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diar-streaming-sortformer-coreml

Automatic Speech Recognition
Core ML
speech-recognition
streaming-asr
apple
ios
macos
FastConformer
RNNT
Parakeet
ASR
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xet
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5

Cleanup + New Models for other configs

#2
by GradientDescent2718 - opened
base: refs/heads/main
←
from: refs/pr/2
Discussion Files changed
+6001
-2340
Files changed (37) hide show
  1. Pipeline_Preprocessor.mlmodelc/analytics/coremldata.bin +0 -3
  2. Pipeline_Preprocessor.mlmodelc/coremldata.bin +0 -3
  3. Pipeline_Preprocessor.mlmodelc/metadata.json +0 -101
  4. Pipeline_Preprocessor.mlmodelc/model.mil +0 -0
  5. Pipeline_Preprocessor.mlmodelc/weights/weight.bin +0 -3
  6. Pipeline_Preprocessor.mlpackage/Data/com.apple.CoreML/weights/weight.bin +0 -3
  7. Pipeline_Preprocessor.mlpackage/Manifest.json +0 -18
  8. {Pipeline_Head_Fixed.mlmodelc → SortformerNvidiaHigh.mlmodelc}/analytics/coremldata.bin +2 -2
  9. SortformerNvidiaHigh.mlmodelc/coremldata.bin +3 -0
  10. {Pipeline_Head_Fixed.mlmodelc → SortformerNvidiaHigh.mlmodelc}/metadata.json +85 -41
  11. {Pipeline_Head_Fixed.mlmodelc → SortformerNvidiaHigh.mlmodelc/model0/analytics}/coremldata.bin +2 -2
  12. {Pipeline_PreEncoder.mlmodelc/analytics → SortformerNvidiaHigh.mlmodelc/model0}/coremldata.bin +2 -2
  13. SortformerNvidiaHigh.mlmodelc/model0/model.mil +201 -0
  14. Pipeline_PreEncoder.mlmodelc/weights/weight.bin → SortformerNvidiaHigh.mlmodelc/model0/weights/0-weight.bin +0 -0
  15. {Pipeline_PreEncoder.mlmodelc → SortformerNvidiaHigh.mlmodelc/model1/analytics}/coremldata.bin +2 -2
  16. SortformerNvidiaHigh.mlmodelc/model1/coremldata.bin +3 -0
  17. SortformerNvidiaHigh.mlmodelc/model1/model.mil +0 -0
  18. SortformerNvidiaHigh.mlmodelc/model1/weights/1-weight.bin +3 -0
  19. {Pipeline_Preprocessor.mlpackage → SortformerNvidiaHigh.mlpackage}/Data/com.apple.CoreML/model.mlmodel +2 -2
  20. Pipeline_PreEncoder.mlpackage/Data/com.apple.CoreML/weights/weight.bin → SortformerNvidiaHigh.mlpackage/Data/com.apple.CoreML/weights/0-weight.bin +0 -0
  21. SortformerNvidiaHigh.mlpackage/Data/com.apple.CoreML/weights/1-weight.bin +3 -0
  22. {Pipeline_Head_Fixed.mlpackage → SortformerNvidiaHigh.mlpackage}/Manifest.json +3 -3
  23. SortformerNvidiaLow.mlmodelc/analytics/coremldata.bin +3 -0
  24. SortformerNvidiaLow.mlmodelc/coremldata.bin +3 -0
  25. {Pipeline_PreEncoder.mlmodelc → SortformerNvidiaLow.mlmodelc}/metadata.json +68 -49
  26. SortformerNvidiaLow.mlmodelc/model0/analytics/coremldata.bin +3 -0
  27. SortformerNvidiaLow.mlmodelc/model0/coremldata.bin +3 -0
  28. {Pipeline_PreEncoder.mlmodelc → SortformerNvidiaLow.mlmodelc/model0}/model.mil +17 -17
  29. Pipeline_Head_Fixed.mlpackage/Data/com.apple.CoreML/model.mlmodel → SortformerNvidiaLow.mlmodelc/model0/weights/0-weight.bin +2 -2
  30. SortformerNvidiaLow.mlmodelc/model1/analytics/coremldata.bin +3 -0
  31. SortformerNvidiaLow.mlmodelc/model1/coremldata.bin +3 -0
  32. {Pipeline_Head_Fixed.mlmodelc → SortformerNvidiaLow.mlmodelc/model1}/model.mil +0 -0
  33. Pipeline_Head_Fixed.mlpackage/Data/com.apple.CoreML/weights/weight.bin → SortformerNvidiaLow.mlmodelc/model1/weights/1-weight.bin +1 -1
  34. {Pipeline_PreEncoder.mlpackage → SortformerNvidiaLow.mlpackage}/Data/com.apple.CoreML/model.mlmodel +2 -2
  35. SortformerNvidiaLow.mlpackage/Data/com.apple.CoreML/weights/0-weight.bin +3 -0
  36. Pipeline_Head_Fixed.mlmodelc/weights/weight.bin → SortformerNvidiaLow.mlpackage/Data/com.apple.CoreML/weights/1-weight.bin +1 -1
  37. {Pipeline_PreEncoder.mlpackage → SortformerNvidiaLow.mlpackage}/Manifest.json +3 -3
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{Pipeline_Head_Fixed.mlmodelc → SortformerNvidiaHigh.mlmodelc}/analytics/coremldata.bin RENAMED
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{Pipeline_Head_Fixed.mlmodelc → SortformerNvidiaHigh.mlmodelc}/metadata.json RENAMED
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  "dataType" : "Int32",
156
  "formattedType" : "MultiArray (Int32 1)",
157
+ "shortDescription" : "Length of the FIFO queue (in frames)",
158
  "shape" : "[1]",
159
+ "name" : "fifo_lengths",
160
  "type" : "MultiArray"
161
  }
162
  ],
163
+ "userDefinedMetadata" : {
164
+ "spkcache_len" : "188",
165
+ "spkcache_update_period" : "300",
166
+ "chunk_len" : "340",
167
+ "mel_feature_frames" : "2720",
168
+ "chunk_right_context" : "40",
169
+ "subsampling_factor" : "8",
170
+ "fifo_len" : "40",
171
+ "chunk_left_context" : "1",
172
+ "frame_duration" : "0.08"
173
+ },
174
+ "generatedClassName" : "SortformerNvidiaHigh",
175
  "method" : "predict"
176
  }
177
  ]
{Pipeline_Head_Fixed.mlmodelc → SortformerNvidiaHigh.mlmodelc/model0/analytics}/coremldata.bin RENAMED
@@ -1,3 +1,3 @@
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  version https://git-lfs.github.com/spec/v1
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- oid sha256:c4c9a35d793dac2cc3d3626adbfb078c115283b0b61aaf52f05ff14916d2eac1
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- size 564
 
1
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+ oid sha256:5a8281049b2a65a3be541cfd9f949e84b8fe1c5251ce90e46da1626fed54e58a
3
+ size 108
{Pipeline_PreEncoder.mlmodelc/analytics → SortformerNvidiaHigh.mlmodelc/model0}/coremldata.bin RENAMED
@@ -1,3 +1,3 @@
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  version https://git-lfs.github.com/spec/v1
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- oid sha256:8ce8a8445c4403eb8acbaafe43e494f313b8ce96012f979363911ec9bc9d81e7
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- size 243
 
