Add Sortformer_streaming.mlmodelc (chunk_len=6, low-latency variant)

Sortformer_streaming.mlmodelc/analytics/coremldata.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b2f5178505a0b70de0e06b5a83e79b4e6964436d52b14744bf4a24b37c743675
+size 202

Sortformer_streaming.mlmodelc/coremldata.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6af2c147181651b84d005e3346ef2320931fe25713d3d73e3413b3a461041021
+size 423

Sortformer_streaming.mlmodelc/metadata.json

@@ -0,0 +1,164 @@
+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "storagePrecision" : "Mixed (Float16, Float32)",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 242 × 4)",
+        "shortDescription" : "",
+        "shape" : "[1, 242, 4]",
+        "name" : "speaker_preds_out",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 14 × 512)",
+        "shortDescription" : "",
+        "shape" : "[1, 14, 512]",
+        "name" : "chunk_pre_encoder_embs_out",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Int32",
+        "formattedType" : "MultiArray (Int32 1)",
+        "shortDescription" : "",
+        "shape" : "[1]",
+        "name" : "chunk_pre_encoder_lengths_out",
+        "type" : "MultiArray"
+      }
+    ],
+    "modelParameters" : [
+
+    ],
+    "specificationVersion" : 8,
+    "mlProgramOperationTypeHistogram" : {
+      "Ios17.logicalAnd" : 2,
+      "Ios17.reshape" : 175,
+      "Ios16.softmax" : 35,
+      "Ios17.matmul" : 87,
+      "Ios17.transpose" : 193,
+      "Ios17.maximum" : 1,
+      "Split" : 17,
+      "Ios17.expandDims" : 25,
+      "Select" : 51,
+      "Ios17.add" : 187,
+      "Tile" : 9,
+      "Ios17.gatherAlongAxis" : 1,
+      "Ios17.sliceByIndex" : 34,
+      "Ios16.sigmoid" : 18,
+      "Pad" : 34,
+      "Ios17.logicalNot" : 2,
+      "Ios17.layerNorm" : 121,
+      "Ios17.less" : 7,
+      "Ios17.sub" : 6,
+      "Ios17.conv" : 56,
+      "Ios16.relu" : 23,
+      "Ios17.cast" : 18,
+      "Ios17.linear" : 248,
+      "Ios17.concat" : 1,
+      "Ios17.floorDiv" : 3,
+      "Ios17.minimum" : 1,
+      "Ios17.greaterEqual" : 2,
+      "Ios16.silu" : 51,
+      "Ios17.mul" : 119
+    },
+    "computePrecision" : "Mixed (Float16, Float32, Int32)",
+    "isUpdatable" : "0",
+    "stateSchema" : [
+
+    ],
+    "availability" : {
+      "macOS" : "14.0",
+      "tvOS" : "17.0",
+      "visionOS" : "1.0",
+      "watchOS" : "10.0",
+      "iOS" : "17.0",
+      "macCatalyst" : "17.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_pipeline",
+      "structure" : [
+        {
+          "name" : "MLModelType_mlProgram"
+        },
+        {
+          "name" : "MLModelType_mlProgram"
+        }
+      ]
+    },
+    "userDefinedMetadata" : {
+
+    },
+    "inputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 112 × 128)",
+        "shortDescription" : "",
+        "shape" : "[1, 112, 128]",
+        "name" : "chunk",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Int32",
+        "formattedType" : "MultiArray (Int32 1)",
+        "shortDescription" : "",
+        "shape" : "[1]",
+        "name" : "chunk_lengths",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 188 × 512)",
+        "shortDescription" : "",
+        "shape" : "[1, 188, 512]",
+        "name" : "spkcache",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Int32",
+        "formattedType" : "MultiArray (Int32 1)",
+        "shortDescription" : "",
+        "shape" : "[1]",
+        "name" : "spkcache_lengths",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 40 × 512)",
+        "shortDescription" : "",
+        "shape" : "[1, 40, 512]",
+        "name" : "fifo",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Int32",
+        "formattedType" : "MultiArray (Int32 1)",
+        "shortDescription" : "",
+        "shape" : "[1]",
+        "name" : "fifo_lengths",
+        "type" : "MultiArray"
+      }
+    ],
+    "generatedClassName" : "Sortformer",
+    "method" : "predict"
+  }
+]

Sortformer_streaming.mlmodelc/model0/analytics/coremldata.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5a8281049b2a65a3be541cfd9f949e84b8fe1c5251ce90e46da1626fed54e58a
+size 108

