Duplicate from FluidInference/speaker-diarization-coreml

Co-authored-by: brandon <bweng@users.noreply.huggingface.co>

.gitattributes

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+plda_coreml.png filter=lfs diff=lfs merge=lfs -text
+plda_parity.png filter=lfs diff=lfs merge=lfs -text
+plots/pipeline_mps_timing.png filter=lfs diff=lfs merge=lfs -text
+plots/summary_speed.png filter=lfs diff=lfs merge=lfs -text

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Embedding.mlmodelc/metadata.json

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+[
+  {
+    "shortDescription" : "pyannote community-1 speaker embedding backend (WeSpeaker ResNet34 consuming 80×998 FBANK features + 589-frame weights, interpolates weights to 125-frame pooling layer internally)",
+    "metadataOutputVersion" : "3.0",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32)",
+        "shortDescription" : "",
+        "shape" : "[]",
+        "name" : "embedding",
+        "type" : "MultiArray"
+      }
+    ],
+    "version" : "pyannote-speaker-diarization-community-1",
+    "modelParameters" : [
+
+    ],
+    "author" : "Fluid Inference",
+    "specificationVersion" : 8,
+    "storagePrecision" : "Float16",
+    "license" : "CC-BY-4.0",
+    "mlProgramOperationTypeHistogram" : {
+      "Ios17.mul" : 4,
+      "Ios17.sqrt" : 1,
+      "Ios17.sub" : 2,
+      "Ios17.conv" : 38,
+      "Ios17.concat" : 1,
+      "Ios17.clip" : 2,
+      "Ios17.add" : 18,
+      "Ios17.realDiv" : 4,
+      "Ios16.relu" : 33,
+      "Ios17.expandDims" : 7,
+      "Ios16.reduceSum" : 4,
+      "Ios16.reduceL2Norm" : 1,
+      "Ios17.squeeze" : 2,
+      "Ios17.reshape" : 2,
+      "Ios17.cast" : 5
+    },
+    "computePrecision" : "Mixed (Float16, Float32, Int32)",
+    "stateSchema" : [
+
+    ],
+    "isUpdatable" : "0",
+    "availability" : {
+      "macOS" : "14.0",
+      "tvOS" : "17.0",
+      "visionOS" : "1.0",
+      "watchOS" : "10.0",
+      "iOS" : "17.0",
+      "macCatalyst" : "17.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "inputSchema" : [
+      {
+        "dataType" : "Float32",
+        "hasShapeFlexibility" : "1",
+        "isOptional" : "0",
+        "shapeFlexibility" : "1...32 × 1 × 80 × 998",
+        "shapeRange" : "[[1, 32], [1, 1], [80, 80], [998, 998]]",
+        "formattedType" : "MultiArray (Float32 1 × 1 × 80 × 998)",
+        "type" : "MultiArray",
+        "shape" : "[1, 1, 80, 998]",
+        "name" : "fbank_features",
+        "shortDescription" : ""
+      },
+      {
+        "dataType" : "Float32",
+        "hasShapeFlexibility" : "1",
+        "isOptional" : "0",
+        "shapeFlexibility" : "1...32 × 589",
+        "shapeRange" : "[[1, 32], [589, 589]]",
+        "formattedType" : "MultiArray (Float32 1 × 589)",
+        "type" : "MultiArray",
+        "shape" : "[1, 589]",
+        "name" : "weights",
+        "shortDescription" : ""
+      }
+    ],
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2025-10-16",
+      "com.github.apple.coremltools.source" : "torch==2.8.0",
+      "com.github.apple.coremltools.version" : "9.0b1",
+      "com.github.apple.coremltools.source_dialect" : "TorchScript"
+    },
+    "generatedClassName" : "embedding_community_1",
+    "method" : "predict"
+  }
+]