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+ oid sha256:2045223a88c6a15d00b08f272d5d4210ffa4d96c860630f7072ab29aa6dee79a
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+ size 634
SortformerNvidiaHigh.mlmodelc/model0/model.mil ADDED
@@ -0,0 +1,201 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ program(1.0)
2
+ [buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.9.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
3
+ {
4
+ func main<ios16>(tensor<fp32, [1, 3048, 128]> chunk, tensor<int32, [1]> chunk_lengths, tensor<fp32, [1, 40, 512]> fifo, tensor<int32, [1]> fifo_lengths, tensor<fp32, [1, 188, 512]> spkcache, tensor<int32, [1]> spkcache_lengths) {
5
+ tensor<fp32, [256]> model_encoder_pre_encode_conv_0_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_0_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(64)))];
6
+ tensor<fp32, [256, 1, 3, 3]> model_encoder_pre_encode_conv_0_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_0_weight"), val = tensor<fp32, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(1152)))];
7
+ tensor<fp32, [256]> model_encoder_pre_encode_conv_2_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(10432)))];
8
+ tensor<fp32, [256, 1, 3, 3]> model_encoder_pre_encode_conv_2_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_2_weight"), val = tensor<fp32, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(11520)))];
9
+ tensor<fp32, [256]> model_encoder_pre_encode_conv_3_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_3_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(20800)))];
10
+ tensor<fp32, [256, 256, 1, 1]> model_encoder_pre_encode_conv_3_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_3_weight"), val = tensor<fp32, [256, 256, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(21888)))];
11
+ tensor<fp32, [256]> model_encoder_pre_encode_conv_5_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_5_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(284096)))];
12
+ tensor<fp32, [256, 1, 3, 3]> model_encoder_pre_encode_conv_5_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_5_weight"), val = tensor<fp32, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(285184)))];
13
+ tensor<fp32, [256]> model_encoder_pre_encode_conv_6_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_6_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(294464)))];
14
+ tensor<fp32, [256, 256, 1, 1]> model_encoder_pre_encode_conv_6_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_6_weight"), val = tensor<fp32, [256, 256, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(295552)))];
15
+ tensor<fp32, [512]> model_encoder_pre_encode_out_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_out_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(557760)))];
16
+ tensor<fp32, [512, 4096]> model_encoder_pre_encode_out_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_out_weight"), val = tensor<fp32, [512, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(559872)))];
17
+ tensor<int32, [1]> tensor_1_axes_0 = const()[name = tensor<string, []>("tensor_1_axes_0"), val = tensor<int32, [1]>([1])];
18
+ tensor<fp32, [1, 1, 3048, 128]> tensor_1 = expand_dims(axes = tensor_1_axes_0, x = chunk)[name = tensor<string, []>("tensor_1")];
19
+ tensor<string, []> current_lengths_1_dtype_0 = const()[name = tensor<string, []>("current_lengths_1_dtype_0"), val = tensor<string, []>("fp32")];
20
+ tensor<int32, [1, 3048]> expand_dims_0 = const()[name = tensor<string, []>("expand_dims_0"), val = tensor<int32, [1, 3048]>([[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, 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2325, 2326, 2327, 2328, 2329, 2330, 2331, 2332, 2333, 2334, 2335, 2336, 2337, 2338, 2339, 2340, 2341, 2342, 2343, 2344, 2345, 2346, 2347, 2348, 2349, 2350, 2351, 2352, 2353, 2354, 2355, 2356, 2357, 2358, 2359, 2360, 2361, 2362, 2363, 2364, 2365, 2366, 2367, 2368, 2369, 2370, 2371, 2372, 2373, 2374, 2375, 2376, 2377, 2378, 2379, 2380, 2381, 2382, 2383, 2384, 2385, 2386, 2387, 2388, 2389, 2390, 2391, 2392, 2393, 2394, 2395, 2396, 2397, 2398, 2399, 2400, 2401, 2402, 2403, 2404, 2405, 2406, 2407, 2408, 2409, 2410, 2411, 2412, 2413, 2414, 2415, 2416, 2417, 2418, 2419, 2420, 2421, 2422, 2423, 2424, 2425, 2426, 2427, 2428, 2429, 2430, 2431, 2432, 2433, 2434, 2435, 2436, 2437, 2438, 2439, 2440, 2441, 2442, 2443, 2444, 2445, 2446, 2447, 2448, 2449, 2450, 2451, 2452, 2453, 2454, 2455, 2456, 2457, 2458, 2459, 2460, 2461, 2462, 2463, 2464, 2465, 2466, 2467, 2468, 2469, 2470, 2471, 2472, 2473, 2474, 2475, 2476, 2477, 2478, 2479, 2480, 2481, 2482, 2483, 2484, 2485, 2486, 2487, 2488, 2489, 2490, 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2657, 2658, 2659, 2660, 2661, 2662, 2663, 2664, 2665, 2666, 2667, 2668, 2669, 2670, 2671, 2672, 2673, 2674, 2675, 2676, 2677, 2678, 2679, 2680, 2681, 2682, 2683, 2684, 2685, 2686, 2687, 2688, 2689, 2690, 2691, 2692, 2693, 2694, 2695, 2696, 2697, 2698, 2699, 2700, 2701, 2702, 2703, 2704, 2705, 2706, 2707, 2708, 2709, 2710, 2711, 2712, 2713, 2714, 2715, 2716, 2717, 2718, 2719, 2720, 2721, 2722, 2723, 2724, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804, 2805, 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820, 2821, 2822, 2823, 2824, 2825, 2826, 2827, 2828, 2829, 2830, 2831, 2832, 2833, 2834, 2835, 2836, 2837, 2838, 2839, 2840, 2841, 2842, 2843, 2844, 2845, 2846, 2847, 2848, 2849, 2850, 2851, 2852, 2853, 2854, 2855, 2856, 2857, 2858, 2859, 2860, 2861, 2862, 2863, 2864, 2865, 2866, 2867, 2868, 2869, 2870, 2871, 2872, 2873, 2874, 2875, 2876, 2877, 2878, 2879, 2880, 2881, 2882, 2883, 2884, 2885, 2886, 2887, 2888, 2889, 2890, 2891, 2892, 2893, 2894, 2895, 2896, 2897, 2898, 2899, 2900, 2901, 2902, 2903, 2904, 2905, 2906, 2907, 2908, 2909, 2910, 2911, 2912, 2913, 2914, 2915, 2916, 2917, 2918, 2919, 2920, 2921, 2922, 2923, 2924, 2925, 2926, 2927, 2928, 2929, 2930, 2931, 2932, 2933, 2934, 2935, 2936, 2937, 2938, 2939, 2940, 2941, 2942, 2943, 2944, 2945, 2946, 2947, 2948, 2949, 2950, 2951, 2952, 2953, 2954, 2955, 2956, 2957, 2958, 2959, 2960, 2961, 2962, 2963, 2964, 2965, 2966, 2967, 2968, 2969, 2970, 2971, 2972, 2973, 2974, 2975, 2976, 2977, 2978, 2979, 2980, 2981, 2982, 2983, 2984, 2985, 2986, 2987, 2988, 2989, 2990, 2991, 2992, 2993, 2994, 2995, 2996, 2997, 2998, 2999, 3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009, 3010, 3011, 3012, 3013, 3014, 3015, 3016, 3017, 3018, 3019, 3020, 3021, 3022, 3023, 3024, 3025, 3026, 3027, 3028, 3029, 3030, 3031, 3032, 3033, 3034, 3035, 3036, 3037, 3038, 3039, 3040, 3041, 3042, 3043, 3044, 3045, 3046, 3047]])];
21
+ tensor<int32, [1]> var_40_axes_0 = const()[name = tensor<string, []>("op_40_axes_0"), val = tensor<int32, [1]>([1])];
22
+ tensor<int32, [1, 1]> var_40 = expand_dims(axes = var_40_axes_0, x = chunk_lengths)[name = tensor<string, []>("op_40")];
23
+ tensor<bool, [1, 3048]> time_mask_1 = less(x = expand_dims_0, y = var_40)[name = tensor<string, []>("time_mask_1")];
24
+ tensor<int32, [1]> var_42_axes_0 = const()[name = tensor<string, []>("op_42_axes_0"), val = tensor<int32, [1]>([-1])];
25
+ tensor<bool, [1, 3048, 1]> var_42 = expand_dims(axes = var_42_axes_0, x = time_mask_1)[name = tensor<string, []>("op_42")];
26
+ tensor<int32, [3]> var_44_reps_0 = const()[name = tensor<string, []>("op_44_reps_0"), val = tensor<int32, [3]>([1, 1, 128])];
27
+ tensor<bool, [1, 3048, 128]> var_44 = tile(reps = var_44_reps_0, x = var_42)[name = tensor<string, []>("op_44")];
28
+ tensor<string, []> mask_1_dtype_0 = const()[name = tensor<string, []>("mask_1_dtype_0"), val = tensor<string, []>("fp32")];
29
+ tensor<int32, [1]> var_50_axes_0 = const()[name = tensor<string, []>("op_50_axes_0"), val = tensor<int32, [1]>([1])];
30
+ tensor<fp32, [1, 3048, 128]> mask_1 = cast(dtype = mask_1_dtype_0, x = var_44)[name = tensor<string, []>("cast_11")];
31
+ tensor<fp32, [1, 1, 3048, 128]> var_50 = expand_dims(axes = var_50_axes_0, x = mask_1)[name = tensor<string, []>("op_50")];
32
