| program(1.0) | |
| [buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"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<ios16>(tensor<fp16, [1, 160000]> waveforms) { | |
| tensor<string, []> cast_0_dtype_0 = const()[name = tensor<string, []>("cast_0_dtype_0"), val = tensor<string, []>("fp32")]; | |
| tensor<fp32, []> var_2_promoted = const()[name = tensor<string, []>("op_2_promoted"), val = tensor<fp32, []>(0x1p+15)]; | |
| tensor<fp32, [1, 160000]> cast_0 = cast(dtype = cast_0_dtype_0, x = waveforms)[name = tensor<string, []>("cast_11")]; | |
| tensor<fp32, [1, 160000]> waveform_1 = mul(x = cast_0, y = var_2_promoted)[name = tensor<string, []>("waveform_1")]; | |
| tensor<int32, [2]> var_6_begin_0 = const()[name = tensor<string, []>("op_6_begin_0"), val = tensor<int32, [2]>([0, 0])]; | |
| tensor<int32, [2]> var_6_end_0 = const()[name = tensor<string, []>("op_6_end_0"), val = tensor<int32, [2]>([1, 160000])]; | |
| tensor<bool, [2]> var_6_end_mask_0 = const()[name = tensor<string, []>("op_6_end_mask_0"), val = tensor<bool, [2]>([false, true])]; | |
| tensor<bool, [2]> var_6_squeeze_mask_0 = const()[name = tensor<string, []>("op_6_squeeze_mask_0"), val = tensor<bool, [2]>([true, false])]; | |
| tensor<fp32, [160000]> 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<string, []>("op_6")]; | |
| tensor<int32, []> sliding_windows_0_axis_0 = const()[name = tensor<string, []>("sliding_windows_0_axis_0"), val = tensor<int32, []>(0)]; | |
| tensor<int32, []> sliding_windows_0_size_0 = const()[name = tensor<string, []>("sliding_windows_0_size_0"), val = tensor<int32, []>(400)]; | |
| tensor<int32, []> sliding_windows_0_stride_0 = const()[name = tensor<string, []>("sliding_windows_0_stride_0"), val = tensor<int32, []>(160)]; | |
| tensor<fp32, [998, 400]> 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<string, []>("sliding_windows_0")]; | |
| tensor<int32, [1]> var_42_axes_0 = const()[name = tensor<string, []>("op_42_axes_0"), val = tensor<int32, [1]>([1])]; | |
| tensor<bool, []> var_42_keep_dims_0 = const()[name = tensor<string, []>("op_42_keep_dims_0"), val = tensor<bool, []>(false)]; | |
| tensor<fp32, [998]> var_42 = reduce_mean(axes = var_42_axes_0, keep_dims = var_42_keep_dims_0, x = sliding_windows_0)[name = tensor<string, []>("op_42")]; | |
| tensor<int32, [1]> row_means_axes_0 = const()[name = tensor<string, []>("row_means_axes_0"), val = tensor<int32, [1]>([1])]; | |
| tensor<fp32, [998, 1]> row_means = expand_dims(axes = row_means_axes_0, x = var_42)[name = tensor<string, []>("row_means")]; | |
| tensor<fp32, [998, 400]> strided_input_3 = sub(x = sliding_windows_0, y = row_means)[name = tensor<string, []>("strided_input_3")]; | |
| tensor<int32, [1]> input_1_axes_0 = const()[name = tensor<string, []>("input_1_axes_0"), val = tensor<int32, [1]>([0])]; | |
| tensor<fp32, [1, 998, 400]> input_1 = expand_dims(axes = input_1_axes_0, x = strided_input_3)[name = tensor<string, []>("input_1")]; | |
| tensor<fp32, []> const_2 = const()[name = tensor<string, []>("const_2"), val = tensor<fp32, []>(0x0p+0)]; | |
| tensor<int32, [6]> var_54_pad_0 = const()[name = tensor<string, []>("op_54_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 1, 0])]; | |
| tensor<string, []> var_54_mode_0 = const()[name = tensor<string, []>("op_54_mode_0"), val = tensor<string, []>("replicate")]; | |
| tensor<fp32, [1, 998, 401]> var_54 = pad(constant_val = const_2, mode = var_54_mode_0, pad = var_54_pad_0, x = input_1)[name = tensor<string, []>("op_54")]; | |
| tensor<int32, [1]> offset_strided_input_axes_0 = const()[name = tensor<string, []>("offset_strided_input_axes_0"), val = tensor<int32, [1]>([0])]; | |
| tensor<fp32, [998, 401]> offset_strided_input = squeeze(axes = offset_strided_input_axes_0, x = var_54)[name = tensor<string, []>("offset_strided_input")]; | |
