program(1.0) [buildInfo = dict, tensor>({{"coremlc-component-MIL", "3405.2.1"}, {"coremlc-version", "3404.23.1"}, {"coremltools-component-torch", "2.5.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] { 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, 160000]]}})))] { tensor var_6 = const()[name = tensor("op_6"), val = tensor(512)]; tensor var_7 = add(x = audio_length, y = var_6)[name = tensor("op_7")]; tensor var_9 = const()[name = tensor("op_9"), val = tensor(512)]; tensor var_10 = sub(x = var_7, y = var_9)[name = tensor("op_10")]; tensor var_11 = const()[name = tensor("op_11"), val = tensor(160)]; tensor floor_div_0 = floor_div(x = var_10, y = var_11)[name = tensor("floor_div_0")]; tensor var_12_to_fp16_dtype_0 = const()[name = tensor("op_12_to_fp16_dtype_0"), val = tensor("fp16")]; tensor var_14_promoted_to_fp16 = const()[name = tensor("op_14_promoted_to_fp16"), val = tensor(0x1p+0)]; tensor floor_div_0_to_fp16 = cast(dtype = var_12_to_fp16_dtype_0, x = floor_div_0)[name = tensor("cast_18")]; tensor seq_len_1_cast_fp16 = add(x = floor_div_0_to_fp16, y = var_14_promoted_to_fp16)[name = tensor("seq_len_1_cast_fp16")]; tensor cast_0_dtype_0 = const()[name = tensor("cast_0_dtype_0"), val = tensor("int32")]; tensor var_28_begin_0 = const()[name = tensor("op_28_begin_0"), val = tensor([0, 0])]; tensor var_28_end_0 = const()[name = tensor("op_28_end_0"), val = tensor([1, 1])]; tensor var_28_end_mask_0 = const()[name = tensor("op_28_end_mask_0"), val = tensor([true, false])]; tensor var_28_squeeze_mask_0 = const()[name = tensor("op_28_squeeze_mask_0"), val = tensor([false, true])]; tensor audio_signal_to_fp16_dtype_0 = const()[name = tensor("audio_signal_to_fp16_dtype_0"), val = tensor("fp16")]; tensor audio_signal_to_fp16 = cast(dtype = audio_signal_to_fp16_dtype_0, x = audio_signal)[name = tensor("cast_16")]; tensor var_28_cast_fp16 = slice_by_index(begin = var_28_begin_0, end = var_28_end_0, end_mask = var_28_end_mask_0, squeeze_mask = var_28_squeeze_mask_0, x = audio_signal_to_fp16)[name = tensor("op_28_cast_fp16")]; tensor var_30_axes_0 = const()[name = tensor("op_30_axes_0"), val = tensor([1])]; tensor var_30_cast_fp16 = expand_dims(axes = var_30_axes_0, x = var_28_cast_fp16)[name = tensor("op_30_cast_fp16")]; tensor var_40_begin_0 = const()[name = tensor("op_40_begin_0"), val = tensor([0, 1])]; tensor var_40_end_0 = const()[name = tensor("op_40_end_0"), val = tensor([1, 0])]; tensor var_40_end_mask_0 = const()[name = tensor("op_40_end_mask_0"), val = tensor([true, true])]; tensor var_40_cast_fp16 = slice_by_index(begin = var_40_begin_0, end = var_40_end_0, end_mask = var_40_end_mask_0, x = audio_signal_to_fp16)[name = tensor("op_40_cast_fp16")]; tensor var_50_begin_0 = const()[name = tensor("op_50_begin_0"), val = tensor([0, 0])]; tensor var_50_end_0 = const()[name = tensor("op_50_end_0"), val = tensor([1, -1])]; tensor var_50_end_mask_0 = const()[name = tensor("op_50_end_mask_0"), val = tensor([true, false])]; tensor var_50_cast_fp16 = slice_by_index(begin = var_50_begin_0, end = var_50_end_0, end_mask = var_50_end_mask_0, x = audio_signal_to_fp16)[name = tensor("op_50_cast_fp16")]; tensor var_51_to_fp16 = const()[name = tensor("op_51_to_fp16"), val = tensor(0x1.f0cp-1)]; tensor var_52_cast_fp16 = mul(x = var_50_cast_fp16, y = var_51_to_fp16)[name = tensor("op_52_cast_fp16")]; tensor var_54_cast_fp16 = sub(x = var_40_cast_fp16, y = var_52_cast_fp16)[name = tensor("op_54_cast_fp16")]; tensor var_56 = const()[name = tensor("op_56"), val = tensor(1)]; tensor input_1_interleave_0 = const()[name = tensor("input_1_interleave_0"), val = tensor(false)]; tensor input_1_cast_fp16 = concat(axis = var_56, interleave = input_1_interleave_0, values = (var_30_cast_fp16, var_54_cast_fp16))[name = tensor("input_1_cast_fp16")]; tensor concat_0x = const()[name = tensor("concat_0x"), val = tensor([1, 1, -1])]; tensor input_3_cast_fp16 = reshape(shape = concat_0x, x = input_1_cast_fp16)[name = tensor("input_3_cast_fp16")]; 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 const_0_to_fp16 = const()[name = tensor("const_0_to_fp16"), val = tensor(0x0p+0)]; tensor input_5_cast_fp16 = pad(constant_val = const_0_to_fp16, mode = input_5_mode_0, pad = input_5_pad_0, x = input_3_cast_fp16)[name = tensor("input_5_cast_fp16")]; tensor concat_1x = const()[name = tensor("concat_1x"), val = tensor([1, -1])]; tensor input_cast_fp16 = reshape(shape = concat_1x, x = input_5_cast_fp16)[name = tensor("input_cast_fp16")]; 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_cast_fp16 = expand_dims(axes = expand_dims_4_axes_0, x = input_cast_fp16)[name = tensor("expand_dims_4_cast_fp16")]; 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 expand_dims_1_to_fp16 = const()[name = tensor("expand_dims_1_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; tensor conv_0_cast_fp16 = 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_to_fp16, x = expand_dims_4_cast_fp16)[name = tensor("conv_0_cast_fp16")]; 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 expand_dims_2_to_fp16 = const()[name = tensor("expand_dims_2_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(263296)))]; tensor conv_1_cast_fp16 = 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_to_fp16, x = expand_dims_4_cast_fp16)[name = tensor("conv_1_cast_fp16")]; tensor stack_0_axis_0 = const()[name = tensor("stack_0_axis_0"), val = tensor(-1)]; tensor stack_0_cast_fp16 = stack(axis = stack_0_axis_0, values = (conv_0_cast_fp16, conv_1_cast_fp16))[name = tensor("stack_0_cast_fp16")]; tensor var_93_promoted_to_fp16 = const()[name = tensor("op_93_promoted_to_fp16"), val = tensor(0x1p+1)]; tensor var_94_cast_fp16 = pow(x = stack_0_cast_fp16, y = var_93_promoted_to_fp16)[name = tensor("op_94_cast_fp16")]; tensor var_99_axes_0 = const()[name = tensor("op_99_axes_0"), val = tensor([-1])]; tensor var_99_keep_dims_0 = const()[name = tensor("op_99_keep_dims_0"), val = tensor(false)]; tensor var_99_cast_fp16 = reduce_sum(axes = var_99_axes_0, keep_dims = var_99_keep_dims_0, x = var_94_cast_fp16)[name = tensor("op_99_cast_fp16")]; tensor x_7_cast_fp16 = identity(x = var_99_cast_fp16)[name = tensor("x_7_cast_fp16")]; tensor x_9_transpose_x_0 = const()[name = tensor("x_9_transpose_x_0"), val = tensor(false)]; tensor x_9_transpose_y_0 = const()[name = tensor("x_9_transpose_y_0"), val = tensor(false)]; tensor filterbanks_to_fp16 = const()[name = tensor("filterbanks_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(526528)))]; tensor x_9_cast_fp16 = matmul(transpose_x = x_9_transpose_x_0, transpose_y = x_9_transpose_y_0, x = filterbanks_to_fp16, y = x_7_cast_fp16)[name = tensor("x_9_cast_fp16")]; tensor var_108_to_fp16 = const()[name = tensor("op_108_to_fp16"), val = tensor(0x1p-24)]; tensor var_109_cast_fp16 = add(x = x_9_cast_fp16, y = var_108_to_fp16)[name = tensor("op_109_cast_fp16")]; tensor x_11_epsilon_0_to_fp16 = const()[name = tensor("x_11_epsilon_0_to_fp16"), val = tensor(0x0p+0)]; tensor x_11_cast_fp16 = log(epsilon = x_11_epsilon_0_to_fp16, x = var_109_cast_fp16)[name = tensor("x_11_cast_fp16")]; tensor var_114 = const()[name = tensor("op_114"), val = tensor(1)]; tensor var_116_shape_cast_fp16 = shape(x = x_11_cast_fp16)[name = tensor("op_116_shape_cast_fp16")]; tensor gather_5_indices_0 = const()[name = tensor("gather_5_indices_0"), val = tensor(2)]; tensor gather_5_axis_0 = const()[name = tensor("gather_5_axis_0"), val = tensor(0)]; tensor gather_5 = gather(axis = gather_5_axis_0, indices = gather_5_indices_0, x = var_116_shape_cast_fp16)[name = tensor("gather_5")]; tensor const_1 = const()[name = tensor("const_1"), val = tensor(0)]; tensor const_2 = const()[name = tensor("const_2"), val = tensor(1)]; tensor var_124 = range_1d(end = gather_5, start = const_1, step = const_2)[name = tensor("op_124")]; tensor var_126_axes_0 = const()[name = tensor("op_126_axes_0"), val = tensor([0])]; tensor var_126 = expand_dims(axes = var_126_axes_0, x = var_124)[name = tensor("op_126")]; 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 = (var_114, gather_5))[name = tensor("concat_2")]; tensor shape_0 = shape(x = var_126)[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_126)[name = tensor("time_steps")]; tensor var_131_axes_0 = const()[name = tensor("op_131_axes_0"), val = tensor([1])]; tensor melspectrogram_length = cast(dtype = cast_0_dtype_0, x = seq_len_1_cast_fp16)[name = tensor("cast_17")]; tensor var_131 = expand_dims(axes = var_131_axes_0, x = melspectrogram_length)[name = tensor("op_131")]; tensor valid_mask = less(x = time_steps, y = var_131)[name = tensor("valid_mask")]; tensor var_134_axes_0 = const()[name = tensor("op_134_axes_0"), val = tensor([1])]; tensor var_134 = expand_dims(axes = var_134_axes_0, x = valid_mask)[name = tensor("op_134")]; tensor var_135_to_fp16 = const()[name = tensor("op_135_to_fp16"), val = tensor(0x0p+0)]; tensor var_136_cast_fp16 = select(a = x_11_cast_fp16, b = var_135_to_fp16, cond = var_134)[name = tensor("op_136_cast_fp16")]; 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_cast_fp16 = reduce_sum(axes = x_mean_numerator_axes_0, keep_dims = x_mean_numerator_keep_dims_0, x = var_136_cast_fp16)[name = tensor("x_mean_numerator_cast_fp16")]; 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_4_to_fp16_dtype_0 = const()[name = tensor("cast_4_to_fp16_dtype_0"), val = tensor("fp16")]; tensor valid_mask_to_fp16 = cast(dtype = cast_4_to_fp16_dtype_0, x = valid_mask)[name = tensor("cast_15")]; tensor x_mean_denominator_cast_fp16 = reduce_sum(axes = x_mean_denominator_axes_0, keep_dims = x_mean_denominator_keep_dims_0, x = valid_mask_to_fp16)[name = tensor("x_mean_denominator_cast_fp16")]; tensor var_148_axes_0 = const()[name = tensor("op_148_axes_0"), val = tensor([1])]; tensor var_148_cast_fp16 = expand_dims(axes = var_148_axes_0, x = x_mean_denominator_cast_fp16)[name = tensor("op_148_cast_fp16")]; tensor x_mean_cast_fp16 = real_div(x = x_mean_numerator_cast_fp16, y = var_148_cast_fp16)[name = tensor("x_mean_cast_fp16")]; tensor var_153_axes_0 = const()[name = tensor("op_153_axes_0"), val = tensor([2])]; tensor var_153_cast_fp16 = expand_dims(axes = var_153_axes_0, x = x_mean_cast_fp16)[name = tensor("op_153_cast_fp16")]; tensor var_155_cast_fp16 = sub(x = x_11_cast_fp16, y = var_153_cast_fp16)[name = tensor("op_155_cast_fp16")]; tensor