+ tensor<fp32, [1, 1, 3048, 128]> input_1 = mul(x = tensor_1, y = var_50)[name = tensor<string, []>("input_1")];
33
+ tensor<string, []> tensor_3_pad_type_0 = const()[name = tensor<string, []>("tensor_3_pad_type_0"), val = tensor<string, []>("custom")];
34
+ tensor<int32, [4]> tensor_3_pad_0 = const()[name = tensor<string, []>("tensor_3_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
35
+ tensor<int32, [2]> tensor_3_strides_0 = const()[name = tensor<string, []>("tensor_3_strides_0"), val = tensor<int32, [2]>([2, 2])];
36
+ tensor<int32, [2]> tensor_3_dilations_0 = const()[name = tensor<string, []>("tensor_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
37
+ tensor<int32, []> tensor_3_groups_0 = const()[name = tensor<string, []>("tensor_3_groups_0"), val = tensor<int32, []>(1)];
38
+ tensor<fp32, [1, 256, 1524, 64]> tensor_3 = conv(bias = model_encoder_pre_encode_conv_0_bias, dilations = tensor_3_dilations_0, groups = tensor_3_groups_0, pad = tensor_3_pad_0, pad_type = tensor_3_pad_type_0, strides = tensor_3_strides_0, weight = model_encoder_pre_encode_conv_0_weight, x = input_1)[name = tensor<string, []>("tensor_3")];
39
+ tensor<fp32, []> var_61_promoted = const()[name = tensor<string, []>("op_61_promoted"), val = tensor<fp32, []>(0x1p+0)];
40
+ tensor<fp32, [1]> current_lengths_1 = cast(dtype = current_lengths_1_dtype_0, x = chunk_lengths)[name = tensor<string, []>("cast_12")];
41
+ tensor<fp32, [1]> var_62 = add(x = current_lengths_1, y = var_61_promoted)[name = tensor<string, []>("op_62")];
42
+ tensor<fp32, []> var_63_promoted = const()[name = tensor<string, []>("op_63_promoted"), val = tensor<fp32, []>(0x1p+0)];
43
+ tensor<fp32, [1]> var_64 = add(x = var_62, y = var_63_promoted)[name = tensor<string, []>("op_64")];
44
+ tensor<fp32, []> var_65_promoted = const()[name = tensor<string, []>("op_65_promoted"), val = tensor<fp32, []>(0x1.8p+1)];
45
+ tensor<fp32, [1]> var_66 = sub(x = var_64, y = var_65_promoted)[name = tensor<string, []>("op_66")];
46
+ tensor<fp32, []> var_21_promoted = const()[name = tensor<string, []>("op_21_promoted"), val = tensor<fp32, []>(0x1p+1)];
47
+ tensor<fp32, [1]> floor_div_0 = floor_div(x = var_66, y = var_21_promoted)[name = tensor<string, []>("floor_div_0")];
48
+ tensor<fp32, []> var_68_promoted = const()[name = tensor<string, []>("op_68_promoted"), val = tensor<fp32, []>(0x1p+0)];
49
+ tensor<fp32, [1]> current_lengths_3 = add(x = floor_div_0, y = var_68_promoted)[name = tensor<string, []>("current_lengths_3")];
50
+ tensor<string, []> lengths_21_dtype_0 = const()[name = tensor<string, []>("lengths_21_dtype_0"), val = tensor<string, []>("int32")];
51
+ tensor<int32, [1, 1524]> expand_dims_1 = const()[name = tensor<string, []>("expand_dims_1"), val = tensor<int32, [1, 1524]>([[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, 1501, 1502, 1503, 1504, 1505, 1506, 1507, 1508, 1509, 1510, 1511, 1512, 1513, 1514, 1515, 1516, 1517, 1518, 1519, 1520, 1521, 1522, 1523]])];
52
+ tensor<int32, [1]> var_77_axes_0 = const()[name = tensor<string, []>("op_77_axes_0"), val = tensor<int32, [1]>([1])];
53
+ tensor<int32, [1]> lengths_21 = cast(dtype = lengths_21_dtype_0, x = current_lengths_3)[name = tensor<string, []>("cast_10")];
54
+ tensor<int32, [1, 1]> var_77 = expand_dims(axes = var_77_axes_0, x = lengths_21)[name = tensor<string, []>("op_77")];
55
+ tensor<bool, [1, 1524]> time_mask_3 = less(x = expand_dims_1, y = var_77)[name = tensor<string, []>("time_mask_3")];
56
+ tensor<int32, [1]> var_79_axes_0 = const()[name = tensor<string, []>("op_79_axes_0"), val = tensor<int32, [1]>([-1])];
57
+ tensor<bool, [1, 1524, 1]> var_79 = expand_dims(axes = var_79_axes_0, x = time_mask_3)[name = tensor<string, []>("op_79")];
58
+ tensor<int32, [3]> var_81_reps_0 = const()[name = tensor<string, []>("op_81_reps_0"), val = tensor<int32, [3]>([1, 1, 64])];
59
+ tensor<bool, [1, 1524, 64]> var_81 = tile(reps = var_81_reps_0, x = var_79)[name = tensor<string, []>("op_81")];
60
+ tensor<string, []> mask_3_dtype_0 = const()[name = tensor<string, []>("mask_3_dtype_0"), val = tensor<string, []>("fp32")];
61
+ tensor<int32, [1]> var_87_axes_0 = const()[name = tensor<string, []>("op_87_axes_0"), val = tensor<int32, [1]>([1])];
62
+ tensor<fp32, [1, 1524, 64]> mask_3 = cast(dtype = mask_3_dtype_0, x = var_81)[name = tensor<string, []>("cast_9")];
63
+ tensor<fp32, [1, 1, 1524, 64]> var_87 = expand_dims(axes = var_87_axes_0, x = mask_3)[name = tensor<string, []>("op_87")];
64
+ tensor<int32, [4]> expanded_mask_3_reps_0 = const()[name = tensor<string, []>("expanded_mask_3_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
65
+ tensor<fp32, [1, 256, 1524, 64]> expanded_mask_3 = tile(reps = expanded_mask_3_reps_0, x = var_87)[name = tensor<string, []>("expanded_mask_3")];
66
+ tensor<fp32, [1, 256, 1524, 64]> input_3 = mul(x = tensor_3, y = expanded_mask_3)[name = tensor<string, []>("input_3")];
67
+ tensor<fp32, [1, 256, 1524, 64]> tensor_5 = relu(x = input_3)[name = tensor<string, []>("tensor_5")];
68
+ tensor<fp32, [1, 256, 1524, 64]> input_5 = mul(x = tensor_5, y = expanded_mask_3)[name = tensor<string, []>("input_5")];
69
+ tensor<string, []> tensor_7_pad_type_0 = const()[name = tensor<string, []>("tensor_7_pad_type_0"), val = tensor<string, []>("custom")];
70
+ tensor<int32, [4]> tensor_7_pad_0 = const()[name = tensor<string, []>("tensor_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
71
+ tensor<int32, [2]> tensor_7_strides_0 = const()[name = tensor<string, []>("tensor_7_strides_0"), val = tensor<int32, [2]>([2, 2])];
72
+ tensor<int32, []> tensor_7_groups_0 = const()[name = tensor<string, []>("tensor_7_groups_0"), val = tensor<int32, []>(256)];
73
+ tensor<int32, [2]> tensor_7_dilations_0 = const()[name = tensor<string, []>("tensor_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
74
+ tensor<fp32, [1, 256, 762, 32]> tensor_7 = conv(bias = model_encoder_pre_encode_conv_2_bias, dilations = tensor_7_dilations_0, groups = tensor_7_groups_0, pad = tensor_7_pad_0, pad_type = tensor_7_pad_type_0, strides = tensor_7_strides_0, weight = model_encoder_pre_encode_conv_2_weight, x = input_5)[name = tensor<string, []>("tensor_7")];
75
+ tensor<fp32, []> var_107_promoted = const()[name = tensor<string, []>("op_107_promoted"), val = tensor<fp32, []>(0x1p+0)];
76
+ tensor<fp32, [1]> var_108 = add(x = current_lengths_3, y = var_107_promoted)[name = tensor<string, []>("op_108")];
77
+ tensor<fp32, []> var_109_promoted = const()[name = tensor<string, []>("op_109_promoted"), val = tensor<fp32, []>(0x1p+0)];
78
+ tensor<fp32, [1]> var_110 = add(x = var_108, y = var_109_promoted)[name = tensor<string, []>("op_110")];
79
+ tensor<fp32, []> var_111_promoted = const()[name = tensor<string, []>("op_111_promoted"), val = tensor<fp32, []>(0x1.8p+1)];
80
+ tensor<fp32, [1]> var_112 = sub(x = var_110, y = var_111_promoted)[name = tensor<string, []>("op_112")];
81
+ tensor<fp32, []> var_21_promoted_1 = const()[name = tensor<string, []>("op_21_promoted_1"), val = tensor<fp32, []>(0x1p+1)];
82
+ tensor<fp32, [1]> floor_div_1 = floor_div(x = var_112, y = var_21_promoted_1)[name = tensor<string, []>("floor_div_1")];
83
+ tensor<fp32, []> var_114_promoted = const()[name = tensor<string, []>("op_114_promoted"), val = tensor<fp32, []>(0x1p+0)];
84
+ tensor<fp32, [1]> current_lengths_5 = add(x = floor_div_1, y = var_114_promoted)[name = tensor<string, []>("current_lengths_5")];
85
+ tensor<string, []> lengths_23_dtype_0 = const()[name = tensor<string, []>("lengths_23_dtype_0"), val = tensor<string, []>("int32")];
86
+ tensor<int32, [1, 762]> expand_dims_2 = const()[name = tensor<string, []>("expand_dims_2"), val = tensor<int32, [1, 762]>([[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]])];
87
+ tensor<int32, [1]> var_123_axes_0 = const()[name = tensor<string, []>("op_123_axes_0"), val = tensor<int32, [1]>([1])];
88
+ tensor<int32, [1]> lengths_23 = cast(dtype = lengths_23_dtype_0, x = current_lengths_5)[name = tensor<string, []>("cast_8")];
89
+ tensor<int32, [1, 1]> var_123 = expand_dims(axes = var_123_axes_0, x = lengths_23)[name = tensor<string, []>("op_123")];
