Upload 22 files

AudioEncoder.mlmodelc/analytics/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:132a16bf732f8b87096f86b8cb0b931e0232a11e41a89b5f2d54fbbcceea3ef5
+size 243

AudioEncoder.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fb2baa7c2e50971adf6a4a0d5eedb4088f764082142e798f65e4f1ff278c5a95
+size 524

AudioEncoder.mlmodelc/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d30cf59ad460c7fc2fb4bfc952b52f77e4fbbda1a9b913bd0fbcb0634eba09ac
+size 2371257856

AudioEncoder_int8.mlmodelc/analytics/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:10b604dd6158221520e6b0dd0e438436710db27f0c26012169aaf262d112833c
+size 243

AudioEncoder_int8.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8c6b1c5072e19ed38b2a548022ecb20cd64afde3db62c7d9aed0d254f62ed1bd
+size 511

AudioEncoder_int8.mlmodelc/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6144e9aaf94e5de3b48903fd57498b72d9edeccac7ebe96bab024bcd78c710e6
+size 597389824

MelSpectrogram.mlmodelc/analytics/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:13e0dda654255a51db1146af5307bfef099bf4039cbe6f02928b3ff178b0c9f6
+size 243

MelSpectrogram.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3fd5943babe43ce9bdcf58808ee6d2c28feda381aae19c7e038493bce68b6616
+size 501

