program(1.0)
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.5.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})]
{
    func main<ios17>(tensor<fp32, [1, ?]> waveform) [FlexibleShapeInformation = tuple<tuple<tensor<string, []>, dict<tensor<string, []>, tensor<int32, [?]>>>, tuple<tensor<string, []>, dict<tensor<string, []>, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"waveform", [1, 160000]}}), ("RangeDims", {{"waveform", [[1, 1], [8000, 4800000]]}})))] {
            tensor<fp32, [400]> cmvn_inv_std = const()[name = tensor<string, []>("cmvn_inv_std"), val = tensor<fp32, [400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
            tensor<fp32, [400]> cmvn_neg_mean = const()[name = tensor<string, []>("cmvn_neg_mean"), val = tensor<fp32, [400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1728)))];
            tensor<fp32, [400, 80, 5]> lfr_kernel = const()[name = tensor<string, []>("lfr_kernel"), val = tensor<fp32, [400, 80, 5]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3392)))];
            tensor<fp32, [400]> window = const()[name = tensor<string, []>("window"), val = tensor<fp32, [400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643456)))];
            tensor<fp32, [400, 1, 400]> frame_kernel = const()[name = tensor<string, []>("frame_kernel"), val = tensor<fp32, [400, 1, 400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(645120)))];
            tensor<int32, [1]> var_11_axes_0 = const()[name = tensor<string, []>("op_11_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1, 1, ?]> var_11 = expand_dims(axes = var_11_axes_0, x = waveform)[name = tensor<string, []>("op_11")];
            tensor<string, []> var_27_pad_type_0 = const()[name = tensor<string, []>("op_27_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> var_27_strides_0 = const()[name = tensor<string, []>("op_27_strides_0"), val = tensor<int32, [1]>([160])];
            tensor<int32, [2]> var_27_pad_0 = const()[name = tensor<string, []>("op_27_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> var_27_dilations_0 = const()[name = tensor<string, []>("op_27_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> var_27_groups_0 = const()[name = tensor<string, []>("op_27_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 400, ?]> var_27 = conv(dilations = var_27_dilations_0, groups = var_27_groups_0, pad = var_27_pad_0, pad_type = var_27_pad_type_0, strides = var_27_strides_0, weight = frame_kernel, x = var_11)[name = tensor<string, []>("op_27")];
            tensor<int32, [3]> var_30_begin_0 = const()[name = tensor<string, []>("op_30_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> var_30_end_0 = const()[name = tensor<string, []>("op_30_end_0"), val = tensor<int32, [3]>([1, 400, 0])];
            tensor<bool, [3]> var_30_end_mask_0 = const()[name = tensor<string, []>("op_30_end_mask_0"), val = tensor<bool, [3]>([false, true, true])];
            tensor<bool, [3]> var_30_squeeze_mask_0 = const()[name = tensor<string, []>("op_30_squeeze_mask_0"), val = tensor<bool, [3]>([true, false, false])];
            tensor<fp32, [400, ?]> var_30 = slice_by_index(begin = var_30_begin_0, end = var_30_end_0, end_mask = var_30_end_mask_0, squeeze_mask = var_30_squeeze_mask_0, x = var_27)[name = tensor<string, []>("op_30")];
            tensor<int32, [2]> frames_1_perm_0 = const()[name = tensor<string, []>("frames_1_perm_0"), val = tensor<int32, [2]>([1, 0])];
            tensor<int32, [1]> var_36_axes_0 = const()[name = tensor<string, []>("op_36_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> var_36_keep_dims_0 = const()[name = tensor<string, []>("op_36_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp32, [?, 400]> frames_1 = transpose(perm = frames_1_perm_0, x = var_30)[name = tensor<string, []>("transpose_5")];
            tensor<fp32, [?, 1]> var_36 = reduce_mean(axes = var_36_axes_0, keep_dims = var_36_keep_dims_0, x = frames_1)[name = tensor<string, []>("op_36")];
            tensor<fp32, [?, 400]> frames_3 = sub(x = frames_1, y = var_36)[name = tensor<string, []>("frames_3")];
            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]>([0, 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 = frames_3)[name = tensor<string, []>("op_48")];