Embedding.mlmodelc/model.mil

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+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3500.14.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.8.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0b1"}})]
+{
+    func main<ios17>(tensor<fp32, [?, 1, 80, 998]> fbank_features, tensor<fp32, [?, 589]> weights) [FlexibleShapeInformation = tuple<tuple<tensor<string, []>, dict<tensor<string, []>, tensor<int32, [?]>>>, tuple<tensor<string, []>, dict<tensor<string, []>, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"fbank_features", [1, 1, 80, 998]}, {"weights", [1, 589]}}), ("RangeDims", {{"fbank_features", [[1, 32], [1, 1], [80, 80], [998, 998]]}, {"weights", [[1, 32], [589, 589]]}})))] {
+            tensor<int32, [1]> weights_1d_axes_0 = const()[name = tensor<string, []>("weights_1d_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<string, []> weights_to_fp16_dtype_0 = const()[name = tensor<string, []>("weights_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<fp16, [?, 589]> weights_to_fp16 = cast(dtype = weights_to_fp16_dtype_0, x = weights)[name = tensor<string, []>("cast_14")];
+            tensor<fp16, [?, 1, 589]> weights_1d_cast_fp16 = expand_dims(axes = weights_1d_axes_0, x = weights_to_fp16)[name = tensor<string, []>("weights_1d_cast_fp16")];
+            tensor<string, []> interpolated_pad_type_0 = const()[name = tensor<string, []>("interpolated_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> interpolated_strides_0 = const()[name = tensor<string, []>("interpolated_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> interpolated_pad_0 = const()[name = tensor<string, []>("interpolated_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> interpolated_dilations_0 = const()[name = tensor<string, []>("interpolated_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> interpolated_groups_0 = const()[name = tensor<string, []>("interpolated_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [125, 1, 589]> const_0_to_fp16 = const()[name = tensor<string, []>("const_0_to_fp16"), val = tensor<fp16, [125, 1, 589]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
+            tensor<fp16, [?, 125, 1]> interpolated_cast_fp16 = conv(dilations = interpolated_dilations_0, groups = interpolated_groups_0, pad = interpolated_pad_0, pad_type = interpolated_pad_type_0, strides = interpolated_strides_0, weight = const_0_to_fp16, x = weights_1d_cast_fp16)[name = tensor<string, []>("interpolated_cast_fp16")];
+            tensor<int32, [1]> weights_3_axes_0 = const()[name = tensor<string, []>("weights_3_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [?, 125]> weights_3_cast_fp16 = squeeze(axes = weights_3_axes_0, x = interpolated_cast_fp16)[name = tensor<string, []>("weights_3_cast_fp16")];
+            tensor<int32, []> var_33 = const()[name = tensor<string, []>("op_33"), val = tensor<int32, []>(-1)];
+            tensor<string, []> input_1_pad_type_0 = const()[name = tensor<string, []>("input_1_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_1_pad_0 = const()[name = tensor<string, []>("input_1_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_1_strides_0 = const()[name = tensor<string, []>("input_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_1_dilations_0 = const()[name = tensor<string, []>("input_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_1_groups_0 = const()[name = tensor<string, []>("input_1_groups_0"), val = tensor<int32, []>(1)];
+            tensor<string, []> fbank_features_to_fp16_dtype_0 = const()[name = tensor<string, []>("fbank_features_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<fp16, [32, 1, 3, 3]> const_3_to_fp16 = const()[name = tensor<string, []>("const_3_to_fp16"), val = tensor<fp16, [32, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(147392)))];
+            tensor<fp16, [32]> const_4_to_fp16 = const()[name = tensor<string, []>("const_4_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(148032)))];
+            tensor<fp16, [?, 1, 80, 998]> fbank_features_to_fp16 = cast(dtype = fbank_features_to_fp16_dtype_0, x = fbank_features)[name = tensor<string, []>("cast_13")];
+            tensor<fp16, [?, 32, 80, 998]> input_3_cast_fp16 = conv(bias = const_4_to_fp16, dilations = input_1_dilations_0, groups = input_1_groups_0, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = input_1_strides_0, weight = const_3_to_fp16, x = fbank_features_to_fp16)[name = tensor<string, []>("input_3_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_5_cast_fp16 = relu(x = input_3_cast_fp16)[name = tensor<string, []>("input_5_cast_fp16")];
+            tensor<string, []> input_7_pad_type_0 = const()[name = tensor<string, []>("input_7_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_7_pad_0 = const()[name = tensor<string, []>("input_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_7_strides_0 = const()[name = tensor<string, []>("input_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_7_dilations_0 = const()[name = tensor<string, []>("input_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_7_groups_0 = const()[name = tensor<string, []>("input_7_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [32, 32, 3, 3]> const_5_to_fp16 = const()[name = tensor<string, []>("const_5_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(148160)))];
+            tensor<fp16, [32]> const_6_to_fp16 = const()[name = tensor<string, []>("const_6_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(166656)))];
+            tensor<fp16, [?, 32, 80, 998]> input_9_cast_fp16 = conv(bias = const_6_to_fp16, dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = const_5_to_fp16, x = input_5_cast_fp16)[name = tensor<string, []>("input_9_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_11_cast_fp16 = relu(x = input_9_cast_fp16)[name = tensor<string, []>("input_11_cast_fp16")];
+            tensor<string, []> input_13_pad_type_0 = const()[name = tensor<string, []>("input_13_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_13_pad_0 = const()[name = tensor<string, []>("input_13_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_13_strides_0 = const()[name = tensor<string, []>("input_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_13_dilations_0 = const()[name = tensor<string, []>("input_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_13_groups_0 = const()[name = tensor<string, []>("input_13_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [32, 32, 3, 3]> const_7_to_fp16 = const()[name = tensor<string, []>("const_7_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(166784)))];
+            tensor<fp16, [32]> const_8_to_fp16 = const()[name = tensor<string, []>("const_8_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(185280)))];
+            tensor<fp16, [?, 32, 80, 998]> out_1_cast_fp16 = conv(bias = const_8_to_fp16, dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = const_7_to_fp16, x = input_11_cast_fp16)[name = tensor<string, []>("out_1_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_15_cast_fp16 = add(x = out_1_cast_fp16, y = input_5_cast_fp16)[name = tensor<string, []>("input_15_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_17_cast_fp16 = relu(x = input_15_cast_fp16)[name = tensor<string, []>("input_17_cast_fp16")];
+            tensor<string, []> input_19_pad_type_0 = const()[name = tensor<string, []>("input_19_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_19_pad_0 = const()[name = tensor<string, []>("input_19_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_19_strides_0 = const()[name = tensor<string, []>("input_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_19_dilations_0 = const()[name = tensor<string, []>("input_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_19_groups_0 = const()[name = tensor<string, []>("input_19_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [32, 32, 3, 3]> const_9_to_fp16 = const()[name = tensor<string, []>("const_9_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(185408)))];
+            tensor<fp16, [32]> const_10_to_fp16 = const()[name = tensor<string, []>("const_10_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(203904)))];
+            tensor<fp16, [?, 32, 80, 998]> input_21_cast_fp16 = conv(bias = const_10_to_fp16, dilations = input_19_dilations_0, groups = input_19_groups_0, pad = input_19_pad_0, pad_type = input_19_pad_type_0, strides = input_19_strides_0, weight = const_9_to_fp16, x = input_17_cast_fp16)[name = tensor<string, []>("input_21_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_23_cast_fp16 = relu(x = input_21_cast_fp16)[name = tensor<string, []>("input_23_cast_fp16")];
+            tensor<string, []> input_25_pad_type_0 = const()[name = tensor<string, []>("input_25_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_25_pad_0 = const()[name = tensor<string, []>("input_25_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_25_strides_0 = const()[name = tensor<string, []>("input_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_25_dilations_0 = const()[name = tensor<string, []>("input_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_25_groups_0 = const()[name = tensor<string, []>("input_25_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [32, 32, 3, 3]> const_11_to_fp16 = const()[name = tensor<string, []>("const_11_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(204032)))];
+            tensor<fp16, [32]> const_12_to_fp16 = const()[name = tensor<string, []>("const_12_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(222528)))];
+            tensor<fp16, [?, 32, 80, 998]> out_3_cast_fp16 = conv(bias = const_12_to_fp16, dilations = input_25_dilations_0, groups = input_25_groups_0, pad = input_25_pad_0, pad_type = input_25_pad_type_0, strides = input_25_strides_0, weight = const_11_to_fp16, x = input_23_cast_fp16)[name = tensor<string, []>("out_3_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_27_cast_fp16 = add(x = out_3_cast_fp16, y = input_17_cast_fp16)[name = tensor<string, []>("input_27_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_29_cast_fp16 = relu(x = input_27_cast_fp16)[name = tensor<string, []>("input_29_cast_fp16")];
+            tensor<string, []> input_31_pad_type_0 = const()[name = tensor<string, []>("input_31_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_31_pad_0 = const()[name = tensor<string, []>("input_31_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_31_strides_0 = const()[name = tensor<string, []>("input_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_31_dilations_0 = const()[name = tensor<string, []>("input_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_31_groups_0 = const()[name = tensor<string, []>("input_31_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [32, 32, 3, 3]> const_13_to_fp16 = const()[name = tensor<string, []>("const_13_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(222656)))];
+            tensor<fp16, [32]> const_14_to_fp16 = const()[name = tensor<string, []>("const_14_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(241152)))];
+            tensor<fp16, [?, 32, 80, 998]> input_33_cast_fp16 = conv(bias = const_14_to_fp16, dilations = input_31_dilations_0, groups = input_31_groups_0, pad = input_31_pad_0, pad_type = input_31_pad_type_0, strides = input_31_strides_0, weight = const_13_to_fp16, x = input_29_cast_fp16)[name = tensor<string, []>("input_33_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_35_cast_fp16 = relu(x = input_33_cast_fp16)[name = tensor<string, []>("input_35_cast_fp16")];
+            tensor<string, []> input_37_pad_type_0 = const()[name = tensor<string, []>("input_37_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_37_pad_0 = const()[name = tensor<string, []>("input_37_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_37_strides_0 = const()[name = tensor<string, []>("input_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_37_dilations_0 = const()[name = tensor<string, []>("input_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_37_groups_0 = const()[name = tensor<string, []>("input_37_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [32, 32, 3, 3]> const_15_to_fp16 = const()[name = tensor<string, []>("const_15_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(241280)))];
+            tensor<fp16, [32]> const_16_to_fp16 = const()[name = tensor<string, []>("const_16_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(259776)))];
+            tensor<fp16, [?, 32, 80, 998]> out_5_cast_fp16 = conv(bias = const_16_to_fp16, dilations = input_37_dilations_0, groups = input_37_groups_0, pad = input_37_pad_0, pad_type = input_37_pad_type_0, strides = input_37_strides_0, weight = const_15_to_fp16, x = input_35_cast_fp16)[name = tensor<string, []>("out_5_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_39_cast_fp16 = add(x = out_5_cast_fp16, y = input_29_cast_fp16)[name = tensor<string, []>("input_39_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_41_cast_fp16 = relu(x = input_39_cast_fp16)[name = tensor<string, []>("input_41_cast_fp16")];
+            tensor<string, []> input_43_pad_type_0 = const()[name = tensor<string, []>("input_43_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_43_pad_0 = const()[name = tensor<string, []>("input_43_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_43_strides_0 = const()[name = tensor<string, []>("input_43_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> input_43_dilations_0 = const()[name = tensor<string, []>("input_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_43_groups_0 = const()[name = tensor<string, []>("input_43_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [64, 32, 3, 3]> const_17_to_fp16 = const()[name = tensor<string, []>("const_17_to_fp16"), val = tensor<fp16, [64, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(259904)))];
+            tensor<fp16, [64]> const_18_to_fp16 = const()[name = tensor<string, []>("const_18_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(296832)))];
+            tensor<fp16, [?, 64, 40, 499]> input_45_cast_fp16 = conv(bias = const_18_to_fp16, dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = const_17_to_fp16, x = input_41_cast_fp16)[name = tensor<string, []>("input_45_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_47_cast_fp16 = relu(x = input_45_cast_fp16)[name = tensor<string, []>("input_47_cast_fp16")];
+            tensor<string, []> input_49_pad_type_0 = const()[name = tensor<string, []>("input_49_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_49_pad_0 = const()[name = tensor<string, []>("input_49_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_49_strides_0 = const()[name = tensor<string, []>("input_49_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_49_dilations_0 = const()[name = tensor<string, []>("input_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_49_groups_0 = const()[name = tensor<string, []>("input_49_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_19_to_fp16 = const()[name = tensor<string, []>("const_19_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(297024)))];
+            tensor<fp16, [64]> const_20_to_fp16 = const()[name = tensor<string, []>("const_20_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(370816)))];
+            tensor<fp16, [?, 64, 40, 499]> out_7_cast_fp16 = conv(bias = const_20_to_fp16, dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = const_19_to_fp16, x = input_47_cast_fp16)[name = tensor<string, []>("out_7_cast_fp16")];
+            tensor<string, []> input_51_pad_type_0 = const()[name = tensor<string, []>("input_51_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> input_51_strides_0 = const()[name = tensor<string, []>("input_51_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> input_51_pad_0 = const()[name = tensor<string, []>("input_51_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_51_dilations_0 = const()[name = tensor<string, []>("input_51_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_51_groups_0 = const()[name = tensor<string, []>("input_51_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [64, 32, 1, 1]> const_21_to_fp16 = const()[name = tensor<string, []>("const_21_to_fp16"), val = tensor<fp16, [64, 32, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(371008)))];
+            tensor<fp16, [64]> const_22_to_fp16 = const()[name = tensor<string, []>("const_22_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(375168)))];
+            tensor<fp16, [?, 64, 40, 499]> var_196_cast_fp16 = conv(bias = const_22_to_fp16, dilations = input_51_dilations_0, groups = input_51_groups_0, pad = input_51_pad_0, pad_type = input_51_pad_type_0, strides = input_51_strides_0, weight = const_21_to_fp16, x = input_41_cast_fp16)[name = tensor<string, []>("op_196_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_53_cast_fp16 = add(x = out_7_cast_fp16, y = var_196_cast_fp16)[name = tensor<string, []>("input_53_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_55_cast_fp16 = relu(x = input_53_cast_fp16)[name = tensor<string, []>("input_55_cast_fp16")];
+            tensor<string, []> input_57_pad_type_0 = const()[name = tensor<string, []>("input_57_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_57_pad_0 = const()[name = tensor<string, []>("input_57_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_57_strides_0 = const()[name = tensor<string, []>("input_57_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_57_dilations_0 = const()[name = tensor<string, []>("input_57_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_57_groups_0 = const()[name = tensor<string, []>("input_57_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_23_to_fp16 = const()[name = tensor<string, []>("const_23_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(375360)))];
+            tensor<fp16, [64]> const_24_to_fp16 = const()[name = tensor<string, []>("const_24_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(449152)))];
+            tensor<fp16, [?, 64, 40, 499]> input_59_cast_fp16 = conv(bias = const_24_to_fp16, dilations = input_57_dilations_0, groups = input_57_groups_0, pad = input_57_pad_0, pad_type = input_57_pad_type_0, strides = input_57_strides_0, weight = const_23_to_fp16, x = input_55_cast_fp16)[name = tensor<string, []>("input_59_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_61_cast_fp16 = relu(x = input_59_cast_fp16)[name = tensor<string, []>("input_61_cast_fp16")];
+            tensor<string, []> input_63_pad_type_0 = const()[name = tensor<string, []>("input_63_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_63_pad_0 = const()[name = tensor<string, []>("input_63_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_63_strides_0 = const()[name = tensor<string, []>("input_63_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_63_dilations_0 = const()[name = tensor<string, []>("input_63_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_63_groups_0 = const()[name = tensor<string, []>("input_63_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_25_to_fp16 = const()[name = tensor<string, []>("const_25_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(449344)))];
+            tensor<fp16, [64]> const_26_to_fp16 = const()[name = tensor<string, []>("const_26_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(523136)))];
+            tensor<fp16, [?, 64, 40, 499]> out_9_cast_fp16 = conv(bias = const_26_to_fp16, dilations = input_63_dilations_0, groups = input_63_groups_0, pad = input_63_pad_0, pad_type = input_63_pad_type_0, strides = input_63_strides_0, weight = const_25_to_fp16, x = input_61_cast_fp16)[name = tensor<string, []>("out_9_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_65_cast_fp16 = add(x = out_9_cast_fp16, y = input_55_cast_fp16)[name = tensor<string, []>("input_65_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_67_cast_fp16 = relu(x = input_65_cast_fp16)[name = tensor<string, []>("input_67_cast_fp16")];
+            tensor<string, []> input_69_pad_type_0 = const()[name = tensor<string, []>("input_69_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_69_pad_0 = const()[name = tensor<string, []>("input_69_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_69_strides_0 = const()[name = tensor<string, []>("input_69_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_69_dilations_0 = const()[name = tensor<string, []>("input_69_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_69_groups_0 = const()[name = tensor<string, []>("input_69_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_27_to_fp16 = const()[name = tensor<string, []>("const_27_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(523328)))];
+            tensor<fp16, [64]> const_28_to_fp16 = const()[name = tensor<string, []>("const_28_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(597120)))];
+            tensor<fp16, [?, 64, 40, 499]> input_71_cast_fp16 = conv(bias = const_28_to_fp16, dilations = input_69_dilations_0, groups = input_69_groups_0, pad = input_69_pad_0, pad_type = input_69_pad_type_0, strides = input_69_strides_0, weight = const_27_to_fp16, x = input_67_cast_fp16)[name = tensor<string, []>("input_71_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_73_cast_fp16 = relu(x = input_71_cast_fp16)[name = tensor<string, []>("input_73_cast_fp16")];
+            tensor<string, []> input_75_pad_type_0 = const()[name = tensor<string, []>("input_75_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_75_pad_0 = const()[name = tensor<string, []>("input_75_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_75_strides_0 = const()[name = tensor<string, []>("input_75_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_75_dilations_0 = const()[name = tensor<string, []>("input_75_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_75_groups_0 = const()[name = tensor<string, []>("input_75_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_29_to_fp16 = const()[name = tensor<string, []>("const_29_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(597312)))];