Sortformer_streaming.mlmodelc/model0/coremldata.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5fedc2c3f13135040e608428555f8c301b0e91ab0259aba1c2f7257957e49156
+size 574

Sortformer_streaming.mlmodelc/model0/model.mil

@@ -0,0 +1,202 @@
+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.11.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})]
+{
+    func main<ios17>(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) {
+            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)))];
+            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)))];
+            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)))];
+            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)))];
+            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)))];
+            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)))];
+            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)))];
+            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)))];
+            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)))];
+            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)))];
+            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)))];
+            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)))];
+            tensor<int32, [1]> tensor_1_axes_0 = const()[name = tensor<string, []>("tensor_1_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [1, 1, 112, 128]> tensor_1 = expand_dims(axes = tensor_1_axes_0, x = chunk)[name = tensor<string, []>("tensor_1")];
+            tensor<string, []> cast_0_dtype_0 = const()[name = tensor<string, []>("cast_0_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<int32, [1, 112]> expand_dims_0 = const()[name = tensor<string, []>("expand_dims_0"), val = tensor<int32, [1, 112]>([[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]])];
+            tensor<int32, [1]> var_40_axes_0 = const()[name = tensor<string, []>("op_40_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1, 1]> var_40 = expand_dims(axes = var_40_axes_0, x = chunk_lengths)[name = tensor<string, []>("op_40")];
+            tensor<bool, [1, 112]> time_mask_1 = less(x = expand_dims_0, y = var_40)[name = tensor<string, []>("time_mask_1")];
+            tensor<int32, [1]> var_42_axes_0 = const()[name = tensor<string, []>("op_42_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, [1, 112, 1]> var_42 = expand_dims(axes = var_42_axes_0, x = time_mask_1)[name = tensor<string, []>("op_42")];
+            tensor<int32, [3]> var_44_reps_0 = const()[name = tensor<string, []>("op_44_reps_0"), val = tensor<int32, [3]>([1, 1, 128])];
+            tensor<bool, [1, 112, 128]> var_44 = tile(reps = var_44_reps_0, x = var_42)[name = tensor<string, []>("op_44")];
+            tensor<string, []> cast_2_dtype_0 = const()[name = tensor<string, []>("cast_2_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<int32, [1]> var_50_axes_0 = const()[name = tensor<string, []>("op_50_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [1, 112, 128]> cast_2 = cast(dtype = cast_2_dtype_0, x = var_44)[name = tensor<string, []>("cast_25")];
+            tensor<fp32, [1, 1, 112, 128]> var_50 = expand_dims(axes = var_50_axes_0, x = cast_2)[name = tensor<string, []>("op_50")];
+            tensor<fp32, [1, 1, 112, 128]> input_1 = mul(x = tensor_1, y = var_50)[name = tensor<string, []>("input_1")];
+            tensor<string, []> tensor_3_pad_type_0 = const()[name = tensor<string, []>("tensor_3_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> tensor_3_pad_0 = const()[name = tensor<string, []>("tensor_3_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> tensor_3_strides_0 = const()[name = tensor<string, []>("tensor_3_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> tensor_3_dilations_0 = const()[name = tensor<string, []>("tensor_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> tensor_3_groups_0 = const()[name = tensor<string, []>("tensor_3_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 256, 56, 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")];
+            tensor<fp32, []> var_61_promoted = const()[name = tensor<string, []>("op_61_promoted"), val = tensor<fp32, []>(0x1p+0)];
