| program(1.0) | |
| [buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"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<ios17>(tensor<int32, [1]> audio_length, tensor<fp32, [1, ?]> audio_signal) [FlexibleShapeInformation = tuple<tuple<tensor<string, []>, dict<tensor<string, []>, tensor<int32, [?]>>>, tuple<tensor<string, []>, dict<tensor<string, []>, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"audio_signal", [1, 1]}}), ("RangeDims", {{"audio_signal", [[1, 1], [1, 240000]]}})))] { | |
| tensor<int32, []> var_9 = const()[name = tensor<string, []>("op_9"), val = tensor<int32, []>(1)]; | |
| tensor<int32, []> var_10 = const()[name = tensor<string, []>("op_10"), val = tensor<int32, []>(160)]; | |
| tensor<fp32, []> var_24 = const()[name = tensor<string, []>("op_24"), val = tensor<fp32, []>(0x0p+0)]; | |
| tensor<fp32, []> var_25 = const()[name = tensor<string, []>("op_25"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; | |
| tensor<int32, []> var_34 = const()[name = tensor<string, []>("op_34"), val = tensor<int32, []>(512)]; | |
| tensor<int32, [1]> var_35 = add(x = audio_length, y = var_34)[name = tensor<string, []>("op_35")]; | |
| tensor<int32, []> var_36 = const()[name = tensor<string, []>("op_36"), val = tensor<int32, []>(512)]; | |
| tensor<int32, [1]> var_37 = sub(x = var_35, y = var_36)[name = tensor<string, []>("op_37")]; | |
| tensor<int32, [1]> floor_div_0 = floor_div(x = var_37, y = var_10)[name = tensor<string, []>("floor_div_0")]; | |
| tensor<string, []> var_38_dtype_0 = const()[name = tensor<string, []>("op_38_dtype_0"), val = tensor<string, []>("fp32")]; | |
| tensor<fp32, []> var_39_promoted = const()[name = tensor<string, []>("op_39_promoted"), val = tensor<fp32, []>(0x1p+0)]; | |
| tensor<fp32, [1]> var_38 = cast(dtype = var_38_dtype_0, x = floor_div_0)[name = tensor<string, []>("cast_13")]; | |
| tensor<fp32, [1]> seq_len_1 = add(x = var_38, y = var_39_promoted)[name = tensor<string, []>("seq_len_1")]; | |
| tensor<string, []> seq_len_dtype_0 = const()[name = tensor<string, []>("seq_len_dtype_0"), val = tensor<string, []>("int32")]; | |
| tensor<int32, [2]> var_43_begin_0 = const()[name = tensor<string, []>("op_43_begin_0"), val = tensor<int32, [2]>([0, 0])]; | |
| tensor<int32, [2]> var_43_end_0 = const()[name = tensor<string, []>("op_43_end_0"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<bool, [2]> var_43_end_mask_0 = const()[name = tensor<string, []>("op_43_end_mask_0"), val = tensor<bool, [2]>([true, false])]; | |
| tensor<bool, [2]> var_43_squeeze_mask_0 = const()[name = tensor<string, []>("op_43_squeeze_mask_0"), val = tensor<bool, [2]>([false, true])]; | |
| tensor<fp32, [1]> 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<string, []>("op_43")]; | |
| tensor<int32, [1]> var_44_axes_0 = const()[name = tensor<string, []>("op_44_axes_0"), val = tensor<int32, [1]>([1])]; | |
| tensor<fp32, [1, 1]> var_44 = expand_dims(axes = var_44_axes_0, x = var_43)[name = tensor<string, []>("op_44")]; | |
| tensor<int32, [2]> var_46_begin_0 = const()[name = tensor<string, []>("op_46_begin_0"), val = tensor<int32, [2]>([0, 1])]; | |
| tensor<int32, [2]> var_46_end_0 = const()[name = tensor<string, []>("op_46_end_0"), val = tensor<int32, [2]>([1, 0])]; | |