var_156_to_fp16 = const()[name = tensor("op_156_to_fp16"), val = tensor(0x0p+0)]; tensor var_157_cast_fp16 = select(a = var_155_cast_fp16, b = var_156_to_fp16, cond = var_134)[name = tensor("op_157_cast_fp16")]; tensor var_158_promoted_to_fp16 = const()[name = tensor("op_158_promoted_to_fp16"), val = tensor(0x1p+1)]; tensor var_159_cast_fp16 = pow(x = var_157_cast_fp16, y = var_158_promoted_to_fp16)[name = tensor("op_159_cast_fp16")]; tensor var_164_axes_0 = const()[name = tensor("op_164_axes_0"), val = tensor([2])]; tensor var_164_keep_dims_0 = const()[name = tensor("op_164_keep_dims_0"), val = tensor(false)]; tensor var_164_cast_fp16 = reduce_sum(axes = var_164_axes_0, keep_dims = var_164_keep_dims_0, x = var_159_cast_fp16)[name = tensor("op_164_cast_fp16")]; tensor var_168_to_fp16 = const()[name = tensor("op_168_to_fp16"), val = tensor(0x1p+0)]; tensor var_169_cast_fp16 = sub(x = var_148_cast_fp16, y = var_168_to_fp16)[name = tensor("op_169_cast_fp16")]; tensor var_170_cast_fp16 = real_div(x = var_164_cast_fp16, y = var_169_cast_fp16)[name = tensor("op_170_cast_fp16")]; tensor x_std_1_cast_fp16 = sqrt(x = var_170_cast_fp16)[name = tensor("x_std_1_cast_fp16")]; tensor var_172_to_fp16 = const()[name = tensor("op_172_to_fp16"), val = tensor(0x1.5p-17)]; tensor x_std_cast_fp16 = add(x = x_std_1_cast_fp16, y = var_172_to_fp16)[name = tensor("x_std_cast_fp16")]; tensor var_180_axes_0 = const()[name = tensor("op_180_axes_0"), val = tensor([2])]; tensor var_180_cast_fp16 = expand_dims(axes = var_180_axes_0, x = x_std_cast_fp16)[name = tensor("op_180_cast_fp16")]; tensor x_cast_fp16 = real_div(x = var_155_cast_fp16, y = var_180_cast_fp16)[name = tensor("x_cast_fp16")]; tensor var_183_shape_cast_fp16 = shape(x = x_cast_fp16)[name = tensor("op_183_shape_cast_fp16")]; tensor gather_6_indices_0 = const()[name = tensor("gather_6_indices_0"), val = tensor(-1)]; tensor gather_6_axis_0 = const()[name = tensor("gather_6_axis_0"), val = tensor(0)]; tensor gather_6 = gather(axis = gather_6_axis_0, indices = gather_6_indices_0, x = var_183_shape_cast_fp16)[name = tensor("gather_6")]; tensor const_3 = const()[name = tensor("const_3"), val = tensor(0)]; tensor const_4 = const()[name = tensor("const_4"), val = tensor(1)]; tensor mask_1 = range_1d(end = gather_6, start = const_3, step = const_4)[name = tensor("mask_1")]; tensor gather_7_indices_0 = const()[name = tensor("gather_7_indices_0"), val = tensor(0)]; tensor gather_7_axis_0 = const()[name = tensor("gather_7_axis_0"), val = tensor(0)]; tensor gather_7 = gather(axis = gather_7_axis_0, indices = gather_7_indices_0, x = var_183_shape_cast_fp16)[name = tensor("gather_7")]; tensor var_195 = const()[name = tensor("op_195"), val = tensor(1)]; 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_195))[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_197 = tile(reps = concat_3, x = expand_dims_0)[name = tensor("op_197")]; tensor mask = greater_equal(x = var_197, y = var_131)[name = tensor("mask")]; tensor var_202_axes_0 = const()[name = tensor("op_202_axes_0"), val = tensor([1])]; tensor var_202 = expand_dims(axes = var_202_axes_0, x = mask)[name = tensor("op_202")]; tensor var_216_to_fp16 = const()[name = tensor("op_216_to_fp16"), val = tensor(0x0p+0)]; tensor var_217_cast_fp16 = select(a = var_216_to_fp16, b = x_cast_fp16, cond = var_202)[name = tensor("op_217_cast_fp16")]; tensor var_217_cast_fp16_to_fp32_dtype_0 = const()[name = tensor