MelSpectrogram.mlmodelc/model.mil

@@ -0,0 +1,122 @@
+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3500.14.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.7.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0b1"}})]
+{
+    func main<ios17>(tensor<fp32, [1, 240000]> audio, tensor<int32, [1]> audio_length) {
+            tensor<int32, []> var_9 = const()[name = tensor<string, []>("op_9"), val = tensor<int32, []>(1)];
+            tensor<int32, []> var_10 = const()[name = tensor<string, []>("op_10"), val = tensor<int32, []>(160)];
+            tensor<fp32, []> var_24 = const()[name = tensor<string, []>("op_24"), val = tensor<fp32, []>(0x0p+0)];
+            tensor<fp32, []> var_25 = const()[name = tensor<string, []>("op_25"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
+            tensor<int32, []> var_34 = const()[name = tensor<string, []>("op_34"), val = tensor<int32, []>(512)];
+            tensor<int32, [1]> var_35 = add(x = audio_length, y = var_34)[name = tensor<string, []>("op_35")];
+            tensor<int32, []> var_36 = const()[name = tensor<string, []>("op_36"), val = tensor<int32, []>(512)];
+            tensor<int32, [1]> var_37 = sub(x = var_35, y = var_36)[name = tensor<string, []>("op_37")];
+            tensor<int32, [1]> floor_div_0 = floor_div(x = var_37, y = var_10)[name = tensor<string, []>("floor_div_0")];
+            tensor<string, []> var_38_dtype_0 = const()[name = tensor<string, []>("op_38_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<fp32, []> var_39_promoted = const()[name = tensor<string, []>("op_39_promoted"), val = tensor<fp32, []>(0x1p+0)];
+            tensor<fp32, [1]> var_38 = cast(dtype = var_38_dtype_0, x = floor_div_0)[name = tensor<string, []>("cast_12")];
+            tensor<fp32, [1]> seq_len_1 = add(x = var_38, y = var_39_promoted)[name = tensor<string, []>("seq_len_1")];
+            tensor<string, []> seq_len_dtype_0 = const()[name = tensor<string, []>("seq_len_dtype_0"), val = tensor<string, []>("int32")];
+            tensor<int32, [2]> var_43_begin_0 = const()[name = tensor<string, []>("op_43_begin_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [2]> var_43_end_0 = const()[name = tensor<string, []>("op_43_end_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<bool, [2]> var_43_end_mask_0 = const()[name = tensor<string, []>("op_43_end_mask_0"), val = tensor<bool, [2]>([true, false])];
+            tensor<bool, [2]> var_43_squeeze_mask_0 = const()[name = tensor<string, []>("op_43_squeeze_mask_0"), val = tensor<bool, [2]>([false, true])];
+            tensor<fp32, [1]> var_43 = slice_by_index(begin = var_43_begin_0, end = var_43_end_0, end_mask = var_43_end_mask_0, squeeze_mask = var_43_squeeze_mask_0, x = audio)[name = tensor<string, []>("op_43")];
+            tensor<int32, [1]> var_44_axes_0 = const()[name = tensor<string, []>("op_44_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [1, 1]> var_44 = expand_dims(axes = var_44_axes_0, x = var_43)[name = tensor<string, []>("op_44")];
+            tensor<int32, [2]> var_46_begin_0 = const()[name = tensor<string, []>("op_46_begin_0"), val = tensor<int32, [2]>([0, 1])];
+            tensor<int32, [2]> var_46_end_0 = const()[name = tensor<string, []>("op_46_end_0"), val = tensor<int32, [2]>([1, 240000])];
+            tensor<bool, [2]> var_46_end_mask_0 = const()[name = tensor<string, []>("op_46_end_mask_0"), val = tensor<bool, [2]>([true, true])];
+            tensor<fp32, [1, 239999]> var_46 = slice_by_index(begin = var_46_begin_0, end = var_46_end_0, end_mask = var_46_end_mask_0, x = audio)[name = tensor<string, []>("op_46")];
+            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]>([1, 239999])];
+            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, 239999]> var_48 = slice_by_index(begin = var_48_begin_0, end = var_48_end_0, end_mask = var_48_end_mask_0, x = audio)[name = tensor<string, []>("op_48")];
+            tensor<fp32, []> var_49 = const()[name = tensor<string, []>("op_49"), val = tensor<fp32, []>(0x1.f0a3d8p-1)];
+            tensor<fp32, [1, 239999]> var_50 = mul(x = var_48, y = var_49)[name = tensor<string, []>("op_50")];
+            tensor<fp32, [1, 239999]> var_51 = sub(x = var_46, y = var_50)[name = tensor<string, []>("op_51")];
+            tensor<bool, []> input_1_interleave_0 = const()[name = tensor<string, []>("input_1_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [1, 240000]> input_1 = concat(axis = var_9, interleave = input_1_interleave_0, values = (var_44, var_51))[name = tensor<string, []>("input_1")];
+            tensor<int32, [3]> var_57 = const()[name = tensor<string, []>("op_57"), val = tensor<int32, [3]>([1, 1, 240000])];
+            tensor<fp32, [1, 1, 240000]> input_3 = reshape(shape = var_57, x = input_1)[name = tensor<string, []>("input_3")];
+            tensor<fp32, []> const_3 = const()[name = tensor<string, []>("const_3"), val = tensor<fp32, []>(0x0p+0)];
+            tensor<int32, [6]> input_5_pad_0 = const()[name = tensor<string, []>("input_5_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 256, 256])];
+            tensor<string, []> input_5_mode_0 = const()[name = tensor<string, []>("input_5_mode_0"), val = tensor<string, []>("reflect")];
+            tensor<fp32, [1, 1, 240512]> input_5 = pad(constant_val = const_3, mode = input_5_mode_0, pad = input_5_pad_0, x = input_3)[name = tensor<string, []>("input_5")];
+            tensor<int32, [2]> var_63 = const()[name = tensor<string, []>("op_63"), val = tensor<int32, [2]>([1, 240512])];
+            tensor<fp32, [1, 240512]> input = reshape(shape = var_63, x = input_5)[name = tensor<string, []>("input")];
+            tensor<fp32, [257, 1, 512]> expand_dims_3 = const()[name = tensor<string, []>("expand_dims_3"), val = tensor<fp32, [257, 1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
+            tensor<fp32, [257, 1, 512]> expand_dims_4 = const()[name = tensor<string, []>("expand_dims_4"), val = tensor<fp32, [257, 1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(526464)))];
+            tensor<int32, [1]> expand_dims_5 = const()[name = tensor<string, []>("expand_dims_5"), val = tensor<int32, [1]>([160])];
+            tensor<int32, [1]> expand_dims_6_axes_0 = const()[name = tensor<string, []>("expand_dims_6_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [1, 1, 240512]> expand_dims_6 = expand_dims(axes = expand_dims_6_axes_0, x = input)[name = tensor<string, []>("expand_dims_6")];
+            tensor<string, []> conv_0_pad_type_0 = const()[name = tensor<string, []>("conv_0_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> conv_0_pad_0 = const()[name = tensor<string, []>("conv_0_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> conv_0_dilations_0 = const()[name = tensor<string, []>("conv_0_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> conv_0_groups_0 = const()[name = tensor<string, []>("conv_0_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 257, 1501]> conv_0 = conv(dilations = conv_0_dilations_0, groups = conv_0_groups_0, pad = conv_0_pad_0, pad_type = conv_0_pad_type_0, strides = expand_dims_5, weight = expand_dims_3, x = expand_dims_6)[name = tensor<string, []>("conv_0")];
+            tensor<string, []> conv_1_pad_type_0 = const()[name = tensor<string, []>("conv_1_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> conv_1_pad_0 = const()[name = tensor<string, []>("conv_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> conv_1_dilations_0 = const()[name = tensor<string, []>("conv_1_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> conv_1_groups_0 = const()[name = tensor<string, []>("conv_1_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 257, 1501]> conv_1 = conv(dilations = conv_1_dilations_0, groups = conv_1_groups_0, pad = conv_1_pad_0, pad_type = conv_1_pad_type_0, strides = expand_dims_5, weight = expand_dims_4, x = expand_dims_6)[name = tensor<string, []>("conv_1")];
+            tensor<int32, []> stack_0_axis_0 = const()[name = tensor<string, []>("stack_0_axis_0"), val = tensor<int32, []>(-1)];
+            tensor<fp32, [1, 257, 1501, 2]> stack_0 = stack(axis = stack_0_axis_0, values = (conv_0, conv_1))[name = tensor<string, []>("stack_0")];