| tensor<bool, [2]> var_46_end_mask_0 = const()[name = tensor<string, []>("op_46_end_mask_0"), val = tensor<bool, [2]>([true, true])]; | |
| tensor<fp32, [1, ?]> 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<string, []>("op_46")]; | |
| tensor<int32, [2]> var_48_begin_0 = const()[name = tensor<string, []>("op_48_begin_0"), val = tensor<int32, [2]>([0, 0])]; | |
| tensor<int32, [2]> var_48_end_0 = const()[name = tensor<string, []>("op_48_end_0"), val = tensor<int32, [2]>([1, -1])]; | |
| tensor<bool, [2]> var_48_end_mask_0 = const()[name = tensor<string, []>("op_48_end_mask_0"), val = tensor<bool, [2]>([true, false])]; | |
| tensor<fp32, [1, ?]> 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<string, []>("op_48")]; | |
| tensor<fp32, []> var_49 = const()[name = tensor<string, []>("op_49"), val = tensor<fp32, []>(0x1.f0a3d8p-1)]; | |
| tensor<fp32, [1, ?]> var_50 = mul(x = var_48, y = var_49)[name = tensor<string, []>("op_50")]; | |
| tensor<fp32, [1, ?]> var_51 = sub(x = var_46, y = var_50)[name = tensor<string, []>("op_51")]; | |
| tensor<bool, []> input_1_interleave_0 = const()[name = tensor<string, []>("input_1_interleave_0"), val = tensor<bool, []>(false)]; | |
| tensor<fp32, [1, ?]> input_1 = concat(axis = var_9, interleave = input_1_interleave_0, values = (var_44, var_51))[name = tensor<string, []>("input_1")]; | |
| tensor<int32, [3]> concat_0x = const()[name = tensor<string, []>("concat_0x"), val = tensor<int32, [3]>([1, 1, -1])]; | |
| tensor<fp32, [1, 1, ?]> input_3 = reshape(shape = concat_0x, x = input_1)[name = tensor<string, []>("input_3")]; | |
| tensor<fp32, []> const_1 = const()[name = tensor<string, []>("const_1"), val = tensor<fp32, []>(0x0p+0)]; | |
| tensor<int32, [6]> input_5_pad_0 = const()[name = tensor<string, []>("input_5_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 256, 256])]; | |
| tensor<string, []> input_5_mode_0 = const()[name = tensor<string, []>("input_5_mode_0"), val = tensor<string, []>("reflect")]; | |
| tensor<fp32, [1, 1, ?]> input_5 = pad(constant_val = const_1, mode = input_5_mode_0, pad = input_5_pad_0, x = input_3)[name = tensor<string, []>("input_5")]; | |
| tensor<int32, [2]> concat_1x = const()[name = tensor<string, []>("concat_1x"), val = tensor<int32, [2]>([1, -1])]; | |
| tensor<fp32, [1, ?]> input = reshape(shape = concat_1x, x = input_5)[name = tensor<string, []>("input")]; | |
| tensor<fp32, [257, 1, 512]> expand_dims_1 = const()[name = tensor<string, []>("expand_dims_1"), val = tensor<fp32, [257, 1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))]; | |
| tensor<fp32, [257, 1, 512]> expand_dims_2 = const()[name = tensor<string, []>("expand_dims_2"), val = tensor<fp32, [257, 1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(526464)))]; | |
| tensor<int32, [1]> expand_dims_3 = const()[name = tensor<string, []>("expand_dims_3"), val = tensor<int32, [1]>([160])]; | |
| tensor<int32, [1]> expand_dims_4_axes_0 = const()[name = tensor<string, []>("expand_dims_4_axes_0"), val = tensor<int32, [1]>([1])]; | |
| tensor<fp32, [1, 1, ?]> expand_dims_4 = expand_dims(axes = expand_dims_4_axes_0, x = input)[name = tensor<string, []>("expand_dims_4")]; | |