+            tensor<fp16, [64]> const_30_to_fp16 = const()[name = tensor<string, []>("const_30_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(671104)))];
+            tensor<fp16, [?, 64, 40, 499]> out_11_cast_fp16 = conv(bias = const_30_to_fp16, dilations = input_75_dilations_0, groups = input_75_groups_0, pad = input_75_pad_0, pad_type = input_75_pad_type_0, strides = input_75_strides_0, weight = const_29_to_fp16, x = input_73_cast_fp16)[name = tensor<string, []>("out_11_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_77_cast_fp16 = add(x = out_11_cast_fp16, y = input_67_cast_fp16)[name = tensor<string, []>("input_77_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_79_cast_fp16 = relu(x = input_77_cast_fp16)[name = tensor<string, []>("input_79_cast_fp16")];
+            tensor<string, []> input_81_pad_type_0 = const()[name = tensor<string, []>("input_81_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_81_pad_0 = const()[name = tensor<string, []>("input_81_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_81_strides_0 = const()[name = tensor<string, []>("input_81_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_81_dilations_0 = const()[name = tensor<string, []>("input_81_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_81_groups_0 = const()[name = tensor<string, []>("input_81_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_31_to_fp16 = const()[name = tensor<string, []>("const_31_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(671296)))];
+            tensor<fp16, [64]> const_32_to_fp16 = const()[name = tensor<string, []>("const_32_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(745088)))];
+            tensor<fp16, [?, 64, 40, 499]> input_83_cast_fp16 = conv(bias = const_32_to_fp16, dilations = input_81_dilations_0, groups = input_81_groups_0, pad = input_81_pad_0, pad_type = input_81_pad_type_0, strides = input_81_strides_0, weight = const_31_to_fp16, x = input_79_cast_fp16)[name = tensor<string, []>("input_83_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_85_cast_fp16 = relu(x = input_83_cast_fp16)[name = tensor<string, []>("input_85_cast_fp16")];
+            tensor<string, []> input_87_pad_type_0 = const()[name = tensor<string, []>("input_87_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_87_pad_0 = const()[name = tensor<string, []>("input_87_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_87_strides_0 = const()[name = tensor<string, []>("input_87_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_87_dilations_0 = const()[name = tensor<string, []>("input_87_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_87_groups_0 = const()[name = tensor<string, []>("input_87_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_33_to_fp16 = const()[name = tensor<string, []>("const_33_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(745280)))];
+            tensor<fp16, [64]> const_34_to_fp16 = const()[name = tensor<string, []>("const_34_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(819072)))];
+            tensor<fp16, [?, 64, 40, 499]> out_13_cast_fp16 = conv(bias = const_34_to_fp16, dilations = input_87_dilations_0, groups = input_87_groups_0, pad = input_87_pad_0, pad_type = input_87_pad_type_0, strides = input_87_strides_0, weight = const_33_to_fp16, x = input_85_cast_fp16)[name = tensor<string, []>("out_13_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_89_cast_fp16 = add(x = out_13_cast_fp16, y = input_79_cast_fp16)[name = tensor<string, []>("input_89_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_91_cast_fp16 = relu(x = input_89_cast_fp16)[name = tensor<string, []>("input_91_cast_fp16")];
+            tensor<string, []> input_93_pad_type_0 = const()[name = tensor<string, []>("input_93_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_93_pad_0 = const()[name = tensor<string, []>("input_93_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_93_strides_0 = const()[name = tensor<string, []>("input_93_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> input_93_dilations_0 = const()[name = tensor<string, []>("input_93_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_93_groups_0 = const()[name = tensor<string, []>("input_93_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [128, 64, 3, 3]> const_35_to_fp16 = const()[name = tensor<string, []>("const_35_to_fp16"), val = tensor<fp16, [128, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(819264)))];
+            tensor<fp16, [128]> const_36_to_fp16 = const()[name = tensor<string, []>("const_36_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(966784)))];
+            tensor<fp16, [?, 128, 20, 250]> input_95_cast_fp16 = conv(bias = const_36_to_fp16, dilations = input_93_dilations_0, groups = input_93_groups_0, pad = input_93_pad_0, pad_type = input_93_pad_type_0, strides = input_93_strides_0, weight = const_35_to_fp16, x = input_91_cast_fp16)[name = tensor<string, []>("input_95_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_97_cast_fp16 = relu(x = input_95_cast_fp16)[name = tensor<string, []>("input_97_cast_fp16")];
+            tensor<string, []> input_99_pad_type_0 = const()[name = tensor<string, []>("input_99_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_99_pad_0 = const()[name = tensor<string, []>("input_99_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_99_strides_0 = const()[name = tensor<string, []>("input_99_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_99_dilations_0 = const()[name = tensor<string, []>("input_99_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_99_groups_0 = const()[name = tensor<string, []>("input_99_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_37_to_fp16 = const()[name = tensor<string, []>("const_37_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(967104)))];
+            tensor<fp16, [128]> const_38_to_fp16 = const()[name = tensor<string, []>("const_38_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1262080)))];
+            tensor<fp16, [?, 128, 20, 250]> out_15_cast_fp16 = conv(bias = const_38_to_fp16, dilations = input_99_dilations_0, groups = input_99_groups_0, pad = input_99_pad_0, pad_type = input_99_pad_type_0, strides = input_99_strides_0, weight = const_37_to_fp16, x = input_97_cast_fp16)[name = tensor<string, []>("out_15_cast_fp16")];
+            tensor<string, []> input_101_pad_type_0 = const()[name = tensor<string, []>("input_101_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> input_101_strides_0 = const()[name = tensor<string, []>("input_101_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> input_101_pad_0 = const()[name = tensor<string, []>("input_101_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_101_dilations_0 = const()[name = tensor<string, []>("input_101_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_101_groups_0 = const()[name = tensor<string, []>("input_101_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [128, 64, 1, 1]> const_39_to_fp16 = const()[name = tensor<string, []>("const_39_to_fp16"), val = tensor<fp16, [128, 64, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1262400)))];
+            tensor<fp16, [128]> const_40_to_fp16 = const()[name = tensor<string, []>("const_40_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1278848)))];
+            tensor<fp16, [?, 128, 20, 250]> var_332_cast_fp16 = conv(bias = const_40_to_fp16, dilations = input_101_dilations_0, groups = input_101_groups_0, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = input_101_strides_0, weight = const_39_to_fp16, x = input_91_cast_fp16)[name = tensor<string, []>("op_332_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_103_cast_fp16 = add(x = out_15_cast_fp16, y = var_332_cast_fp16)[name = tensor<string, []>("input_103_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_105_cast_fp16 = relu(x = input_103_cast_fp16)[name = tensor<string, []>("input_105_cast_fp16")];
+            tensor<string, []> input_107_pad_type_0 = const()[name = tensor<string, []>("input_107_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_107_pad_0 = const()[name = tensor<string, []>("input_107_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_107_strides_0 = const()[name = tensor<string, []>("input_107_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_107_dilations_0 = const()[name = tensor<string, []>("input_107_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_107_groups_0 = const()[name = tensor<string, []>("input_107_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_41_to_fp16 = const()[name = tensor<string, []>("const_41_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1279168)))];
+            tensor<fp16, [128]> const_42_to_fp16 = const()[name = tensor<string, []>("const_42_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1574144)))];
+            tensor<fp16, [?, 128, 20, 250]> input_109_cast_fp16 = conv(bias = const_42_to_fp16, dilations = input_107_dilations_0, groups = input_107_groups_0, pad = input_107_pad_0, pad_type = input_107_pad_type_0, strides = input_107_strides_0, weight = const_41_to_fp16, x = input_105_cast_fp16)[name = tensor<string, []>("input_109_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_111_cast_fp16 = relu(x = input_109_cast_fp16)[name = tensor<string, []>("input_111_cast_fp16")];
+            tensor<string, []> input_113_pad_type_0 = const()[name = tensor<string, []>("input_113_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_113_pad_0 = const()[name = tensor<string, []>("input_113_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_113_strides_0 = const()[name = tensor<string, []>("input_113_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_113_dilations_0 = const()[name = tensor<string, []>("input_113_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_113_groups_0 = const()[name = tensor<string, []>("input_113_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_43_to_fp16 = const()[name = tensor<string, []>("const_43_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1574464)))];
+            tensor<fp16, [128]> const_44_to_fp16 = const()[name = tensor<string, []>("const_44_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1869440)))];
+            tensor<fp16, [?, 128, 20, 250]> out_17_cast_fp16 = conv(bias = const_44_to_fp16, dilations = input_113_dilations_0, groups = input_113_groups_0, pad = input_113_pad_0, pad_type = input_113_pad_type_0, strides = input_113_strides_0, weight = const_43_to_fp16, x = input_111_cast_fp16)[name = tensor<string, []>("out_17_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_115_cast_fp16 = add(x = out_17_cast_fp16, y = input_105_cast_fp16)[name = tensor<string, []>("input_115_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_117_cast_fp16 = relu(x = input_115_cast_fp16)[name = tensor<string, []>("input_117_cast_fp16")];
+            tensor<string, []> input_119_pad_type_0 = const()[name = tensor<string, []>("input_119_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_119_pad_0 = const()[name = tensor<string, []>("input_119_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_119_strides_0 = const()[name = tensor<string, []>("input_119_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_119_dilations_0 = const()[name = tensor<string, []>("input_119_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_119_groups_0 = const()[name = tensor<string, []>("input_119_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_45_to_fp16 = const()[name = tensor<string, []>("const_45_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1869760)))];
+            tensor<fp16, [128]> const_46_to_fp16 = const()[name = tensor<string, []>("const_46_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2164736)))];
+            tensor<fp16, [?, 128, 20, 250]> input_121_cast_fp16 = conv(bias = const_46_to_fp16, dilations = input_119_dilations_0, groups = input_119_groups_0, pad = input_119_pad_0, pad_type = input_119_pad_type_0, strides = input_119_strides_0, weight = const_45_to_fp16, x = input_117_cast_fp16)[name = tensor<string, []>("input_121_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_123_cast_fp16 = relu(x = input_121_cast_fp16)[name = tensor<string, []>("input_123_cast_fp16")];
+            tensor<string, []> input_125_pad_type_0 = const()[name = tensor<string, []>("input_125_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_125_pad_0 = const()[name = tensor<string, []>("input_125_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_125_strides_0 = const()[name = tensor<string, []>("input_125_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_125_dilations_0 = const()[name = tensor<string, []>("input_125_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_125_groups_0 = const()[name = tensor<string, []>("input_125_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_47_to_fp16 = const()[name = tensor<string, []>("const_47_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2165056)))];
+            tensor<fp16, [128]> const_48_to_fp16 = const()[name = tensor<string, []>("const_48_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2460032)))];
+            tensor<fp16, [?, 128, 20, 250]> out_19_cast_fp16 = conv(bias = const_48_to_fp16, dilations = input_125_dilations_0, groups = input_125_groups_0, pad = input_125_pad_0, pad_type = input_125_pad_type_0, strides = input_125_strides_0, weight = const_47_to_fp16, x = input_123_cast_fp16)[name = tensor<string, []>("out_19_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_127_cast_fp16 = add(x = out_19_cast_fp16, y = input_117_cast_fp16)[name = tensor<string, []>("input_127_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_129_cast_fp16 = relu(x = input_127_cast_fp16)[name = tensor<string, []>("input_129_cast_fp16")];
+            tensor<string, []> input_131_pad_type_0 = const()[name = tensor<string, []>("input_131_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_131_pad_0 = const()[name = tensor<string, []>("input_131_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_131_strides_0 = const()[name = tensor<string, []>("input_131_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_131_dilations_0 = const()[name = tensor<string, []>("input_131_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_131_groups_0 = const()[name = tensor<string, []>("input_131_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_49_to_fp16 = const()[name = tensor<string, []>("const_49_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2460352)))];
+            tensor<fp16, [128]> const_50_to_fp16 = const()[name = tensor<string, []>("const_50_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2755328)))];
+            tensor<fp16, [?, 128, 20, 250]> input_133_cast_fp16 = conv(bias = const_50_to_fp16, dilations = input_131_dilations_0, groups = input_131_groups_0, pad = input_131_pad_0, pad_type = input_131_pad_type_0, strides = input_131_strides_0, weight = const_49_to_fp16, x = input_129_cast_fp16)[name = tensor<string, []>("input_133_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_135_cast_fp16 = relu(x = input_133_cast_fp16)[name = tensor<string, []>("input_135_cast_fp16")];
+            tensor<string, []> input_137_pad_type_0 = const()[name = tensor<string, []>("input_137_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_137_pad_0 = const()[name = tensor<string, []>("input_137_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_137_strides_0 = const()[name = tensor<string, []>("input_137_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_137_dilations_0 = const()[name = tensor<string, []>("input_137_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_137_groups_0 = const()[name = tensor<string, []>("input_137_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_51_to_fp16 = const()[name = tensor<string, []>("const_51_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2755648)))];
+            tensor<fp16, [128]> const_52_to_fp16 = const()[name = tensor<string, []>("const_52_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3050624)))];
+            tensor<fp16, [?, 128, 20, 250]> out_21_cast_fp16 = conv(bias = const_52_to_fp16, dilations = input_137_dilations_0, groups = input_137_groups_0, pad = input_137_pad_0, pad_type = input_137_pad_type_0, strides = input_137_strides_0, weight = const_51_to_fp16, x = input_135_cast_fp16)[name = tensor<string, []>("out_21_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_139_cast_fp16 = add(x = out_21_cast_fp16, y = input_129_cast_fp16)[name = tensor<string, []>("input_139_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_141_cast_fp16 = relu(x = input_139_cast_fp16)[name = tensor<string, []>("input_141_cast_fp16")];
+            tensor<string, []> input_143_pad_type_0 = const()[name = tensor<string, []>("input_143_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_143_pad_0 = const()[name = tensor<string, []>("input_143_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_143_strides_0 = const()[name = tensor<string, []>("input_143_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_143_dilations_0 = const()[name = tensor<string, []>("input_143_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_143_groups_0 = const()[name = tensor<string, []>("input_143_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_53_to_fp16 = const()[name = tensor<string, []>("const_53_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3050944)))];
+            tensor<fp16, [128]> const_54_to_fp16 = const()[name = tensor<string, []>("const_54_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3345920)))];
+            tensor<fp16, [?, 128, 20, 250]> input_145_cast_fp16 = conv(bias = const_54_to_fp16, dilations = input_143_dilations_0, groups = input_143_groups_0, pad = input_143_pad_0, pad_type = input_143_pad_type_0, strides = input_143_strides_0, weight = const_53_to_fp16, x = input_141_cast_fp16)[name = tensor<string, []>("input_145_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_147_cast_fp16 = relu(x = input_145_cast_fp16)[name = tensor<string, []>("input_147_cast_fp16")];
+            tensor<string, []> input_149_pad_type_0 = const()[name = tensor<string, []>("input_149_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_149_pad_0 = const()[name = tensor<string, []>("input_149_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_149_strides_0 = const()[name = tensor<string, []>("input_149_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_149_dilations_0 = const()[name = tensor<string, []>("input_149_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_149_groups_0 = const()[name = tensor<string, []>("input_149_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_55_to_fp16 = const()[name = tensor<string, []>("const_55_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3346240)))];
+            tensor<fp16, [128]> const_56_to_fp16 = const()[name = tensor<string, []>("const_56_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3641216)))];
+            tensor<fp16, [?, 128, 20, 250]> out_23_cast_fp16 = conv(bias = const_56_to_fp16, dilations = input_149_dilations_0, groups = input_149_groups_0, pad = input_149_pad_0, pad_type = input_149_pad_type_0, strides = input_149_strides_0, weight = const_55_to_fp16, x = input_147_cast_fp16)[name = tensor<string, []>("out_23_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_151_cast_fp16 = add(x = out_23_cast_fp16, y = input_141_cast_fp16)[name = tensor<string, []>("input_151_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_153_cast_fp16 = relu(x = input_151_cast_fp16)[name = tensor<string, []>("input_153_cast_fp16")];
+            tensor<string, []> input_155_pad_type_0 = const()[name = tensor<string, []>("input_155_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_155_pad_0 = const()[name = tensor<string, []>("input_155_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_155_strides_0 = const()[name = tensor<string, []>("input_155_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_155_dilations_0 = const()[name = tensor<string, []>("input_155_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_155_groups_0 = const()[name = tensor<string, []>("input_155_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_57_to_fp16 = const()[name = tensor<string, []>("const_57_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3641536)))];
+            tensor<fp16, [128]> const_58_to_fp16 = const()[name = tensor<string, []>("const_58_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3936512)))];
+            tensor<fp16, [?, 128, 20, 250]> input_157_cast_fp16 = conv(bias = const_58_to_fp16, dilations = input_155_dilations_0, groups = input_155_groups_0, pad = input_155_pad_0, pad_type = input_155_pad_type_0, strides = input_155_strides_0, weight = const_57_to_fp16, x = input_153_cast_fp16)[name = tensor<string, []>("input_157_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_159_cast_fp16 = relu(x = input_157_cast_fp16)[name = tensor<string, []>("input_159_cast_fp16")];
+            tensor<string, []> input_161_pad_type_0 = const()[name = tensor<string, []>("input_161_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_161_pad_0 = const()[name = tensor<string, []>("input_161_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_161_strides_0 = const()[name = tensor<string, []>("input_161_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_161_dilations_0 = const()[name = tensor<string, []>("input_161_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_161_groups_0 = const()[name = tensor<string, []>("input_161_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_59_to_fp16 = const()[name = tensor<string, []>("const_59_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3936832)))];