90
+ tensor<bool, [1, 762]> time_mask_5 = less(x = expand_dims_2, y = var_123)[name = tensor<string, []>("time_mask_5")];
91
+ tensor<int32, [1]> var_125_axes_0 = const()[name = tensor<string, []>("op_125_axes_0"), val = tensor<int32, [1]>([-1])];
92
+ tensor<bool, [1, 762, 1]> var_125 = expand_dims(axes = var_125_axes_0, x = time_mask_5)[name = tensor<string, []>("op_125")];
93
+ tensor<int32, [3]> var_127_reps_0 = const()[name = tensor<string, []>("op_127_reps_0"), val = tensor<int32, [3]>([1, 1, 32])];
94
+ tensor<bool, [1, 762, 32]> var_127 = tile(reps = var_127_reps_0, x = var_125)[name = tensor<string, []>("op_127")];
95
+ tensor<string, []> mask_5_dtype_0 = const()[name = tensor<string, []>("mask_5_dtype_0"), val = tensor<string, []>("fp32")];
96
+ tensor<int32, [1]> var_133_axes_0 = const()[name = tensor<string, []>("op_133_axes_0"), val = tensor<int32, [1]>([1])];
97
+ tensor<fp32, [1, 762, 32]> mask_5 = cast(dtype = mask_5_dtype_0, x = var_127)[name = tensor<string, []>("cast_7")];
98
+ tensor<fp32, [1, 1, 762, 32]> var_133 = expand_dims(axes = var_133_axes_0, x = mask_5)[name = tensor<string, []>("op_133")];
99
+ tensor<int32, [4]> expanded_mask_7_reps_0 = const()[name = tensor<string, []>("expanded_mask_7_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
100
+ tensor<fp32, [1, 256, 762, 32]> expanded_mask_7 = tile(reps = expanded_mask_7_reps_0, x = var_133)[name = tensor<string, []>("expanded_mask_7")];
101
+ tensor<fp32, [1, 256, 762, 32]> input_7 = mul(x = tensor_7, y = expanded_mask_7)[name = tensor<string, []>("input_7")];
102
+ tensor<string, []> tensor_9_pad_type_0 = const()[name = tensor<string, []>("tensor_9_pad_type_0"), val = tensor<string, []>("valid")];
103
+ tensor<int32, [2]> tensor_9_strides_0 = const()[name = tensor<string, []>("tensor_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
104
+ tensor<int32, [4]> tensor_9_pad_0 = const()[name = tensor<string, []>("tensor_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
105
+ tensor<int32, [2]> tensor_9_dilations_0 = const()[name = tensor<string, []>("tensor_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
106
+ tensor<int32, []> tensor_9_groups_0 = const()[name = tensor<string, []>("tensor_9_groups_0"), val = tensor<int32, []>(1)];
107
+ tensor<fp32, [1, 256, 762, 32]> tensor_9 = conv(bias = model_encoder_pre_encode_conv_3_bias, dilations = tensor_9_dilations_0, groups = tensor_9_groups_0, pad = tensor_9_pad_0, pad_type = tensor_9_pad_type_0, strides = tensor_9_strides_0, weight = model_encoder_pre_encode_conv_3_weight, x = input_7)[name = tensor<string, []>("tensor_9")];
108
+ tensor<fp32, [1, 256, 762, 32]> input_9 = mul(x = tensor_9, y = expanded_mask_7)[name = tensor<string, []>("input_9")];
109
+ tensor<fp32, [1, 256, 762, 32]> tensor_11 = relu(x = input_9)[name = tensor<string, []>("tensor_11")];
110
+ tensor<fp32, [1, 256, 762, 32]> input_11 = mul(x = tensor_11, y = expanded_mask_7)[name = tensor<string, []>("input_11")];
111
+ tensor<string, []> tensor_13_pad_type_0 = const()[name = tensor<string, []>("tensor_13_pad_type_0"), val = tensor<string, []>("custom")];
112
+ tensor<int32, [4]> tensor_13_pad_0 = const()[name = tensor<string, []>("tensor_13_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
113
+ tensor<int32, [2]> tensor_13_strides_0 = const()[name = tensor<string, []>("tensor_13_strides_0"), val = tensor<int32, [2]>([2, 2])];
114
+ tensor<int32, []> tensor_13_groups_0 = const()[name = tensor<string, []>("tensor_13_groups_0"), val = tensor<int32, []>(256)];
115
+ tensor<int32, [2]> tensor_13_dilations_0 = const()[name = tensor<string, []>("tensor_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
116
+ tensor<fp32, [1, 256, 381, 16]> tensor_13 = conv(bias = model_encoder_pre_encode_conv_5_bias, dilations = tensor_13_dilations_0, groups = tensor_13_groups_0, pad = tensor_13_pad_0, pad_type = tensor_13_pad_type_0, strides = tensor_13_strides_0, weight = model_encoder_pre_encode_conv_5_weight, x = input_11)[name = tensor<string, []>("tensor_13")];
117
+ tensor<fp32, []> var_168_promoted = const()[name = tensor<string, []>("op_168_promoted"), val = tensor<fp32, []>(0x1p+0)];
118
+ tensor<fp32, [1]> var_169 = add(x = current_lengths_5, y = var_168_promoted)[name = tensor<string, []>("op_169")];
119
+ tensor<fp32, []> var_170_promoted = const()[name = tensor<string, []>("op_170_promoted"), val = tensor<fp32, []>(0x1p+0)];
120
+ tensor<fp32, [1]> var_171 = add(x = var_169, y = var_170_promoted)[name = tensor<string, []>("op_171")];
121
+ tensor<fp32, []> var_172_promoted = const()[name = tensor<string, []>("op_172_promoted"), val = tensor<fp32, []>(0x1.8p+1)];
122
+ tensor<fp32, [1]> var_173 = sub(x = var_171, y = var_172_promoted)[name = tensor<string, []>("op_173")];
123
+ tensor<fp32, []> var_21_promoted_2 = const()[name = tensor<string, []>("op_21_promoted_2"), val = tensor<fp32, []>(0x1p+1)];
124
+ tensor<fp32, [1]> floor_div_2 = floor_div(x = var_173, y = var_21_promoted_2)[name = tensor<string, []>("floor_div_2")];
125
+ tensor<fp32, []> var_175_promoted = const()[name = tensor<string, []>("op_175_promoted"), val = tensor<fp32, []>(0x1p+0)];
126
+ tensor<fp32, [1]> current_lengths = add(x = floor_div_2, y = var_175_promoted)[name = tensor<string, []>("current_lengths")];
127
+ tensor<string, []> lengths_dtype_0 = const()[name = tensor<string, []>("lengths_dtype_0"), val = tensor<string, []>("int32")];
128
+ tensor<int32, [1, 381]> expand_dims_3 = const()[name = tensor<string, []>("expand_dims_3"), val = tensor<int32, [1, 381]>([[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]])];
129
+ tensor<int32, [1]> var_184_axes_0 = const()[name = tensor<string, []>("op_184_axes_0"), val = tensor<int32, [1]>([1])];
130
+ tensor<int32, [1]> lengths = cast(dtype = lengths_dtype_0, x = current_lengths)[name = tensor<string, []>("cast_6")];
131
+ tensor<int32, [1, 1]> var_184 = expand_dims(axes = var_184_axes_0, x = lengths)[name = tensor<string, []>("op_184")];
132
+ tensor<bool, [1, 381]> time_mask = less(x = expand_dims_3, y = var_184)[name = tensor<string, []>("time_mask")];
133
+ tensor<int32, [1]> var_186_axes_0 = const()[name = tensor<string, []>("op_186_axes_0"), val = tensor<int32, [1]>([-1])];
134
+ tensor<bool, [1, 381, 1]> var_186 = expand_dims(axes = var_186_axes_0, x = time_mask)[name = tensor<string, []>("op_186")];
135
+ tensor<int32, [3]> var_188_reps_0 = const()[name = tensor<string, []>("op_188_reps_0"), val = tensor<int32, [3]>([1, 1, 16])];
136
+ tensor<bool, [1, 381, 16]> var_188 = tile(reps = var_188_reps_0, x = var_186)[name = tensor<string, []>("op_188")];
137
+ tensor<string, []> mask_dtype_0 = const()[name = tensor<string, []>("mask_dtype_0"), val = tensor<string, []>("fp32")];
138
+ tensor<int32, [1]> var_194_axes_0 = const()[name = tensor<string, []>("op_194_axes_0"), val = tensor<int32, [1]>([1])];
139
+ tensor<fp32, [1, 381, 16]> mask = cast(dtype = mask_dtype_0, x = var_188)[name = tensor<string, []>("cast_5")];
140
+ tensor<fp32, [1, 1, 381, 16]> var_194 = expand_dims(axes = var_194_axes_0, x = mask)[name = tensor<string, []>("op_194")];
141
+ tensor<int32, [4]> expanded_mask_13_reps_0 = const()[name = tensor<string, []>("expanded_mask_13_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
142
+ tensor<fp32, [1, 256, 381, 16]> expanded_mask_13 = tile(reps = expanded_mask_13_reps_0, x = var_194)[name = tensor<string, []>("expanded_mask_13")];
143
+ tensor<fp32, [1, 256, 381, 16]> input_13 = mul(x = tensor_13, y = expanded_mask_13)[name = tensor<string, []>("input_13")];
144
+ tensor<string, []> tensor_15_pad_type_0 = const()[name = tensor<string, []>("tensor_15_pad_type_0"), val = tensor<string, []>("valid")];
145
+ tensor<int32, [2]> tensor_15_strides_0 = const()[name = tensor<string, []>("tensor_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
146
+ tensor<int32, [4]> tensor_15_pad_0 = const()[name = tensor<string, []>("tensor_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
147