| tensor<string, []> conv_0_pad_type_0 = const()[name = tensor<string, []>("conv_0_pad_type_0"), val = tensor<string, []>("valid")]; | |
| tensor<int32, [2]> conv_0_pad_0 = const()[name = tensor<string, []>("conv_0_pad_0"), val = tensor<int32, [2]>([0, 0])]; | |
| tensor<int32, [1]> conv_0_dilations_0 = const()[name = tensor<string, []>("conv_0_dilations_0"), val = tensor<int32, [1]>([1])]; | |
| tensor<int32, []> conv_0_groups_0 = const()[name = tensor<string, []>("conv_0_groups_0"), val = tensor<int32, []>(1)]; | |
| tensor<fp32, [1, 257, ?]> 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<string, []>("conv_0")]; | |
| tensor<string, []> conv_1_pad_type_0 = const()[name = tensor<string, []>("conv_1_pad_type_0"), val = tensor<string, []>("valid")]; | |
| tensor<int32, [2]> conv_1_pad_0 = const()[name = tensor<string, []>("conv_1_pad_0"), val = tensor<int32, [2]>([0, 0])]; | |
| tensor<int32, [1]> conv_1_dilations_0 = const()[name = tensor<string, []>("conv_1_dilations_0"), val = tensor<int32, [1]>([1])]; | |
| tensor<int32, []> conv_1_groups_0 = const()[name = tensor<string, []>("conv_1_groups_0"), val = tensor<int32, []>(1)]; | |
| tensor<fp32, [1, 257, ?]> 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<string, []>("conv_1")]; | |
| tensor<int32, []> stack_0_axis_0 = const()[name = tensor<string, []>("stack_0_axis_0"), val = tensor<int32, []>(-1)]; | |
| tensor<fp32, [1, 257, ?, 2]> stack_0 = stack(axis = stack_0_axis_0, values = (conv_0, conv_1))[name = tensor<string, []>("stack_0")]; | |
| tensor<fp32, []> var_17_promoted = const()[name = tensor<string, []>("op_17_promoted"), val = tensor<fp32, []>(0x1p+1)]; | |
| tensor<fp32, [1, 257, ?, 2]> var_67 = pow(x = stack_0, y = var_17_promoted)[name = tensor<string, []>("op_67")]; | |
| tensor<int32, [1]> var_69_axes_0 = const()[name = tensor<string, []>("op_69_axes_0"), val = tensor<int32, [1]>([-1])]; | |
| tensor<bool, []> var_69_keep_dims_0 = const()[name = tensor<string, []>("op_69_keep_dims_0"), val = tensor<bool, []>(false)]; | |
| tensor<fp32, [1, 257, ?]> var_69 = reduce_sum(axes = var_69_axes_0, keep_dims = var_69_keep_dims_0, x = var_67)[name = tensor<string, []>("op_69")]; | |
| tensor<fp32, [1, 257, ?]> x_9 = identity(x = var_69)[name = tensor<string, []>("x_9")]; | |
| tensor<fp32, [1, 128, 257]> const_2 = const()[name = tensor<string, []>("const_2"), val = tensor<fp32, [1, 128, 257]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1052864)))]; | |
| tensor<bool, []> x_11_transpose_x_0 = const()[name = tensor<string, []>("x_11_transpose_x_0"), val = tensor<bool, []>(false)]; | |
| tensor<bool, []> x_11_transpose_y_0 = const()[name = tensor<string, []>("x_11_transpose_y_0"), val = tensor<bool, []>(false)]; | |
| tensor<fp32, [1, 128, ?]> 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<string, []>("x_11")]; | |
| tensor<fp32, []> var_76 = const()[name = tensor<string, []>("op_76"), val = tensor<fp32, []>(0x1p-24)]; | |
| tensor<fp32, [1, 128, ?]> var_77 = add(x = x_11, y = var_76)[name = tensor<string, []>("op_77")]; | |
| tensor<fp32, []> x_13_epsilon_0 = const()[name = tensor<string, []>("x_13_epsilon_0"), val = tensor<fp32, []>(0x1p-149)]; | |
| tensor<fp32, [1, 128, ?]> x_13 = log(epsilon = x_13_epsilon_0, x = var_77)[name = tensor<string, []>("x_13")]; | |