+            tensor<fp32, []> var_17_promoted = const()[name = tensor<string, []>("op_17_promoted"), val = tensor<fp32, []>(0x1p+1)];
+            tensor<fp32, [1, 257, 1501, 2]> var_67 = pow(x = stack_0, y = var_17_promoted)[name = tensor<string, []>("op_67")];
+            tensor<int32, [1]> var_69_axes_0 = const()[name = tensor<string, []>("op_69_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, []> var_69_keep_dims_0 = const()[name = tensor<string, []>("op_69_keep_dims_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [1, 257, 1501]> var_69 = reduce_sum(axes = var_69_axes_0, keep_dims = var_69_keep_dims_0, x = var_67)[name = tensor<string, []>("op_69")];
+            tensor<fp32, [1, 257, 1501]> x_9 = identity(x = var_69)[name = tensor<string, []>("x_9")];
+            tensor<fp32, [1, 80, 257]> const_6 = const()[name = tensor<string, []>("const_6"), val = tensor<fp32, [1, 80, 257]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1052864)))];
+            tensor<bool, []> x_11_transpose_x_0 = const()[name = tensor<string, []>("x_11_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_11_transpose_y_0 = const()[name = tensor<string, []>("x_11_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [1, 80, 1501]> x_11 = matmul(transpose_x = x_11_transpose_x_0, transpose_y = x_11_transpose_y_0, x = const_6, y = x_9)[name = tensor<string, []>("x_11")];
+            tensor<fp32, []> var_76 = const()[name = tensor<string, []>("op_76"), val = tensor<fp32, []>(0x1p-24)];
+            tensor<fp32, [1, 80, 1501]> var_77 = add(x = x_11, y = var_76)[name = tensor<string, []>("op_77")];
+            tensor<fp32, []> x_13_epsilon_0 = const()[name = tensor<string, []>("x_13_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
+            tensor<fp32, [1, 80, 1501]> x_13 = log(epsilon = x_13_epsilon_0, x = var_77)[name = tensor<string, []>("x_13")];
+            tensor<int32, [1, 1501]> var_82 = const()[name = tensor<string, []>("op_82"), val = tensor<int32, [1, 1501]>([[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, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 1470, 1471, 1472, 1473, 1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1482, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1490, 1491, 1492, 1493, 1494, 1495, 1496, 1497, 1498, 1499, 1500]])];
+            tensor<int32, [1]> var_85_axes_0 = const()[name = tensor<string, []>("op_85_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> mel_length = cast(dtype = seq_len_dtype_0, x = seq_len_1)[name = tensor<string, []>("cast_11")];
+            tensor<int32, [1, 1]> var_85 = expand_dims(axes = var_85_axes_0, x = mel_length)[name = tensor<string, []>("op_85")];
+            tensor<bool, [1, 1501]> valid_mask = less(x = var_82, y = var_85)[name = tensor<string, []>("valid_mask")];
+            tensor<int32, [1]> var_87_axes_0 = const()[name = tensor<string, []>("op_87_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 1501]> var_87 = expand_dims(axes = var_87_axes_0, x = valid_mask)[name = tensor<string, []>("op_87")];
+            tensor<int32, [3]> var_87_after_broadcast_reps_0 = const()[name = tensor<string, []>("op_87_after_broadcast_reps_0"), val = tensor<int32, [3]>([1, 80, 1])];
+            tensor<bool, [1, 80, 1501]> var_87_after_broadcast = tile(reps = var_87_after_broadcast_reps_0, x = var_87)[name = tensor<string, []>("op_87_after_broadcast")];
+            tensor<fp32, [1, 80, 1501]> var_24_after_broadcast = const()[name = tensor<string, []>("op_24_after_broadcast"), val = tensor<fp32, [1, 80, 1501]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1135168)))];
+            tensor<fp32, [1, 80, 1501]> var_88 = select(a = x_13, b = var_24_after_broadcast, cond = var_87_after_broadcast)[name = tensor<string, []>("op_88")];
+            tensor<int32, [1]> x_mean_numerator_axes_0 = const()[name = tensor<string, []>("x_mean_numerator_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<bool, []> x_mean_numerator_keep_dims_0 = const()[name = tensor<string, []>("x_mean_numerator_keep_dims_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [1, 80]> x_mean_numerator = reduce_sum(axes = x_mean_numerator_axes_0, keep_dims = x_mean_numerator_keep_dims_0, x = var_88)[name = tensor<string, []>("x_mean_numerator")];
+            tensor<string, []> cast_2_dtype_0 = const()[name = tensor<string, []>("cast_2_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<int32, [1]> x_mean_denominator_axes_0 = const()[name = tensor<string, []>("x_mean_denominator_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, []> x_mean_denominator_keep_dims_0 = const()[name = tensor<string, []>("x_mean_denominator_keep_dims_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [1, 1501]> cast_2 = cast(dtype = cast_2_dtype_0, x = valid_mask)[name = tensor<string, []>("cast_10")];
+            tensor<fp32, [1]> x_mean_denominator = reduce_sum(axes = x_mean_denominator_axes_0, keep_dims = x_mean_denominator_keep_dims_0, x = cast_2)[name = tensor<string, []>("x_mean_denominator")];
+            tensor<int32, [1]> var_93_axes_0 = const()[name = tensor<string, []>("op_93_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [1, 1]> var_93 = expand_dims(axes = var_93_axes_0, x = x_mean_denominator)[name = tensor<string, []>("op_93")];
+            tensor<fp32, [1, 80]> x_mean = real_div(x = x_mean_numerator, y = var_93)[name = tensor<string, []>("x_mean")];
+            tensor<int32, [1]> var_96_axes_0 = const()[name = tensor<string, []>("op_96_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp32, [1, 80, 1]> var_96 = expand_dims(axes = var_96_axes_0, x = x_mean)[name = tensor<string, []>("op_96")];
+            tensor<fp32, [1, 80, 1501]> var_97 = sub(x = x_13, y = var_96)[name = tensor<string, []>("op_97")];
+            tensor<fp32, [1, 80, 1501]> var_98 = select(a = var_97, b = var_24_after_broadcast, cond = var_87_after_broadcast)[name = tensor<string, []>("op_98")];
+            tensor<fp32, []> var_17_promoted_1 = const()[name = tensor<string, []>("op_17_promoted_1"), val = tensor<fp32, []>(0x1p+1)];
+            tensor<fp32, [1, 80, 1501]> var_99 = pow(x = var_98, y = var_17_promoted_1)[name = tensor<string, []>("op_99")];
+            tensor<int32, [1]> var_101_axes_0 = const()[name = tensor<string, []>("op_101_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<bool, []> var_101_keep_dims_0 = const()[name = tensor<string, []>("op_101_keep_dims_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [1, 80]> var_101 = reduce_sum(axes = var_101_axes_0, keep_dims = var_101_keep_dims_0, x = var_99)[name = tensor<string, []>("op_101")];
+            tensor<fp32, []> var_103 = const()[name = tensor<string, []>("op_103"), val = tensor<fp32, []>(0x1p+0)];
+            tensor<fp32, [1, 1]> var_104 = sub(x = var_93, y = var_103)[name = tensor<string, []>("op_104")];
+            tensor<fp32, [1, 80]> var_105 = real_div(x = var_101, y = var_104)[name = tensor<string, []>("op_105")];
+            tensor<fp32, [1, 80]> x_std_1 = sqrt(x = var_105)[name = tensor<string, []>("x_std_1")];
+            tensor<fp32, [1, 80]> x_std = add(x = x_std_1, y = var_25)[name = tensor<string, []>("x_std")];
+            tensor<int32, [1]> var_110_axes_0 = const()[name = tensor<string, []>("op_110_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp32, [1, 80, 1]> var_110 = expand_dims(axes = var_110_axes_0, x = x_std)[name = tensor<string, []>("op_110")];
+            tensor<fp32, [1, 80, 1501]> x = real_div(x = var_97, y = var_110)[name = tensor<string, []>("x")];
+            tensor<bool, [1, 1501]> mask = greater_equal(x = var_82, y = var_85)[name = tensor<string, []>("mask")];
+            tensor<int32, [1]> var_119_axes_0 = const()[name = tensor<string, []>("op_119_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 1501]> var_119 = expand_dims(axes = var_119_axes_0, x = mask)[name = tensor<string, []>("op_119")];
+            tensor<fp32, [1, 80, 1501]> melspectrogram_features = select(a = var_24, b = x, cond = var_119)[name = tensor<string, []>("processed_signal")];
+        } -> (melspectrogram_features, mel_length);
+}