+            tensor<fp32, [1]> cast_0 = cast(dtype = cast_0_dtype_0, x = chunk_lengths)[name = tensor<string, []>("cast_26")];
+            tensor<fp32, [1]> var_62 = add(x = cast_0, y = var_61_promoted)[name = tensor<string, []>("op_62")];
+            tensor<fp32, []> var_63_promoted = const()[name = tensor<string, []>("op_63_promoted"), val = tensor<fp32, []>(0x1p+0)];
+            tensor<fp32, [1]> var_64 = add(x = var_62, y = var_63_promoted)[name = tensor<string, []>("op_64")];
+            tensor<fp32, []> var_65_promoted = const()[name = tensor<string, []>("op_65_promoted"), val = tensor<fp32, []>(0x1.8p+1)];
+            tensor<fp32, [1]> var_66 = sub(x = var_64, y = var_65_promoted)[name = tensor<string, []>("op_66")];
+            tensor<fp32, []> var_21_promoted = const()[name = tensor<string, []>("op_21_promoted"), val = tensor<fp32, []>(0x1p+1)];
+            tensor<fp32, [1]> floor_div_0 = floor_div(x = var_66, y = var_21_promoted)[name = tensor<string, []>("floor_div_0")];
+            tensor<fp32, []> var_68_promoted = const()[name = tensor<string, []>("op_68_promoted"), val = tensor<fp32, []>(0x1p+0)];
+            tensor<fp32, [1]> current_lengths_3 = add(x = floor_div_0, y = var_68_promoted)[name = tensor<string, []>("current_lengths_3")];
+            tensor<string, []> cast_3_dtype_0 = const()[name = tensor<string, []>("cast_3_dtype_0"), val = tensor<string, []>("int32")];
+            tensor<int32, [1, 56]> expand_dims_1 = const()[name = tensor<string, []>("expand_dims_1"), val = tensor<int32, [1, 56]>([[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]])];
+            tensor<int32, [1]> var_77_axes_0 = const()[name = tensor<string, []>("op_77_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> cast_3 = cast(dtype = cast_3_dtype_0, x = current_lengths_3)[name = tensor<string, []>("cast_24")];
+            tensor<int32, [1, 1]> var_77 = expand_dims(axes = var_77_axes_0, x = cast_3)[name = tensor<string, []>("op_77")];
+            tensor<bool, [1, 56]> time_mask_3 = less(x = expand_dims_1, y = var_77)[name = tensor<string, []>("time_mask_3")];
+            tensor<int32, [1]> var_79_axes_0 = const()[name = tensor<string, []>("op_79_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, [1, 56, 1]> var_79 = expand_dims(axes = var_79_axes_0, x = time_mask_3)[name = tensor<string, []>("op_79")];
+            tensor<int32, [3]> var_81_reps_0 = const()[name = tensor<string, []>("op_81_reps_0"), val = tensor<int32, [3]>([1, 1, 64])];
+            tensor<bool, [1, 56, 64]> var_81 = tile(reps = var_81_reps_0, x = var_79)[name = tensor<string, []>("op_81")];
+            tensor<string, []> cast_4_dtype_0 = const()[name = tensor<string, []>("cast_4_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<int32, [1]> var_87_axes_0 = const()[name = tensor<string, []>("op_87_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [1, 56, 64]> cast_4 = cast(dtype = cast_4_dtype_0, x = var_81)[name = tensor<string, []>("cast_23")];
+            tensor<fp32, [1, 1, 56, 64]> var_87 = expand_dims(axes = var_87_axes_0, x = cast_4)[name = tensor<string, []>("op_87")];
+            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])];
+            tensor<fp32, [1, 256, 56, 64]> expanded_mask_3 = tile(reps = expanded_mask_3_reps_0, x = var_87)[name = tensor<string, []>("expanded_mask_3")];
+            tensor<fp32, [1, 256, 56, 64]> input_3 = mul(x = tensor_3, y = expanded_mask_3)[name = tensor<string, []>("input_3")];
+            tensor<fp32, [1, 256, 56, 64]> tensor_5 = relu(x = input_3)[name = tensor<string, []>("tensor_5")];
+            tensor<fp32, [1, 256, 56, 64]> input_5 = mul(x = tensor_5, y = expanded_mask_3)[name = tensor<string, []>("input_5")];
+            tensor<string, []> tensor_7_pad_type_0 = const()[name = tensor<string, []>("tensor_7_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> tensor_7_pad_0 = const()[name = tensor<string, []>("tensor_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> tensor_7_strides_0 = const()[name = tensor<string, []>("tensor_7_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, []> tensor_7_groups_0 = const()[name = tensor<string, []>("tensor_7_groups_0"), val = tensor<int32, []>(256)];
