tensor<int32, [2]> var_3651_pad_0 = const()[val = tensor<int32, [2]>([40, 40])];
            tensor<fp16, [256, 1, 80]> resynthesizer_weight_to_fp16 = const()[val = tensor<fp16, [256, 1, 80]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/vi-nnet-voice.weight.bin"), offset = tensor<uint64, []>(2205120)))];
            tensor<fp16, [1, 256, 13]> input1_3_cast_padded = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input1_3_cast_in_state, input1_3_cast));
            tensor<fp16, [1, 256, 1]> input1_3_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -1]), size = tensor<int32, [3]>([1, 256, 1]), x = input1_3_cast_padded);
            tensor<fp16, [1, 1, 480]> var_3651_cast = conv_transpose(dilations = var_3649, groups = var_3641, pad = var_3651_pad_0, pad_type = var_3651_pad_type_0, strides = var_3647, weight = resynthesizer_weight_to_fp16, x = input1_3_cast_padded);
            tensor<string, []> var_3651_cast_to_fp32_dtype_0 = const()[val = tensor<string, []>("fp32")];
            tensor<fp32, [1, 1, 480]> var_3651 = cast(dtype = var_3651_cast_to_fp32_dtype_0, x = var_3651_cast);
        } -> (var_3651, input1_3_cast_out_state, var_3624_cast_elementwise_out_state, input_4_cast_out_state, input_587_cast_out_state, input_577_cast_out_state, input_567_cast_out_state, input_557_cast_out_state, input_547_cast_out_state, input_537_cast_out_state, input_527_cast_out_state, input_517_cast_out_state, input_507_cast_out_state, input_497_cast_out_state, input_487_cast_out_state, input_477_cast_out_state, input_467_cast_out_state, input_457_cast_out_state, input_447_cast_out_state, input_437_cast_out_state, input_427_cast_out_state, input_417_cast_out_state, input_407_cast_out_state, input_397_cast_out_state, input_387_cast_out_state, input_377_cast_out_state, input_367_cast_out_state, input_357_cast_out_state, input_347_cast_out_state, input_337_cast_out_state, input_327_cast_out_state, input_317_cast_out_state, input_307_cast_out_state, input_297_cast_out_state, input_287_cast_out_state, input_277_cast_out_state, input_267_cast_out_state, input_257_cast_out_state, input_247_cast_out_state, input_237_cast_out_state, input_227_cast_out_state, input_217_cast_out_state, input_207_cast_out_state, input_197_cast_out_state, input_187_cast_out_state, input_177_cast_out_state, input_167_cast_out_state, input_157_cast_out_state, input_147_cast_out_state, input_137_cast_out_state, input_127_cast_out_state, input_117_cast_out_state, input_107_cast_out_state, input_97_cast_out_state, input_87_cast_out_state, input_77_cast_out_state, input_67_cast_out_state, input_57_cast_out_state, input_47_cast_out_state, input_37_cast_out_state, input_23_cast_elementwise_out_state, input_27_cast_out_state, input_13_cast_elementwise_out_state, input_17_cast_out_state, input_597_cast_elementwise_out_state, input_7_cast_out_state, cast_1702_out_state);
}