MelSpectrogram.mlmodelc/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:866c3c5d69f1ab1063273ce7e01d26b696e8f41483191409996c853f06b9aa75
+size 1615552

MelSpectrogram_int8.mlmodelc/analytics/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5b06d99331817967ae1cd7613837f473e8824529c4cf04849378e939c348a2d9
+size 243

MelSpectrogram_int8.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8ec8a0769843f459dc29b9c9338b813891b5739eb2da9cddcefdb0f2ea607579
+size 492

MelSpectrogram_int8.mlmodelc/model.mil

@@ -0,0 +1,122 @@
+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3500.14.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.7.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0b1"}})]
+{
+    func main<ios17>(tensor<fp32, [1, 240000]> audio, tensor<int32, [1]> audio_length) {
+            tensor<int32, []> var_9 = const()[name = tensor<string, []>("op_9"), val = tensor<int32, []>(1)];
+            tensor<int32, []> var_10 = const()[name = tensor<string, []>("op_10"), val = tensor<int32, []>(160)];
+            tensor<fp32, []> var_24 = const()[name = tensor<string, []>("op_24"), val = tensor<fp32, []>(0x0p+0)];
+            tensor<fp32, []> var_25 = const()[name = tensor<string, []>("op_25"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
+            tensor<int32, []> var_34 = const()[name = tensor<string, []>("op_34"), val = tensor<int32, []>(512)];
+            tensor<int32, [1]> var_35 = add(x = audio_length, y = var_34)[name = tensor<string, []>("op_35")];
+            tensor<int32, []> var_36 = const()[name = tensor<string, []>("op_36"), val = tensor<int32, []>(512)];
+            tensor<int32, [1]> var_37 = sub(x = var_35, y = var_36)[name = tensor<string, []>("op_37")];
+            tensor<int32, [1]> floor_div_0 = floor_div(x = var_37, y = var_10)[name = tensor<string, []>("floor_div_0")];
+            tensor<string, []> var_38_dtype_0 = const()[name = tensor<string, []>("op_38_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<fp32, []> var_39_promoted = const()[name = tensor<string, []>("op_39_promoted"), val = tensor<fp32, []>(0x1p+0)];
+            tensor<fp32, [1]> var_38 = cast(dtype = var_38_dtype_0, x = floor_div_0)[name = tensor<string, []>("cast_12")];
+            tensor<fp32, [1]> seq_len_1 = add(x = var_38, y = var_39_promoted)[name = tensor<string, []>("seq_len_1")];
+            tensor<string, []> seq_len_dtype_0 = const()[name = tensor<string, []>("seq_len_dtype_0"), val = tensor<string, []>("int32")];
+            tensor<int32, [2]> var_43_begin_0 = const()[name = tensor<string, []>("op_43_begin_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [2]> var_43_end_0 = const()[name = tensor<string, []>("op_43_end_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<bool, [2]> var_43_end_mask_0 = const()[name = tensor<string, []>("op_43_end_mask_0"), val = tensor<bool, [2]>([true, false])];
+            tensor<bool, [2]> var_43_squeeze_mask_0 = const()[name = tensor<string, []>("op_43_squeeze_mask_0"), val = tensor<bool, [2]>([false, true])];
+            tensor<fp32, [1]> var_43 = slice_by_index(begin = var_43_begin_0, end = var_43_end_0, end_mask = var_43_end_mask_0, squeeze_mask = var_43_squeeze_mask_0, x = audio)[name = tensor<string, []>("op_43")];
+            tensor<int32, [1]> var_44_axes_0 = const()[name = tensor<string, []>("op_44_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [1, 1]> var_44 = expand_dims(axes = var_44_axes_0, x = var_43)[name = tensor<string, []>("op_44")];
+            tensor<int32, [2]> var_46_begin_0 = const()[name = tensor<string, []>("op_46_begin_0"), val = tensor<int32, [2]>([0, 1])];
+            tensor<int32, [2]> var_46_end_0 = const()[name = tensor<string, []>("op_46_end_0"), val = tensor<int32, [2]>([1, 240000])];
+            tensor<bool, [2]> var_46_end_mask_0 = const()[name = tensor<string, []>("op_46_end_mask_0"), val = tensor<bool, [2]>([true, true])];
+            tensor<fp32, [1, 239999]> var_46 = slice_by_index(begin = var_46_begin_0, end = var_46_end_0, end_mask = var_46_end_mask_0, x = audio)[name = tensor<string, []>("op_46")];
+            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]>([1, 239999])];
+            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, 239999]> var_48 = slice_by_index(begin = var_48_begin_0, end = var_48_end_0, end_mask = var_48_end_mask_0, x = audio)[name = tensor<string, []>("op_48")];
+            tensor<fp32, []> var_49 = const()[name = tensor<string, []>("op_49"), val = tensor<fp32, []>(0x1.f0a3d8p-1)];
+            tensor<fp32, [1, 239999]> var_50 = mul(x = var_48, y = var_49)[name = tensor<string, []>("op_50")];
+            tensor<fp32, [1, 239999]> var_51 = sub(x = var_46, y = var_50)[name = tensor<string, []>("op_51")];
+            tensor<bool, []> input_1_interleave_0 = const()[name = tensor<string, []>("input_1_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [1, 240000]> input_1 = concat(axis = var_9, interleave = input_1_interleave_0, values = (var_44, var_51))[name = tensor<string, []>("input_1")];
+            tensor<int32, [3]> var_57 = const()[name = tensor<string, []>("op_57"), val = tensor<int32, [3]>([1, 1, 240000])];
+            tensor<fp32, [1, 1, 240000]> input_3 = reshape(shape = var_57, x = input_1)[name = tensor<string, []>("input_3")];
+            tensor<fp32, []> const_3 = const()[name = tensor<string, []>("const_3"), val = tensor<fp32, []>(0x0p+0)];
+            tensor<int32, [6]> input_5_pad_0 = const()[name = tensor<string, []>("input_5_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 256, 256])];
+            tensor<string, []> input_5_mode_0 = const()[name = tensor<string, []>("input_5_mode_0"), val = tensor<string, []>("reflect")];
+            tensor<fp32, [1, 1, 240512]> input_5 = pad(constant_val = const_3, mode = input_5_mode_0, pad = input_5_pad_0, x = input_3)[name = tensor<string, []>("input_5")];
+            tensor<int32, [2]> var_63 = const()[name = tensor<string, []>("op_63"), val = tensor<int32, [2]>([1, 240512])];
+            tensor<fp32, [1, 240512]> input = reshape(shape = var_63, x = input_5)[name = tensor<string, []>("input")];
+            tensor<fp32, [257, 1, 512]> expand_dims_3 = const()[name = tensor<string, []>("expand_dims_3"), val = tensor<fp32, [257, 1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
+            tensor<fp32, [257, 1, 512]> expand_dims_4 = const()[name = tensor<string, []>("expand_dims_4"), val = tensor<fp32, [257, 1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(526464)))];
+            tensor<int32, [1]> expand_dims_5 = const()[name = tensor<string, []>("expand_dims_5"), val = tensor<int32, [1]>([160])];
+            tensor<int32, [1]> expand_dims_6_axes_0 = const()[name = tensor<string, []>("expand_dims_6_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [1, 1, 240512]> expand_dims_6 = expand_dims(axes = expand_dims_6_axes_0, x = input)[name = tensor<string, []>("expand_dims_6")];
+            tensor<string, []> conv_0_pad_type_0 = const()[name = tensor<string, []>("conv_0_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> conv_0_pad_0 = const()[name = tensor<string, []>("conv_0_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> conv_0_dilations_0 = const()[name = tensor<string, []>("conv_0_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> conv_0_groups_0 = const()[name = tensor<string, []>("conv_0_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 257, 1501]> conv_0 = conv(dilations = conv_0_dilations_0, groups = conv_0_groups_0, pad = conv_0_pad_0, pad_type = conv_0_pad_type_0, strides = expand_dims_5, weight = expand_dims_3, x = expand_dims_6)[name = tensor<string, []>("conv_0")];
+            tensor<string, []> conv_1_pad_type_0 = const()[name = tensor<string, []>("conv_1_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> conv_1_pad_0 = const()[name = tensor<string, []>("conv_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> conv_1_dilations_0 = const()[name = tensor<string, []>("conv_1_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> conv_1_groups_0 = const()[name = tensor<string, []>("conv_1_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 257, 1501]> conv_1 = conv(dilations = conv_1_dilations_0, groups = conv_1_groups_0, pad = conv_1_pad_0, pad_type = conv_1_pad_type_0, strides = expand_dims_5, weight = expand_dims_4, x = expand_dims_6)[name = tensor<string, []>("conv_1")];