+            tensor<fp16, [128]> const_60_to_fp16 = const()[name = tensor<string, []>("const_60_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4231808)))];
+            tensor<fp16, [?, 128, 20, 250]> out_25_cast_fp16 = conv(bias = const_60_to_fp16, dilations = input_161_dilations_0, groups = input_161_groups_0, pad = input_161_pad_0, pad_type = input_161_pad_type_0, strides = input_161_strides_0, weight = const_59_to_fp16, x = input_159_cast_fp16)[name = tensor<string, []>("out_25_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_163_cast_fp16 = add(x = out_25_cast_fp16, y = input_153_cast_fp16)[name = tensor<string, []>("input_163_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_165_cast_fp16 = relu(x = input_163_cast_fp16)[name = tensor<string, []>("input_165_cast_fp16")];
+            tensor<string, []> input_167_pad_type_0 = const()[name = tensor<string, []>("input_167_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_167_pad_0 = const()[name = tensor<string, []>("input_167_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_167_strides_0 = const()[name = tensor<string, []>("input_167_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> input_167_dilations_0 = const()[name = tensor<string, []>("input_167_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_167_groups_0 = const()[name = tensor<string, []>("input_167_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 128, 3, 3]> const_61_to_fp16 = const()[name = tensor<string, []>("const_61_to_fp16"), val = tensor<fp16, [256, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4232128)))];
+            tensor<fp16, [256]> const_62_to_fp16 = const()[name = tensor<string, []>("const_62_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4822016)))];
+            tensor<fp16, [?, 256, 10, 125]> input_169_cast_fp16 = conv(bias = const_62_to_fp16, dilations = input_167_dilations_0, groups = input_167_groups_0, pad = input_167_pad_0, pad_type = input_167_pad_type_0, strides = input_167_strides_0, weight = const_61_to_fp16, x = input_165_cast_fp16)[name = tensor<string, []>("input_169_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_171_cast_fp16 = relu(x = input_169_cast_fp16)[name = tensor<string, []>("input_171_cast_fp16")];
+            tensor<string, []> input_173_pad_type_0 = const()[name = tensor<string, []>("input_173_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_173_pad_0 = const()[name = tensor<string, []>("input_173_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_173_strides_0 = const()[name = tensor<string, []>("input_173_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_173_dilations_0 = const()[name = tensor<string, []>("input_173_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_173_groups_0 = const()[name = tensor<string, []>("input_173_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 256, 3, 3]> const_63_to_fp16 = const()[name = tensor<string, []>("const_63_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4822592)))];
+            tensor<fp16, [256]> const_64_to_fp16 = const()[name = tensor<string, []>("const_64_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6002304)))];
+            tensor<fp16, [?, 256, 10, 125]> out_27_cast_fp16 = conv(bias = const_64_to_fp16, dilations = input_173_dilations_0, groups = input_173_groups_0, pad = input_173_pad_0, pad_type = input_173_pad_type_0, strides = input_173_strides_0, weight = const_63_to_fp16, x = input_171_cast_fp16)[name = tensor<string, []>("out_27_cast_fp16")];
+            tensor<string, []> input_175_pad_type_0 = const()[name = tensor<string, []>("input_175_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> input_175_strides_0 = const()[name = tensor<string, []>("input_175_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> input_175_pad_0 = const()[name = tensor<string, []>("input_175_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_175_dilations_0 = const()[name = tensor<string, []>("input_175_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_175_groups_0 = const()[name = tensor<string, []>("input_175_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 128, 1, 1]> const_65_to_fp16 = const()[name = tensor<string, []>("const_65_to_fp16"), val = tensor<fp16, [256, 128, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6002880)))];
+            tensor<fp16, [256]> const_66_to_fp16 = const()[name = tensor<string, []>("const_66_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6068480)))];
+            tensor<fp16, [?, 256, 10, 125]> var_523_cast_fp16 = conv(bias = const_66_to_fp16, dilations = input_175_dilations_0, groups = input_175_groups_0, pad = input_175_pad_0, pad_type = input_175_pad_type_0, strides = input_175_strides_0, weight = const_65_to_fp16, x = input_165_cast_fp16)[name = tensor<string, []>("op_523_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_177_cast_fp16 = add(x = out_27_cast_fp16, y = var_523_cast_fp16)[name = tensor<string, []>("input_177_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_179_cast_fp16 = relu(x = input_177_cast_fp16)[name = tensor<string, []>("input_179_cast_fp16")];
+            tensor<string, []> input_181_pad_type_0 = const()[name = tensor<string, []>("input_181_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_181_pad_0 = const()[name = tensor<string, []>("input_181_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_181_strides_0 = const()[name = tensor<string, []>("input_181_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_181_dilations_0 = const()[name = tensor<string, []>("input_181_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_181_groups_0 = const()[name = tensor<string, []>("input_181_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 256, 3, 3]> const_67_to_fp16 = const()[name = tensor<string, []>("const_67_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6069056)))];
+            tensor<fp16, [256]> const_68_to_fp16 = const()[name = tensor<string, []>("const_68_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7248768)))];
+            tensor<fp16, [?, 256, 10, 125]> input_183_cast_fp16 = conv(bias = const_68_to_fp16, dilations = input_181_dilations_0, groups = input_181_groups_0, pad = input_181_pad_0, pad_type = input_181_pad_type_0, strides = input_181_strides_0, weight = const_67_to_fp16, x = input_179_cast_fp16)[name = tensor<string, []>("input_183_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_185_cast_fp16 = relu(x = input_183_cast_fp16)[name = tensor<string, []>("input_185_cast_fp16")];
+            tensor<string, []> input_187_pad_type_0 = const()[name = tensor<string, []>("input_187_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_187_pad_0 = const()[name = tensor<string, []>("input_187_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_187_strides_0 = const()[name = tensor<string, []>("input_187_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_187_dilations_0 = const()[name = tensor<string, []>("input_187_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_187_groups_0 = const()[name = tensor<string, []>("input_187_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 256, 3, 3]> const_69_to_fp16 = const()[name = tensor<string, []>("const_69_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7249344)))];
+            tensor<fp16, [256]> const_70_to_fp16 = const()[name = tensor<string, []>("const_70_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8429056)))];
+            tensor<fp16, [?, 256, 10, 125]> out_29_cast_fp16 = conv(bias = const_70_to_fp16, dilations = input_187_dilations_0, groups = input_187_groups_0, pad = input_187_pad_0, pad_type = input_187_pad_type_0, strides = input_187_strides_0, weight = const_69_to_fp16, x = input_185_cast_fp16)[name = tensor<string, []>("out_29_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_189_cast_fp16 = add(x = out_29_cast_fp16, y = input_179_cast_fp16)[name = tensor<string, []>("input_189_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_191_cast_fp16 = relu(x = input_189_cast_fp16)[name = tensor<string, []>("input_191_cast_fp16")];
+            tensor<string, []> input_193_pad_type_0 = const()[name = tensor<string, []>("input_193_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_193_pad_0 = const()[name = tensor<string, []>("input_193_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_193_strides_0 = const()[name = tensor<string, []>("input_193_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_193_dilations_0 = const()[name = tensor<string, []>("input_193_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_193_groups_0 = const()[name = tensor<string, []>("input_193_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 256, 3, 3]> const_71_to_fp16 = const()[name = tensor<string, []>("const_71_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8429632)))];
+            tensor<fp16, [256]> const_72_to_fp16 = const()[name = tensor<string, []>("const_72_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9609344)))];
+            tensor<fp16, [?, 256, 10, 125]> input_195_cast_fp16 = conv(bias = const_72_to_fp16, dilations = input_193_dilations_0, groups = input_193_groups_0, pad = input_193_pad_0, pad_type = input_193_pad_type_0, strides = input_193_strides_0, weight = const_71_to_fp16, x = input_191_cast_fp16)[name = tensor<string, []>("input_195_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_197_cast_fp16 = relu(x = input_195_cast_fp16)[name = tensor<string, []>("input_197_cast_fp16")];
+            tensor<string, []> input_199_pad_type_0 = const()[name = tensor<string, []>("input_199_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_199_pad_0 = const()[name = tensor<string, []>("input_199_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_199_strides_0 = const()[name = tensor<string, []>("input_199_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_199_dilations_0 = const()[name = tensor<string, []>("input_199_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_199_groups_0 = const()[name = tensor<string, []>("input_199_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 256, 3, 3]> const_73_to_fp16 = const()[name = tensor<string, []>("const_73_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9609920)))];
+            tensor<fp16, [256]> const_74_to_fp16 = const()[name = tensor<string, []>("const_74_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10789632)))];
+            tensor<fp16, [?, 256, 10, 125]> out_cast_fp16 = conv(bias = const_74_to_fp16, dilations = input_199_dilations_0, groups = input_199_groups_0, pad = input_199_pad_0, pad_type = input_199_pad_type_0, strides = input_199_strides_0, weight = const_73_to_fp16, x = input_197_cast_fp16)[name = tensor<string, []>("out_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_201_cast_fp16 = add(x = out_cast_fp16, y = input_191_cast_fp16)[name = tensor<string, []>("input_201_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> features_cast_fp16 = relu(x = input_201_cast_fp16)[name = tensor<string, []>("features_cast_fp16")];
+            tensor<int32, [3]> concat_0x = const()[name = tensor<string, []>("concat_0x"), val = tensor<int32, [3]>([-1, 2560, 125])];
+            tensor<fp16, [?, 2560, 125]> sequences_1_cast_fp16 = reshape(shape = concat_0x, x = features_cast_fp16)[name = tensor<string, []>("sequences_1_cast_fp16")];
+            tensor<int32, [1]> weights_fp32_axes_0 = const()[name = tensor<string, []>("weights_fp32_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [?, 1, 125]> weights_fp32_cast_fp16 = expand_dims(axes = weights_fp32_axes_0, x = weights_3_cast_fp16)[name = tensor<string, []>("weights_fp32_cast_fp16")];
+            tensor<int32, [1]> weights_expanded_axes_0 = const()[name = tensor<string, []>("weights_expanded_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp16, [?, 1, 1, 125]> weights_expanded_cast_fp16 = expand_dims(axes = weights_expanded_axes_0, x = weights_fp32_cast_fp16)[name = tensor<string, []>("weights_expanded_cast_fp16")];
+            tensor<int32, [1]> var_599_axes_0 = const()[name = tensor<string, []>("op_599_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, []> var_599_keep_dims_0 = const()[name = tensor<string, []>("op_599_keep_dims_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [?, 1, 1]> var_599_cast_fp16 = reduce_sum(axes = var_599_axes_0, keep_dims = var_599_keep_dims_0, x = weights_expanded_cast_fp16)[name = tensor<string, []>("op_599_cast_fp16")];
+            tensor<fp16, []> var_600_to_fp16 = const()[name = tensor<string, []>("op_600_to_fp16"), val = tensor<fp16, []>(0x1.a38p-14)];
+            tensor<fp16, [?, 1, 1]> v1_cast_fp16 = add(x = var_599_cast_fp16, y = var_600_to_fp16)[name = tensor<string, []>("v1_cast_fp16")];
+            tensor<int32, [1]> var_602_axes_0 = const()[name = tensor<string, []>("op_602_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [?, 1, 2560, 125]> var_602_cast_fp16 = expand_dims(axes = var_602_axes_0, x = sequences_1_cast_fp16)[name = tensor<string, []>("op_602_cast_fp16")];
+            tensor<fp16, [?, 1, 2560, 125]> weighted_cast_fp16 = mul(x = var_602_cast_fp16, y = weights_expanded_cast_fp16)[name = tensor<string, []>("weighted_cast_fp16")];
+            tensor<int32, [1]> var_605_axes_0 = const()[name = tensor<string, []>("op_605_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, []> var_605_keep_dims_0 = const()[name = tensor<string, []>("op_605_keep_dims_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [?, 1, 2560]> var_605_cast_fp16 = reduce_sum(axes = var_605_axes_0, keep_dims = var_605_keep_dims_0, x = weighted_cast_fp16)[name = tensor<string, []>("op_605_cast_fp16")];
+            tensor<fp16, [?, 1, 2560]> mean_cast_fp16 = real_div(x = var_605_cast_fp16, y = v1_cast_fp16)[name = tensor<string, []>("mean_cast_fp16")];
+            tensor<int32, [1]> var_608_axes_0 = const()[name = tensor<string, []>("op_608_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [?, 1, 2560, 1]> var_608_cast_fp16 = expand_dims(axes = var_608_axes_0, x = mean_cast_fp16)[name = tensor<string, []>("op_608_cast_fp16")];
+            tensor<fp16, [?, 1, 2560, 125]> diff_cast_fp16 = sub(x = var_602_cast_fp16, y = var_608_cast_fp16)[name = tensor<string, []>("diff_cast_fp16")];
+            tensor<fp16, [?, 1, 1, 125]> var_610_cast_fp16 = mul(x = weights_expanded_cast_fp16, y = weights_expanded_cast_fp16)[name = tensor<string, []>("op_610_cast_fp16")];
+            tensor<int32, [1]> v2_axes_0 = const()[name = tensor<string, []>("v2_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, []> v2_keep_dims_0 = const()[name = tensor<string, []>("v2_keep_dims_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [?, 1, 1]> v2_cast_fp16 = reduce_sum(axes = v2_axes_0, keep_dims = v2_keep_dims_0, x = var_610_cast_fp16)[name = tensor<string, []>("v2_cast_fp16")];
+            tensor<fp16, [?, 1, 1]> var_613_cast_fp16 = real_div(x = v2_cast_fp16, y = v1_cast_fp16)[name = tensor<string, []>("op_613_cast_fp16")];
+            tensor<fp16, [?, 1, 1]> var_614_cast_fp16 = sub(x = v1_cast_fp16, y = var_613_cast_fp16)[name = tensor<string, []>("op_614_cast_fp16")];
+            tensor<fp16, []> var_615_to_fp16 = const()[name = tensor<string, []>("op_615_to_fp16"), val = tensor<fp16, []>(0x1.a38p-14)];
+            tensor<fp16, [?, 1, 1]> denom_cast_fp16 = add(x = var_614_cast_fp16, y = var_615_to_fp16)[name = tensor<string, []>("denom_cast_fp16")];
+            tensor<fp16, [?, 1, 2560, 125]> var_617_cast_fp16 = mul(x = diff_cast_fp16, y = diff_cast_fp16)[name = tensor<string, []>("op_617_cast_fp16")];
+            tensor<fp16, [?, 1, 2560, 125]> var_618_cast_fp16 = mul(x = var_617_cast_fp16, y = weights_expanded_cast_fp16)[name = tensor<string, []>("op_618_cast_fp16")];
+            tensor<int32, [1]> var_620_axes_0 = const()[name = tensor<string, []>("op_620_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, []> var_620_keep_dims_0 = const()[name = tensor<string, []>("op_620_keep_dims_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [?, 1, 2560]> var_620_cast_fp16 = reduce_sum(axes = var_620_axes_0, keep_dims = var_620_keep_dims_0, x = var_618_cast_fp16)[name = tensor<string, []>("op_620_cast_fp16")];
+            tensor<fp16, [?, 1, 2560]> var_cast_fp16 = real_div(x = var_620_cast_fp16, y = denom_cast_fp16)[name = tensor<string, []>("var_cast_fp16")];
+            tensor<fp16, []> var_34_to_fp16 = const()[name = tensor<string, []>("op_34_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
+            tensor<fp16, []> const_1_to_fp16 = const()[name = tensor<string, []>("const_1_to_fp16"), val = tensor<fp16, []>(inf)];
+            tensor<fp16, [?, 1, 2560]> clip_0_cast_fp16 = clip(alpha = var_34_to_fp16, beta = const_1_to_fp16, x = var_cast_fp16)[name = tensor<string, []>("clip_0_cast_fp16")];
+            tensor<fp16, [?, 1, 2560]> std_cast_fp16 = sqrt(x = clip_0_cast_fp16)[name = tensor<string, []>("std_cast_fp16")];
+            tensor<bool, []> output_interleave_0 = const()[name = tensor<string, []>("output_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [?, 1, 5120]> output_cast_fp16 = concat(axis = var_33, interleave = output_interleave_0, values = (mean_cast_fp16, std_cast_fp16))[name = tensor<string, []>("output_cast_fp16")];
+            tensor<int32, [1]> var_626_axes_0 = const()[name = tensor<string, []>("op_626_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [?, 5120]> var_626_cast_fp16 = squeeze(axes = var_626_axes_0, x = output_cast_fp16)[name = tensor<string, []>("op_626_cast_fp16")];
+            tensor<int32, [1]> var_628_axes_0 = const()[name = tensor<string, []>("op_628_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [?, 5120, 1]> var_628_cast_fp16 = expand_dims(axes = var_628_axes_0, x = var_626_cast_fp16)[name = tensor<string, []>("op_628_cast_fp16")];
+            tensor<int32, [1]> input_203_axes_0 = const()[name = tensor<string, []>("input_203_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [?, 5120, 1, 1]> input_203_cast_fp16 = expand_dims(axes = input_203_axes_0, x = var_628_cast_fp16)[name = tensor<string, []>("input_203_cast_fp16")];
+            tensor<string, []> var_636_pad_type_0 = const()[name = tensor<string, []>("op_636_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> var_636_strides_0 = const()[name = tensor<string, []>("op_636_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> var_636_pad_0 = const()[name = tensor<string, []>("op_636_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> var_636_dilations_0 = const()[name = tensor<string, []>("op_636_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> var_636_groups_0 = const()[name = tensor<string, []>("op_636_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 5120, 1, 1]> resnet_seg_1_weight_to_fp16 = const()[name = tensor<string, []>("resnet_seg_1_weight_to_fp16"), val = tensor<fp16, [256, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10790208)))];
+            tensor<fp16, [256]> resnet_seg_1_bias_to_fp16 = const()[name = tensor<string, []>("resnet_seg_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13411712)))];
+            tensor<fp16, [?, 256, 1, 1]> var_636_cast_fp16 = conv(bias = resnet_seg_1_bias_to_fp16, dilations = var_636_dilations_0, groups = var_636_groups_0, pad = var_636_pad_0, pad_type = var_636_pad_type_0, strides = var_636_strides_0, weight = resnet_seg_1_weight_to_fp16, x = input_203_cast_fp16)[name = tensor<string, []>("op_636_cast_fp16")];
+            tensor<int32, [2]> concat_1x = const()[name = tensor<string, []>("concat_1x"), val = tensor<int32, [2]>([-1, 256])];
+            tensor<fp16, [?, 256]> input_cast_fp16 = reshape(shape = concat_1x, x = var_636_cast_fp16)[name = tensor<string, []>("input_cast_fp16")];
+            tensor<string, []> input_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("input_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<int32, [1]> var_640 = const()[name = tensor<string, []>("op_640"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, []> var_641 = const()[name = tensor<string, []>("op_641"), val = tensor<bool, []>(true)];
+            tensor<fp32, [?, 256]> input_cast_fp16_to_fp32 = cast(dtype = input_cast_fp16_to_fp32_dtype_0, x = input_cast_fp16)[name = tensor<string, []>("cast_12")];
+            tensor<fp32, [?, 1]> norms_1 = reduce_l2_norm(axes = var_640, keep_dims = var_641, x = input_cast_fp16_to_fp32)[name = tensor<string, []>("norms_1")];
+            tensor<string, []> norms_1_to_fp16_dtype_0 = const()[name = tensor<string, []>("norms_1_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<fp16, []> var_644_to_fp16 = const()[name = tensor<string, []>("op_644_to_fp16"), val = tensor<fp16, []>(0x1.a38p-14)];
+            tensor<fp16, []> const_2_to_fp16 = const()[name = tensor<string, []>("const_2_to_fp16"), val = tensor<fp16, []>(inf)];
+            tensor<fp16, [?, 1]> norms_1_to_fp16 = cast(dtype = norms_1_to_fp16_dtype_0, x = norms_1)[name = tensor<string, []>("cast_11")];
+            tensor<fp16, [?, 1]> clip_1_cast_fp16 = clip(alpha = var_644_to_fp16, beta = const_2_to_fp16, x = norms_1_to_fp16)[name = tensor<string, []>("clip_1_cast_fp16")];
+            tensor<fp16, [?, 256]> var_647_cast_fp16 = real_div(x = input_cast_fp16, y = clip_1_cast_fp16)[name = tensor<string, []>("op_647_cast_fp16")];
+            tensor<string, []> var_647_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("op_647_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<fp32, [?, 256]> embedding = cast(dtype = var_647_cast_fp16_to_fp32_dtype_0, x = var_647_cast_fp16)[name = tensor<string, []>("cast_10")];
+        } -> (embedding);
+}