+ tensor<int32, [2]> tensor_15_dilations_0 = const()[name = tensor<string, []>("tensor_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
148
+ tensor<int32, []> tensor_15_groups_0 = const()[name = tensor<string, []>("tensor_15_groups_0"), val = tensor<int32, []>(1)];
149
+ tensor<fp32, [1, 256, 381, 16]> tensor_15 = conv(bias = model_encoder_pre_encode_conv_6_bias, dilations = tensor_15_dilations_0, groups = tensor_15_groups_0, pad = tensor_15_pad_0, pad_type = tensor_15_pad_type_0, strides = tensor_15_strides_0, weight = model_encoder_pre_encode_conv_6_weight, x = input_13)[name = tensor<string, []>("tensor_15")];
150
+ tensor<fp32, [1, 256, 381, 16]> input_15 = mul(x = tensor_15, y = expanded_mask_13)[name = tensor<string, []>("input_15")];
151
+ tensor<fp32, [1, 256, 381, 16]> tensor_workaround = relu(x = input_15)[name = tensor<string, []>("tensor_workaround")];
152
+ tensor<fp32, [1, 256, 381, 16]> x = mul(x = tensor_workaround, y = expanded_mask_13)[name = tensor<string, []>("x")];
153
+ tensor<int32, [4]> var_228_perm_0 = const()[name = tensor<string, []>("op_228_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
154
+ tensor<int32, [3]> var_229 = const()[name = tensor<string, []>("op_229"), val = tensor<int32, [3]>([1, 381, -1])];
155
+ tensor<fp32, [1, 381, 256, 16]> var_228 = transpose(perm = var_228_perm_0, x = x)[name = tensor<string, []>("transpose_0")];
156
+ tensor<fp32, [1, 381, 4096]> input = reshape(shape = var_229, x = var_228)[name = tensor<string, []>("input")];
157
+ tensor<fp32, [1, 381, 512]> chunk_pre_encoder_embs = linear(bias = model_encoder_pre_encode_out_bias, weight = model_encoder_pre_encode_out_weight, x = input)[name = tensor<string, []>("linear_0")];
158
+ tensor<string, []> var_241_dtype_0 = const()[name = tensor<string, []>("op_241_dtype_0"), val = tensor<string, []>("int32")];
159
+ tensor<int32, [1]> size0 = const()[name = tensor<string, []>("size0"), val = tensor<int32, [1]>([188])];
160
+ tensor<int32, [1]> size1 = const()[name = tensor<string, []>("size1"), val = tensor<int32, [1]>([40])];
161
+ tensor<int32, []> var_264 = const()[name = tensor<string, []>("op_264"), val = tensor<int32, []>(1)];
162
+ tensor<bool, []> full_concat_interleave_0 = const()[name = tensor<string, []>("full_concat_interleave_0"), val = tensor<bool, []>(false)];
163
+ tensor<fp32, [1, 609, 512]> full_concat = concat(axis = var_264, interleave = full_concat_interleave_0, values = (spkcache, fifo, chunk_pre_encoder_embs))[name = tensor<string, []>("full_concat")];
164
+ tensor<int32, [1]> var_273 = add(x = spkcache_lengths, y = fifo_lengths)[name = tensor<string, []>("op_273")];
165
+ tensor<int32, [1]> chunk_pre_encoder_lengths = cast(dtype = var_241_dtype_0, x = current_lengths)[name = tensor<string, []>("cast_4")];
166
+ tensor<int32, [1]> pre_encoder_lengths = add(x = var_273, y = chunk_pre_encoder_lengths)[name = tensor<string, []>("total_length")];
167
+ tensor<int32, [609]> out_pos = const()[name = tensor<string, []>("out_pos"), val = tensor<int32, [609]>([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])];
168
+ tensor<bool, [609]> var_284 = greater_equal(x = out_pos, y = spkcache_lengths)[name = tensor<string, []>("op_284")];
169
+ tensor<string, []> in_seg1_or_2_dtype_0 = const()[name = tensor<string, []>("in_seg1_or_2_dtype_0"), val = tensor<string, []>("int32")];
170
+ tensor<bool, [609]> var_290 = greater_equal(x = out_pos, y = var_273)[name = tensor<string, []>("op_290")];
171
+ tensor<string, []> in_seg2_dtype_0 = const()[name = tensor<string, []>("in_seg2_dtype_0"), val = tensor<string, []>("int32")];
172
+ tensor<int32, [1]> var_297 = sub(x = size0, y = spkcache_lengths)[name = tensor<string, []>("op_297")];
173
+ tensor<int32, [609]> in_seg1_or_2 = cast(dtype = in_seg1_or_2_dtype_0, x = var_284)[name = tensor<string, []>("cast_3")];
174
+ tensor<int32, [609]> var_298 = mul(x = in_seg1_or_2, y = var_297)[name = tensor<string, []>("op_298")];
175
+ tensor<int32, [1]> var_300 = sub(x = size1, y = fifo_lengths)[name = tensor<string, []>("op_300")];
176
+ tensor<int32, [609]> in_seg2 = cast(dtype = in_seg2_dtype_0, x = var_290)[name = tensor<string, []>("cast_2")];
177
+ tensor<int32, [609]> var_301 = mul(x = in_seg2, y = var_300)[name = tensor<string, []>("op_301")];
178
+ tensor<int32, [609]> offset = add(x = var_298, y = var_301)[name = tensor<string, []>("offset")];
179
+ tensor<int32, [609]> var_305 = add(x = out_pos, y = offset)[name = tensor<string, []>("op_305")];
180
+ tensor<int32, []> var_309 = const()[name = tensor<string, []>("op_309"), val = tensor<int32, []>(608)];
181
+ tensor<int32, []> var_310 = const()[name = tensor<string, []>("op_310"), val = tensor<int32, []>(0)];
182
+ tensor<int32, [609]> minimum_0 = minimum(x = var_305, y = var_309)[name = tensor<string, []>("minimum_0")];
183
+ tensor<int32, [609]> maximum_0 = maximum(x = minimum_0, y = var_310)[name = tensor<string, []>("maximum_0")];
184
+ tensor<int32, [1]> var_313_axes_0 = const()[name = tensor<string, []>("op_313_axes_0"), val = tensor<int32, [1]>([0])];
185
+ tensor<int32, [1, 609]> var_313 = expand_dims(axes = var_313_axes_0, x = maximum_0)[name = tensor<string, []>("op_313")];
186
+ tensor<int32, [1]> var_315_axes_0 = const()[name = tensor<string, []>("op_315_axes_0"), val = tensor<int32, [1]>([-1])];
187
+ tensor<int32, [1, 609, 1]> var_315 = expand_dims(axes = var_315_axes_0, x = var_313)[name = tensor<string, []>("op_315")];
188
+ tensor<int32, [3]> gather_idx_reps_0 = const()[name = tensor<string, []>("gather_idx_reps_0"), val = tensor<int32, [3]>([1, 1, 512])];
189
+ tensor<int32, [1, 609, 512]> gather_idx = tile(reps = gather_idx_reps_0, x = var_315)[name = tensor<string, []>("gather_idx")];
190
+ tensor<int32, []> var_320 = const()[name = tensor<string, []>("op_320"), val = tensor<int32, []>(1)];
191
+ tensor<fp32, [1, 609, 512]> output = gather_along_axis(axis = var_320, indices = gather_idx, x = full_concat)[name = tensor<string, []>("output")];
192
+ tensor<bool, [609]> var_323 = less(x = out_pos, y = pre_encoder_lengths)[name = tensor<string, []>("op_323")];
193
+ tensor<string, []> var_328_dtype_0 = const()[name = tensor<string, []>("op_328_dtype_0"), val = tensor<string, []>("fp32")];
194
+ tensor<int32, [1]> var_330_axes_0 = const()[name = tensor<string, []>("op_330_axes_0"), val = tensor<int32, [1]>([0])];
195
+ tensor<fp32, [609]> var_328 = cast(dtype = var_328_dtype_0, x = var_323)[name = tensor<string, []>("cast_1")];
196
+ tensor<fp32, [1, 609]> var_330 = expand_dims(axes = var_330_axes_0, x = var_328)[name = tensor<string, []>("op_330")];
197
+ tensor<int32, [1]> var_332_axes_0 = const()[name = tensor<string, []>("op_332_axes_0"), val = tensor<int32, [1]>([-1])];
198
+ tensor<fp32, [1, 609, 1]> var_332 = expand_dims(axes = var_332_axes_0, x = var_330)[name = tensor<string, []>("op_332")];
199
+ tensor<fp32, [1, 609, 512]> pre_encoder_embs = mul(x = output, y = var_332)[name = tensor<string, []>("op_333")];
200
+ } -> (pre_encoder_embs, pre_encoder_lengths, chunk_pre_encoder_embs, chunk_pre_encoder_lengths);
201
+ }
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10
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11
  "author": "com.apple.CoreML",
12
  "description": "CoreML Model Specification",
13
  "name": "model.mlmodel",
14
  "path": "com.apple.CoreML/model.mlmodel"
15
  }
16
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17
+ "rootModelIdentifier": "F250FEE8-A027-4BAB-B7D1-630291725950"
18
  }
SortformerNvidiaLow.mlmodelc/analytics/coremldata.bin ADDED
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+ oid sha256:7a96be4debfe5612eb3f316a5d91baee98be511526a4f9f59974a2c2bcf5d323
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SortformerNvidiaLow.mlmodelc/coremldata.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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{Pipeline_PreEncoder.mlmodelc → SortformerNvidiaLow.mlmodelc}/metadata.json RENAMED
@@ -1,26 +1,16 @@
1
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2
  {
 