+            tensor<int32, []> stack_0_axis_0 = const()[name = tensor<string, []>("stack_0_axis_0"), val = tensor<int32, []>(-1)];
+            tensor<fp32, [1, 257, 1501, 2]> stack_0 = stack(axis = stack_0_axis_0, values = (conv_0, conv_1))[name = tensor<string, []>("stack_0")];
+            tensor<fp32, []> var_17_promoted = const()[name = tensor<string, []>("op_17_promoted"), val = tensor<fp32, []>(0x1p+1)];
+            tensor<fp32, [1, 257, 1501, 2]> var_67 = pow(x = stack_0, y = var_17_promoted)[name = tensor<string, []>("op_67")];
+            tensor<int32, [1]> var_69_axes_0 = const()[name = tensor<string, []>("op_69_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, []> var_69_keep_dims_0 = const()[name = tensor<string, []>("op_69_keep_dims_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [1, 257, 1501]> var_69 = reduce_sum(axes = var_69_axes_0, keep_dims = var_69_keep_dims_0, x = var_67)[name = tensor<string, []>("op_69")];
+            tensor<fp32, [1, 257, 1501]> x_9 = identity(x = var_69)[name = tensor<string, []>("x_9")];
+            tensor<fp32, [1, 80, 257]> const_6 = const()[name = tensor<string, []>("const_6"), val = tensor<fp32, [1, 80, 257]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1052864)))];
+            tensor<bool, []> x_11_transpose_x_0 = const()[name = tensor<string, []>("x_11_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_11_transpose_y_0 = const()[name = tensor<string, []>("x_11_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [1, 80, 1501]> x_11 = matmul(transpose_x = x_11_transpose_x_0, transpose_y = x_11_transpose_y_0, x = const_6, y = x_9)[name = tensor<string, []>("x_11")];
+            tensor<fp32, []> var_76 = const()[name = tensor<string, []>("op_76"), val = tensor<fp32, []>(0x1p-24)];
+            tensor<fp32, [1, 80, 1501]> var_77 = add(x = x_11, y = var_76)[name = tensor<string, []>("op_77")];
+            tensor<fp32, []> x_13_epsilon_0 = const()[name = tensor<string, []>("x_13_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
+            tensor<fp32, [1, 80, 1501]> x_13 = log(epsilon = x_13_epsilon_0, x = var_77)[name = tensor<string, []>("x_13")];
+            tensor<int32, [1, 1501]> var_82 = const()[name = tensor<string, []>("op_82"), val = tensor<int32, [1, 1501]>([[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, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 1470, 1471, 1472, 1473, 1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1482, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1490, 1491, 1492, 1493, 1494, 1495, 1496, 1497, 1498, 1499, 1500]])];
+            tensor<int32, [1]> var_85_axes_0 = const()[name = tensor<string, []>("op_85_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> mel_length = cast(dtype = seq_len_dtype_0, x = seq_len_1)[name = tensor<string, []>("cast_11")];
+            tensor<int32, [1, 1]> var_85 = expand_dims(axes = var_85_axes_0, x = mel_length)[name = tensor<string, []>("op_85")];
+            tensor<bool, [1, 1501]> valid_mask = less(x = var_82, y = var_85)[name = tensor<string, []>("valid_mask")];
+            tensor<int32, [1]> var_87_axes_0 = const()[name = tensor<string, []>("op_87_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 1501]> var_87 = expand_dims(axes = var_87_axes_0, x = valid_mask)[name = tensor<string, []>("op_87")];
+            tensor<int32, [3]> var_87_after_broadcast_reps_0 = const()[name = tensor<string, []>("op_87_after_broadcast_reps_0"), val = tensor<int32, [3]>([1, 80, 1])];
+            tensor<bool, [1, 80, 1501]> var_87_after_broadcast = tile(reps = var_87_after_broadcast_reps_0, x = var_87)[name = tensor<string, []>("op_87_after_broadcast")];
+            tensor<fp32, [1, 80, 1501]> var_24_after_broadcast = const()[name = tensor<string, []>("op_24_after_broadcast"), val = tensor<fp32, [1, 80, 1501]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1135168)))];
+            tensor<fp32, [1, 80, 1501]> var_88 = select(a = x_13, b = var_24_after_broadcast, cond = var_87_after_broadcast)[name = tensor<string, []>("op_88")];
+            tensor<int32, [1]> x_mean_numerator_axes_0 = const()[name = tensor<string, []>("x_mean_numerator_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<bool, []> x_mean_numerator_keep_dims_0 = const()[name = tensor<string, []>("x_mean_numerator_keep_dims_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [1, 80]> x_mean_numerator = reduce_sum(axes = x_mean_numerator_axes_0, keep_dims = x_mean_numerator_keep_dims_0, x = var_88)[name = tensor<string, []>("x_mean_numerator")];
+            tensor<string, []> cast_2_dtype_0 = const()[name = tensor<string, []>("cast_2_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<int32, [1]> x_mean_denominator_axes_0 = const()[name = tensor<string, []>("x_mean_denominator_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, []> x_mean_denominator_keep_dims_0 = const()[name = tensor<string, []>("x_mean_denominator_keep_dims_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [1, 1501]> cast_2 = cast(dtype = cast_2_dtype_0, x = valid_mask)[name = tensor<string, []>("cast_10")];
+            tensor<fp32, [1]> x_mean_denominator = reduce_sum(axes = x_mean_denominator_axes_0, keep_dims = x_mean_denominator_keep_dims_0, x = cast_2)[name = tensor<string, []>("x_mean_denominator")];
+            tensor<int32, [1]> var_93_axes_0 = const()[name = tensor<string, []>("op_93_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [1, 1]> var_93 = expand_dims(axes = var_93_axes_0, x = x_mean_denominator)[name = tensor<string, []>("op_93")];
+            tensor<fp32, [1, 80]> x_mean = real_div(x = x_mean_numerator, y = var_93)[name = tensor<string, []>("x_mean")];
+            tensor<int32, [1]> var_96_axes_0 = const()[name = tensor<string, []>("op_96_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp32, [1, 80, 1]> var_96 = expand_dims(axes = var_96_axes_0, x = x_mean)[name = tensor<string, []>("op_96")];
+            tensor<fp32, [1, 80, 1501]> var_97 = sub(x = x_13, y = var_96)[name = tensor<string, []>("op_97")];
+            tensor<fp32, [1, 80, 1501]> var_98 = select(a = var_97, b = var_24_after_broadcast, cond = var_87_after_broadcast)[name = tensor<string, []>("op_98")];
+            tensor<fp32, []> var_17_promoted_1 = const()[name = tensor<string, []>("op_17_promoted_1"), val = tensor<fp32, []>(0x1p+1)];
+            tensor<fp32, [1, 80, 1501]> var_99 = pow(x = var_98, y = var_17_promoted_1)[name = tensor<string, []>("op_99")];
+            tensor<int32, [1]> var_101_axes_0 = const()[name = tensor<string, []>("op_101_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<bool, []> var_101_keep_dims_0 = const()[name = tensor<string, []>("op_101_keep_dims_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [1, 80]> var_101 = reduce_sum(axes = var_101_axes_0, keep_dims = var_101_keep_dims_0, x = var_99)[name = tensor<string, []>("op_101")];
+            tensor<fp32, []> var_103 = const()[name = tensor<string, []>("op_103"), val = tensor<fp32, []>(0x1p+0)];
+            tensor<fp32, [1, 1]> var_104 = sub(x = var_93, y = var_103)[name = tensor<string, []>("op_104")];
+            tensor<fp32, [1, 80]> var_105 = real_div(x = var_101, y = var_104)[name = tensor<string, []>("op_105")];
+            tensor<fp32, [1, 80]> x_std_1 = sqrt(x = var_105)[name = tensor<string, []>("x_std_1")];
+            tensor<fp32, [1, 80]> x_std = add(x = x_std_1, y = var_25)[name = tensor<string, []>("x_std")];
+            tensor<int32, [1]> var_110_axes_0 = const()[name = tensor<string, []>("op_110_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp32, [1, 80, 1]> var_110 = expand_dims(axes = var_110_axes_0, x = x_std)[name = tensor<string, []>("op_110")];
+            tensor<fp32, [1, 80, 1501]> x = real_div(x = var_97, y = var_110)[name = tensor<string, []>("x")];
+            tensor<bool, [1, 1501]> mask = greater_equal(x = var_82, y = var_85)[name = tensor<string, []>("mask")];
+            tensor<int32, [1]> var_119_axes_0 = const()[name = tensor<string, []>("op_119_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 1501]> var_119 = expand_dims(axes = var_119_axes_0, x = mask)[name = tensor<string, []>("op_119")];
+            tensor<fp32, [1, 80, 1501]> melspectrogram_features = select(a = var_24, b = x, cond = var_119)[name = tensor<string, []>("processed_signal")];
+        } -> (melspectrogram_features, mel_length);
+}