+            tensor<int32, [2]> tensor_7_dilations_0 = const()[name = tensor<string, []>("tensor_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<fp32, [1, 256, 28, 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")];
+            tensor<fp32, []> var_107_promoted = const()[name = tensor<string, []>("op_107_promoted"), val = tensor<fp32, []>(0x1p+0)];
+            tensor<fp32, [1]> var_108 = add(x = current_lengths_3, y = var_107_promoted)[name = tensor<string, []>("op_108")];
+            tensor<fp32, []> var_109_promoted = const()[name = tensor<string, []>("op_109_promoted"), val = tensor<fp32, []>(0x1p+0)];
+            tensor<fp32, [1]> var_110 = add(x = var_108, y = var_109_promoted)[name = tensor<string, []>("op_110")];
+            tensor<fp32, []> var_111_promoted = const()[name = tensor<string, []>("op_111_promoted"), val = tensor<fp32, []>(0x1.8p+1)];
+            tensor<fp32, [1]> var_112 = sub(x = var_110, y = var_111_promoted)[name = tensor<string, []>("op_112")];
+            tensor<fp32, []> var_21_promoted_1 = const()[name = tensor<string, []>("op_21_promoted_1"), val = tensor<fp32, []>(0x1p+1)];
+            tensor<fp32, [1]> floor_div_1 = floor_div(x = var_112, y = var_21_promoted_1)[name = tensor<string, []>("floor_div_1")];
+            tensor<fp32, []> var_114_promoted = const()[name = tensor<string, []>("op_114_promoted"), val = tensor<fp32, []>(0x1p+0)];
+            tensor<fp32, [1]> current_lengths_5 = add(x = floor_div_1, y = var_114_promoted)[name = tensor<string, []>("current_lengths_5")];
+            tensor<string, []> cast_5_dtype_0 = const()[name = tensor<string, []>("cast_5_dtype_0"), val = tensor<string, []>("int32")];
+            tensor<int32, [1, 28]> expand_dims_2 = const()[name = tensor<string, []>("expand_dims_2"), val = tensor<int32, [1, 28]>([[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]])];
+            tensor<int32, [1]> var_123_axes_0 = const()[name = tensor<string, []>("op_123_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> cast_5 = cast(dtype = cast_5_dtype_0, x = current_lengths_5)[name = tensor<string, []>("cast_22")];
+            tensor<int32, [1, 1]> var_123 = expand_dims(axes = var_123_axes_0, x = cast_5)[name = tensor<string, []>("op_123")];
+            tensor<bool, [1, 28]> time_mask_5 = less(x = expand_dims_2, y = var_123)[name = tensor<string, []>("time_mask_5")];
+            tensor<int32, [1]> var_125_axes_0 = const()[name = tensor<string, []>("op_125_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, [1, 28, 1]> var_125 = expand_dims(axes = var_125_axes_0, x = time_mask_5)[name = tensor<string, []>("op_125")];
+            tensor<int32, [3]> var_127_reps_0 = const()[name = tensor<string, []>("op_127_reps_0"), val = tensor<int32, [3]>([1, 1, 32])];
+            tensor<bool, [1, 28, 32]> var_127 = tile(reps = var_127_reps_0, x = var_125)[name = tensor<string, []>("op_127")];
+            tensor<string, []> cast_6_dtype_0 = const()[name = tensor<string, []>("cast_6_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<int32, [1]> var_133_axes_0 = const()[name = tensor<string, []>("op_133_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [1, 28, 32]> cast_6 = cast(dtype = cast_6_dtype_0, x = var_127)[name = tensor<string, []>("cast_21")];
+            tensor<fp32, [1, 1, 28, 32]> var_133 = expand_dims(axes = var_133_axes_0, x = cast_6)[name = tensor<string, []>("op_133")];
+            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])];
+            tensor<fp32, [1, 256, 28, 32]> expanded_mask_7 = tile(reps = expanded_mask_7_reps_0, x = var_133)[name = tensor<string, []>("expanded_mask_7")];
+            tensor<fp32, [1, 256, 28, 32]> input_7 = mul(x = tensor_7, y = expanded_mask_7)[name = tensor<string, []>("input_7")];