3
  "metadataOutputVersion" : "3.0",
4
- "storagePrecision" : "Float32",
5
  "outputSchema" : [
6
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7
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8
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10
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14
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22
- "shape" : "[1]",
23
- "name" : "pre_encoder_lengths",
24
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25
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26
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@@ -28,9 +18,9 @@
28
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30
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31
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32
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33
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34
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35
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36
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@@ -38,41 +28,57 @@
38
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39
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40
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41
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42
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43
- "name" : "chunk_lens_in",
44
  "type" : "MultiArray"
45
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46
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47
  "modelParameters" : [
48
 
49
  ],
 
50
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51
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52
- "Concat" : 1,
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54
- "Ios16.mul" : 12,
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  "Ios16.floorDiv" : 3,
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- "Ios16.conv" : 5,
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62
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63
- "Ios16.linear" : 1,
64
  "Ios16.greaterEqual" : 2,
65
- "ExpandDims" : 17,
 
66
  "Ios16.minimum" : 1,
 
 
 
 
 
 
 
 
 
 
 
67
  "Ios16.maximum" : 1,
68
- "Ios16.reshape" : 1,
69
- "Ios16.gatherAlongAxis" : 1
 
 
 
70
  },
71
- "computePrecision" : "Mixed (Float32, Int32)",
72
- "isUpdatable" : "0",
73
  "stateSchema" : [
74
 
75
  ],
 
76
  "availability" : {
77
  "macOS" : "13.0",
78
  "tvOS" : "16.0",
@@ -82,13 +88,15 @@
82
  "macCatalyst" : "16.0"
83
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84
  "modelType" : {
85
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86
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87
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88
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89
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90
- "com.github.apple.coremltools.version" : "9.0",
91
- "com.github.apple.coremltools.source_dialect" : "TorchScript"
 
 
92
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93
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@@ -96,7 +104,7 @@
96
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97
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98
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99
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100
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101
  "name" : "chunk",
102
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@@ -106,7 +114,7 @@
106
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107
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108
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109
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110
  "shape" : "[1]",
111
  "name" : "chunk_lengths",
112
  "type" : "MultiArray"
@@ -116,7 +124,7 @@
116
  "isOptional" : "0",
117
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118
  "formattedType" : "MultiArray (Float32 1 × 188 × 512)",
119
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120
  "shape" : "[1, 188, 512]",
121
  "name" : "spkcache",
122
  "type" : "MultiArray"
@@ -126,7 +134,7 @@
126
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127
  "dataType" : "Int32",
128
  "formattedType" : "MultiArray (Int32 1)",
129
- "shortDescription" : "",
130
  "shape" : "[1]",
131
  "name" : "spkcache_lengths",
132
  "type" : "MultiArray"
@@ -136,7 +144,7 @@
136
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137
  "dataType" : "Float32",
138
  "formattedType" : "MultiArray (Float32 1 × 40 × 512)",
139
- "shortDescription" : "",
140
  "shape" : "[1, 40, 512]",
141
  "name" : "fifo",
142
  "type" : "MultiArray"
@@ -146,13 +154,24 @@
146
  "isOptional" : "0",
147
  "dataType" : "Int32",
148
  "formattedType" : "MultiArray (Int32 1)",
149
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150
  "shape" : "[1]",
151
  "name" : "fifo_lengths",
152
  "type" : "MultiArray"
153
  }
154
  ],
155
- "generatedClassName" : "Pipeline_PreEncoder",
 