MelSpectrogram_int8.mlmodelc/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:866c3c5d69f1ab1063273ce7e01d26b696e8f41483191409996c853f06b9aa75
+size 1615552

config.json

@@ -0,0 +1,7 @@
+{
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "nemo_model_type": "parakeet",
+  "pad_token_id": 0,
+  "vocab_size": 1024,
+}

preprocessor_config.json

@@ -0,0 +1,13 @@
+{
+  "feature_extractor_type": "ParakeetFeatureExtractor",
+  "feature_size": 80,
+  "hop_length": 160,
+  "n_fft": 512,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "preemphasis": 0.97,
+  "processor_class": "ParakeetProcessor",
+  "return_attention_mask": true,
+  "sampling_rate": 16000,
+  "win_length": 400
+}

special_tokens_map.json

@@ -0,0 +1,16 @@
+{
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,27 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1024": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "extra_special_tokens": {},
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "processor_class": "ParakeetProcessor",
+  "tokenizer_class": "PreTrainedTokenizer",
+  "unk_token": "<unk>"
+}

vocab.json

@@ -0,0 +1 @@
+{"0": "<unk>", "1": " t", "2": " th", "3": " a", "4": "in", "5": "re", "6": " the", "7": " w", "8": " s", "9": " o", "10": "er", "11": "ou", "12": "at", "13": "nd", "14": "it", "15": " h", "16": " c", "17": " b", "18": "is", "19": "en", "20": "on", "21": "ing", "22": " f", "23": " to", "24": " m", "25": "es", "26": " p", "27": "or", "28": "an", "29": " d", "30": "ll", "31": " I", "32": "ed", "33": " and", "34": " l", "35": " of", "36": " in", "37": " y", "38": "ar", "39": " g", "40": " you", "41": "as", "42": "om", "43": " n", "44": "ve", "45": " that", "46": "le", "47": "ic", "48": "us", "49": "ow", "50": "et", "51": "al", "52": " e", "53": "ut", "54": " it", "55": "ot", "56": " be", "57": " T", "58": "ion", "59": " is", "60": " wh", "61": " re", "62": " on", "63": " we", "64": "ent", "65": " A", "66": "ay", "67": " ha", "68": " Th", "69": "id", "70": " S", "71": "ac", "72": "gh", "73": "ver", "74": "ke", "75": " for", "76": "im", "77": "ly", "78": "ur", "79": "ld", "80": " he", "81": " st", "82": "all", "83": "ro", "84": "st", "85": "se", "86": "ct", "87": "ith", "88": "ir", "89": "am", "90": " this", "91": "if", "92": " W", "93": "oo", "94": "ri", "95": " was", "96": "ght", "97": " u", "98": " with", "99": "ad", "100": "ch", "101": " se", "102": " k", "103": " an", "104": " The", "105": " li", "106": " do", "107": " B", "108": " have", "109": " as", "110": "th", "111": " are", "112": " sh", "113": "ust", "114": "ce", "115": "ally", "116": "ill", "117": " H", "118": " j", "119": "ter", "120": " go", "121": " And", "122": "ation", "123": " C", "124": " so", "125": "ome", "126": " not", "127": "op", "128": "il", "129": "ore", "130": " ne", "131": " can", "132": " me", "133": " at", "134": "ould", "135": "ant", "136": " M", "137": " like", "138": "ere", "139": " they", "140": "ra", "141": "ers", "142": " ab", "143": " de", "144": " kn", "145": "ge", "146": " Y", "147": " ch", "148": "ul", "149": "pp", "150": " or", "151": " al", "152": " con", "153": " com", "154": "ess", "155": " su", "156": "out", "157": " your", "158": " So", "159": "ate", "160": " one", "161": " all", "162": " ex", "163": "est", "164": " fr", "165": " just", "166": " pro", "167": " know", "168": " O", "169": "ain", "170": " but", "171": "ol", "172": "ive", "173": " v", "174": "use", "175": "very", "176": "art", "177": "qu", "178": " my", "179": "el", "180": " N", "181": "nt", "182": " It", "183": " what", "184": "ab", "185": " P", "186": " wor", "187": " out", "188": " there", "189": " up", "190": "um", "191": " from", "192": "pe", "193": " tw", "194": " r", "195": "and", "196": "ight", "197": "ort", "198": "un", "199": " L", "200": "ist", "201": " about", "202": "ide", "203": "ig", "204": "ake", "205": " D", "206": "em", "207": "os", "208": "king", "209": "rou", "210": "ind", "211": "our", "212": "res", "213": " We", "214": " get", "215": " E", "216": " G", "217": "ack", "218": " le", "219": "ity", "220": "od", "221": " F", "222": "ard", "223": " pl", "224": " our", "225": " int", "226": "ment", "227": " will", "228": "ies", "229": " by", "230": "ink", "231": "ca", "232": " if", "233": "red", "234": "her", "235": "ie", "236": " us", "237": " some", "238": " don", "239": "ven", "240": "ood", "241": "ast", "242": " R", "243": " his", "244": " tim", "245": " tr", "246": " more", "247": "ich", "248": "ous", "249": "ame", "250": " going", "251": " had", "252": " them", "253": "ook", "254": " pe", "255": " Wh", "256": " You", "257": " But", "258": "ine", "259": " here", "260": " would", "261": "cause", "262": "right", "263": "so", "264": "ost", "265": "ure", "266": " has", "267": "ect", "268": " think", "269": " fe", "270": "ong", "271": " see", "272": " when", "273": " who", "274": " were", "275": " really", "276": " their", "277": " want", "278": "one", "279": "ople", "280": " then", "281": " time", "282": " sa", "283": "ap", "284": " te", "285": " He", "286": " ye", "287": "ck", "288": " her", "289": " thing", "290": " right", "291": " which", "292": "itt", "293": "ice", "294": "act", "295": " people", "296": "ty", "297": " two", "298": " J", "299": " im", "300": "ther", "301": "ci", "302": "ose", "303": " cl", "304": " qu", "305": " man", "306": " also", "307": "ree", "308": " en", "309