program(1.0) [buildInfo = dict, tensor>({{"coremlc-component-MIL", "3405.2.1"}, {"coremlc-version", "3404.23.1"}, {"coremltools-component-torch", "2.7.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0b1"}})] { func main(tensor audio_length, tensor audio_signal) [FlexibleShapeInformation = tuple, dict, tensor>>, tuple, dict, list, ?>>>>((("DefaultShapes", {{"audio_signal", [1, 1]}}), ("RangeDims", {{"audio_signal", [[1, 1], [1, 240000]]}})))] { tensor var_9 = const()[name = tensor("op_9"), val = tensor(1)]; tensor var_10 = const()[name = tensor("op_10"), val = tensor(160)]; tensor var_24 = const()[name = tensor("op_24"), val = tensor(0x0p+0)]; tensor var_25 = const()[name = tensor("op_25"), val = tensor(0x1.4f8b58p-17)]; tensor var_34 = const()[name = tensor("op_34"), val = tensor(512)]; tensor var_35 = add(x = audio_length, y = var_34)[name = tensor("op_35")]; tensor var_36 = const()[name = tensor("op_36"), val = tensor(512)]; tensor var_37 = sub(x = var_35, y = var_36)[name = tensor("op_37")]; tensor floor_div_0 = floor_div(x = var_37, y = var_10)[name = tensor("floor_div_0")]; tensor var_38_dtype_0 = const()[name = tensor("op_38_dtype_0"), val = tensor("fp32")]; tensor var_39_promoted = const()[name = tensor("op_39_promoted"), val = tensor(0x1p+0)]; tensor var_38 = cast(dtype = var_38_dtype_0, x = floor_div_0)[name = tensor("cast_13")]; tensor seq_len_1 = add(x = var_38, y = var_39_promoted)[name = tensor("seq_len_1")]; tensor seq_len_dtype_0 = const()[name = tensor("seq_len_dtype_0"), val = tensor("int32")]; tensor var_43_begin_0 = const()[name = tensor("op_43_begin_0"), val = tensor([0, 0])]; tensor var_43_end_0 = const()[name = tensor("op_43_end_0"), val = tensor([1, 1])]; tensor var_43_end_mask_0 = const()[name = tensor("op_43_end_mask_0"), val = tensor([true, false])]; tensor var_43_squeeze_mask_0 = const()[name = tensor("op_43_squeeze_mask_0"), val = tensor([false, true])]; tensor var_43 = slice_by_index(begin = var_43_begin_0, end = var_43_end_0, end_mask = var_43_end_mask_0, squeeze_mask = var_43_squeeze_mask_0, x = audio_signal)[name = tensor("op_43")]; tensor var_44_axes_0 = const()[name = tensor("op_44_axes_0"), val = tensor([1])]; tensor var_44 = expand_dims(axes = var_44_axes_0, x = var_43)[name = tensor("op_44")]; tensor var_46_begin_0 = const()[name = tensor("op_46_begin_0"), val = tensor([0, 1])]; tensor var_46_end_0 = const()[name = tensor("op_46_end_0"), val = tensor([1, 0])]; tensor var_46_end_mask_0 = const()[name = tensor("op_46_end_mask_0"), val = tensor([true, true])]; tensor var_46 = slice_by_index(begin = var_46_begin_0, end = var_46_end_0, end_mask = var_46_end_mask_0, x = audio_signal)[name = tensor("op_46")]; tensor var_48_begin_0 = const()[name = tensor("op_48_begin_0"), val = tensor([0, 0])]; tensor var_48_end_0 = const()[name = tensor("op_48_end_0"), val = tensor([1, -1])]; tensor var_48_end_mask_0 = const()[name = tensor("op_48_end_mask_0"), val = tensor([true, false])]; tensor var_48 = slice_by_index(begin = var_48_begin_0, end = var_48_end_0, end_mask = var_48_end_mask_0, x = audio_signal)[name = tensor("op_48")]; tensor var_49 = const()[name = tensor("op_49"), val = tensor(0x1.f0a3d8p-1)]; tensor var_50 = mul(x = var_48, y = var_49)[name = tensor("op_50")]; tensor var_51 = sub(x = var_46, y = var_50)[name = tensor("op_51")]; tensor input_1_interleave_0 = const()[name = tensor("input_1_interleave_0"), val = tensor(false)]; tensor input_1 = concat(axis = var_9, interleave = input_1_interleave_0, values = (var_44, var_51))[name = tensor("input_1")]; tensor concat_0x = const()[name = tensor("concat_0x"), val = tensor([1, 1, -1])]; tensor