            tensor<int32, [2]> var_58_begin_0 = const()[name = tensor<string, []>("op_58_begin_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [2]> var_58_end_0 = const()[name = tensor<string, []>("op_58_end_0"), val = tensor<int32, [2]>([0, 399])];
            tensor<bool, [2]> var_58_end_mask_0 = const()[name = tensor<string, []>("op_58_end_mask_0"), val = tensor<bool, [2]>([true, false])];
            tensor<fp32, [?, 399]> var_58 = slice_by_index(begin = var_58_begin_0, end = var_58_end_0, end_mask = var_58_end_mask_0, x = frames_3)[name = tensor<string, []>("op_58")];
            tensor<int32, []> var_60 = const()[name = tensor<string, []>("op_60"), val = tensor<int32, []>(1)];
            tensor<bool, []> shifted_interleave_0 = const()[name = tensor<string, []>("shifted_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [?, 400]> shifted = concat(axis = var_60, interleave = shifted_interleave_0, values = (var_48, var_58))[name = tensor<string, []>("shifted")];
            tensor<fp32, []> var_62 = const()[name = tensor<string, []>("op_62"), val = tensor<fp32, []>(0x1.f0a3d8p-1)];
            tensor<fp32, [?, 400]> var_63 = mul(x = shifted, y = var_62)[name = tensor<string, []>("op_63")];
            tensor<fp32, [?, 400]> frames_5 = sub(x = frames_3, y = var_63)[name = tensor<string, []>("frames_5")];
            tensor<fp32, [?, 400]> input = mul(x = frames_5, y = window)[name = tensor<string, []>("input")];
            tensor<fp32, []> const_0 = const()[name = tensor<string, []>("const_0"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [4]> frames_pad_0 = const()[name = tensor<string, []>("frames_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 112])];
            tensor<string, []> frames_mode_0 = const()[name = tensor<string, []>("frames_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [?, 512]> frames = pad(constant_val = const_0, mode = frames_mode_0, pad = frames_pad_0, x = input)[name = tensor<string, []>("frames")];
            tensor<fp32, [257, 512]> transpose_0 = const()[name = tensor<string, []>("transpose_0"), val = tensor<fp32, [257, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1285184)))];
            tensor<fp32, [257]> re_bias_0 = const()[name = tensor<string, []>("re_bias_0"), val = tensor<fp32, [257]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1811584)))];
            tensor<fp32, [?, 257]> re = linear(bias = re_bias_0, weight = transpose_0, x = frames)[name = tensor<string, []>("re")];
            tensor<fp32, [257, 512]> transpose_1 = const()[name = tensor<string, []>("transpose_1"), val = tensor<fp32, [257, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1812736)))];
            tensor<fp32, [?, 257]> im = linear(bias = re_bias_0, weight = transpose_1, x = frames)[name = tensor<string, []>("im")];
            tensor<fp32, [?, 257]> var_75 = mul(x = re, y = re)[name = tensor<string, []>("op_75")];
            tensor<fp32, [?, 257]> var_76 = mul(x = im, y = im)[name = tensor<string, []>("op_76")];
            tensor<fp32, [?, 257]> power = add(x = var_75, y = var_76)[name = tensor<string, []>("power")];
            tensor<fp32, [80, 257]> transpose_2 = const()[name = tensor<string, []>("transpose_2"), val = tensor<fp32, [80, 257]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2339136)))];
            tensor<fp32, [80]> var_79_bias_0 = const()[name = tensor<string, []>("op_79_bias_0"), val = tensor<fp32, [80]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2421440)))];
            tensor<fp32, [?, 80]> var_79 = linear(bias = var_79_bias_0, weight = transpose_2, x = power)[name = tensor<string, []>("op_79")];
            tensor<fp32, []> var_80 = const()[name = tensor<string, []>("op_80"), val = tensor<fp32, []>(0x1p-23)];
            tensor<fp32, []> const_1 = const()[name = tensor<string, []>("const_1"), val = tensor<fp32, []>(0x1.fffffep+127)];
            tensor<fp32, [?, 80]> clip_0 = clip(alpha = var_80, beta = const_1, x = var_79)[name = tensor<string, []>("clip_0")];
            tensor<fp32, []> fbank_1_epsilon_0 = const()[name = tensor<string, []>("fbank_1_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp32, [?, 80]> fbank_1 = log(epsilon = fbank_1_epsilon_0, x = clip_0)[name = tensor<string, []>("fbank_1")];