Embedding.mlmodelc/weights/weight.bin

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+size 13412288

FBank.mlmodelc/analytics/coremldata.bin

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+size 243

FBank.mlmodelc/coremldata.bin

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+size 853

FBank.mlmodelc/metadata.json

@@ -0,0 +1,80 @@
+[
+  {
+    "shortDescription" : "pyannote community-1 FBANK frontend (10 s audio preprocessing to 80×998 features, batch 1-32, CPU preferred)",
+    "metadataOutputVersion" : "3.0",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32)",
+        "shortDescription" : "",
+        "shape" : "[]",
+        "name" : "fbank_features",
+        "type" : "MultiArray"
+      }
+    ],
+    "version" : "pyannote-speaker-diarization-community-1",
+    "modelParameters" : [
+
+    ],
+    "author" : "Fluid Inference",
+    "specificationVersion" : 8,
+    "storagePrecision" : "Float32",
+    "license" : "CC-BY-4.0",
+    "mlProgramOperationTypeHistogram" : {
+      "Ios17.mul" : 3,
+      "Ios17.transpose" : 2,
+      "Ios17.sub" : 3,
+      "Ios17.conv" : 4,
+      "Ios17.log" : 1,
+      "Ios17.sliceByIndex" : 1,
+      "Ios16.reduceMean" : 2,
+      "Ios17.add" : 2,
+      "Ios17.clip" : 1,
+      "Ios17.pow" : 2,
+      "Ios17.expandDims" : 4,
+      "Ios17.squeeze" : 4,
+      "Ios17.reshape" : 2,
+      "Pad" : 2
+    },
+    "computePrecision" : "Mixed (Float32, Int32)",
+    "stateSchema" : [
+
+    ],
+    "isUpdatable" : "0",
+    "availability" : {
+      "macOS" : "14.0",
+      "tvOS" : "17.0",
+      "visionOS" : "1.0",
+      "watchOS" : "10.0",
+      "iOS" : "17.0",
+      "macCatalyst" : "17.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "inputSchema" : [
+      {
+        "shortDescription" : "",
+        "dataType" : "Float32",
+        "hasShapeFlexibility" : "1",
+        "isOptional" : "0",
+        "shapeFlexibility" : "1 × 1 × 160000 | 2 × 1 × 160000 | 3 × 1 × 160000 | 4 × 1 × 160000 | 5 × 1 × 160000 | 6 × 1 × 160000 | 7 × 1 × 160000 | 8 × 1 × 160000 | 9 × 1 × 160000 | 10 × 1 × 160000 | 11 × 1 × 160000 | 12 × 1 × 160000 | 13 × 1 × 160000 | 14 × 1 × 160000 | 15 × 1 × 160000 | 16 × 1 × 160000 | 17 × 1 × 160000 | 18 × 1 × 160000 | 19 × 1 × 160000 | 20 × 1 × 160000 | 21 × 1 × 160000 | 22 × 1 × 160000 | 23 × 1 × 160000 | 24 × 1 × 160000 | 25 × 1 × 160000 | 26 × 1 × 160000 | 27 × 1 × 160000 | 28 × 1 × 160000 | 29 × 1 × 160000 | 30 × 1 × 160000 | 31 × 1 × 160000 | 32 × 1 × 160000",
+        "formattedType" : "MultiArray (Float32 1 × 1 × 160000)",
+        "type" : "MultiArray",
+        "shape" : "[1, 1, 160000]",
+        "name" : "audio",
+        "enumeratedShapes" : "[[1, 1, 160000], [2, 1, 160000], [3, 1, 160000], [4, 1, 160000], [5, 1, 160000], [6, 1, 160000], [7, 1, 160000], [8, 1, 160000], [9, 1, 160000], [10, 1, 160000], [11, 1, 160000], [12, 1, 160000], [13, 1, 160000], [14, 1, 160000], [15, 1, 160000], [16, 1, 160000], [17, 1, 160000], [18, 1, 160000], [19, 1, 160000], [20, 1, 160000], [21, 1, 160000], [22, 1, 160000], [23, 1, 160000], [24, 1, 160000], [25, 1, 160000], [26, 1, 160000], [27, 1, 160000], [28, 1, 160000], [29, 1, 160000], [30, 1, 160000], [31, 1, 160000], [32, 1, 160000]]"
+      }
+    ],
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2025-10-15",
+      "com.github.apple.coremltools.source" : "torch==2.8.0",
+      "com.github.apple.coremltools.version" : "9.0b1",
+      "com.github.apple.coremltools.source_dialect" : "TorchScript"
+    },
+    "generatedClassName" : "fbank_community_1",
+    "method" : "predict"
+  }
+]