| tensor<int32, [2]> var_66_begin_0 = const()[name = tensor<string, []>("op_66_begin_0"), val = tensor<int32, [2]>([0, 0])]; | |
| tensor<int32, [2]> var_66_end_0 = const()[name = tensor<string, []>("op_66_end_0"), val = tensor<int32, [2]>([998, 400])]; | |
| tensor<bool, [2]> var_66_end_mask_0 = const()[name = tensor<string, []>("op_66_end_mask_0"), val = tensor<bool, [2]>([true, false])]; | |
| tensor<fp32, [998, 400]> 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<string, []>("op_66")]; | |
| tensor<fp32, []> var_67 = const()[name = tensor<string, []>("op_67"), val = tensor<fp32, []>(0x1.f0a3d8p-1)]; | |
| tensor<fp32, [998, 400]> var_68 = mul(x = var_66, y = var_67)[name = tensor<string, []>("op_68")]; | |
| tensor<fp32, [998, 400]> strided_input_5 = sub(x = strided_input_3, y = var_68)[name = tensor<string, []>("strided_input_5")]; | |
| tensor<fp32, [1, 400]> window_function = const()[name = tensor<string, []>("window_function"), val = tensor<fp32, [1, 400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))]; | |
| tensor<fp32, [998, 400]> strided_input_7 = mul(x = strided_input_5, y = window_function)[name = tensor<string, []>("strided_input_7")]; | |
| tensor<int32, [1]> input_3_axes_0 = const()[name = tensor<string, []>("input_3_axes_0"), val = tensor<int32, [1]>([0])]; | |
| tensor<fp32, [1, 998, 400]> input_3 = expand_dims(axes = input_3_axes_0, x = strided_input_7)[name = tensor<string, []>("input_3")]; | |
| tensor<fp32, []> const_3 = const()[name = tensor<string, []>("const_3"), val = tensor<fp32, []>(0x0p+0)]; | |
| tensor<int32, [6]> var_90_pad_0 = const()[name = tensor<string, []>("op_90_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 0, 112])]; | |
| tensor<string, []> var_90_mode_0 = const()[name = tensor<string, []>("op_90_mode_0"), val = tensor<string, []>("constant")]; | |
| tensor<fp32, [1, 998, 512]> var_90 = pad(constant_val = const_3, mode = var_90_mode_0, pad = var_90_pad_0, x = input_3)[name = tensor<string, []>("op_90")]; | |
| tensor<int32, [1]> strided_input_axes_0 = const()[name = tensor<string, []>("strided_input_axes_0"), val = tensor<int32, [1]>([0])]; | |
| tensor<fp32, [998, 512]> strided_input = squeeze(axes = strided_input_axes_0, x = var_90)[name = tensor<string, []>("strided_input")]; | |
| tensor<fp32, [512, 512]> cos_0 = const()[name = tensor<string, []>("cos_0"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1728)))]; | |
| tensor<fp32, [512, 512]> sin_0 = const()[name = tensor<string, []>("sin_0"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1050368)))]; | |
| tensor<bool, []> matmul_1_transpose_x_1 = const()[name = tensor<string, []>("matmul_1_transpose_x_1"), val = tensor<bool, []>(false)]; | |
| tensor<bool, []> matmul_1_transpose_y_1 = const()[name = tensor<string, []>("matmul_1_transpose_y_1"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [512, 998]> 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<string, []>("matmul_1")]; | |
| tensor<bool, []> matmul_3_transpose_x_1 = const()[name = tensor<string, []>("matmul_3_transpose_x_1"), val = tensor<bool, []>(false)]; | |
| tensor<bool, []> matmul_3_transpose_y_1 = const()[name = tensor<string, []>("matmul_3_transpose_y_1"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [512, 998]> 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<string, []>("matmul_3")]; | |
| tensor<fp32, []> mul_1_y_0 = const()[name = tensor<string, []>("mul_1_y_0"), val = tensor<fp32, []>(-0x1p+0)]; | |
| tensor<fp32, [512, 998]> mul_1 = mul(x = matmul_3, y = mul_1_y_0)[name = tensor<string, []>("mul_1")]; | |
| tensor<int32, [2]> transpose_3_perm_0 = const()[name = tensor<string, []>("transpose_3_perm_0"), val = tensor<int32, [2]>([-1, 0])]; | |
| tensor<int32, [2]> transpose_4_perm_0 = const()[name = tensor<string, []>("transpose_4_perm_0"), val = tensor<int32, [2]>([-1, 0])]; | |