+            tensor<string, []> tensor_9_pad_type_0 = const()[name = tensor<string, []>("tensor_9_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> tensor_9_strides_0 = const()[name = tensor<string, []>("tensor_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> tensor_9_pad_0 = const()[name = tensor<string, []>("tensor_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> tensor_9_dilations_0 = const()[name = tensor<string, []>("tensor_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> tensor_9_groups_0 = const()[name = tensor<string, []>("tensor_9_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 256, 28, 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")];
+            tensor<fp32, [1, 256, 28, 32]> input_9 = mul(x = tensor_9, y = expanded_mask_7)[name = tensor<string, []>("input_9")];
+            tensor<fp32, [1, 256, 28, 32]> tensor_11 = relu(x = input_9)[name = tensor<string, []>("tensor_11")];
+            tensor<fp32, [1, 256, 28, 32]> input_11 = mul(x = tensor_11, y = expanded_mask_7)[name = tensor<string, []>("input_11")];
+            tensor<string, []> tensor_13_pad_type_0 = const()[name = tensor<string, []>("tensor_13_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> tensor_13_pad_0 = const()[name = tensor<string, []>("tensor_13_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> tensor_13_strides_0 = const()[name = tensor<string, []>("tensor_13_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, []> tensor_13_groups_0 = const()[name = tensor<string, []>("tensor_13_groups_0"), val = tensor<int32, []>(256)];
+            tensor<int32, [2]> tensor_13_dilations_0 = const()[name = tensor<string, []>("tensor_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<fp32, [1, 256, 14, 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")];
+            tensor<fp32, []> var_168_promoted = const()[name = tensor<string, []>("op_168_promoted"), val = tensor<fp32, []>(0x1p+0)];
+            tensor<fp32, [1]> var_169 = add(x = current_lengths_5, y = var_168_promoted)[name = tensor<string, []>("op_169")];
+            tensor<fp32, []> var_170_promoted = const()[name = tensor<string, []>("op_170_promoted"), val = tensor<fp32, []>(0x1p+0)];
+            tensor<fp32, [1]> var_171 = add(x = var_169, y = var_170_promoted)[name = tensor<string, []>("op_171")];
+            tensor<fp32, []> var_172_promoted = const()[name = tensor<string, []>("op_172_promoted"), val = tensor<fp32, []>(0x1.8p+1)];
+            tensor<fp32, [1]> var_173 = sub(x = var_171, y = var_172_promoted)[name = tensor<string, []>("op_173")];
+            tensor<fp32, []> var_21_promoted_2 = const()[name = tensor<string, []>("op_21_promoted_2"), val = tensor<fp32, []>(0x1p+1)];
+            tensor<fp32, [1]> floor_div_2 = floor_div(x = var_173, y = var_21_promoted_2)[name = tensor<string, []>("floor_div_2")];
+            tensor<fp32, []> var_175_promoted = const()[name = tensor<string, []>("op_175_promoted"), val = tensor<fp32, []>(0x1p+0)];
+            tensor<fp32, [1]> current_lengths = add(x = floor_div_2, y = var_175_promoted)[name = tensor<string, []>("current_lengths")];
+            tensor<string, []> cast_7_dtype_0 = const()[name = tensor<string, []>("cast_7_dtype_0"), val = tensor<string, []>("int32")];
+            tensor<int32, [1, 14]> expand_dims_3 = const()[name = tensor<string, []>("expand_dims_3"), val = tensor<int32, [1, 14]>([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]])];
+            tensor<int32, [1]> var_184_axes_0 = const()[name = tensor<string, []>("op_184_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> cast_7 = cast(dtype = cast_7_dtype_0, x = current_lengths)[name = tensor<string, []>("cast_20")];
+            tensor<int32, [1, 1]> var_184 = expand_dims(axes = var_184_axes_0, x = cast_7)[name = tensor<string, []>("op_184")];
+            tensor<bool, [1, 14]> time_mask = less(x = expand_dims_3, y = var_184)[name = tensor<string, []>("time_mask")];
+            tensor<int32, [1]> var_186_axes_0 = const()[name = tensor<string, []>("op_186_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, [1, 14, 1]> var_186 = expand_dims(axes = var_186_axes_0, x = time_mask)[name = tensor<string, []>("op_186")];
+            tensor<int32, [3]> var_188_reps_0 = const()[name = tensor<string, []>("op_188_reps_0"), val = tensor<int32, [3]>([1, 1, 16])];
+            tensor<bool, [1, 14, 16]> var_188 = tile(reps = var_188_reps_0, x = var_186)[name = tensor<string, []>("op_188")];