FBank.mlmodelc/model.mil

@@ -0,0 +1,96 @@
+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3500.14.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.8.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0b1"}})]
+{
+    func main<ios17>(tensor<fp32, [?, 1, 160000]> audio) [FlexibleShapeInformation = tuple<tuple<tensor<string, []>, dict<tensor<string, []>, tensor<int32, [?]>>>, tuple<tensor<string, []>, dict<tensor<string, []>, dict<tensor<string, []>, tensor<int32, [?]>>>>>((("DefaultShapes", {{"audio", [1, 1, 160000]}}), ("EnumeratedShapes", {{"audio_1_1_10_1_160000_", {{"audio", [10, 1, 160000]}}}, {"audio_1_1_11_1_160000_", {{"audio", [11, 1, 160000]}}}, {"audio_1_1_12_1_160000_", {{"audio", [12, 1, 160000]}}}, {"audio_1_1_13_1_160000_", {{"audio", [13, 1, 160000]}}}, {"audio_1_1_14_1_160000_", {{"audio", [14, 1, 160000]}}}, {"audio_1_1_15_1_160000_", {{"audio", [15, 1, 160000]}}}, {"audio_1_1_16_1_160000_", {{"audio", [16, 1, 160000]}}}, {"audio_1_1_17_1_160000_", {{"audio", [17, 1, 160000]}}}, {"audio_1_1_18_1_160000_", {{"audio", [18, 1, 160000]}}}, {"audio_1_1_19_1_160000_", {{"audio", [19, 1, 160000]}}}, {"audio_1_1_1_1_160000_", {{"audio", [1, 1, 160000]}}}, {"audio_1_1_20_1_160000_", {{"audio", [20, 1, 160000]}}}, {"audio_1_1_21_1_160000_", {{"audio", [21, 1, 160000]}}}, {"audio_1_1_22_1_160000_", {{"audio", [22, 1, 160000]}}}, {"audio_1_1_23_1_160000_", {{"audio", [23, 1, 160000]}}}, {"audio_1_1_24_1_160000_", {{"audio", [24, 1, 160000]}}}, {"audio_1_1_25_1_160000_", {{"audio", [25, 1, 160000]}}}, {"audio_1_1_26_1_160000_", {{"audio", [26, 1, 160000]}}}, {"audio_1_1_27_1_160000_", {{"audio", [27, 1, 160000]}}}, {"audio_1_1_28_1_160000_", {{"audio", [28, 1, 160000]}}}, {"audio_1_1_29_1_160000_", {{"audio", [29, 1, 160000]}}}, {"audio_1_1_2_1_160000_", {{"audio", [2, 1, 160000]}}}, {"audio_1_1_30_1_160000_", {{"audio", [30, 1, 160000]}}}, {"audio_1_1_31_1_160000_", {{"audio", [31, 1, 160000]}}}, {"audio_1_1_32_1_160000_", {{"audio", [32, 1, 160000]}}}, {"audio_1_1_3_1_160000_", {{"audio", [3, 1, 160000]}}}, {"audio_1_1_4_1_160000_", {{"audio", [4, 1, 160000]}}}, {"audio_1_1_5_1_160000_", {{"audio", [5, 1, 160000]}}}, {"audio_1_1_6_1_160000_", {{"audio", [6, 1, 160000]}}}, {"audio_1_1_7_1_160000_", {{"audio", [7, 1, 160000]}}}, {"audio_1_1_8_1_160000_", {{"audio", [8, 1, 160000]}}}, {"audio_1_1_9_1_160000_", {{"audio", [9, 1, 160000]}}}})))] {
+            tensor<fp32, [80, 257, 1]> _fbank_mel_weight = const()[name = tensor<string, []>("_fbank_mel_weight"), val = tensor<fp32, [80, 257, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
+            tensor<fp32, [257, 1, 512]> _fbank_dft_imag_weight = const()[name = tensor<string, []>("_fbank_dft_imag_weight"), val = tensor<fp32, [257, 1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(82368)))];
+            tensor<fp32, [257, 1, 512]> _fbank_dft_real_weight = const()[name = tensor<string, []>("_fbank_dft_real_weight"), val = tensor<fp32, [257, 1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(608768)))];
+            tensor<fp32, [1, 400]> _fbank_window = const()[name = tensor<string, []>("_fbank_window"), val = tensor<fp32, [1, 400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1135168)))];
+            tensor<fp32, []> _fbank_eps = const()[name = tensor<string, []>("_fbank_eps"), val = tensor<fp32, []>(0x1.0c6f7ap-20)];
+            tensor<fp32, [400, 1, 400]> _fbank_frame_kernel = const()[name = tensor<string, []>("_fbank_frame_kernel"), val = tensor<fp32, [400, 1, 400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1136832)))];
+            tensor<fp32, []> var_3_promoted = const()[name = tensor<string, []>("op_3_promoted"), val = tensor<fp32, []>(0x1p+15)];
+            tensor<fp32, [?, 1, 160000]> waveforms_3 = mul(x = audio, y = var_3_promoted)[name = tensor<string, []>("waveforms_3")];
+            tensor<string, []> frames_1_pad_type_0 = const()[name = tensor<string, []>("frames_1_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> frames_1_strides_0 = const()[name = tensor<string, []>("frames_1_strides_0"), val = tensor<int32, [1]>([160])];
+            tensor<int32, [2]> frames_1_pad_0 = const()[name = tensor<string, []>("frames_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> frames_1_dilations_0 = const()[name = tensor<string, []>("frames_1_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> frames_1_groups_0 = const()[name = tensor<string, []>("frames_1_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [?, 400, 998]> frames_1 = conv(dilations = frames_1_dilations_0, groups = frames_1_groups_0, pad = frames_1_pad_0, pad_type = frames_1_pad_type_0, strides = frames_1_strides_0, weight = _fbank_frame_kernel, x = waveforms_3)[name = tensor<string, []>("frames_1")];
+            tensor<int32, [3]> frames_3_perm_0 = const()[name = tensor<string, []>("frames_3_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [2]> concat_0x = const()[name = tensor<string, []>("concat_0x"), val = tensor<int32, [2]>([-1, 400])];
+            tensor<fp32, [?, 998, 400]> frames_3 = transpose(perm = frames_3_perm_0, x = frames_1)[name = tensor<string, []>("transpose_1")];
+            tensor<fp32, [?, 400]> frames_5 = reshape(shape = concat_0x, x = frames_3)[name = tensor<string, []>("frames_5")];
+            tensor<int32, [1]> var_53_axes_0 = const()[name = tensor<string, []>("op_53_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, []> var_53_keep_dims_0 = const()[name = tensor<string, []>("op_53_keep_dims_0"), val = tensor<bool, []>(true)];
+            tensor<fp32, [?, 1]> var_53 = reduce_mean(axes = var_53_axes_0, keep_dims = var_53_keep_dims_0, x = frames_5)[name = tensor<string, []>("op_53")];
+            tensor<fp32, [?, 400]> frames_7 = sub(x = frames_5, y = var_53)[name = tensor<string, []>("frames_7")];
+            tensor<int32, [1]> input_1_axes_0 = const()[name = tensor<string, []>("input_1_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [?, 1, 400]> input_1 = expand_dims(axes = input_1_axes_0, x = frames_7)[name = tensor<string, []>("input_1")];
+            tensor<fp32, []> const_0 = const()[name = tensor<string, []>("const_0"), val = tensor<fp32, []>(0x0p+0)];
+            tensor<int32, [6]> var_57_pad_0 = const()[name = tensor<string, []>("op_57_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 1, 0])];
+            tensor<string, []> var_57_mode_0 = const()[name = tensor<string, []>("op_57_mode_0"), val = tensor<string, []>("replicate")];
+            tensor<fp32, [?, 1, 401]> var_57 = pad(constant_val = const_0, mode = var_57_mode_0, pad = var_57_pad_0, x = input_1)[name = tensor<string, []>("op_57")];
+            tensor<int32, [1]> padded_axes_0 = const()[name = tensor<string, []>("padded_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [?, 401]> padded = squeeze(axes = padded_axes_0, x = var_57)[name = tensor<string, []>("padded")];
+            tensor<int32, [2]> var_60_begin_0 = const()[name = tensor<string, []>("op_60_begin_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [2]> var_60_end_0 = const()[name = tensor<string, []>("op_60_end_0"), val = tensor<int32, [2]>([0, 400])];
+            tensor<bool, [2]> var_60_end_mask_0 = const()[name = tensor<string, []>("op_60_end_mask_0"), val = tensor<bool, [2]>([true, false])];
+            tensor<fp32, [?, 400]> var_60 = slice_by_index(begin = var_60_begin_0, end = var_60_end_0, end_mask = var_60_end_mask_0, x = padded)[name = tensor<string, []>("op_60")];
+            tensor<fp32, []> var_61 = const()[name = tensor<string, []>("op_61"), val = tensor<fp32, []>(0x1.f0a3d8p-1)];
+            tensor<fp32, [?, 400]> var_62 = mul(x = var_60, y = var_61)[name = tensor<string, []>("op_62")];
+            tensor<fp32, [?, 400]> frames_9 = sub(x = frames_7, y = var_62)[name = tensor<string, []>("frames_9")];
+            tensor<fp32, [?, 400]> frames_11 = mul(x = frames_9, y = _fbank_window)[name = tensor<string, []>("frames_11")];
+            tensor<int32, [1]> input_axes_0 = const()[name = tensor<string, []>("input_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [?, 1, 400]> input = expand_dims(axes = input_axes_0, x = frames_11)[name = tensor<string, []>("input")];
+            tensor<fp32, []> const_1 = const()[name = tensor<string, []>("const_1"), val = tensor<fp32, []>(0x0p+0)];
+            tensor<int32, [6]> var_67_pad_0 = const()[name = tensor<string, []>("op_67_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 0, 112])];
+            tensor<string, []> var_67_mode_0 = const()[name = tensor<string, []>("op_67_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp32, [?, 1, 512]> var_67 = pad(constant_val = const_1, mode = var_67_mode_0, pad = var_67_pad_0, x = input)[name = tensor<string, []>("op_67")];
+            tensor<string, []> var_74_pad_type_0 = const()[name = tensor<string, []>("op_74_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> var_74_strides_0 = const()[name = tensor<string, []>("op_74_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> var_74_pad_0 = const()[name = tensor<string, []>("op_74_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_74_dilations_0 = const()[name = tensor<string, []>("op_74_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> var_74_groups_0 = const()[name = tensor<string, []>("op_74_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [?, 257, 1]> var_74 = conv(dilations = var_74_dilations_0, groups = var_74_groups_0, pad = var_74_pad_0, pad_type = var_74_pad_type_0, strides = var_74_strides_0, weight = _fbank_dft_real_weight, x = var_67)[name = tensor<string, []>("op_74")];
+            tensor<int32, [1]> real_axes_0 = const()[name = tensor<string, []>("real_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp32, [?, 257]> real = squeeze(axes = real_axes_0, x = var_74)[name = tensor<string, []>("real")];
+            tensor<string, []> var_80_pad_type_0 = const()[name = tensor<string, []>("op_80_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> var_80_strides_0 = const()[name = tensor<string, []>("op_80_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> var_80_pad_0 = const()[name = tensor<string, []>("op_80_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_80_dilations_0 = const()[name = tensor<string, []>("op_80_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> var_80_groups_0 = const()[name = tensor<string, []>("op_80_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [?, 257, 1]> var_80 = conv(dilations = var_80_dilations_0, groups = var_80_groups_0, pad = var_80_pad_0, pad_type = var_80_pad_type_0, strides = var_80_strides_0, weight = _fbank_dft_imag_weight, x = var_67)[name = tensor<string, []>("op_80")];
+            tensor<int32, [1]> imag_axes_0 = const()[name = tensor<string, []>("imag_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp32, [?, 257]> imag = squeeze(axes = imag_axes_0, x = var_80)[name = tensor<string, []>("imag")];
+            tensor<fp32, []> var_22_promoted = const()[name = tensor<string, []>("op_22_promoted"), val = tensor<fp32, []>(0x1p+1)];
+            tensor<fp32, [?, 257]> var_82 = pow(x = real, y = var_22_promoted)[name = tensor<string, []>("op_82")];
+            tensor<fp32, []> var_22_promoted_1 = const()[name = tensor<string, []>("op_22_promoted_1"), val = tensor<fp32, []>(0x1p+1)];
+            tensor<fp32, [?, 257]> var_83 = pow(x = imag, y = var_22_promoted_1)[name = tensor<string, []>("op_83")];
+            tensor<fp32, [?, 257]> power = add(x = var_82, y = var_83)[name = tensor<string, []>("power")];
+            tensor<int32, [1]> var_85_axes_0 = const()[name = tensor<string, []>("op_85_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp32, [?, 257, 1]> var_85 = expand_dims(axes = var_85_axes_0, x = power)[name = tensor<string, []>("op_85")];
+            tensor<string, []> var_90_pad_type_0 = const()[name = tensor<string, []>("op_90_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> var_90_strides_0 = const()[name = tensor<string, []>("op_90_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> var_90_pad_0 = const()[name = tensor<string, []>("op_90_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_90_dilations_0 = const()[name = tensor<string, []>("op_90_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> var_90_groups_0 = const()[name = tensor<string, []>("op_90_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [?, 80, 1]> var_90 = conv(dilations = var_90_dilations_0, groups = var_90_groups_0, pad = var_90_pad_0, pad_type = var_90_pad_type_0, strides = var_90_strides_0, weight = _fbank_mel_weight, x = var_85)[name = tensor<string, []>("op_90")];
+            tensor<int32, [1]> mel_1_axes_0 = const()[name = tensor<string, []>("mel_1_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp32, [?, 80]> mel_1 = squeeze(axes = mel_1_axes_0, x = var_90)[name = tensor<string, []>("mel_1")];
+            tensor<fp32, [?, 80]> mel_3 = add(x = mel_1, y = _fbank_eps)[name = tensor<string, []>("mel_3")];
+            tensor<fp32, []> const_2 = const()[name = tensor<string, []>("const_2"), val = tensor<fp32, []>(0x1.fffffep+127)];
+            tensor<fp32, [?, 80]> clip_0 = clip(alpha = _fbank_eps, beta = const_2, x = mel_3)[name = tensor<string, []>("clip_0")];
+            tensor<fp32, []> mel_epsilon_0 = const()[name = tensor<string, []>("mel_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
+            tensor<fp32, [?, 80]> mel = log(epsilon = mel_epsilon_0, x = clip_0)[name = tensor<string, []>("mel")];
+            tensor<int32, [3]> concat_1x = const()[name = tensor<string, []>("concat_1x"), val = tensor<int32, [3]>([-1, 998, 80])];
+            tensor<fp32, [?, 998, 80]> var_96 = reshape(shape = concat_1x, x = mel)[name = tensor<string, []>("op_96")];
+            tensor<int32, [1]> centered_axes_0 = const()[name = tensor<string, []>("centered_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, []> centered_keep_dims_0 = const()[name = tensor<string, []>("centered_keep_dims_0"), val = tensor<bool, []>(true)];
+            tensor<fp32, [?, 1, 80]> centered = reduce_mean(axes = centered_axes_0, keep_dims = centered_keep_dims_0, x = var_96)[name = tensor<string, []>("centered")];
+            tensor<fp32, [?, 998, 80]> features = sub(x = var_96, y = centered)[name = tensor<string, []>("features")];
+            tensor<int32, [3]> var_115 = const()[name = tensor<string, []>("op_115"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> var_118_axes_0 = const()[name = tensor<string, []>("op_118_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [?, 80, 998]> var_116 = transpose(perm = var_115, x = features)[name = tensor<string, []>("transpose_0")];
+            tensor<fp32, [?, 1, 80, 998]> fbank_features = expand_dims(axes = var_118_axes_0, x = var_116)[name = tensor<string, []>("op_118")];
+        } -> (fbank_features);
+}