 
 
 
 
 
 
 
 
 
 
156
  "method" : "predict"
157
  }
158
  ]
 
1
  [
2
  {
3
+ "shortDescription" : "CoreML port of Nvidia's Streaming Sortformer diarization model",
4
  "metadataOutputVersion" : "3.0",
 
5
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7
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12
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13
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15
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  {
 
18
  "isOptional" : "0",
19
  "dataType" : "Float32",
20
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22
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23
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24
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25
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28
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29
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31
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32
  "shape" : "[1]",
33
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34
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35
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36
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37
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38
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39
 
40
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41
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42
  "specificationVersion" : 7,
43
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44
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45
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47
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57
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  "Ios16.maximum" : 1,
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+ "computePrecision" : "Mixed (Float16, Float32, Int32)",
 
78
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80
  ],
81
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  "availability" : {
83
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84
  "tvOS" : "16.0",
 
88
  "macCatalyst" : "16.0"
89
  },
90
  "modelType" : {
91
+ "name" : "MLModelType_pipeline",
92
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93
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94
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95
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96
+ {
97
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98
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99
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100
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101
  "inputSchema" : [
102
  {
 
104
  "isOptional" : "0",
105
  "dataType" : "Float32",
106
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107
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108
  "shape" : "[1, 112, 128]",
109
  "name" : "chunk",
110
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114
  "isOptional" : "0",
115
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116
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117
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118
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119
  "name" : "chunk_lengths",
120
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124
  "isOptional" : "0",
125
  "dataType" : "Float32",
126
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127
+ "shortDescription" : "Order of Arrival Speaker Cache",
128
  "shape" : "[1, 188, 512]",
129
  "name" : "spkcache",
130
  "type" : "MultiArray"
 
134
  "isOptional" : "0",
135
  "dataType" : "Int32",
136
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137
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138
  "shape" : "[1]",
139
  "name" : "spkcache_lengths",
140
  "type" : "MultiArray"
 
144
  "isOptional" : "0",
145
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146
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147
+ "shortDescription" : "First-In-First-Out speech queue",
148
  "shape" : "[1, 40, 512]",
149
  "name" : "fifo",
150
  "type" : "MultiArray"
 
154
  "isOptional" : "0",
155
  "dataType" : "Int32",
156
  "formattedType" : "MultiArray (Int32 1)",
157
+ "shortDescription" : "Length of the FIFO queue (in frames)",
158
  "shape" : "[1]",
159
  "name" : "fifo_lengths",
160
  "type" : "MultiArray"
161
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162
  ],
163
+ "userDefinedMetadata" : {
164
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165
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166
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167
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168
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169
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170
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171
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172
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173
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174
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175
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176
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177
  ]
SortformerNvidiaLow.mlmodelc/model0/analytics/coremldata.bin ADDED
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SortformerNvidiaLow.mlmodelc/model0/coremldata.bin ADDED
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{Pipeline_PreEncoder.mlmodelc → SortformerNvidiaLow.mlmodelc/model0}/model.mil RENAMED
@@ -2,18 +2,18 @@ program(1.0)
2
  [buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.9.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
3
  {
4
  func main<ios16>(tensor<fp32, [1, 112, 128]> chunk, tensor<int32, [1]> chunk_lengths, tensor<fp32, [1, 40, 512]> fifo, tensor<int32, [1]> fifo_lengths, tensor<fp32, [1, 188, 512]> spkcache, tensor<int32, [1]> spkcache_lengths) {
5
- tensor<fp32, [256]> model_encoder_pre_encode_conv_0_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_0_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
6
- tensor<fp32, [256, 1, 3, 3]> model_encoder_pre_encode_conv_0_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_0_weight"), val = tensor<fp32, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1152)))];
7
- tensor<fp32, [256]> model_encoder_pre_encode_conv_2_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10432)))];
8
- tensor<fp32, [256, 1, 3, 3]> model_encoder_pre_encode_conv_2_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_2_weight"), val = tensor<fp32, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11520)))];
9
- tensor<fp32, [256]> model_encoder_pre_encode_conv_3_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_3_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20800)))];
10
- tensor<fp32, [256, 256, 1, 1]> model_encoder_pre_encode_conv_3_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_3_weight"), val = tensor<fp32, [256, 256, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21888)))];
11
- tensor<fp32, [256]> model_encoder_pre_encode_conv_5_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_5_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(284096)))];
12
- tensor<fp32, [256, 1, 3, 3]> model_encoder_pre_encode_conv_5_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_5_weight"), val = tensor<fp32, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(285184)))];
13
- tensor<fp32, [256]> model_encoder_pre_encode_conv_6_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_6_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(294464)))];
14
- tensor<fp32, [256, 256, 1, 1]> model_encoder_pre_encode_conv_6_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_6_weight"), val = tensor<fp32, [256, 256, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(295552)))];
15
- tensor<fp32, [512]> model_encoder_pre_encode_out_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_out_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(557760)))];
16
- tensor<fp32, [512, 4096]> model_encoder_pre_encode_out_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_out_weight"), val = tensor<fp32, [512, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(559872)))];
17
  tensor<int32, [1]> tensor_1_axes_0 = const()[name = tensor<string, []>("tensor_1_axes_0"), val = tensor<int32, [1]>([1])];
18
  tensor<fp32, [1, 1, 112, 128]> tensor_1 = expand_dims(axes = tensor_1_axes_0, x = chunk)[name = tensor<string, []>("tensor_1")];
19
  tensor<string, []> current_lengths_1_dtype_0 = const()[name = tensor<string, []>("current_lengths_1_dtype_0"), val = tensor<string, []>("fp32")];
@@ -154,16 +154,16 @@ program(1.0)
154
  tensor<int32, [3]> var_229 = const()[name = tensor<string, []>("op_229"), val = tensor<int32, [3]>([1, 14, -1])];
155
  tensor<fp32, [1, 14, 256, 16]> var_228 = transpose(perm = var_228_perm_0, x = x)[name = tensor<string, []>("transpose_0")];
156
  tensor<fp32, [1, 14, 4096]> input = reshape(shape = var_229, x = var_228)[name = tensor<string, []>("input")];
157
- tensor<fp32, [1, 14, 512]> chunk_embs_in = linear(bias = model_encoder_pre_encode_out_bias, weight = model_encoder_pre_encode_out_weight, x = input)[name = tensor<string, []>("linear_0")];
158
  tensor<string, []> var_241_dtype_0 = const()[name = tensor<string, []>("op_241_dtype_0"), val = tensor<string, []>("int32")];
159
  tensor<int32, [1]> size0 = const()[name = tensor<string, []>("size0"), val = tensor<int32, [1]>([188])];
160
  tensor<int32, [1]> size1 = const()[name = tensor<string, []>("size1"), val = tensor<int32, [1]>([40])];
161
  tensor<int32, []> var_264 = const()[name = tensor<string, []>("op_264"), val = tensor<int32, []>(1)];
162
  tensor<bool, []> full_concat_interleave_0 = const()[name = tensor<string, []>("full_concat_interleave_0"), val = tensor<bool, []>(false)];
163
- tensor<fp32, [1, 242, 512]> full_concat = concat(axis = var_264, interleave = full_concat_interleave_0, values = (spkcache, fifo, chunk_embs_in))[name = tensor<string, []>("full_concat")];
164
  tensor<int32, [1]> var_273 = add(x = spkcache_lengths, y = fifo_lengths)[name = tensor<string, []>("op_273")];
165
- tensor<int32, [1]> chunk_lens_in = cast(dtype = var_241_dtype_0, x = current_lengths)[name = tensor<string, []>("cast_4")];
166
- tensor<int32, [1]> pre_encoder_lengths = add(x = var_273, y = chunk_lens_in)[name = tensor<string, []>("total_length")];
167
  tensor<int32, [242]> out_pos = const()[name = tensor<string, []>("out_pos"), val = tensor<int32, [242]>([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])];
168
  tensor<bool, [242]> var_284 = greater_equal(x = out_pos, y = spkcache_lengths)[name = tensor<string, []>("op_284")];
169
  tensor<string, []> in_seg1_or_2_dtype_0 = const()[name = tensor<string, []>("in_seg1_or_2_dtype_0"), val = tensor<string, []>("int32")];
@@ -197,5 +197,5 @@ program(1.0)
197
  tensor<int32, [1]> var_332_axes_0 = const()[name = tensor<string, []>("op_332_axes_0"), val = tensor<int32, [1]>([-1])];
198
  tensor<fp32, [1, 242, 1]> var_332 = expand_dims(axes = var_332_axes_0, x = var_330)[name = tensor<string, []>("op_332")];
199
  tensor<fp32, [1, 242, 512]> pre_encoder_embs = mul(x = output, y = var_332)[name = tensor<string, []>("op_333")];
200
- } -> (pre_encoder_embs, pre_encoder_lengths, chunk_embs_in, chunk_lens_in);
201
  }
 