            tensor<int32, [2]> var_88_begin_0 = const()[name = tensor<string, []>("op_88_begin_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [2]> var_88_end_0 = const()[name = tensor<string, []>("op_88_end_0"), val = tensor<int32, [2]>([1, 80])];
            tensor<bool, [2]> var_88_end_mask_0 = const()[name = tensor<string, []>("op_88_end_mask_0"), val = tensor<bool, [2]>([false, true])];
            tensor<fp32, [1, 80]> var_88 = slice_by_index(begin = var_88_begin_0, end = var_88_end_0, end_mask = var_88_end_mask_0, x = fbank_1)[name = tensor<string, []>("op_88")];
            tensor<int32, [2]> var_91 = const()[name = tensor<string, []>("op_91"), val = tensor<int32, [2]>([2, 1])];
            tensor<fp32, [2, 80]> var_92 = tile(reps = var_91, x = var_88)[name = tensor<string, []>("op_92")];
            tensor<int32, []> var_94 = const()[name = tensor<string, []>("op_94"), val = tensor<int32, []>(0)];
            tensor<bool, []> fbank_interleave_0 = const()[name = tensor<string, []>("fbank_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [?, 80]> fbank = concat(axis = var_94, interleave = fbank_interleave_0, values = (var_92, fbank_1))[name = tensor<string, []>("fbank")];
            tensor<int32, [2]> var_96_perm_0 = const()[name = tensor<string, []>("op_96_perm_0"), val = tensor<int32, [2]>([1, 0])];
            tensor<int32, [1]> var_98_axes_0 = const()[name = tensor<string, []>("op_98_axes_0"), val = tensor<int32, [1]>([0])];
            tensor<fp32, [80, ?]> var_96 = transpose(perm = var_96_perm_0, x = fbank)[name = tensor<string, []>("transpose_4")];
            tensor<fp32, [1, 80, ?]> var_98 = expand_dims(axes = var_98_axes_0, x = var_96)[name = tensor<string, []>("op_98")];
            tensor<string, []> var_114_pad_type_0 = const()[name = tensor<string, []>("op_114_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> var_114_strides_0 = const()[name = tensor<string, []>("op_114_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> var_114_pad_0 = const()[name = tensor<string, []>("op_114_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> var_114_dilations_0 = const()[name = tensor<string, []>("op_114_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> var_114_groups_0 = const()[name = tensor<string, []>("op_114_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 400, ?]> var_114 = conv(dilations = var_114_dilations_0, groups = var_114_groups_0, pad = var_114_pad_0, pad_type = var_114_pad_type_0, strides = var_114_strides_0, weight = lfr_kernel, x = var_98)[name = tensor<string, []>("op_114")];
            tensor<int32, [3]> var_117_begin_0 = const()[name = tensor<string, []>("op_117_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> var_117_end_0 = const()[name = tensor<string, []>("op_117_end_0"), val = tensor<int32, [3]>([1, 400, 0])];
            tensor<bool, [3]> var_117_end_mask_0 = const()[name = tensor<string, []>("op_117_end_mask_0"), val = tensor<bool, [3]>([false, true, true])];
            tensor<bool, [3]> var_117_squeeze_mask_0 = const()[name = tensor<string, []>("op_117_squeeze_mask_0"), val = tensor<bool, [3]>([true, false, false])];
            tensor<fp32, [400, ?]> var_117 = slice_by_index(begin = var_117_begin_0, end = var_117_end_0, end_mask = var_117_end_mask_0, squeeze_mask = var_117_squeeze_mask_0, x = var_114)[name = tensor<string, []>("op_117")];
            tensor<int32, [2]> lfr_perm_0 = const()[name = tensor<string, []>("lfr_perm_0"), val = tensor<int32, [2]>([1, 0])];
            tensor<fp32, [?, 400]> lfr = transpose(perm = lfr_perm_0, x = var_117)[name = tensor<string, []>("transpose_3")];
            tensor<fp32, [?, 400]> var_120 = add(x = lfr, y = cmvn_neg_mean)[name = tensor<string, []>("op_120")];
            tensor<fp32, [?, 400]> feats = mul(x = var_120, y = cmvn_inv_std)[name = tensor<string, []>("feats")];
            tensor<int32, [1]> var_123_axes_0 = const()[name = tensor<string, []>("op_123_axes_0"), val = tensor<int32, [1]>([0])];
            tensor<fp32, [1, ?, 400]> features = expand_dims(axes = var_123_axes_0, x = feats)[name = tensor<string, []>("op_123")];
        } -> (features);
}