FBank.mlmodelc/weights/weight.bin

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PLDA.mlmodelc/analytics/coremldata.bin

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PLDA.mlmodelc/coremldata.bin

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PLDA.mlmodelc/metadata.json

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+[
+  {
+    "shortDescription" : "pyannote community-1 PLDA transformation (x-vector whitening + PLDA projection)",
+    "metadataOutputVersion" : "3.0",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 128)",
+        "shortDescription" : "",
+        "shape" : "[1, 128]",
+        "name" : "plda_features",
+        "type" : "MultiArray"
+      }
+    ],
+    "version" : "pyannote-speaker-diarization-community-1",
+    "modelParameters" : [
+
+    ],
+    "author" : "Fluid Inference",
+    "specificationVersion" : 8,
+    "storagePrecision" : "Float32",
+    "license" : "CC-BY-4.0",
+    "mlProgramOperationTypeHistogram" : {
+      "Ios16.reduceSum" : 2,
+      "Ios17.clip" : 2,
+      "Ios17.sqrt" : 2,
+      "Ios17.linear" : 2,
+      "Ios17.realDiv" : 2,
+      "Ios17.mul" : 4,
+      "Ios17.sub" : 3
+    },
+    "computePrecision" : "Mixed (Float32, Int32)",
+    "stateSchema" : [
+
+    ],
+    "isUpdatable" : "0",
+    "availability" : {
+      "macOS" : "14.0",
+      "tvOS" : "17.0",
+      "visionOS" : "1.0",
+      "watchOS" : "10.0",
+      "iOS" : "17.0",
+      "macCatalyst" : "17.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "inputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 256)",
+        "shortDescription" : "",
+        "shape" : "[1, 256]",
+        "name" : "embeddings",
+        "type" : "MultiArray"
+      }
+    ],
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2025-10-01",
+      "com.github.apple.coremltools.source" : "torch==2.8.0",
+      "com.github.apple.coremltools.version" : "9.0b1",
+      "com.github.apple.coremltools.source_dialect" : "TorchScript"
+    },
+    "generatedClassName" : "plda_community_1",
+    "method" : "predict"
+  }
+]

PLDA.mlmodelc/model.mil

@@ -0,0 +1,39 @@
+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3500.14.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.8.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0b1"}})]
+{
+    func main<ios17>(tensor<fp32, [1, 256]> embeddings) {
+            tensor<fp32, [128, 128]> plda_tr = const()[name = tensor<string, []>("plda_tr"), val = tensor<fp32, [128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
+            tensor<fp32, [128]> mu = const()[name = tensor<string, []>("mu"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(65664)))];
+            tensor<fp32, []> lda_dim_scale = const()[name = tensor<string, []>("lda_dim_scale"), val = tensor<fp32, []>(0x1.6a09e6p+3)];
+            tensor<fp32, [128]> mean2 = const()[name = tensor<string, []>("mean2"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(66240)))];
+            tensor<fp32, []> lda_scale = const()[name = tensor<string, []>("lda_scale"), val = tensor<fp32, []>(0x1p+4)];
+            tensor<fp32, [256]> mean1 = const()[name = tensor<string, []>("mean1"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(66816)))];
+            tensor<fp32, [1, 256]> x_1 = sub(x = embeddings, y = mean1)[name = tensor<string, []>("x_1")];
+            tensor<fp32, [1, 256]> var_11 = mul(x = x_1, y = x_1)[name = tensor<string, []>("op_11")];
+            tensor<int32, [1]> var_16_axes_0 = const()[name = tensor<string, []>("op_16_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, []> var_16_keep_dims_0 = const()[name = tensor<string, []>("op_16_keep_dims_0"), val = tensor<bool, []>(true)];
+            tensor<fp32, [1, 1]> var_16 = reduce_sum(axes = var_16_axes_0, keep_dims = var_16_keep_dims_0, x = var_11)[name = tensor<string, []>("op_16")];
+            tensor<fp32, []> var_17 = const()[name = tensor<string, []>("op_17"), val = tensor<fp32, []>(0x1.197998p-40)];
+            tensor<fp32, []> const_0 = const()[name = tensor<string, []>("const_0"), val = tensor<fp32, []>(0x1.fffffep+127)];
+            tensor<fp32, [1, 1]> clip_0 = clip(alpha = var_17, beta = const_0, x = var_16)[name = tensor<string, []>("clip_0")];
+            tensor<fp32, [1, 1]> norm_1 = sqrt(x = clip_0)[name = tensor<string, []>("norm_1")];
+            tensor<fp32, [1, 256]> normalized1 = real_div(x = x_1, y = norm_1)[name = tensor<string, []>("normalized1")];
+            tensor<fp32, [128, 256]> transpose_0 = const()[name = tensor<string, []>("transpose_0"), val = tensor<fp32, [128, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(67904)))];
+            tensor<fp32, [128]> var_22_bias_0 = const()[name = tensor<string, []>("op_22_bias_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(199040)))];
+            tensor<fp32, [1, 128]> var_22 = linear(bias = var_22_bias_0, weight = transpose_0, x = normalized1)[name = tensor<string, []>("op_22")];
+            tensor<fp32, [1, 128]> projected = mul(x = var_22, y = lda_scale)[name = tensor<string, []>("projected")];
+            tensor<fp32, [1, 128]> x = sub(x = projected, y = mean2)[name = tensor<string, []>("x")];
+            tensor<fp32, [1, 128]> var_26 = mul(x = x, y = x)[name = tensor<string, []>("op_26")];
+            tensor<int32, [1]> var_31_axes_0 = const()[name = tensor<string, []>("op_31_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, []> var_31_keep_dims_0 = const()[name = tensor<string, []>("op_31_keep_dims_0"), val = tensor<bool, []>(true)];
+            tensor<fp32, [1, 1]> var_31 = reduce_sum(axes = var_31_axes_0, keep_dims = var_31_keep_dims_0, x = var_26)[name = tensor<string, []>("op_31")];
+            tensor<fp32, []> var_32 = const()[name = tensor<string, []>("op_32"), val = tensor<fp32, []>(0x1.197998p-40)];
+            tensor<fp32, []> const_1 = const()[name = tensor<string, []>("const_1"), val = tensor<fp32, []>(0x1.fffffep+127)];
+            tensor<fp32, [1, 1]> clip_1 = clip(alpha = var_32, beta = const_1, x = var_31)[name = tensor<string, []>("clip_1")];
+            tensor<fp32, [1, 1]> norm = sqrt(x = clip_1)[name = tensor<string, []>("norm")];
+            tensor<fp32, [1, 128]> var_36 = real_div(x = x, y = norm)[name = tensor<string, []>("op_36")];
+            tensor<fp32, [1, 128]> normalized2 = mul(x = var_36, y = lda_dim_scale)[name = tensor<string, []>("normalized2")];
+            tensor<fp32, [1, 128]> plda_centered = sub(x = normalized2, y = mu)[name = tensor<string, []>("plda_centered")];
+            tensor<fp32, [1, 128]> plda_features = linear(bias = var_22_bias_0, weight = plda_tr, x = plda_centered)[name = tensor<string, []>("op_41")];
+        } -> (plda_features);
+}

PLDA.mlmodelc/weights/weight.bin

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PldaRho.mlmodelc/analytics/coremldata.bin

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PldaRho.mlmodelc/coremldata.bin

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+size 763

PldaRho.mlmodelc/metadata.json

@@ -0,0 +1,73 @@
+[
+  {
+    "shortDescription" : "pyannote community-1 PLDA rho (features scaled by sqrt(phi) for VBx clustering, batch 1-32)",
+    "metadataOutputVersion" : "3.0",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32)",
+        "shortDescription" : "",
+        "shape" : "[]",
+        "name" : "rho",
+        "type" : "MultiArray"
+      }
+    ],
+    "version" : "pyannote-speaker-diarization-community-1",
+    "modelParameters" : [
+
+    ],
+    "author" : "Fluid Inference",
+    "specificationVersion" : 8,
+    "storagePrecision" : "Float32",
+    "license" : "CC-BY-4.0",
+    "mlProgramOperationTypeHistogram" : {
+      "Ios16.reduceSum" : 2,
+      "Ios17.clip" : 2,
+      "Ios17.sqrt" : 2,
+      "Ios17.linear" : 2,
+      "Ios17.realDiv" : 2,
+      "Ios17.mul" : 5,
+      "Ios17.sub" : 3
+    },
+    "computePrecision" : "Mixed (Float32, Int32)",
+    "stateSchema" : [
+
+    ],
+    "isUpdatable" : "0",
+    "availability" : {
+      "macOS" : "14.0",
+      "tvOS" : "17.0",
+      "visionOS" : "1.0",
+      "watchOS" : "10.0",
+      "iOS" : "17.0",
+      "macCatalyst" : "17.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "inputSchema" : [
+      {
+        "shortDescription" : "",
+        "dataType" : "Float32",
+        "hasShapeFlexibility" : "1",
+        "isOptional" : "0",
+        "shapeFlexibility" : "32 × 256 | 1 × 256 | 2 × 256 | 3 × 256 | 4 × 256 | 5 × 256 | 6 × 256 | 7 × 256 | 8 × 256 | 9 × 256 | 10 × 256 | 11 × 256 | 12 × 256 | 13 × 256 | 14 × 256 | 15 × 256 | 16 × 256 | 17 × 256 | 18 × 256 | 19 × 256 | 20 × 256 | 21 × 256 | 22 × 256 | 23 × 256 | 24 × 256 | 25 × 256 | 26 × 256 | 27 × 256 | 28 × 256 | 29 × 256 | 30 × 256 | 31 × 256",
+        "formattedType" : "MultiArray (Float32 32 × 256)",
+        "type" : "MultiArray",
+        "shape" : "[32, 256]",
+        "name" : "embeddings",
+        "enumeratedShapes" : "[[32, 256], [1, 256], [2, 256], [3, 256], [4, 256], [5, 256], [6, 256], [7, 256], [8, 256], [9, 256], [10, 256], [11, 256], [12, 256], [13, 256], [14, 256], [15, 256], [16, 256], [17, 256], [18, 256], [19, 256], [20, 256], [21, 256], [22, 256], [23, 256], [24, 256], [25, 256], [26, 256], [27, 256], [28, 256], [29, 256], [30, 256], [31, 256]]"
+      }
+    ],
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2025-10-04",
+      "com.github.apple.coremltools.source" : "torch==2.8.0",
+      "com.github.apple.coremltools.version" : "9.0b1",
+      "com.github.apple.coremltools.source_dialect" : "TorchScript"
+    },
+    "generatedClassName" : "plda_rho_community_1",
+    "method" : "predict"
+  }
+]