| tensor<int32, [3]> var_79_shape = shape(x = x_13)[name = tensor<string, []>("op_79_shape")]; | |
| tensor<int32, []> gather_4 = const()[name = tensor<string, []>("gather_4"), val = tensor<int32, []>(1)]; | |
| tensor<int32, []> gather_5_batch_dims_0 = const()[name = tensor<string, []>("gather_5_batch_dims_0"), val = tensor<int32, []>(0)]; | |
| tensor<bool, []> gather_5_validate_indices_0 = const()[name = tensor<string, []>("gather_5_validate_indices_0"), val = tensor<bool, []>(false)]; | |
| tensor<int32, []> select_2 = const()[name = tensor<string, []>("select_2"), val = tensor<int32, []>(2)]; | |
| tensor<int32, []> gather_5_axis_1 = const()[name = tensor<string, []>("gather_5_axis_1"), val = tensor<int32, []>(0)]; | |
| tensor<int32, []> 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<string, []>("gather_5")]; | |
| tensor<int32, []> const_3 = const()[name = tensor<string, []>("const_3"), val = tensor<int32, []>(0)]; | |
| tensor<int32, []> const_4 = const()[name = tensor<string, []>("const_4"), val = tensor<int32, []>(1)]; | |
| tensor<int32, [?]> var_81 = range_1d(end = gather_5, start = const_3, step = const_4)[name = tensor<string, []>("op_81")]; | |
| tensor<int32, [1]> var_82_axes_0 = const()[name = tensor<string, []>("op_82_axes_0"), val = tensor<int32, [1]>([0])]; | |
| tensor<int32, [1, ?]> var_82 = expand_dims(axes = var_82_axes_0, x = var_81)[name = tensor<string, []>("op_82")]; | |
| tensor<int32, []> concat_2_axis_0 = const()[name = tensor<string, []>("concat_2_axis_0"), val = tensor<int32, []>(0)]; | |
| tensor<bool, []> concat_2_interleave_0 = const()[name = tensor<string, []>("concat_2_interleave_0"), val = tensor<bool, []>(false)]; | |
| tensor<int32, [2]> concat_2 = concat(axis = concat_2_axis_0, interleave = concat_2_interleave_0, values = (gather_4, gather_5))[name = tensor<string, []>("concat_2")]; | |
| tensor<int32, [2]> shape_0 = shape(x = var_82)[name = tensor<string, []>("shape_0")]; | |
| tensor<int32, [2]> real_div_0 = real_div(x = concat_2, y = shape_0)[name = tensor<string, []>("real_div_0")]; | |
| tensor<int32, [?, ?]> time_steps = tile(reps = real_div_0, x = var_82)[name = tensor<string, []>("time_steps")]; | |
| tensor<int32, [1]> var_85_axes_0 = const()[name = tensor<string, []>("op_85_axes_0"), val = tensor<int32, [1]>([1])]; | |
| tensor<int32, [1]> mel_length = cast(dtype = seq_len_dtype_0, x = seq_len_1)[name = tensor<string, []>("cast_12")]; | |
| tensor<int32, [1, 1]> var_85 = expand_dims(axes = var_85_axes_0, x = mel_length)[name = tensor<string, []>("op_85")]; | |
| tensor<bool, [?, ?]> valid_mask = less(x = time_steps, y = var_85)[name = tensor<string, []>("valid_mask")]; | |
| tensor<int32, [1]> var_87_axes_0 = const()[name = tensor<string, []>("op_87_axes_0"), val = tensor<int32, [1]>([1])]; | |
| tensor<bool, [?, 1, ?]> var_87 = expand_dims(axes = var_87_axes_0, x = valid_mask)[name = tensor<string, []>("op_87")]; | |
| tensor<fp32, [1, 128, ?]> var_88 = select(a = x_13, b = var_24, cond = var_87)[name = tensor<string, []>("op_88")]; | |
| tensor<int32, [1]> x_mean_numerator_axes_0 = const()[name = tensor<string, []>("x_mean_numerator_axes_0"), val = tensor<int32, [1]>([2])]; | |