2
  [buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.9.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
3
  {
4
  func main<ios16>(tensor<fp32, [1, 112, 128]> chunk, tensor<int32, [1]> chunk_lengths, tensor<fp32, [1, 40, 512]> fifo, tensor<int32, [1]> fifo_lengths, tensor<fp32, [1, 188, 512]> spkcache, tensor<int32, [1]> spkcache_lengths) {
5
+ tensor<fp32, [256]> model_encoder_pre_encode_conv_0_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_0_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(64)))];
6
+ tensor<fp32, [256, 1, 3, 3]> model_encoder_pre_encode_conv_0_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_0_weight"), val = tensor<fp32, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(1152)))];
7
+ tensor<fp32, [256]> model_encoder_pre_encode_conv_2_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(10432)))];
8
+ tensor<fp32, [256, 1, 3, 3]> model_encoder_pre_encode_conv_2_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_2_weight"), val = tensor<fp32, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(11520)))];
9
+ tensor<fp32, [256]> model_encoder_pre_encode_conv_3_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_3_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(20800)))];
10
+ tensor<fp32, [256, 256, 1, 1]> model_encoder_pre_encode_conv_3_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_3_weight"), val = tensor<fp32, [256, 256, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(21888)))];
11
+ tensor<fp32, [256]> model_encoder_pre_encode_conv_5_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_5_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(284096)))];
12
+ tensor<fp32, [256, 1, 3, 3]> model_encoder_pre_encode_conv_5_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_5_weight"), val = tensor<fp32, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(285184)))];
13
+ tensor<fp32, [256]> model_encoder_pre_encode_conv_6_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_6_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(294464)))];
14
+ tensor<fp32, [256, 256, 1, 1]> model_encoder_pre_encode_conv_6_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_6_weight"), val = tensor<fp32, [256, 256, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(295552)))];
15
+ tensor<fp32, [512]> model_encoder_pre_encode_out_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_out_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(557760)))];
16
+ tensor<fp32, [512, 4096]> model_encoder_pre_encode_out_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_out_weight"), val = tensor<fp32, [512, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(559872)))];
17
  tensor<int32, [1]> tensor_1_axes_0 = const()[name = tensor<string, []>("tensor_1_axes_0"), val = tensor<int32, [1]>([1])];
18
  tensor<fp32, [1, 1, 112, 128]> tensor_1 = expand_dims(axes = tensor_1_axes_0, x = chunk)[name = tensor<string, []>("tensor_1")];
19
  tensor<string, []> current_lengths_1_dtype_0 = const()[name = tensor<string, []>("current_lengths_1_dtype_0"), val = tensor<string, []>("fp32")];
 
154
  tensor<int32, [3]> var_229 = const()[name = tensor<string, []>("op_229"), val = tensor<int32, [3]>([1, 14, -1])];
155
  tensor<fp32, [1, 14, 256, 16]> var_228 = transpose(perm = var_228_perm_0, x = x)[name = tensor<string, []>("transpose_0")];
156
  tensor<fp32, [1, 14, 4096]> input = reshape(shape = var_229, x = var_228)[name = tensor<string, []>("input")];
157
+ tensor<fp32, [1, 14, 512]> chunk_pre_encoder_embs = linear(bias = model_encoder_pre_encode_out_bias, weight = model_encoder_pre_encode_out_weight, x = input)[name = tensor<string, []>("linear_0")];
158
  tensor<string, []> var_241_dtype_0 = const()[name = tensor<string, []>("op_241_dtype_0"), val = tensor<string, []>("int32")];
159
  tensor<int32, [1]> size0 = const()[name = tensor<string, []>("size0"), val = tensor<int32, [1]>([188])];
160
  tensor<int32, [1]> size1 = const()[name = tensor<string, []>("size1"), val = tensor<int32, [1]>([40])];
161
  tensor<int32, []> var_264 = const()[name = tensor<string, []>("op_264"), val = tensor<int32, []>(1)];
162
  tensor<bool, []> full_concat_interleave_0 = const()[name = tensor<string, []>("full_concat_interleave_0"), val = tensor<bool, []>(false)];
163
+ tensor<fp32, [1, 242, 512]> full_concat = concat(axis = var_264, interleave = full_concat_interleave_0, values = (spkcache, fifo, chunk_pre_encoder_embs))[name = tensor<string, []>("full_concat")];
164
  tensor<int32, [1]> var_273 = add(x = spkcache_lengths, y = fifo_lengths)[name = tensor<string, []>("op_273")];
165
+ tensor<int32, [1]> chunk_pre_encoder_lengths = cast(dtype = var_241_dtype_0, x = current_lengths)[name = tensor<string, []>("cast_4")];
166
+ tensor<int32, [1]> pre_encoder_lengths = add(x = var_273, y = chunk_pre_encoder_lengths)[name = tensor<string, []>("total_length")];
167
  tensor<int32, [242]> out_pos = const()[name = tensor<string, []>("out_pos"), val = tensor<int32, [242]>([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])];
168
  tensor<bool, [242]> var_284 = greater_equal(x = out_pos, y = spkcache_lengths)[name = tensor<string, []>("op_284")];
169
  tensor<string, []> in_seg1_or_2_dtype_0 = const()[name = tensor<string, []>("in_seg1_or_2_dtype_0"), val = tensor<string, []>("int32")];
 
197
  tensor<int32, [1]> var_332_axes_0 = const()[name = tensor<string, []>("op_332_axes_0"), val = tensor<int32, [1]>([-1])];
198
  tensor<fp32, [1, 242, 1]> var_332 = expand_dims(axes = var_332_axes_0, x = var_330)[name = tensor<string, []>("op_332")];
199
  tensor<fp32, [1, 242, 512]> pre_encoder_embs = mul(x = output, y = var_332)[name = tensor<string, []>("op_333")];
200
+ } -> (pre_encoder_embs, pre_encoder_lengths, chunk_pre_encoder_embs, chunk_pre_encoder_lengths);
201
  }
Pipeline_Head_Fixed.mlpackage/Data/com.apple.CoreML/model.mlmodel → SortformerNvidiaLow.mlmodelc/model0/weights/0-weight.bin RENAMED
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{Pipeline_Head_Fixed.mlmodelc → SortformerNvidiaLow.mlmodelc/model1}/model.mil RENAMED
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{Pipeline_PreEncoder.mlpackage → SortformerNvidiaLow.mlpackage}/Data/com.apple.CoreML/model.mlmodel RENAMED
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Pipeline_Head_Fixed.mlmodelc/weights/weight.bin → SortformerNvidiaLow.mlpackage/Data/com.apple.CoreML/weights/1-weight.bin RENAMED
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{Pipeline_PreEncoder.mlpackage → SortformerNvidiaLow.mlpackage}/Manifest.json RENAMED
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14
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15
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16
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18
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14
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15
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16
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