| program(1.3) |
| [buildInfo = dict<string, string>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.14.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] |
| { |
| func main<ios18>(tensor<int32, [1]> audio_length, tensor<fp32, [1, 240000]> audio_signal) { |
| tensor<fp32, [1, 1, 1501]> fidx = const()[name = string("fidx"), val = tensor<fp32, [1, 1, 1501]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; |
| tensor<fp32, [128, 257]> mel_1 = const()[name = string("mel"), val = tensor<fp32, [128, 257]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6144)))]; |
| tensor<fp32, [514, 1, 512]> basis = const()[name = string("basis"), val = tensor<fp32, [514, 1, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(137792)))]; |
| tensor<fp32, [1, 240000]> idx = const()[name = string("idx"), val = tensor<fp32, [1, 240000]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1190528)))]; |
| string L_dtype_0 = const()[name = string("L_dtype_0"), val = string("fp32")]; |
| tensor<int32, [2]> var_21_begin_0 = const()[name = string("op_21_begin_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [2]> var_21_end_0 = const()[name = string("op_21_end_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<bool, [2]> var_21_end_mask_0 = const()[name = string("op_21_end_mask_0"), val = tensor<bool, [2]>([true, false])]; |
| tensor<fp32, [1, 1]> var_21 = slice_by_index(begin = var_21_begin_0, end = var_21_end_0, end_mask = var_21_end_mask_0, x = audio_signal)[name = string("op_21")]; |
| tensor<int32, [2]> var_31_begin_0 = const()[name = string("op_31_begin_0"), val = tensor<int32, [2]>([0, 1])]; |
| tensor<int32, [2]> var_31_end_0 = const()[name = string("op_31_end_0"), val = tensor<int32, [2]>([1, 240000])]; |
| tensor<bool, [2]> var_31_end_mask_0 = const()[name = string("op_31_end_mask_0"), val = tensor<bool, [2]>([true, true])]; |
| tensor<fp32, [1, 239999]> var_31 = slice_by_index(begin = var_31_begin_0, end = var_31_end_0, end_mask = var_31_end_mask_0, x = audio_signal)[name = string("op_31")]; |
| tensor<int32, [2]> var_41_begin_0 = const()[name = string("op_41_begin_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [2]> var_41_end_0 = const()[name = string("op_41_end_0"), val = tensor<int32, [2]>([1, 239999])]; |
| tensor<bool, [2]> var_41_end_mask_0 = const()[name = string("op_41_end_mask_0"), val = tensor<bool, [2]>([true, false])]; |
| tensor<fp32, [1, 239999]> var_41 = slice_by_index(begin = var_41_begin_0, end = var_41_end_0, end_mask = var_41_end_mask_0, x = audio_signal)[name = string("op_41")]; |
| fp32 var_42 = const()[name = string("op_42"), val = fp32(0x1.f0a3d8p-1)]; |
| tensor<fp32, [1, 239999]> var_43 = mul(x = var_41, y = var_42)[name = string("op_43")]; |
| tensor<fp32, [1, 239999]> var_45 = sub(x = var_31, y = var_43)[name = string("op_45")]; |
| int32 var_47 = const()[name = string("op_47"), val = int32(1)]; |
| bool x_1_interleave_0 = const()[name = string("x_1_interleave_0"), val = bool(false)]; |
| tensor<fp32, [1, 240000]> x_1 = concat(axis = var_47, interleave = x_1_interleave_0, values = (var_21, var_45))[name = string("x_1")]; |
| tensor<int32, [1]> var_55_axes_0 = const()[name = string("op_55_axes_0"), val = tensor<int32, [1]>([1])]; |
| tensor<fp32, [1]> L = cast(dtype = L_dtype_0, x = audio_length)[name = string("cast_11")]; |
| tensor<fp32, [1, 1]> var_55 = expand_dims(axes = var_55_axes_0, x = L)[name = string("op_55")]; |