+            tensor<string, []> cast_8_dtype_0 = const()[name = tensor<string, []>("cast_8_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<int32, [1]> var_194_axes_0 = const()[name = tensor<string, []>("op_194_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [1, 14, 16]> cast_8 = cast(dtype = cast_8_dtype_0, x = var_188)[name = tensor<string, []>("cast_19")];
+            tensor<fp32, [1, 1, 14, 16]> var_194 = expand_dims(axes = var_194_axes_0, x = cast_8)[name = tensor<string, []>("op_194")];
+            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])];
+            tensor<fp32, [1, 256, 14, 16]> expanded_mask_13 = tile(reps = expanded_mask_13_reps_0, x = var_194)[name = tensor<string, []>("expanded_mask_13")];
+            tensor<fp32, [1, 256, 14, 16]> input_13 = mul(x = tensor_13, y = expanded_mask_13)[name = tensor<string, []>("input_13")];
+            tensor<string, []> tensor_15_pad_type_0 = const()[name = tensor<string, []>("tensor_15_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> tensor_15_strides_0 = const()[name = tensor<string, []>("tensor_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> tensor_15_pad_0 = const()[name = tensor<string, []>("tensor_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> tensor_15_dilations_0 = const()[name = tensor<string, []>("tensor_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> tensor_15_groups_0 = const()[name = tensor<string, []>("tensor_15_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 256, 14, 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")];
+            tensor<fp32, [1, 256, 14, 16]> input_15 = mul(x = tensor_15, y = expanded_mask_13)[name = tensor<string, []>("input_15")];
+            tensor<fp32, [1, 256, 14, 16]> tensor_workaround = relu(x = input_15)[name = tensor<string, []>("tensor_workaround")];
+            tensor<fp32, [1, 256, 14, 16]> x = mul(x = tensor_workaround, y = expanded_mask_13)[name = tensor<string, []>("x")];
+            tensor<int32, [4]> var_228_perm_0 = const()[name = tensor<string, []>("op_228_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_229 = const()[name = tensor<string, []>("op_229"), val = tensor<int32, [3]>([1, 14, -1])];
+            tensor<fp32, [1, 14, 256, 16]> var_228 = transpose(perm = var_228_perm_0, x = x)[name = tensor<string, []>("transpose_0")];
+            tensor<fp32, [1, 14, 4096]> input = reshape(shape = var_229, x = var_228)[name = tensor<string, []>("input")];
+            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")];
+            tensor<string, []> cast_11_dtype_0 = const()[name = tensor<string, []>("cast_11_dtype_0"), val = tensor<string, []>("int32")];
+            tensor<int32, [1]> cap0 = const()[name = tensor<string, []>("cap0"), val = tensor<int32, [1]>([188])];
+            tensor<int32, [1]> cap1 = const()[name = tensor<string, []>("cap1"), val = tensor<int32, [1]>([40])];
+            tensor<int32, []> var_264 = const()[name = tensor<string, []>("op_264"), val = tensor<int32, []>(1)];
+            tensor<bool, []> full_interleave_0 = const()[name = tensor<string, []>("full_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [1, 242, 512]> full = concat(axis = var_264, interleave = full_interleave_0, values = (spkcache, fifo, chunk_pre_encoder_embs))[name = tensor<string, []>("full")];
+            tensor<int32, [1]> var_273 = add(x = spkcache_lengths, y = fifo_lengths)[name = tensor<string, []>("op_273")];
+            tensor<int32, [1]> chunk_pre_encoder_lengths = cast(dtype = cast_11_dtype_0, x = current_lengths)[name = tensor<string, []>("cast_18")];
+            tensor<int32, [1]> pre_encoder_lengths = add(x = var_273, y = chunk_pre_encoder_lengths)[name = tensor<string, []>("total_length")];
+            tensor<int32, [242]> positions = const()[name = tensor<string, []>("positions"), 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])];
+            tensor<bool, [242]> var_284 = greater_equal(x = positions, y = spkcache_lengths)[name = tensor<string, []>("op_284")];
+            tensor<string, []> cast_12_dtype_0 = const()[name = tensor<string, []>("cast_12_dtype_0"), val = tensor<string, []>("int32")];