| tensor<bool, []> x_mean_numerator_keep_dims_0 = const()[name = tensor<string, []>("x_mean_numerator_keep_dims_0"), val = tensor<bool, []>(false)]; | |
| tensor<fp32, [1, 128]> x_mean_numerator = reduce_sum(axes = x_mean_numerator_axes_0, keep_dims = x_mean_numerator_keep_dims_0, x = var_88)[name = tensor<string, []>("x_mean_numerator")]; | |
| tensor<string, []> cast_3_dtype_0 = const()[name = tensor<string, []>("cast_3_dtype_0"), val = tensor<string, []>("fp32")]; | |
| tensor<int32, [1]> x_mean_denominator_axes_0 = const()[name = tensor<string, []>("x_mean_denominator_axes_0"), val = tensor<int32, [1]>([1])]; | |
| tensor<bool, []> x_mean_denominator_keep_dims_0 = const()[name = tensor<string, []>("x_mean_denominator_keep_dims_0"), val = tensor<bool, []>(false)]; | |
| tensor<fp32, [?, ?]> cast_3 = cast(dtype = cast_3_dtype_0, x = valid_mask)[name = tensor<string, []>("cast_11")]; | |
| tensor<fp32, [?]> x_mean_denominator = reduce_sum(axes = x_mean_denominator_axes_0, keep_dims = x_mean_denominator_keep_dims_0, x = cast_3)[name = tensor<string, []>("x_mean_denominator")]; | |
| tensor<int32, [1]> var_93_axes_0 = const()[name = tensor<string, []>("op_93_axes_0"), val = tensor<int32, [1]>([1])]; | |
| tensor<fp32, [?, 1]> var_93 = expand_dims(axes = var_93_axes_0, x = x_mean_denominator)[name = tensor<string, []>("op_93")]; | |
| tensor<fp32, [?, 128]> x_mean = real_div(x = x_mean_numerator, y = var_93)[name = tensor<string, []>("x_mean")]; | |
| tensor<int32, [1]> var_96_axes_0 = const()[name = tensor<string, []>("op_96_axes_0"), val = tensor<int32, [1]>([2])]; | |
| tensor<fp32, [?, 128, 1]> var_96 = expand_dims(axes = var_96_axes_0, x = x_mean)[name = tensor<string, []>("op_96")]; | |
| tensor<fp32, [?, 128, ?]> var_97 = sub(x = x_13, y = var_96)[name = tensor<string, []>("op_97")]; | |
| tensor<fp32, [?, 128, ?]> var_98 = select(a = var_97, b = var_24, cond = var_87)[name = tensor<string, []>("op_98")]; | |
| tensor<fp32, []> var_17_promoted_1 = const()[name = tensor<string, []>("op_17_promoted_1"), val = tensor<fp32, []>(0x1p+1)]; | |
| tensor<fp32, [?, 128, ?]> var_99 = pow(x = var_98, y = var_17_promoted_1)[name = tensor<string, []>("op_99")]; | |
| tensor<int32, [1]> var_101_axes_0 = const()[name = tensor<string, []>("op_101_axes_0"), val = tensor<int32, [1]>([2])]; | |
| tensor<bool, []> var_101_keep_dims_0 = const()[name = tensor<string, []>("op_101_keep_dims_0"), val = tensor<bool, []>(false)]; | |
| tensor<fp32, [?, 128]> var_101 = reduce_sum(axes = var_101_axes_0, keep_dims = var_101_keep_dims_0, x = var_99)[name = tensor<string, []>("op_101")]; | |
| tensor<fp32, []> var_103 = const()[name = tensor<string, []>("op_103"), val = tensor<fp32, []>(0x1p+0)]; | |
| tensor<fp32, [?, 1]> var_104 = sub(x = var_93, y = var_103)[name = tensor<string, []>("op_104")]; | |
| tensor<fp32, [?, 128]> var_105 = real_div(x = var_101, y = var_104)[name = tensor<string, []>("op_105")]; | |
| tensor<fp32, [?, 128]> x_std_1 = sqrt(x = var_105)[name = tensor<string, []>("x_std_1")]; | |
| tensor<fp32, [?, 128]> x_std = add(x = x_std_1, y = var_25)[name = tensor<string, []>("x_std")]; | |
| tensor<int32, [1]> var_110_axes_0 = const()[name = tensor<string, []>("op_110_axes_0"), val = tensor<int32, [1]>([2])]; | |