| tensor<bool, [1, 240000]> var_56 = less(x = idx, y = var_55)[name = string("op_56")]; |
| string var_61_dtype_0 = const()[name = string("op_61_dtype_0"), val = string("fp32")]; |
| tensor<fp32, [1, 240000]> var_61 = cast(dtype = var_61_dtype_0, x = var_56)[name = string("cast_10")]; |
| tensor<fp32, [1, 240000]> input = mul(x = x_1, y = var_61)[name = string("input")]; |
| fp32 const_0 = const()[name = string("const_0"), val = fp32(0x0p+0)]; |
| tensor<int32, [4]> var_68_pad_0 = const()[name = string("op_68_pad_0"), val = tensor<int32, [4]>([0, 0, 256, 256])]; |
| string var_68_mode_0 = const()[name = string("op_68_mode_0"), val = string("constant")]; |
| tensor<fp32, [1, 240512]> var_68 = pad(constant_val = const_0, mode = var_68_mode_0, pad = var_68_pad_0, x = input)[name = string("op_68")]; |
| tensor<int32, [1]> x_axes_0 = const()[name = string("x_axes_0"), val = tensor<int32, [1]>([1])]; |
| tensor<fp32, [1, 1, 240512]> x = expand_dims(axes = x_axes_0, x = var_68)[name = string("x")]; |
| string spec_pad_type_0 = const()[name = string("spec_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> spec_strides_0 = const()[name = string("spec_strides_0"), val = tensor<int32, [1]>([160])]; |
| tensor<int32, [2]> spec_pad_0 = const()[name = string("spec_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> spec_dilations_0 = const()[name = string("spec_dilations_0"), val = tensor<int32, [1]>([1])]; |
| int32 spec_groups_0 = const()[name = string("spec_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 514, 1501]> spec = conv(dilations = spec_dilations_0, groups = spec_groups_0, pad = spec_pad_0, pad_type = spec_pad_type_0, strides = spec_strides_0, weight = basis, x = x)[name = string("spec")]; |
| tensor<int32, [3]> re_begin_0 = const()[name = string("re_begin_0"), val = tensor<int32, [3]>([0, 0, 0])]; |
| tensor<int32, [3]> re_end_0 = const()[name = string("re_end_0"), val = tensor<int32, [3]>([1, 257, 1501])]; |
| tensor<bool, [3]> re_end_mask_0 = const()[name = string("re_end_mask_0"), val = tensor<bool, [3]>([true, false, true])]; |
| tensor<fp32, [1, 257, 1501]> re = slice_by_index(begin = re_begin_0, end = re_end_0, end_mask = re_end_mask_0, x = spec)[name = string("re")]; |
| tensor<int32, [3]> im_begin_0 = const()[name = string("im_begin_0"), val = tensor<int32, [3]>([0, 257, 0])]; |
| tensor<int32, [3]> im_end_0 = const()[name = string("im_end_0"), val = tensor<int32, [3]>([1, 514, 1501])]; |
| tensor<bool, [3]> im_end_mask_0 = const()[name = string("im_end_mask_0"), val = tensor<bool, [3]>([true, true, true])]; |
| tensor<fp32, [1, 257, 1501]> im = slice_by_index(begin = im_begin_0, end = im_end_0, end_mask = im_end_mask_0, x = spec)[name = string("im")]; |
| tensor<fp32, [1, 257, 1501]> var_112 = mul(x = re, y = re)[name = string("op_112")]; |
| tensor<fp32, [1, 257, 1501]> var_113 = mul(x = im, y = im)[name = string("op_113")]; |
| tensor<fp32, [1, 257, 1501]> p = add(x = var_112, y = var_113)[name = string("p")]; |
| bool var_116_transpose_x_0 = const()[name = string("op_116_transpose_x_0"), val = bool(false)]; |
| bool var_116_transpose_y_0 = const()[name = string("op_116_transpose_y_0"), val = bool(false)]; |
| tensor<fp32, [1, 128, 1501]> var_116 = matmul(transpose_x = var_116_transpose_x_0, transpose_y = var_116_transpose_y_0, x = mel_1, y = p)[name = string("op_116")]; |
| fp32 var_118 = const()[name = string("op_118"), val = fp32(0x1p-24)]; |
