program(1.0) [buildInfo = dict, tensor>({{"coremlc-component-MIL", "3401.3.1"}, {"coremlc-version", "3401.4.1"}, {"coremltools-component-torch", "2.5.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.1"}})] { func main(tensor waveforms) { tensor cast_0_dtype_0 = const()[name = tensor("cast_0_dtype_0"), val = tensor("fp32")]; tensor var_2_promoted = const()[name = tensor("op_2_promoted"), val = tensor(0x1p+15)]; tensor cast_0 = cast(dtype = cast_0_dtype_0, x = waveforms)[name = tensor("cast_11")]; tensor waveform_1 = mul(x = cast_0, y = var_2_promoted)[name = tensor("waveform_1")]; tensor var_6_begin_0 = const()[name = tensor("op_6_begin_0"), val = tensor([0, 0])]; tensor var_6_end_0 = const()[name = tensor("op_6_end_0"), val = tensor([1, 160000])]; tensor var_6_end_mask_0 = const()[name = tensor("op_6_end_mask_0"), val = tensor([false, true])]; tensor var_6_squeeze_mask_0 = const()[name = tensor("op_6_squeeze_mask_0"), val = tensor([true, false])]; tensor var_6 = slice_by_index(begin = var_6_begin_0, end = var_6_end_0, end_mask = var_6_end_mask_0, squeeze_mask = var_6_squeeze_mask_0, x = waveform_1)[name = tensor("op_6")]; tensor sliding_windows_0_axis_0 = const()[name = tensor("sliding_windows_0_axis_0"), val = tensor(0)]; tensor sliding_windows_0_size_0 = const()[name = tensor("sliding_windows_0_size_0"), val = tensor(400)]; tensor sliding_windows_0_stride_0 = const()[name = tensor("sliding_windows_0_stride_0"), val = tensor(160)]; tensor sliding_windows_0 = sliding_windows(axis = sliding_windows_0_axis_0, size = sliding_windows_0_size_0, stride = sliding_windows_0_stride_0, x = var_6)[name = tensor("sliding_windows_0")]; tensor var_42_axes_0 = const()[name = tensor("op_42_axes_0"), val = tensor([1])]; tensor var_42_keep_dims_0 = const()[name = tensor("op_42_keep_dims_0"), val = tensor(false)]; tensor var_42 = reduce_mean(axes = var_42_axes_0, keep_dims = var_42_keep_dims_0, x = sliding_windows_0)[name = tensor("op_42")]; tensor row_means_axes_0 = const()[name = tensor("row_means_axes_0"), val = tensor([1])]; tensor row_means = expand_dims(axes = row_means_axes_0, x = var_42)[name = tensor("row_means")]; tensor strided_input_3 = sub(x = sliding_windows_0, y = row_means)[name = tensor("strided_input_3")]; tensor input_1_axes_0 = const()[name = tensor("input_1_axes_0"), val = tensor([0])]; tensor input_1 = expand_dims(axes = input_1_axes_0, x = strided_input_3)[name = tensor("input_1")]; tensor const_2 = const()[name = tensor("const_2"), val = tensor(0x0p+0)]; tensor var_54_pad_0 = const()[name = tensor("op_54_pad_0"), val = tensor([0, 0, 0, 0, 1, 0])]; tensor var_54_mode_0 = const()[name = tensor("op_54_mode_0"), val = tensor("replicate")]; tensor var_54 = pad(constant_val = const_2, mode = var_54_mode_0, pad = var_54_pad_0, x = input_1)[name = tensor("op_54")]; tensor offset_strided_input_axes_0 = const()[name = tensor("offset_strided_input_axes_0"), val = tensor([0])]; tensor offset_strided_input = squeeze(axes = offset_strided_input_axes_0, x = var_54)[name = tensor("offset_strided_input")]; tensor var_66_begin_0 = const()[name = tensor("op_66_begin_0"), val = tensor([0, 0])]; tensor var_66_end_0 = const()[name = tensor("op_66_end_0"), val = tensor([998, 400])]; tensor var_66_end_mask_0 = const()[name = tensor("op_66_end_mask_0"), val = tensor([true, false])]; tensor var_66 = slice_by_index(begin = var_66_begin_0, end = var_66_end_0, end_mask = var_66_end_mask_0, x = offset_strided_input)[name = tensor("op_66")]; tensor var_67 = const()[name = tensor("op_67"), val = tensor(0x1.f0a3d8p-1)]; tensor var_68 = mul(x = var_66, y = var_67)[name = tensor("op_68")]; tensor strided_input_5 = sub(x = strided_input_3, y = var_68)[name = tensor("strided_input_5")]; tensor window_function = const()[name = tensor("window_function"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; tensor strided_input_7 = mul(x = strided_input_5, y = window_function)[name = tensor("strided_input_7")]; tensor input_3_axes_0 = const()[name = tensor("input_3_axes_0"), val = tensor([0])]; tensor input_3 = expand_dims(axes = input_3_axes_0, x = strided_input_7)[name = tensor("input_3")]; tensor const_3 = const()[name = tensor("const_3"), val = tensor(0x0p+0)]; tensor var_90_pad_0 = const()[name = tensor("op_90_pad_0"), val = tensor([0, 0, 0, 0, 0, 112])]; tensor var_90_mode_0 = const()[name = tensor("op_90_mode_0"), val = tensor("constant")]; tensor var_90 = pad(constant_val = const_3, mode = var_90_mode_0, pad = var_90_pad_0, x = input_3)[name = tensor("op_90")]; tensor strided_input_axes_0 = const()[name = tensor("strided_input_axes_0"), val = tensor([0])]; tensor strided_input = squeeze(axes = strided_input_axes_0, x = var_90)[name = tensor("strided_input")]; tensor cos_0 = const()[name = tensor("cos_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1728)))]; tensor sin_0 = const()[name = tensor("sin_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1050368)))]; tensor matmul_1_transpose_x_1 = const()[name = tensor("matmul_1_transpose_x_1"), val = tensor(false)]; tensor matmul_1_transpose_y_1 = const()[name = tensor("matmul_1_transpose_y_1"), val = tensor(true)]; tensor matmul_1 = matmul(transpose_x = matmul_1_transpose_x_1, transpose_y = matmul_1_transpose_y_1, x = cos_0, y = strided_input)[name = tensor("matmul_1")]; tensor matmul_3_transpose_x_1 = const()[name = tensor("matmul_3_transpose_x_1"), val = tensor(false)]; tensor matmul_3_transpose_y_1 = const()[name = tensor("matmul_3_transpose_y_1"), val = tensor(true)]; tensor matmul_3 = matmul(transpose_x = matmul_3_transpose_x_1, transpose_y = matmul_3_transpose_y_1, x = sin_0, y = strided_input)[name = tensor("matmul_3")]; tensor mul_1_y_0 = const()[name = tensor("mul_1_y_0"), val = tensor(-0x1p+0)]; tensor mul_1 = mul(x = matmul_3, y = mul_1_y_0)[name = tensor("mul_1")]; tensor transpose_3_perm_0 = const()[name = tensor("transpose_3_perm_0"), val = tensor([-1, 0])]; tensor transpose_4_perm_0 = const()[name = tensor("transpose_4_perm_0"), val = tensor([-1, 0])]; tensor range_1d_2 = const()[name = tensor("range_1d_2"), val = tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256])]; tensor gather_0_axis_0 = const()[name = tensor("gather_0_axis_0"), val = tensor(-1)]; tensor gather_0_batch_dims_0 = const()[name = tensor("gather_0_batch_dims_0"), val = tensor(0)]; tensor transpose_3 = transpose(perm = transpose_3_perm_0, x = matmul_1)[name = tensor("transpose_6")]; tensor gather_0 = gather(axis = gather_0_axis_0, batch_dims = gather_0_batch_dims_0, indices = range_1d_2, x = transpose_3)[name = tensor("gather_0")]; tensor gather_1_axis_0 = const()[name = tensor("gather_1_axis_0"), val = tensor(-1)]; tensor gather_1_batch_dims_0 = const()[name = tensor("gather_1_batch_dims_0"), val = tensor(0)]; tensor transpose_4 = transpose(perm = transpose_4_perm_0, x = mul_1)[name = tensor("transpose_5")]; tensor gather_1 = gather(axis = gather_1_axis_0, batch_dims = gather_1_batch_dims_0, indices = range_1d_2, x = transpose_4)[name = tensor("gather_1")]; tensor square_0 = square(x = gather_0)[name = tensor("square_0")]; tensor square_1 = square(x = gather_1)[name = tensor("square_1")]; tensor add_1 = add(x = square_0, y = square_1)[name = tensor("add_1")]; tensor spectrum = identity(x = add_1)[name = tensor("spectrum")]; tensor mel_energies_3 = const()[name = tensor("mel_energies_3"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2099008)))]; tensor mel_energies_bias_0 = const()[name = tensor("mel_energies_bias_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2181312)))]; tensor mel_energies = linear(bias = mel_energies_bias_0, weight = mel_energies_3, x = spectrum)[name = tensor("mel_energies")]; tensor const_10 = const()[name = tensor("const_10"), val = tensor(0x1p-23)]; tensor var_186 = maximum(x = mel_energies, y = const_10)[name = tensor("op_186")]; tensor filter_banks_epsilon_0 = const()[name = tensor("filter_banks_epsilon_0"), val = tensor(0x1p-149)]; tensor filter_banks = log(epsilon = filter_banks_epsilon_0, x = var_186)[name = tensor("filter_banks")]; tensor var_192_axes_0 = const()[name = tensor("op_192_axes_0"), val = tensor([0])]; tensor var_192_keep_dims_0 = const()[name = tensor("op_192_keep_dims_0"), val = tensor(true)]; tensor var_192 = reduce_mean(axes = var_192_axes_0, keep_dims = var_192_keep_dims_0, x = filter_banks)[name = tensor("op_192")]; tensor var_194 = sub(x = filter_banks, y = var_192)[name = tensor("op_194")]; tensor obj_axes_0 = const()[name = tensor("obj_axes_0"), val = tensor([0])]; tensor preprocessor_output_1_type_fp32 = expand_dims(axes = obj_axes_0, x = var_194)[name = tensor("obj")]; tensor cast_9_dtype_0 = const()[name = tensor("cast_9_dtype_0"), val = tensor("fp16")]; tensor preprocessor_output_1 = cast(dtype = cast_9_dtype_0, x = preprocessor_output_1_type_fp32)[name = tensor("cast_10")]; } -> (preprocessor_output_1); }