program(1.0) [buildInfo = dict, tensor>({{"coremlc-component-MIL", "3500.14.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.8.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0b1"}})] { func main(tensor audio) [FlexibleShapeInformation = tuple, dict, tensor>>, tuple, dict, dict, tensor>>>>((("DefaultShapes", {{"audio", [32, 1, 160000]}}), ("EnumeratedShapes", {{"audio_1_1_10_1_160000_", {{"audio", [10, 1, 160000]}}}, {"audio_1_1_11_1_160000_", {{"audio", [11, 1, 160000]}}}, {"audio_1_1_12_1_160000_", {{"audio", [12, 1, 160000]}}}, {"audio_1_1_13_1_160000_", {{"audio", [13, 1, 160000]}}}, {"audio_1_1_14_1_160000_", {{"audio", [14, 1, 160000]}}}, {"audio_1_1_15_1_160000_", {{"audio", [15, 1, 160000]}}}, {"audio_1_1_16_1_160000_", {{"audio", [16, 1, 160000]}}}, {"audio_1_1_17_1_160000_", {{"audio", [17, 1, 160000]}}}, {"audio_1_1_18_1_160000_", {{"audio", [18, 1, 160000]}}}, {"audio_1_1_19_1_160000_", {{"audio", [19, 1, 160000]}}}, {"audio_1_1_1_1_160000_", {{"audio", [1, 1, 160000]}}}, {"audio_1_1_20_1_160000_", {{"audio", [20, 1, 160000]}}}, {"audio_1_1_21_1_160000_", {{"audio", [21, 1, 160000]}}}, {"audio_1_1_22_1_160000_", {{"audio", [22, 1, 160000]}}}, {"audio_1_1_23_1_160000_", {{"audio", [23, 1, 160000]}}}, {"audio_1_1_24_1_160000_", {{"audio", [24, 1, 160000]}}}, {"audio_1_1_25_1_160000_", {{"audio", [25, 1, 160000]}}}, {"audio_1_1_26_1_160000_", {{"audio", [26, 1, 160000]}}}, {"audio_1_1_27_1_160000_", {{"audio", [27, 1, 160000]}}}, {"audio_1_1_28_1_160000_", {{"audio", [28, 1, 160000]}}}, {"audio_1_1_29_1_160000_", {{"audio", [29, 1, 160000]}}}, {"audio_1_1_2_1_160000_", {{"audio", [2, 1, 160000]}}}, {"audio_1_1_30_1_160000_", {{"audio", [30, 1, 160000]}}}, {"audio_1_1_31_1_160000_", {{"audio", [31, 1, 160000]}}}, {"audio_1_1_32_1_160000_", {{"audio", [32, 1, 160000]}}}, {"audio_1_1_3_1_160000_", {{"audio", [3, 1, 160000]}}}, {"audio_1_1_4_1_160000_", {{"audio", [4, 1, 160000]}}}, {"audio_1_1_5_1_160000_", {{"audio", [5, 1, 160000]}}}, {"audio_1_1_6_1_160000_", {{"audio", [6, 1, 160000]}}}, {"audio_1_1_7_1_160000_", {{"audio", [7, 1, 160000]}}}, {"audio_1_1_8_1_160000_", {{"audio", [8, 1, 160000]}}}, {"audio_1_1_9_1_160000_", {{"audio", [9, 1, 160000]}}}})))] { tensor sincnet_wav_norm1d_bias = const()[name = tensor("sincnet_wav_norm1d_bias"), val = tensor([0x1.73505ep-5])]; tensor sincnet_wav_norm1d_weight = const()[name = tensor("sincnet_wav_norm1d_weight"), val = tensor([0x1.43f862p-7])]; tensor sincnet_norm1d_0_bias = const()[name = tensor("sincnet_norm1d_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; tensor sincnet_norm1d_0_weight = const()[name = tensor("sincnet_norm1d_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(448)))]; tensor sincnet_conv1d_1_bias = const()[name = tensor("sincnet_conv1d_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(832)))]; tensor sincnet_conv1d_1_weight = const()[name = tensor("sincnet_conv1d_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1152)))]; tensor sincnet_norm1d_1_bias = const()[name = tensor("sincnet_norm1d_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97216)))]; tensor sincnet_norm1d_1_weight = const()[name = tensor("sincnet_norm1d_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97536)))]; tensor sincnet_conv1d_2_bias = const()[name = tensor("sincnet_conv1d_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97856)))]; tensor sincnet_conv1d_2_weight = const()[name = tensor("sincnet_conv1d_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98176)))]; tensor sincnet_norm1d_2_bias = const()[name = tensor("sincnet_norm1d_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170240)))]; tensor sincnet_norm1d_2_weight = const()[name = tensor("sincnet_norm1d_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170560)))]; tensor linear_0_bias = const()[name = tensor("linear_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170880)))]; tensor linear_0_weight = const()[name = tensor("linear_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(171456)))]; tensor linear_1_bias = const()[name = tensor("linear_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(302592)))]; tensor linear_1_weight = const()[name = tensor("linear_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(303168)))]; tensor classifier_bias = const()[name = tensor("classifier_bias"), val = tensor([-0x1.00e888p+0, 0x1.67cb52p-2, 0x1.3d87fp-1, 0x1.c8aa8p-2, -0x1.445f5ep-2, -0x1.591274p-1, -0x1.8fb70ep-2])]; tensor classifier_weight = const()[name = tensor("classifier_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(368768)))]; tensor var_9 = const()[name = tensor("op_9"), val = tensor(0x1.47ae14p-7)]; tensor var_24 = const()[name = tensor("op_24"), val = tensor(0x1.4f8b58p-17)]; tensor waveform = instance_norm(beta = sincnet_wav_norm1d_bias, epsilon = var_24, gamma = sincnet_wav_norm1d_weight, x = audio)[name = tensor("waveform")]; tensor filters = const()[name = tensor("filters"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(372416)))]; tensor outputs_pad_type_0 = const()[name = tensor("outputs_pad_type_0"), val = tensor("valid")]; tensor outputs_strides_0 = const()[name = tensor("outputs_strides_0"), val = tensor([10])]; tensor outputs_pad_0 = const()[name = tensor("outputs_pad_0"), val = tensor([0, 0])]; tensor outputs_dilations_0 = const()[name = tensor("outputs_dilations_0"), val = tensor([1])]; tensor outputs_groups_0 = const()[name = tensor("outputs_groups_0"), val = tensor(1)]; tensor outputs = conv(dilations = outputs_dilations_0, groups = outputs_groups_0, pad = outputs_pad_0, pad_type = outputs_pad_type_0, strides = outputs_strides_0, weight = filters, x = waveform)[name = tensor("outputs")]; tensor input_1 = abs(x = outputs)[name = tensor("input_1")]; tensor var_119 = const()[name = tensor("op_119"), val = tensor([3])]; tensor var_120 = const()[name = tensor("op_120"), val = tensor([3])]; tensor input_3_pad_type_0 = const()[name = tensor("input_3_pad_type_0"), val = tensor("custom")]; tensor input_3_pad_0 = const()[name = tensor("input_3_pad_0"), val = tensor([0, 0])]; tensor input_3_ceil_mode_0 = const()[name = tensor("input_3_ceil_mode_0"), val = tensor(false)]; tensor input_3 = max_pool(ceil_mode = input_3_ceil_mode_0, kernel_sizes = var_119, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_120, x = input_1)[name = tensor("input_3")]; tensor input_5 = instance_norm(beta = sincnet_norm1d_0_bias, epsilon = var_24, gamma = sincnet_norm1d_0_weight, x = input_3)[name = tensor("input_5")]; tensor input_7 = leaky_relu(alpha = var_9, x = input_5)[name = tensor("input_7")]; tensor input_9_pad_type_0 = const()[name = tensor("input_9_pad_type_0"), val = tensor("valid")]; tensor input_9_strides_0 = const()[name = tensor("input_9_strides_0"), val = tensor([1])]; tensor input_9_pad_0 = const()[name = tensor("input_9_pad_0"), val = tensor([0, 0])]; tensor input_9_dilations_0 = const()[name = tensor("input_9_dilations_0"), val = tensor([1])]; tensor input_9_groups_0 = const()[name = tensor("input_9_groups_0"), val = tensor(1)]; tensor input_9 = conv(bias = sincnet_conv1d_1_bias, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = sincnet_conv1d_1_weight, x = input_7)[name = tensor("input_9")]; tensor var_135 = const()[name = tensor("op_135"), val = tensor([3])]; tensor var_136 = const()[name = tensor("op_136"), val = tensor([3])]; tensor input_11_pad_type_0 = const()[name = tensor("input_11_pad_type_0"), val = tensor("custom")]; tensor input_11_pad_0 = const()[name = tensor("input_11_pad_0"), val = tensor([0, 0])]; tensor input_11_ceil_mode_0 = const()[name = tensor("input_11_ceil_mode_0"), val = tensor(false)]; tensor input_11 = max_pool(ceil_mode = input_11_ceil_mode_0, kernel_sizes = var_135, pad = input_11_pad_0, pad_type = input_11_pad_type_0, strides = var_136, x = input_9)[name = tensor("input_11")]; tensor input_13 = instance_norm(beta = sincnet_norm1d_1_bias, epsilon = var_24, gamma = sincnet_norm1d_1_weight, x = input_11)[name = tensor("input_13")]; tensor input_15 = leaky_relu(alpha = var_9, x = input_13)[name = tensor("input_15")]; tensor input_17_pad_type_0 = const()[name = tensor("input_17_pad_type_0"), val = tensor("valid")]; tensor input_17_strides_0 = const()[name = tensor("input_17_strides_0"), val = tensor([1])]; tensor input_17_pad_0 = const()[name = tensor("input_17_pad_0"), val = tensor([0, 0])]; tensor input_17_dilations_0 = const()[name = tensor("input_17_dilations_0"), val = tensor([1])]; tensor input_17_groups_0 = const()[name = tensor("input_17_groups_0"), val = tensor(1)]; tensor input_17 = conv(bias = sincnet_conv1d_2_bias, dilations = input_17_dilations_0, groups = input_17_groups_0, pad = input_17_pad_0, pad_type = input_17_pad_type_0, strides = input_17_strides_0, weight = sincnet_conv1d_2_weight, x = input_15)[name = tensor("input_17")]; tensor var_151 = const()[name = tensor("op_151"), val = tensor([3])]; tensor var_152 = const()[name = tensor("op_152"), val = tensor([3])]; tensor input_19_pad_type_0 = const()[name = tensor("input_19_pad_type_0"), val = tensor("custom")]; tensor input_19_pad_0 = const()[name = tensor("input_19_pad_0"), val = tensor([0, 0])]; tensor input_19_ceil_mode_0 = const()[name = tensor("input_19_ceil_mode_0"), val = tensor(false)]; tensor input_19 = max_pool(ceil_mode = input_19_ceil_mode_0, kernel_sizes = var_151, pad = input_19_pad_0, pad_type = input_19_pad_type_0, strides = var_152, x = input_17)[name = tensor("input_19")]; tensor input_21 = instance_norm(beta = sincnet_norm1d_2_bias, epsilon = var_24, gamma = sincnet_norm1d_2_weight, x = input_19)[name = tensor("input_21")]; tensor x = leaky_relu(alpha = var_9, x = input_21)[name = tensor("x")]; tensor var_163 = const()[name = tensor("op_163"), val = tensor([0, 2, 1])]; tensor var_172 = const()[name = tensor("op_172"), val = tensor(128)]; tensor var_173 = const()[name = tensor("op_173"), val = tensor(8)]; tensor input_23 = transpose(perm = var_163, x = x)[name = tensor("transpose_6")]; tensor var_207_shape = shape(x = input_23)[name = tensor("op_207_shape")]; tensor gather_0_batch_dims_0 = const()[name = tensor("gather_0_batch_dims_0"), val = tensor(0)]; tensor gather_0_validate_indices_0 = const()[name = tensor("gather_0_validate_indices_0"), val = tensor(false)]; tensor select_0 = const()[name = tensor("select_0"), val = tensor(0)]; tensor gather_0_axis_1 = const()[name = tensor("gather_0_axis_1"), val = tensor(0)]; tensor gather_0 = gather(axis = gather_0_axis_1, batch_dims = gather_0_batch_dims_0, indices = select_0, validate_indices = gather_0_validate_indices_0, x = var_207_shape)[name = tensor("gather_0")]; tensor concat_0_axis_0 = const()[name = tensor("concat_0_axis_0"), val = tensor(0)]; tensor concat_0_interleave_0 = const()[name = tensor("concat_0_interleave_0"), val = tensor(false)]; tensor concat_0 = concat(axis = concat_0_axis_0, interleave = concat_0_interleave_0, values = (var_173, gather_0, var_172))[name = tensor("concat_0")]; tensor hx_1_value_0 = const()[name = tensor("hx_1_value_0"), val = tensor(0x0p+0)]; tensor hx_1 = fill(shape = concat_0, value = hx_1_value_0)[name = tensor("hx_1")]; tensor input_23_batch_first_transpose_perm_0 = const()[name = tensor("input_23_batch_first_transpose_perm_0"), val = tensor([1, 0, 2])]; tensor split_0_num_splits_0 = const()[name = tensor("split_0_num_splits_0"), val = tensor(4)]; tensor split_0_axis_0 = const()[name = tensor("split_0_axis_0"), val = tensor(0)]; tensor split_0_0, tensor split_0_1, tensor split_0_2, tensor split_0_3 = split(axis = split_0_axis_0, num_splits = split_0_num_splits_0, x = hx_1)[name = tensor("split_0")]; tensor split_1_num_splits_0 = const()[name = tensor("split_1_num_splits_0"), val = tensor(4)]; tensor split_1_axis_0 = const()[name = tensor("split_1_axis_0"), val = tensor(0)]; tensor split_1_0, tensor split_1_1, tensor split_1_2, tensor split_1_3 = split(axis = split_1_axis_0, num_splits = split_1_num_splits_0, x = hx_1)[name = tensor("split_1")]; tensor add_0 = const()[name = tensor("add_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(452800)))]; tensor add_1 = const()[name = tensor("add_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(454912)))]; tensor concat_6 = const()[name = tensor("concat_6"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(457024)))]; tensor concat_7 = const()[name = tensor("concat_7"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(579968)))]; tensor concat_8 = const()[name = tensor("concat_8"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(842176)))]; tensor concat_9 = const()[name = tensor("concat_9"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(965120)))]; tensor split_10_split_sizes_0 = const()[name = tensor("split_10_split_sizes_0"), val = tensor([1, 1])]; tensor split_10_axis_0 = const()[name = tensor("split_10_axis_0"), val = tensor(0)]; tensor split_10_0, tensor split_10_1 = split(axis = split_10_axis_0, split_sizes = split_10_split_sizes_0, x = split_0_0)[name = tensor("split_10")]; tensor concat_10_axis_0 = const()[name = tensor("concat_10_axis_0"), val = tensor(2)]; tensor concat_10_interleave_0 = const()[name = tensor("concat_10_interleave_0"), val = tensor(false)]; tensor concat_10 = concat(axis = concat_10_axis_0, interleave = concat_10_interleave_0, values = (split_10_0, split_10_1))[name = tensor("concat_10")]; tensor input_25_lstm_layer_0_lstm_h0_reshaped_axes_0 = const()[name = tensor("input_25_lstm_layer_0_lstm_h0_reshaped_axes_0"), val = tensor([0])]; tensor input_25_lstm_layer_0_lstm_h0_reshaped = squeeze(axes = input_25_lstm_layer_0_lstm_h0_reshaped_axes_0, x = concat_10)[name = tensor("input_25_lstm_layer_0_lstm_h0_reshaped")]; tensor split_11_split_sizes_0 = const()[name = tensor("split_11_split_sizes_0"), val = tensor([1, 1])]; tensor split_11_axis_0 = const()[name = tensor("split_11_axis_0"), val = tensor(0)]; tensor split_11_0, tensor split_11_1 = split(axis = split_11_axis_0, split_sizes = split_11_split_sizes_0, x = split_1_0)[name = tensor("split_11")]; tensor concat_11_axis_0 = const()[name = tensor("concat_11_axis_0"), val = tensor(2)]; tensor concat_11_interleave_0 = const()[name = tensor("concat_11_interleave_0"), val = tensor(false)]; tensor concat_11 = concat(axis = concat_11_axis_0, interleave = concat_11_interleave_0, values = (split_11_0, split_11_1))[name = tensor("concat_11")]; tensor input_25_lstm_layer_0_lstm_c0_reshaped_axes_0 = const()[name = tensor("input_25_lstm_layer_0_lstm_c0_reshaped_axes_0"), val = tensor([0])]; tensor input_25_lstm_layer_0_lstm_c0_reshaped = squeeze(axes = input_25_lstm_layer_0_lstm_c0_reshaped_axes_0, x = concat_11)[name = tensor("input_25_lstm_layer_0_lstm_c0_reshaped")]; tensor input_25_lstm_layer_0_direction_0 = const()[name = tensor("input_25_lstm_layer_0_direction_0"), val = tensor("bidirectional")]; tensor input_25_lstm_layer_0_output_sequence_0 = const()[name = tensor("input_25_lstm_layer_0_output_sequence_0"), val = tensor(true)]; tensor input_25_lstm_layer_0_recurrent_activation_0 = const()[name = tensor("input_25_lstm_layer_0_recurrent_activation_0"), val = tensor("sigmoid")]; tensor input_25_lstm_layer_0_cell_activation_0 = const()[name = tensor("input_25_lstm_layer_0_cell_activation_0"), val = tensor("tanh")]; tensor input_25_lstm_layer_0_activation_0 = const()[name = tensor("input_25_lstm_layer_0_activation_0"), val = tensor("tanh")]; tensor input_23_batch_first_transpose = transpose(perm = input_23_batch_first_transpose_perm_0, x = input_23)[name = tensor("transpose_5")]; tensor input_25_lstm_layer_0_0, tensor input_25_lstm_layer_0_1, tensor input_25_lstm_layer_0_2 = lstm(activation = input_25_lstm_layer_0_activation_0, bias = add_0, bias_back = add_1, cell_activation = input_25_lstm_layer_0_cell_activation_0, direction = input_25_lstm_layer_0_direction_0, initial_c = input_25_lstm_layer_0_lstm_c0_reshaped, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped, output_sequence = input_25_lstm_layer_0_output_sequence_0, recurrent_activation = input_25_lstm_layer_0_recurrent_activation_0, weight_hh = concat_7, weight_hh_back = concat_9, weight_ih = concat_6, weight_ih_back = concat_8, x = input_23_batch_first_transpose)[name = tensor("input_25_lstm_layer_0")]; tensor add_2 = const()[name = tensor("add_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1227328)))]; tensor add_3 = const()[name = tensor("add_3"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1229440)))]; tensor concat_16 = const()[name = tensor("concat_16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1231552)))]; tensor concat_17 = const()[name = tensor