| tensor<fp32, [1, 128, 1501]> var_119 = add(x = var_116, y = var_118)[name = string("op_119")]; |
| fp32 m_epsilon_0 = const()[name = string("m_epsilon_0"), val = fp32(0x1p-149)]; |
| tensor<fp32, [1, 128, 1501]> m = log(epsilon = m_epsilon_0, x = var_119)[name = string("m")]; |
| fp32 _inversed_122_y_0 = const()[name = string("_inversed_122_y_0"), val = fp32(0x1.99999ap-8)]; |
| tensor<fp32, [1]> _inversed_122 = mul(x = L, y = _inversed_122_y_0)[name = string("_inversed_122")]; |
| tensor<fp32, [1]> valid = floor(x = _inversed_122)[name = string("valid")]; |
| tensor<int32, [1]> var_130_axes_0 = const()[name = string("op_130_axes_0"), val = tensor<int32, [1]>([1])]; |
| tensor<fp32, [1, 1]> var_130 = expand_dims(axes = var_130_axes_0, x = valid)[name = string("op_130")]; |
| tensor<int32, [1]> var_132_axes_0 = const()[name = string("op_132_axes_0"), val = tensor<int32, [1]>([2])]; |
| tensor<fp32, [1, 1, 1]> var_132 = expand_dims(axes = var_132_axes_0, x = var_130)[name = string("op_132")]; |
| tensor<bool, [1, 1, 1501]> var_133 = less(x = fidx, y = var_132)[name = string("op_133")]; |
| string mask_dtype_0 = const()[name = string("mask_dtype_0"), val = string("fp32")]; |
| tensor<fp32, [1, 1, 1501]> mask = cast(dtype = mask_dtype_0, x = var_133)[name = string("cast_9")]; |
| tensor<fp32, [1, 128, 1501]> var_139 = mul(x = m, y = mask)[name = string("op_139")]; |
| tensor<int32, [1]> var_144_axes_0 = const()[name = string("op_144_axes_0"), val = tensor<int32, [1]>([-1])]; |
| bool var_144_keep_dims_0 = const()[name = string("op_144_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 128, 1]> var_144 = reduce_sum(axes = var_144_axes_0, keep_dims = var_144_keep_dims_0, x = var_139)[name = string("op_144")]; |
| tensor<fp32, [1, 128, 1]> mean = real_div(x = var_144, y = var_132)[name = string("mean")]; |
| tensor<fp32, [1, 128, 1501]> var_156 = sub(x = m, y = mean)[name = string("op_156")]; |
| tensor<fp32, [1, 128, 1501]> d = mul(x = var_156, y = mask)[name = string("d")]; |
| tensor<fp32, [1, 128, 1501]> var_158 = mul(x = d, y = d)[name = string("op_158")]; |
| tensor<int32, [1]> var_163_axes_0 = const()[name = string("op_163_axes_0"), val = tensor<int32, [1]>([-1])]; |
| bool var_163_keep_dims_0 = const()[name = string("op_163_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 128, 1]> var_163 = reduce_sum(axes = var_163_axes_0, keep_dims = var_163_keep_dims_0, x = var_158)[name = string("op_163")]; |
| fp32 var_174 = const()[name = string("op_174"), val = fp32(0x1p+0)]; |
| tensor<fp32, [1, 1, 1]> var_175 = sub(x = var_132, y = var_174)[name = string("op_175")]; |
| tensor<fp32, [1, 128, 1]> var_176 = real_div(x = var_163, y = var_175)[name = string("op_176")]; |
| tensor<fp32, [1, 128, 1]> std = sqrt(x = var_176)[name = string("std")]; |
| fp32 var_181 = const()[name = string("op_181"), val = fp32(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 128, 1]> var_182 = add(x = std, y = var_181)[name = string("op_182")]; |
| tensor<fp32, [1, 128, 1501]> var_183 = real_div(x = var_156, y = var_182)[name = string("op_183")]; |
| tensor<fp32, [1, 128, 1501]> mel = mul(x = var_183, y = mask)[name = string("op_184")]; |
| string var_189_dtype_0 = const()[name = string("op_189_dtype_0"), val = string("int32")]; |
| tensor<int32, [1]> mel_length = cast(dtype = var_189_dtype_0, x = valid)[name = string("cast_8")]; |
| } -> (mel, mel_length); |
| } |