input_3 = reshape(shape = concat_0x, x = input_1)[name = tensor("input_3")]; tensor const_1 = const()[name = tensor("const_1"), val = tensor(0x0p+0)]; tensor input_5_pad_0 = const()[name = tensor("input_5_pad_0"), val = tensor([0, 0, 0, 0, 256, 256])]; tensor input_5_mode_0 = const()[name = tensor("input_5_mode_0"), val = tensor("reflect")]; tensor input_5 = pad(constant_val = const_1, mode = input_5_mode_0, pad = input_5_pad_0, x = input_3)[name = tensor("input_5")]; tensor concat_1x = const()[name = tensor("concat_1x"), val = tensor([1, -1])]; tensor input = reshape(shape = concat_1x, x = input_5)[name = tensor("input")]; tensor expand_dims_1 = const()[name = tensor("expand_dims_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; tensor expand_dims_2 = const()[name = tensor("expand_dims_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(526464)))]; tensor expand_dims_3 = const()[name = tensor("expand_dims_3"), val = tensor([160])]; tensor expand_dims_4_axes_0 = const()[name = tensor("expand_dims_4_axes_0"), val = tensor([1])]; tensor expand_dims_4 = expand_dims(axes = expand_dims_4_axes_0, x = input)[name = tensor("expand_dims_4")]; tensor conv_0_pad_type_0 = const()[name = tensor("conv_0_pad_type_0"), val = tensor("valid")]; tensor conv_0_pad_0 = const()[name = tensor("conv_0_pad_0"), val = tensor([0, 0])]; tensor conv_0_dilations_0 = const()[name = tensor("conv_0_dilations_0"), val = tensor([1])]; tensor conv_0_groups_0 = const()[name = tensor("conv_0_groups_0"), val = tensor(1)]; tensor conv_0 = conv(dilations = conv_0_dilations_0, groups = conv_0_groups_0, pad = conv_0_pad_0, pad_type = conv_0_pad_type_0, strides = expand_dims_3, weight = expand_dims_1, x = expand_dims_4)[name = tensor("conv_0")]; tensor conv_1_pad_type_0 = const()[name = tensor("conv_1_pad_type_0"), val = tensor("valid")]; tensor conv_1_pad_0 = const()[name = tensor("conv_1_pad_0"), val = tensor([0, 0])]; tensor conv_1_dilations_0 = const()[name = tensor("conv_1_dilations_0"), val = tensor([1])]; tensor conv_1_groups_0 = const()[name = tensor("conv_1_groups_0"), val = tensor(1)]; tensor conv_1 = conv(dilations = conv_1_dilations_0, groups = conv_1_groups_0, pad = conv_1_pad_0, pad_type = conv_1_pad_type_0, strides = expand_dims_3, weight = expand_dims_2, x = expand_dims_4)[name = tensor("conv_1")]; tensor stack_0_axis_0 = const()[name = tensor("stack_0_axis_0"), val = tensor(-1)]; tensor stack_0 = stack(axis = stack_0_axis_0, values = (conv_0, conv_1))[name = tensor("stack_0")]; tensor var_17_promoted = const()[name = tensor("op_17_promoted"), val = tensor(0x1p+1)]; tensor var_67 = pow(x = stack_0, y = var_17_promoted)[name = tensor("op_67")]; tensor var_69_axes_0 = const()[name = tensor("op_69_axes_0"), val = tensor([-1])]; tensor var_69_keep_dims_0 = const()[name = tensor("op_69_keep_dims_0"), val = tensor(false)]; tensor var_69 = reduce_sum(axes = var_69_axes_0, keep_dims = var_69_keep_dims_0, x = var_67)[name = tensor("op_69")]; tensor x_9 = identity(x = var_69)[name = tensor("x_9")]; tensor const_2 = const()[name = tensor("const_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1052864)))]; tensor x_11_transpose_x_0 = const()[name = tensor("x_11_transpose_x_0"), val = tensor(false)]; tensor x_11_transpose_y_0 = const()[name = tensor("x_11_transpose_y_0"), val = tensor(false)]; tensor x_11 = matmul(transpose_x = x_11_transpose_x_0, transpose_y = x_11_transpose_y_0, x = const_2, y = x_9)[name = tensor("x_11")]; tensor var_76 = const()[name = tensor("op_76"), val = tensor(0x1p-24)]; tensor var_77 = add(x = x_11, y = var_76)[name = tensor("op_77")]; tensor x_13_epsilon_0 = const()[name = tensor("x_13_epsilon_0"), val = tensor(0x1p-149)]; tensor x_13 = log(epsilon = x_13_epsilon_0, x = var_77)[name = tensor("x_13")]; tensor var_79_shape = shape(x = x_13)[name = tensor("op_79_shape")]; tensor gather_4 = const()[name = tensor("gather_4"), val = tensor(1)]; tensor gather_5_batch_dims_0 = const()[name = tensor("gather_5_batch_dims_0"), val = tensor(0)]; tensor gather_5_validate_indices_0 = const()[name = tensor("gather_5_validate_indices_0"), val = tensor(false)]; tensor select_2 = const()[name = tensor("select_2"), val = tensor(2)]; tensor gather_5_axis_1 = const()[name = tensor("gather_5_axis_1"), val = tensor(0)]; tensor gather_5 = gather(axis = gather_5_axis_1, batch_dims = gather_5_batch_dims_0, indices = select_2, validate_indices = gather_5_validate_indices_0, x = var_79_shape)[name = tensor("gather_5")]; tensor const_3 = const()[name = tensor("const_3"), val = tensor(0)]; tensor const_4 = const()[name = tensor("const_4"), val = tensor(1)]; tensor var_81 = range_1d(end = gather_5, start = const_3, step = const_4)[name = tensor("op_81")]; tensor var_82_axes_0 = const()[name = tensor("op_82_axes_0"), val = tensor([0])]; tensor var_82 = expand_dims(axes = var_82_axes_0, x = var_81)[name = tensor("op_82")]; tensor concat_2_axis_0 = const()[name = tensor("concat_2_axis_0"), val = tensor(0)]; tensor concat_2_interleave_0 = const()[name = tensor("concat_2_interleave_0"), val = tensor(false)]; tensor concat_2 = concat(axis = concat_2_axis_0, interleave = concat_2_interleave_0, values = (gather_4, gather_5))[name = tensor("concat_2")]; tensor shape_0 = shape(x = var_82)[name = tensor("shape_0")]; tensor real_div_0 = real_div(x = concat_2, y = shape_0)[name = tensor("real_div_0")]; tensor time_steps = tile(reps = real_div_0, x = var_82)[name = tensor("time_steps")]; tensor var_85_axes_0 = const()[name = tensor("op_85_axes_0"), val = tensor([1])]; tensor mel_length = cast(dtype = seq_len_dtype_0, x = seq_len_1)[name = tensor("cast_12")]; tensor var_85 = expand_dims(axes = var_85_axes_0, x = mel_length)[name = tensor("op_85")]; tensor valid_mask = less(x = time_steps, y = var_85)[name = tensor("valid_mask")]; tensor var_87_axes_0 = const()[name = tensor("op_87_axes_0"), val = tensor([1])]; tensor var_87 = expand_dims(axes = var_87_axes_0, x = valid_mask)[name = tensor("op_87")]; tensor var_88 = select(a = x_13, b = var_24, cond = var_87)[name = tensor("op_88")]; tensor x_mean_numerator_axes_0 = const()[name = tensor("x_mean_numerator_axes_0"), val = tensor([2])]; tensor x_mean_numerator_keep_dims_0 = const()[name = tensor("x_mean_numerator_keep_dims_0"), val = tensor(false)]; tensor x_mean_numerator = reduce_sum(axes = x_mean_numerator_axes_0, keep_dims = x_mean_numerator_keep_dims_0, x = var_88)[name = tensor("x_mean_numerator")]; tensor cast_3_dtype_0 = const()[name = tensor("cast_3_dtype_0"), val = tensor("fp32")]; tensor x_mean_denominator_axes_0 = const()[name = tensor("x_mean_denominator_axes_0"), val = tensor([1])]; tensor x_mean_denominator_keep_dims_0 = const()[name = tensor("x_mean_denominator_keep_dims_0"), val = tensor(false)]; tensor cast_3 = cast(dtype = cast_3_dtype_0, x = valid_mask)[name = tensor("cast_11")]; tensor x_mean_denominator = reduce_sum(axes = x_mean_denominator_axes_0, keep_dims = x_mean_denominator_keep_dims_0, x = cast_3)[name = tensor("x_mean_denominator")]; tensor var_93_axes_0 = const()[name = tensor("op_93_axes_0"), val = tensor([1])]; tensor var_93 = expand_dims(axes = var_93_axes_0, x = x_mean_denominator)[name = tensor("op_93")]; tensor x_mean = real_div(x = x_mean_numerator, y = var_93)[name = tensor("x_mean")]; tensor var_96_axes_0 = const()[name = tensor("op_96_axes_0"), val = tensor([2])]; tensor var_96 = expand_dims(axes = var_96_axes_0, x = x_mean)[name = tensor("op_96")]; tensor var_97 = sub(x = x_13, y = var_96)[name = tensor("op_97")]; tensor var_98 = select(a = var_97, b = var_24, cond = var_87)[name = tensor("op_98")]; tensor var_17_promoted_1 = const()[name = tensor("op_17_promoted_1"), val = tensor(0x1p+1)]; tensor var_99 = pow(x = var_98, y = var_17_promoted_1)[name = tensor("op_99")]; tensor var_101_axes_0 = const()[name = tensor("op_101_axes_0"), val = tensor([2])]; tensor var_101_keep_dims_0 = const()[name = tensor("op_101_keep_dims_0"), val = tensor(false)]; tensor var_101 = reduce_sum(axes = var_101_axes_0, keep_dims = var_101_keep_dims_0, x = var_99)[name = tensor("op_101")]; tensor var_103 = const()[name = tensor("op_103"), val = tensor(0x1p+0)]; tensor var_104 = sub(x = var_93, y = var_103)[name = tensor("op_104")]; tensor var_105 = real_div(x = var_101, y = var_104)[name = tensor("op_105")]; tensor x_std_1 = sqrt(x = var_105)[name = tensor("x_std_1")]; tensor x_std = add(x = x_std_1, y = var_25)[name = tensor("x_std")]; tensor var_110_axes_0 = const()[name = tensor("op_110_axes_0"), val = tensor([2])]; tensor var_110 = expand_dims(axes = var_110_axes_0, x = x_std)[name = tensor("op_110")]; tensor x = real_div(x = var_97, y = var_110)[name = tensor("x")]; tensor var_112_shape = shape(x = x)[name = tensor("op_112_shape")]; tensor gather_6_batch_dims_0 = const()[name = tensor("gather_6_batch_dims_0"), val = tensor(0)]; tensor gather_6_validate_indices_0 = const()[name = tensor("gather_6_validate_indices_0"), val = tensor(false)]; tensor select_3 = const()[name = tensor("select_3"), val = tensor(2)]; tensor gather_6_axis_1 = const()[name = tensor("gather_6_axis_1"), val = tensor(0)]; tensor gather_6 = gather(axis = gather_6_axis_1, batch_dims = gather_6_batch_dims_0, indices = select_3, validate_indices = gather_6_validate_indices_0, x = var_112_shape)[name = tensor("gather_6")]; tensor const_5 = const()[name = tensor("const_5"), val = tensor(0)]; tensor const_6 = const()[name = tensor("const_6"), val = tensor(1)]; tensor mask_1 = range_1d(end = gather_6, start = const_5, step = const_6)[name = tensor("mask_1")]; tensor gather_7_batch_dims_0 = const()[name = tensor("gather_7_batch_dims_0"), val = tensor(0)]; tensor gather_7_validate_indices_0 = const()[name = tensor("gather_7_validate_indices_0"), val = tensor(false)]; tensor select_4 = const()[name = tensor("select_4"), val = tensor(0)]; tensor gather_7_axis_1 = const()[name = tensor("gather_7_axis_1"), val = tensor(0)]; tensor gather_7 = gather(axis = gather_7_axis_1, batch_dims = gather_7_batch_dims_0, indices = select_4, validate_indices = gather_7_validate_indices_0, x = var_112_shape)[name = tensor("gather_7")]; tensor concat_3_axis_0 = const()[name = tensor("concat_3_axis_0"), val = tensor(0)]; tensor concat_3_interleave_0 = const()[name = tensor("concat_3_interleave_0"), val = tensor(false)]; tensor concat_3 = concat(axis = concat_3_axis_0, interleave = concat_3_interleave_0, values = (gather_7, var_9))[name = tensor("concat_3")]; tensor expand_dims_0_axes_0 = const()[name = tensor("expand_dims_0_axes_0"), val = tensor([0])]; tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = mask_1)[name = tensor("expand_dims_0")]; tensor var_116 = tile(reps = concat_3, x = expand_dims_0)[name = tensor("op_116")]; tensor mask = greater_equal(x = var_116, y = var_85)[name = tensor("mask")]; tensor var_119_axes_0 = const()[name = tensor("op_119_axes_0"), val = tensor([1])]; tensor var_119 = expand_dims(axes = var_119_axes_0, x = mask)[name = tensor