| tensor<fp32, [?, 128, 1]> var_110 = expand_dims(axes = var_110_axes_0, x = x_std)[name = tensor<string, []>("op_110")]; | |
| tensor<fp32, [?, 128, ?]> x = real_div(x = var_97, y = var_110)[name = tensor<string, []>("x")]; | |
| tensor<int32, [3]> var_112_shape = shape(x = x)[name = tensor<string, []>("op_112_shape")]; | |
| tensor<int32, []> gather_6_batch_dims_0 = const()[name = tensor<string, []>("gather_6_batch_dims_0"), val = tensor<int32, []>(0)]; | |
| tensor<bool, []> gather_6_validate_indices_0 = const()[name = tensor<string, []>("gather_6_validate_indices_0"), val = tensor<bool, []>(false)]; | |
| tensor<int32, []> select_3 = const()[name = tensor<string, []>("select_3"), val = tensor<int32, []>(2)]; | |
| tensor<int32, []> gather_6_axis_1 = const()[name = tensor<string, []>("gather_6_axis_1"), val = tensor<int32, []>(0)]; | |
| tensor<int32, []> 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<string, []>("gather_6")]; | |
| tensor<int32, []> const_5 = const()[name = tensor<string, []>("const_5"), val = tensor<int32, []>(0)]; | |
| tensor<int32, []> const_6 = const()[name = tensor<string, []>("const_6"), val = tensor<int32, []>(1)]; | |
| tensor<int32, [?]> mask_1 = range_1d(end = gather_6, start = const_5, step = const_6)[name = tensor<string, []>("mask_1")]; | |
| tensor<int32, []> gather_7_batch_dims_0 = const()[name = tensor<string, []>("gather_7_batch_dims_0"), val = tensor<int32, []>(0)]; | |
| tensor<bool, []> gather_7_validate_indices_0 = const()[name = tensor<string, []>("gather_7_validate_indices_0"), val = tensor<bool, []>(false)]; | |
| tensor<int32, []> select_4 = const()[name = tensor<string, []>("select_4"), val = tensor<int32, []>(0)]; | |
| tensor<int32, []> gather_7_axis_1 = const()[name = tensor<string, []>("gather_7_axis_1"), val = tensor<int32, []>(0)]; | |
| tensor<int32, []> 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<string, []>("gather_7")]; | |
| tensor<int32, []> concat_3_axis_0 = const()[name = tensor<string, []>("concat_3_axis_0"), val = tensor<int32, []>(0)]; | |
| tensor<bool, []> concat_3_interleave_0 = const()[name = tensor<string, []>("concat_3_interleave_0"), val = tensor<bool, []>(false)]; | |
| tensor<int32, [2]> concat_3 = concat(axis = concat_3_axis_0, interleave = concat_3_interleave_0, values = (gather_7, var_9))[name = tensor<string, []>("concat_3")]; | |
| tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = tensor<string, []>("expand_dims_0_axes_0"), val = tensor<int32, [1]>([0])]; | |
| tensor<int32, [1, ?]> expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = mask_1)[name = tensor<string, []>("expand_dims_0")]; | |
| tensor<int32, [?, ?]> var_116 = tile(reps = concat_3, x = expand_dims_0)[name = tensor<string, []>("op_116")]; | |
| tensor<bool, [?, ?]> mask = greater_equal(x = var_116, y = var_85)[name = tensor<string, []>("mask")]; | |
| tensor<int32, [1]> var_119_axes_0 = const()[name = tensor<string, []>("op_119_axes_0"), val = tensor<int32, [1]>([1])]; | |
| tensor<bool, [?, 1, ?]> var_119 = expand_dims(axes = var_119_axes_0, x = mask)[name = tensor<string, []>("op_119")]; | |
| tensor<fp32, [?, 128, ?]> mel = select(a = var_24, b = x, cond = var_119)[name = tensor<string, []>("processed_signal")]; | |
| } -> (mel, mel_length); | |
| } |