+            tensor<bool, [242]> var_290 = greater_equal(x = positions, y = var_273)[name = tensor<string, []>("op_290")];
+            tensor<string, []> cast_13_dtype_0 = const()[name = tensor<string, []>("cast_13_dtype_0"), val = tensor<string, []>("int32")];
+            tensor<int32, [1]> var_297 = sub(x = cap0, y = spkcache_lengths)[name = tensor<string, []>("op_297")];
+            tensor<int32, [242]> cast_12 = cast(dtype = cast_12_dtype_0, x = var_284)[name = tensor<string, []>("cast_17")];
+            tensor<int32, [242]> var_298 = mul(x = cast_12, y = var_297)[name = tensor<string, []>("op_298")];
+            tensor<int32, [1]> var_300 = sub(x = cap1, y = fifo_lengths)[name = tensor<string, []>("op_300")];
+            tensor<int32, [242]> cast_13 = cast(dtype = cast_13_dtype_0, x = var_290)[name = tensor<string, []>("cast_16")];
+            tensor<int32, [242]> var_301 = mul(x = cast_13, y = var_300)[name = tensor<string, []>("op_301")];
+            tensor<int32, [242]> offset = add(x = var_298, y = var_301)[name = tensor<string, []>("offset")];
+            tensor<int32, [242]> var_305 = add(x = positions, y = offset)[name = tensor<string, []>("op_305")];
+            tensor<int32, []> var_309 = const()[name = tensor<string, []>("op_309"), val = tensor<int32, []>(241)];
+            tensor<int32, []> var_310 = const()[name = tensor<string, []>("op_310"), val = tensor<int32, []>(0)];
+            tensor<int32, [242]> minimum_0 = minimum(x = var_305, y = var_309)[name = tensor<string, []>("minimum_0")];
+            tensor<int32, [242]> maximum_0 = maximum(x = minimum_0, y = var_310)[name = tensor<string, []>("maximum_0")];
+            tensor<int32, [1]> var_313_axes_0 = const()[name = tensor<string, []>("op_313_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<int32, [1, 242]> var_313 = expand_dims(axes = var_313_axes_0, x = maximum_0)[name = tensor<string, []>("op_313")];
+            tensor<int32, [1]> var_315_axes_0 = const()[name = tensor<string, []>("op_315_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<int32, [1, 242, 1]> var_315 = expand_dims(axes = var_315_axes_0, x = var_313)[name = tensor<string, []>("op_315")];
+            tensor<int32, [3]> gather_idx_reps_0 = const()[name = tensor<string, []>("gather_idx_reps_0"), val = tensor<int32, [3]>([1, 1, 512])];
+            tensor<int32, [1, 242, 512]> gather_idx = tile(reps = gather_idx_reps_0, x = var_315)[name = tensor<string, []>("gather_idx")];
+            tensor<int32, []> var_320 = const()[name = tensor<string, []>("op_320"), val = tensor<int32, []>(1)];
+            tensor<bool, []> packed_validate_indices_0 = const()[name = tensor<string, []>("packed_validate_indices_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [1, 242, 512]> packed = gather_along_axis(axis = var_320, indices = gather_idx, validate_indices = packed_validate_indices_0, x = full)[name = tensor<string, []>("packed")];
+            tensor<bool, [242]> var_323 = less(x = positions, y = pre_encoder_lengths)[name = tensor<string, []>("op_323")];
+            tensor<string, []> cast_14_dtype_0 = const()[name = tensor<string, []>("cast_14_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<int32, [1]> var_330_axes_0 = const()[name = tensor<string, []>("op_330_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp32, [242]> cast_14 = cast(dtype = cast_14_dtype_0, x = var_323)[name = tensor<string, []>("cast_15")];
+            tensor<fp32, [1, 242]> var_330 = expand_dims(axes = var_330_axes_0, x = cast_14)[name = tensor<string, []>("op_330")];
+            tensor<int32, [1]> valid_mask_axes_0 = const()[name = tensor<string, []>("valid_mask_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp32, [1, 242, 1]> valid_mask = expand_dims(axes = valid_mask_axes_0, x = var_330)[name = tensor<string, []>("valid_mask")];
+            tensor<fp32, [1, 242, 512]> pre_encoder_embs = mul(x = packed, y = valid_mask)[name = tensor<string, []>("op_333")];
+        } -> (pre_encoder_embs, pre_encoder_lengths, chunk_pre_encoder_embs, chunk_pre_encoder_lengths);
+}

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