| tensor<int32, [257]> range_1d_2 = const()[name = tensor<string, []>("range_1d_2"), val = tensor<int32, [257]>([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<int32, []> gather_0_axis_0 = const()[name = tensor<string, []>("gather_0_axis_0"), val = tensor<int32, []>(-1)]; | |
| tensor<int32, []> gather_0_batch_dims_0 = const()[name = tensor<string, []>("gather_0_batch_dims_0"), val = tensor<int32, []>(0)]; | |
| tensor<fp32, [998, 512]> transpose_3 = transpose(perm = transpose_3_perm_0, x = matmul_1)[name = tensor<string, []>("transpose_6")]; | |
| tensor<fp32, [998, 257]> gather_0 = gather(axis = gather_0_axis_0, batch_dims = gather_0_batch_dims_0, indices = range_1d_2, x = transpose_3)[name = tensor<string, []>("gather_0")]; | |
| tensor<int32, []> gather_1_axis_0 = const()[name = tensor<string, []>("gather_1_axis_0"), val = tensor<int32, []>(-1)]; | |
| tensor<int32, []> gather_1_batch_dims_0 = const()[name = tensor<string, []>("gather_1_batch_dims_0"), val = tensor<int32, []>(0)]; | |
| tensor<fp32, [998, 512]> transpose_4 = transpose(perm = transpose_4_perm_0, x = mul_1)[name = tensor<string, []>("transpose_5")]; | |
| tensor<fp32, [998, 257]> gather_1 = gather(axis = gather_1_axis_0, batch_dims = gather_1_batch_dims_0, indices = range_1d_2, x = transpose_4)[name = tensor<string, []>("gather_1")]; | |
| tensor<fp32, [998, 257]> square_0 = square(x = gather_0)[name = tensor<string, []>("square_0")]; | |
| tensor<fp32, [998, 257]> square_1 = square(x = gather_1)[name = tensor<string, []>("square_1")]; | |
| tensor<fp32, [998, 257]> add_1 = add(x = square_0, y = square_1)[name = tensor<string, []>("add_1")]; | |
| tensor<fp32, [998, 257]> spectrum = identity(x = add_1)[name = tensor<string, []>("spectrum")]; | |
| tensor<fp32, [80, 257]> mel_energies_3 = const()[name = tensor<string, []>("mel_energies_3"), val = tensor<fp32, [80, 257]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2099008)))]; | |
| tensor<fp32, [80]> mel_energies_bias_0 = const()[name = tensor<string, []>("mel_energies_bias_0"), val = tensor<fp32, [80]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2181312)))]; | |
| tensor<fp32, [998, 80]> mel_energies = linear(bias = mel_energies_bias_0, weight = mel_energies_3, x = spectrum)[name = tensor<string, []>("mel_energies")]; | |
| tensor<fp32, []> const_10 = const()[name = tensor<string, []>("const_10"), val = tensor<fp32, []>(0x1p-23)]; | |
| tensor<fp32, [998, 80]> var_186 = maximum(x = mel_energies, y = const_10)[name = tensor<string, []>("op_186")]; | |
| tensor<fp32, []> filter_banks_epsilon_0 = const()[name = tensor<string, []>("filter_banks_epsilon_0"), val = tensor<fp32, []>(0x1p-149)]; | |
| tensor<fp32, [998, 80]> filter_banks = log(epsilon = filter_banks_epsilon_0, x = var_186)[name = tensor<string, []>("filter_banks")]; | |
| tensor<int32, [1]> var_192_axes_0 = const()[name = tensor<string, []>("op_192_axes_0"), val = tensor<int32, [1]>([0])]; | |
| tensor<bool, []> var_192_keep_dims_0 = const()[name = tensor<string, []>("op_192_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 80]> var_192 = reduce_mean(axes = var_192_axes_0, keep_dims = var_192_keep_dims_0, x = filter_banks)[name = tensor<string, []>("op_192")]; | |
| tensor<fp32, [998, 80]> var_194 = sub(x = filter_banks, y = var_192)[name = tensor<string, []>("op_194")]; | |
| tensor<int32, [1]> obj_axes_0 = const()[name = tensor<string, []>("obj_axes_0"), val = tensor<int32, [1]>([0])]; | |
| tensor<fp32, [1, 998, 80]> preprocessor_output_1_type_fp32 = expand_dims(axes = obj_axes_0, x = var_194)[name = tensor<string, []>("obj")]; | |
| tensor<string, []> cast_9_dtype_0 = const()[name = tensor<string, []>("cast_9_dtype_0"), val = tensor<string, []>("fp16")]; | |
| tensor<fp16, [1, 998, 80]> preprocessor_output_1 = cast(dtype = cast_9_dtype_0, x = preprocessor_output_1_type_fp32)[name = tensor<string, []>("cast_10")]; | |
| } -> (preprocessor_output_1); | |
| } |