": "ud", "310": " how", "311": "reat", "312": "ak", "313": "hing", "314": "ag", "315": " any", "316": "ff", "317": "ace", "318": "per", "319": " because", "320": " very", "321": "own", "322": " ad", "323": " act", "324": " been", "325": " now", "326": " ag", "327": " into", "328": " comp", "329": "ars", "330": "ions", "331": "are", "332": "ite", "333": "iv", "334": " these", "335": "ays", "336": "ep", "337": " This", "338": " she", "339": "ans", "340": "ah", "341": "een", "342": " over", "343": "ry", "344": " lo", "345": "age", "346": " pr", "347": " sp", "348": "ue", "349": " co", "350": "ick", "351": "ber", "352": " did", "353": "ip", "354": "ach", "355": " back", "356": " no", "357": " cont", "358": " other", "359": " every", "360": "pt", "361": " need", "362": " him", "363": " U", "364": " In", "365": " work", "366": "irst", "367": " part", "368": " look", "369": "ittle", "370": "ble", "371": "iz", "372": " un", "373": " make", "374": "omet", "375": "nder", "376": "ish", "377": "na", "378": " little", "379": " off", "380": " than", "381": " got", "382": "ually", "383": " per", "384": " good", "385": " way", "386": " could", "387": " ac", "388": " imp", "389": "able", "390": " where", "391": "iff", "392": " That", "393": " res", "394": "ount", "395": "pl", "396": "ance", "397": " first", "398": " ro", "399": " pre", "400": "ass", "401": " say", "402": "int", "403": "ated", "404": "ire", "405": "uch", "406": "ase", "407": " somet", "408": "ound", "409": " down", "410": " diff", "411": "sel", "412": " gu", "413": " am", "414": "ress", "415": " lot", "416": "ence", "417": " dis", "418": "orm", "419": "ix", "420": " po", "421": "ving", "422": "enty", "423": " K", "424": " spe", "425": "und", "426": "he", "427": " much", "428": " ar", "429": "round", "430": " app", "431": "co", "432": "ark", "433": " new", "434": "ater", "435": "ult", "436": "end", "437": " even", "438": " start", "439": "ations", "440": "rough", "441": "ile", "442": "fter", "443": " well", "444": "be", "445": " They", "446": " three", "447": "ign", "448": "ild", "449": " said", "450": "ough", "451": "ang", "452": " too", "453": "ade", "454": " bl", "455": "ens", "456": " inc", "457": "ia", "458": " those", "459": " mo", "460": " take", "461": " through", "462": " fl", "463": " kind", "464": " things", "465": " bet", "466": " only", "467": " St", "468": " let", "469": "cess", "470": " Ch", "471": "ary", "472": "vel", "473": " If", "474": "xt", "475": "other", "476": "av", "477": "ical", "478": "ord", "479": " again", "480": " something", "481": "onna", "482": "fore", "483": " may", "484": "ting", "485": " bu", "486": " differe", "487": "urn", "488": " gonna", "489": " does", "490": "uct", "491": "og", "492": " twenty", "493": " gr", "494": " Ye", "495": "wn", "496": " should", "497": " comm", "498": "ition", "499": " under", "500": " hel", "501": "ory", "502": " fo", "503": " use", "504": "igh", "505": "ife", "506": " actually", "507": " tal", "508": " call", "509": "ents", "510": "ious", "511": "ull", "512": " There", "513": " Yeah", "514": " most", "515": " ke", "516": "ors", "517": "ved", "518": "ys", "519": " sc", "520": " happ", "521": "ope", "522": " help", "523": "atch", "524": " What", "525": " rem", "526": "ple", "527": " Now", "528": " br", "529": "ool", "530": "oth", "531": " four", "532": "self", "533": " str", "534": "ne", "535": "thing", "536": " put", "537": "ial", "538": " great", "539": "ail", "540": "ub", "541": "ning", "542": " sm", "543": " feel", "544": " five", "545": "ody", "546": "undred", "547": "iss", "548": "ank", "549": "get", "550": "aking", "551": " many", "552": " hundred", "553": " years", "554": " being", "555": " come", "556": " mean", "557": "ily", "558": " different", "559": " after", "560": " ser", "561": " show", "562": "form", "563": "ful", "564": "oy", "565": " six", "566": " vide", "567": " V", "568": " its", "569": " point", "570": " day", "571": " des", "572": "ons", "573": " bit", "574": " bel", "575": " before", "576": " aw", "577": " end", "578": " Oh", "579": " still", "580": "ath", "581": " long", "582": " '", "583": "ise", "584": "ob", "585": "day", "586": " add", "587": "ft", "588": "ves", "589": "ces", "590": "ady", "591": " cr", "592": " around", "593": " try", "594": "les", "595": "vers", "596": "kay", "597": "ian", "598": "ates", "599": " find", "600": "ward", "601": " As", "602": " eight", "603": "lic", "604": " same", "605": " pos", "606": " em", "607": " made", "608": " supp", "609": " life", "610": " Be", "611": "pect", "612": " dec", "613": " play", "614": "ange", "615": " att", "616": " pers", "617": "ways", "618": " high", "619": " hand", "620": " next", "621": " cons", "622": " own", "623": " inv", "624": "ower", "625": " ind", "626": "ert", "627": "ng", "628": "ave", "629": " year", "630": " big", "631": "ating", "632": " world", "633": " rel", "634": " sure", "635": " tra", "636": "ew", "637": "ered", "638": " fin", "639": " Well", "640": " sl", "641": " doing", "642": "bs", "643": " set", "644": " rec", "645": "ual", "646": "cial", "647": " ph", "648": "erm", "649": " love", "650": "ph", "651": " real", "652": " last", "653": "ict", "654": " bo", "655": " ra", "656": "ible", "657": " wr", "658": "mer", "659": " count", "660": "ities", "661": " always", "662": "inet", "663": "ments", "664": "uc", "665": " might", "666": " inter", "667": " video", "668": "gin", "669": " tell", "670": " never", "671": "vent", "672": " import", "673": "ied", "674": " sy", "675": " How", "676": "ically", "677": "ought", "678": " thir", "679": " rep", "680": "ks", "681": "ib", "682": " fam", "683": "ject", "684": " bas", "685": " She", "686": " give", "687": "akes", "688": " ninet", "689": " reg", "690": " min", "691": " op", "692": " def", "693": " didn", "694": "te", "695": " cour", "696": " why", "697": " ent", "698": " place", "699": " ins", "700": " car", "701": "ather", "702": " person", "703": "ular", "704": " inst", "705": " prod", "706": "lect", "707": " Al", "708": " today", "709": " bec", "710": " sur", "711": " All", "712": " another", "713": " bus", "714": " keep", "715": "ell", "716": "ese", "717": "riend", "718": " quest", "719": " talk", "720": "als", "721": "ings", "722": " mon", "723": "cond", "724": "old", "725": " acc", "726": " la", "727": " num", "728": "ident", "729": " che", "730": "iness", "731": " turn", "732": " ear", "733": " No", "734": "ousand", "735": " better", "736": "ific", "737": " loo", "738": " gl", "739": "oc", "740": " important", "741": "ited", "742": " An", "743": " thousand", "744": "ility", "745": "llow", "746": " used", "747": " gen", "748": " sim", "749": "li", "750": " happen", "751": " Un", "752": " Let", "753": "air", "754": "ock", "755": "ably", "756": "gg", "757": " watch", "758": " For", "759": " sw", "760": "ren", "761": "ute", "762": "ever", "763": " pol", "764": " sch", "765": " When", "766": " such", "767": " fif", "768": " home", "769": " cle", "770": " contin", "771": "ouse", "772": " friend", "773": "uring", "774": " Okay", "775": "gr", "776": " able", "777": " stud", "778": " eff", "779": "hip", "780": "body", "781": " top", "782": "ness", "783": " exper", "784": " pret", "785": " both", "786": " done", "787": "cri", "788": " mark", "789": " while", "790": " old", "791": "ros", "792": "ont", "793": " second", "794": "ative", "795": " thought", "796": " best", "797": " found", "798": "iew", "799": " belie", "800": " each", "801": "erest", "802": " tri", "803": " eas", "804": " ca", "805": " fact", "806": " care", "807": " fun", "808": "atter", "809": "ures", "810": " head", "811": " lear", "812": " water", "813": " hard", "814": " few", "815": " side", "816": "ween", "817": " exp", "818": " away", "819": "its", "820": " ext", "821": "lud", "822": " run", "823": " trans", "824": "ince", "825": " sk", "826": " open", "827": "cus", "828": " between", "829": " called", "830": " wee", "831": " pretty", "832": "ason", "833": " far", "834": "ember", "835": "omm", "836": " interest", "837": "any", "838": "ner", "839": "uff", "840": " pres", "841": " cur", "842": " child", "843": "ee", "844": " toget", "845": " together", "846": "olog", "847": " God", "848": "ond", "849": " char", "850": " looking", "851": "stem", "852": "az", "853": "cent", "854": " ob", "855": " ass", "856": "land", "857": " doesn", "858": " business", "859": " course", "860": " ten", "861": "ps", "862": "arch", "863": "ced", "864": "ms", "865": "ize", "866": "nce", "867": " ref", "868": " name", "869": "ross", "870": " grow", "871": "oney", "872": " went", "873": "ics", "874": "teen", "875": " cou", "876": " prob", "877": " ret", "878": " guys", "879": " came", "880": "ash", "881": "led", "882": " Eur", "883": "ues", "884": " ide", "885": "gan", "886": " everything", "887": " getting", "888": " ask", "889": " cor", "890": " build", "891": " sign", "892": " small", "893": "uck", "894": " el", "895": " col", "896": " Is", "897": "ational", "898": "stand", "899": "cy", "900": " conf", "901": "der", "902": " bre", "903": " cap", "904": " mod", "905": "ets", "906": "ike", "907": " number", "908": " comple", "909": "ertain", "910": " ever", "911": " coll", "912": " hum", "913": " Europe", "914": " cre", "915": " met", "916": " exam", "917": " move", "918": " pass", "919": " left", "920": " system", "921": " includ", "922": " Thank", "923": "cept", "924": " wom", "925": " product", "926": "ten", "927": " rest", "928": " probably", "929": " dri", "930": " Do", "931": " gener", "932": " anything", "933": " lar", "934": " My", "935": " school", "936": " lead", "937": " sub", "938": " ty", "939": " plan", "940": " seem", "941": " whole", "942": "irect", "943": " light", "944": " must", "945": " mom", "946": " opp", "947": " support", "948": " family", "949": "ices", "950": "amp", "951": " proble", "952": " dr", "953": "ready", "954": " using", "955": "ense", "956": " prov", "957": "ush", "958": "ax", "959": " power", "960": " Re", "961": "alth", "962": " ev", "963": " stand", "964": " war", "965": "ts", "966": " ", "967": "e", "968": "t", "969": "o", "970": "a", "971": "n", "972": "i", "973": "s", "974": "r", "975": "h", "976": "l", "977": "d", "978": "u", "979": "c", "980": "m", "981": "y", "982": "g", "983": "w", "984": "f", "985": "p", "986": ".", "987": "b", "988": ",", "989": "v", "990": "k", "991": "'", "992": "I", "993": "T", "994": "A", "995": "S", "996": "x", "997": "W", "998": "j", "999": "B", "1000": "C", "1001": "H", "1002": "?", "1003": "M", "1004": "O", "1005": "Y", "1006": "N", "1007": "P", "1008": "E", "1009": "q", "1010": "L", "1011": "D", "1012": "z", "1013": "G", "1014": "F", "1015": "R", "1016": "!", "1017": "J", "1018": "U", "1019": "K", "1020": "V", "1021": "Q", "1022": "Z", "1023": "X"}