program(1.0) [buildInfo = dict, tensor>({{"coremlc-component-MIL", "3500.14.1"}, {"coremlc-version", "3500.32.1"}})] { 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_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_to_fp16_dtype_0 = const()[name = tensor("op_38_to_fp16_dtype_0"), val = tensor("fp16")]; tensor var_39_promoted_to_fp16 = const()[name = tensor("op_39_promoted_to_fp16"), val = tensor(0x1p+0)]; tensor floor_div_0_to_fp16 = cast(dtype = var_38_to_fp16_dtype_0, x = floor_div_0)[name = tensor("cast_9")]; tensor seq_len_1_cast_fp16 = add(x = floor_div_0_to_fp16, y = var_39_promoted_to_fp16)[name = tensor("seq_len_1_cast_fp16")]; 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 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_8")]; tensor var_43_cast_fp16 = 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_to_fp16)[name = tensor("op_43_cast_fp16")]; tensor var_44_axes_0 = const()[name = tensor("op_44_axes_0"), val = tensor([1])]; tensor var_44_cast_fp16 = expand_dims(axes = var_44_axes_0, x = var_43_cast_fp16)[name = tensor("op_44_cast_fp16")]; 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_cast_fp16 = slice_by_index(begin = var_46_begin_0, end = var_46_end_0, end_mask = var_46_end_mask_0, x = audio_signal_to_fp16)[name = tensor("op_46_cast_fp16")]; 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_cast_fp16 = slice_by_index(begin = var_48_begin_0, end = var_48_end_0, end_mask = var_48_end_mask_0, x = audio_signal_to_fp16)[name = tensor("op_48_cast_fp16")]; tensor var_49_to_fp16 = const()[name = tensor("op_49_to_fp16"), val = tensor(0x1.f0cp-1)]; tensor var_50_cast_fp16 = mul(x = var_48_cast_fp16, y = var_49_to_fp16)[name = tensor("op_50_cast_fp16")]; tensor var_51_cast_fp16 = sub(x = var_46_cast_fp16, y = var_50_cast_fp16)[name = tensor("op_51_cast_fp16")]; tensor input_1_interleave_0 = const()[name = tensor("input_1_interleave_0"), val = tensor(false)]; tensor input_1_cast_fp16 = concat(axis = var_9, interleave = input_1_interleave_0, values = (var_44_cast_fp16, var_51_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_1_to_fp16 = const()[name = tensor("const_1_to_fp16"), val = tensor(0x0p+0)]; tensor input_5_cast_fp16 = pad(constant_val = const_1_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_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("expand_dims_1_to_fp16_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132096))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131712)))]; 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_quantized, 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_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("expand_dims_2_to_fp16_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132736))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(264768))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(264384)))]; 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_quantized, 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_17_promoted_to_fp16 = const()[name = tensor("op_17_promoted_to_fp16"), val = tensor(0x1p+1)]; tensor var_67_cast_fp16 = pow(x = stack_0_cast_fp16, y = var_17_promoted_to_fp16)[name = tensor("op_67_cast_fp16")]; 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_cast_fp16 = reduce_sum(axes = var_69_axes_0, keep_dims = var_69_keep_dims_0, x = var_67_cast_fp16)[name = tensor("op_69_cast_fp16")]; 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 const_2_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor(1), name = tensor("const_2_to_fp16_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(265408))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(298560))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(298368)))]; tensor x_11_cast_fp16 = matmul(transpose_x = x_11_transpose_x_0, transpose_y = x_11_transpose_y_0, x = const_2_to_fp16_quantized, y = var_69_cast_fp16)[name = tensor("x_11_cast_fp16")]; tensor var_76_to_fp16 = const()[name = tensor("op_76_to_fp16"), val = tensor(0x1p-24)]; tensor var_77_cast_fp16 = add(x = x_11_cast_fp16, y = var_76_to_fp16)[name = tensor("op_77_cast_fp16")]; tensor x_13_epsilon_0 = const()[name = tensor("x_13_epsilon_0"), val = tensor(0x1p-149)]; tensor x_13_cast_fp16 = log(epsilon = x_13_epsilon_0, x = var_77_cast_fp16)[name = tensor("x_13_cast_fp16")]; tensor var_79_shape_cast_fp16 = shape(x = x_13_cast_fp16)[name = tensor("op_79_shape_cast_fp16")]; tensor gather_4 = const()[name = tensor("gather_4"), val = tensor(1)]; tensor gather_5_axis_0 = const()[name = tensor("gather_5_axis_0"), val = tensor(0)]; 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 var_79_shape_cast_fp16_to_uint16_dtype_0 = const()[name = tensor("op_79_shape_cast_fp16_to_uint16_dtype_0"), val = tensor("uint16")]; tensor gather_5_indices_0_to_uint16 = const()[name = tensor("gather_5_indices_0_to_uint16"), val = tensor(2)]; tensor var_79_shape_cast_fp16_to_uint16 = cast(dtype = var_79_shape_cast_fp16_to_uint16_dtype_0, x = var_79_shape_cast_fp16)[name = tensor("cast_7")]; tensor gather_5_cast_uint16 = gather(axis = gather_5_axis_0, batch_dims = gather_5_batch_dims_0, indices = gather_5_indices_0_to_uint16, validate_indices = gather_5_validate_indices_0, x = var_79_shape_cast_fp16_to_uint16)[name = tensor("gather_5_cast_uint16")]; tensor gather_5_cast_uint16_to_int32_dtype_0 = const()[name = tensor("gather_5_cast_uint16_to_int32_dtype_0"), val = tensor("int32")]; tensor const_3 = const()[name = tensor("const_3"), val = tensor(0)]; tensor const_4 = const()[name = tensor("const_4"), val = tensor(1)]; tensor gather_5_cast_uint16_to_int32 = cast(dtype = gather_5_cast_uint16_to_int32_dtype_0, x = gather_5_cast_uint16)[name = tensor("cast_6")]; tensor var_81 = range_1d(end = gather_5_cast_uint16_to_int32, 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_cast_uint16_to_int32))[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_cast_fp16)[name = tensor("cast_5")]; 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_24_to_fp16 = const()[name = tensor("op_24_to_fp16"), val = tensor(0x0p+0)]; tensor var_88_cast_fp16 = select(a = x_13_cast_fp16, b = var_24_to_fp16, cond = var_87)[name = tensor("op_88_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_88_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_3_to_fp16_dtype_0 = const()[name = tensor("cast_3_to_fp16_dtype_0"), val = tensor("fp16")]; tensor valid_mask_to_fp16 = cast(dtype = cast_3_to_fp16_dtype_0, x = valid_mask)[name = tensor("cast_4")]; 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_93_axes_0 = const()[name = tensor("op_93_axes_0"), val = tensor([1])]; tensor var_93_cast_fp16 = expand_dims(axes = var_93_axes_0, x = x_mean_denominator_cast_fp16)[name = tensor("op_93_cast_fp16")]; tensor x_mean_cast_fp16 = real_div(x = x_mean_numerator_cast_fp16, y = var_93_cast_fp16)[name = tensor("x_mean_cast_fp16")]; tensor var_96_axes_0 = const()[name = tensor("op_96_axes_0"), val = tensor([2])]; tensor var_96_cast_fp16 = expand_dims(axes = var_96_axes_0, x = x_mean_cast_fp16)[name = tensor("op_96_cast_fp16")]; tensor var_97_cast_fp16 = sub(x = x_13_cast_fp16, y = var_96_cast_fp16)[name = tensor("op_97_cast_fp16")]; tensor var_98_cast_fp16 = select(a = var_97_cast_fp16, b = var_24_to_fp16, cond = var_87)[name = tensor("op_98_cast_fp16")]; tensor var_17_promoted_1_to_fp16 = const()[name = tensor("op_17_promoted_1_to_fp16"), val = tensor(0x1p+1)]; tensor var_99_cast_fp16 = pow(x = var_98_cast_fp16, y = var_17_promoted_1_to_fp16)[name = tensor("op_99_cast_fp16")]; 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_cast_fp16 = reduce_sum(axes = var_101_axes_0, keep_dims = var_101_keep_dims_0, x = var_99_cast_fp16)[name = tensor("op_101_cast_fp16")]; tensor var_103_to_fp16 = const()[name = tensor("op_103_to_fp16"), val = tensor(0x1p+0)]; tensor var_104_cast_fp16 = sub(x = var_93_cast_fp16, y = var_103_to_fp16)[name = tensor("op_104_cast_fp16")]; tensor var_105_cast_fp16 = real_div(x = var_101_cast_fp16, y = var_104_cast_fp16)[name = tensor("op_105_cast_fp16")]; tensor x_std_1_cast_fp16 = sqrt(x = var_105_cast_fp16)[name = tensor("x_std_1_cast_fp16")]; tensor var_25_to_fp16 = const()[name = tensor("op_25_to_fp16"), val = tensor(0x1.5p-17)]; tensor x_std_cast_fp16 = add(x = x_std_1_cast_fp16, y = var_25_to_fp16)[name = tensor("x_std_cast_fp16")]; tensor var_110_axes_0 = const()[name = tensor("op_110_axes_0"), val = tensor([2])]; tensor var_110_cast_fp16 = expand_dims(axes = var_110_axes_0, x = x_std_cast_fp16)[name = tensor("op_110_cast_fp16")]; tensor x_cast_fp16 = real_div(x = var_97_cast_fp16, y = var_110_cast_fp16)[name = tensor("x_cast_fp16")]; tensor var_112_shape_cast_fp16 = shape(x = x_cast_fp16)[name = tensor("op_112_shape_cast_fp16")]; 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 var_112_shape_cast_fp16_to_uint16_dtype_0 = const()[name = tensor("op_112_shape_cast_fp16_to_uint16_dtype_0"), val = tensor("uint16")]; tensor gather_6_cast_uint16_axis_0 = const()[name = tensor("gather_6_cast_uint16_axis_0"), val = tensor(0)]; tensor select_0_to_uint16 = const()[name = tensor("select_0_to_uint16"), val = tensor(2)]; tensor var_112_shape_cast_fp16_to_uint16 = cast(dtype = var_112_shape_cast_fp16_to_uint16_dtype_0, x = var_112_shape_cast_fp16)[name = tensor("cast_3")]; tensor gather_6_cast_uint16_cast_uint16 = gather(axis = gather_6_cast_uint16_axis_0, batch_dims = gather_6_batch_dims_0, indices = select_0_to_uint16, validate_indices = gather_6_validate_indices_0, x = var_112_shape_cast_fp16_to_uint16)[name = tensor("gather_6_cast_uint16_cast_uint16")]; tensor gather_6_cast_uint16_to_int32_dtype_0 = const()[name = tensor("gather_6_cast_uint16_to_int32_dtype_0"), val = tensor("int32")]; tensor const_5 = const()[name = tensor