PldaRho.mlmodelc/model.mil

@@ -0,0 +1,40 @@
+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3500.14.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.8.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0b1"}})]
+{
+    func main<ios17>(tensor<fp32, [?, 256]> embeddings) [FlexibleShapeInformation = tuple<tuple<tensor<string, []>, dict<tensor<string, []>, tensor<int32, [?]>>>, tuple<tensor<string, []>, dict<tensor<string, []>, dict<tensor<string, []>, tensor<int32, [?]>>>>>((("DefaultShapes", {{"embeddings", [32, 256]}}), ("EnumeratedShapes", {{"embeddings_1_1_1_10_256_", {{"embeddings", [10, 256]}}}, {"embeddings_1_1_1_11_256_", {{"embeddings", [11, 256]}}}, {"embeddings_1_1_1_12_256_", {{"embeddings", [12, 256]}}}, {"embeddings_1_1_1_13_256_", {{"embeddings", [13, 256]}}}, {"embeddings_1_1_1_14_256_", {{"embeddings", [14, 256]}}}, {"embeddings_1_1_1_15_256_", {{"embeddings", [15, 256]}}}, {"embeddings_1_1_1_16_256_", {{"embeddings", [16, 256]}}}, {"embeddings_1_1_1_17_256_", {{"embeddings", [17, 256]}}}, {"embeddings_1_1_1_18_256_", {{"embeddings", [18, 256]}}}, {"embeddings_1_1_1_19_256_", {{"embeddings", [19, 256]}}}, {"embeddings_1_1_1_1_256_", {{"embeddings", [1, 256]}}}, {"embeddings_1_1_1_20_256_", {{"embeddings", [20, 256]}}}, {"embeddings_1_1_1_21_256_", {{"embeddings", [21, 256]}}}, {"embeddings_1_1_1_22_256_", {{"embeddings", [22, 256]}}}, {"embeddings_1_1_1_23_256_", {{"embeddings", [23, 256]}}}, {"embeddings_1_1_1_24_256_", {{"embeddings", [24, 256]}}}, {"embeddings_1_1_1_25_256_", {{"embeddings", [25, 256]}}}, {"embeddings_1_1_1_26_256_", {{"embeddings", [26, 256]}}}, {"embeddings_1_1_1_27_256_", {{"embeddings", [27, 256]}}}, {"embeddings_1_1_1_28_256_", {{"embeddings", [28, 256]}}}, {"embeddings_1_1_1_29_256_", {{"embeddings", [29, 256]}}}, {"embeddings_1_1_1_2_256_", {{"embeddings", [2, 256]}}}, {"embeddings_1_1_1_30_256_", {{"embeddings", [30, 256]}}}, {"embeddings_1_1_1_31_256_", {{"embeddings", [31, 256]}}}, {"embeddings_1_1_1_32_256_", {{"embeddings", [32, 256]}}}, {"embeddings_1_1_1_3_256_", {{"embeddings", [3, 256]}}}, {"embeddings_1_1_1_4_256_", {{"embeddings", [4, 256]}}}, {"embeddings_1_1_1_5_256_", {{"embeddings", [5, 256]}}}, {"embeddings_1_1_1_6_256_", {{"embeddings", [6, 256]}}}, {"embeddings_1_1_1_7_256_", {{"embeddings", [7, 256]}}}, {"embeddings_1_1_1_8_256_", {{"embeddings", [8, 256]}}}, {"embeddings_1_1_1_9_256_", {{"embeddings", [9, 256]}}}})))] {
+            tensor<fp32, [128]> sqrt_phi = const()[name = tensor<string, []>("sqrt_phi"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
+            tensor<fp32, [128, 128]> transform_plda_tr = const()[name = tensor<string, []>("transform_plda_tr"), val = tensor<fp32, [128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(640)))];
+            tensor<fp32, [128]> transform_mu = const()[name = tensor<string, []>("transform_mu"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(66240)))];
+            tensor<fp32, []> transform_lda_dim_scale = const()[name = tensor<string, []>("transform_lda_dim_scale"), val = tensor<fp32, []>(0x1.6a09e6p+3)];
+            tensor<fp32, [128]> transform_mean2 = const()[name = tensor<string, []>("transform_mean2"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(66816)))];
+            tensor<fp32, []> transform_lda_scale = const()[name = tensor<string, []>("transform_lda_scale"), val = tensor<fp32, []>(0x1p+4)];
+            tensor<fp32, [256]> transform_mean1 = const()[name = tensor<string, []>("transform_mean1"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(67392)))];
+            tensor<fp32, []> var_4 = const()[name = tensor<string, []>("op_4"), val = tensor<fp32, []>(0x1.197998p-40)];
+            tensor<fp32, [?, 256]> x_1 = sub(x = embeddings, y = transform_mean1)[name = tensor<string, []>("x_1")];
+            tensor<fp32, [?, 256]> var_17 = mul(x = x_1, y = x_1)[name = tensor<string, []>("op_17")];
+            tensor<int32, [1]> var_19_axes_0 = const()[name = tensor<string, []>("op_19_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, []> var_19_keep_dims_0 = const()[name = tensor<string, []>("op_19_keep_dims_0"), val = tensor<bool, []>(true)];
+            tensor<fp32, [?, 1]> var_19 = reduce_sum(axes = var_19_axes_0, keep_dims = var_19_keep_dims_0, x = var_17)[name = tensor<string, []>("op_19")];
+            tensor<fp32, []> const_0 = const()[name = tensor<string, []>("const_0"), val = tensor<fp32, []>(0x1.fffffep+127)];
+            tensor<fp32, [?, 1]> clip_0 = clip(alpha = var_4, beta = const_0, x = var_19)[name = tensor<string, []>("clip_0")];
+            tensor<fp32, [?, 1]> norm_1 = sqrt(x = clip_0)[name = tensor<string, []>("norm_1")];
+            tensor<fp32, [?, 256]> normalized1 = real_div(x = x_1, y = norm_1)[name = tensor<string, []>("normalized1")];
+            tensor<fp32, [128, 256]> transpose_0 = const()[name = tensor<string, []>("transpose_0"), val = tensor<fp32, [128, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(68480)))];
+            tensor<fp32, [128]> var_23_bias_0 = const()[name = tensor<string, []>("op_23_bias_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(199616)))];
+            tensor<fp32, [?, 128]> var_23 = linear(bias = var_23_bias_0, weight = transpose_0, x = normalized1)[name = tensor<string, []>("op_23")];
+            tensor<fp32, [?, 128]> projected = mul(x = var_23, y = transform_lda_scale)[name = tensor<string, []>("projected")];
+            tensor<fp32, [?, 128]> x = sub(x = projected, y = transform_mean2)[name = tensor<string, []>("x")];
+            tensor<fp32, [?, 128]> var_26 = mul(x = x, y = x)[name = tensor<string, []>("op_26")];
+            tensor<int32, [1]> var_28_axes_0 = const()[name = tensor<string, []>("op_28_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, []> var_28_keep_dims_0 = const()[name = tensor<string, []>("op_28_keep_dims_0"), val = tensor<bool, []>(true)];
+            tensor<fp32, [?, 1]> var_28 = reduce_sum(axes = var_28_axes_0, keep_dims = var_28_keep_dims_0, x = var_26)[name = tensor<string, []>("op_28")];
+            tensor<fp32, []> const_1 = const()[name = tensor<string, []>("const_1"), val = tensor<fp32, []>(0x1.fffffep+127)];
+            tensor<fp32, [?, 1]> clip_1 = clip(alpha = var_4, beta = const_1, x = var_28)[name = tensor<string, []>("clip_1")];
+            tensor<fp32, [?, 1]> norm = sqrt(x = clip_1)[name = tensor<string, []>("norm")];
+            tensor<fp32, [?, 128]> var_31 = real_div(x = x, y = norm)[name = tensor<string, []>("op_31")];
+            tensor<fp32, [?, 128]> normalized2 = mul(x = var_31, y = transform_lda_dim_scale)[name = tensor<string, []>("normalized2")];
+            tensor<fp32, [?, 128]> plda_centered = sub(x = normalized2, y = transform_mu)[name = tensor<string, []>("plda_centered")];
+            tensor<fp32, [?, 128]> features = linear(bias = var_23_bias_0, weight = transform_plda_tr, x = plda_centered)[name = tensor<string, []>("features")];
+            tensor<fp32, [?, 128]> rho = mul(x = features, y = sqrt_phi)[name = tensor<string, []>("op_36")];
+        } -> (rho);
+}

PldaRho.mlmodelc/weights/weight.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:80f7d229202636d372428c90596f11a91545f07da77259f07153aaf225914a36
+size 200192

README.md

@@ -0,0 +1,93 @@
+---
+license: cc-by-4.0
+tags:
+- speech
+- audio
+- voice
+- speaker-diarization
+- speaker-change-detection
+- coreml
+- speaker-segmentation
+base_model:
+- pyannote/speaker-diarization-community-1
+base_model_relation: finetune
+pipeline_tag: voice-activity-detection
+---
+
+
+# **<span style="color:#5DAF8D">🧃 Speaker Diarization CoreML </span>**
+[![Discord](https://img.shields.io/badge/Discord-Join%20Chat-7289da.svg)](https://discord.gg/WNsvaCtmDe)
+[![GitHub Repo stars](https://img.shields.io/github/stars/FluidInference/FluidAudio?style=flat&logo=github)](https://github.com/FluidInference/FluidAudio)
+
+Speaker diarization based on [pyannote](https://github.com/pyannote) models optimized for Apple Neural Engine.
+
+Models are trained on acoustic signatures so it supports any lanugage.
+
+## Usage
+
+See the SDK for more details [https://github.com/FluidInference/FluidAudio](https://github.com/FluidInference/FluidAudio)
+
+Please note that the SDK itself is Apache 2.0, but the parent model from Pyannote is `cc-by-4.0`
+
+### Technical Specifications
+- **Input**: 16kHz mono audio
+- **Output**: Speaker segments with timestamps and IDs
+- **Framework**: CoreML (converted from PyTorch)
+- **Optimization**: Apple Neural Engine (ANE) optimized operations
+- **Precision**: FP32 on CPU/GPU, FP16 on ANE
+
+
+## Performance
+
+See the [origianl model](https://huggingface.co/pyannote/speaker-diarization-community-1) for detailed DER benchmark, for the purpose of our conversion, we tried to match the original model as much as possible: 
+
+The models on CoreML exhibit a ~10x Speedup on CPU and ~20x speed up on GPU. 
+
+![plots/pipeline_timing.png](plots/pipeline_timing.png)
+
+Due to different precisions, there are minor differences in the values generated but the differences are mostly negilible, though it does account for some errors that needs to be adjusted during clustering:
+
+![plots/metrics_timeseries.png](plots/metrics_timeseries.png)
+
+
+We see this when running the end to end pipeline with the Pytorch model versus the Core ML model (patched the Pyannote pipeline to run the Core ML model instead). The DER and JER is ~1% compared to the Pytorch model as we're dropping the precision to fp32
+![plots/pipeline_overview.png](plots/pipeline_overview.png)
+
+
+
+## Citations (from original model)
+
+1. Speaker segmentation model
+
+```bibtex
+@inproceedings{Plaquet23,
+  author={Alexis Plaquet and Hervé Bredin},
+  title={{Powerset multi-class cross entropy loss for neural speaker diarization}},
+  year=2023,
+  booktitle={Proc. INTERSPEECH 2023},
+}
+```
+
+2. Speaker embedding model
+
+```bibtex
+@inproceedings{Wang2023,
+  title={Wespeaker: A research and production oriented speaker embedding learning toolkit},
+  author={Wang, Hongji and Liang, Chengdong and Wang, Shuai and Chen, Zhengyang and Zhang, Binbin and Xiang, Xu and Deng, Yanlei and Qian, Yanmin},
+  booktitle={ICASSP 2023, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+  pages={1--5},
+  year={2023},
+  organization={IEEE}
+}
+```
+
+
+3. Speaker clustering
+
+```bibtex
+@article{Landini2022,
+  author={Landini, Federico and Profant, J{\'a}n and Diez, Mireia and Burget, Luk{\'a}{\v{s}}},
+  title={{Bayesian HMM clustering of x-vector sequences (VBx) in speaker diarization: theory, implementation and analysis on standard tasks}},
+  year={2022},
+  journal={Computer Speech \& Language},
+}

Segmentation.mlmodelc/analytics/coremldata.bin

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Segmentation.mlmodelc/coremldata.bin

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Segmentation.mlmodelc/metadata.json

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+[
+  {
+    "shortDescription" : "pyannote community-1 segmentation (10 s powerset diarization, batch 1-32)",
+    "metadataOutputVersion" : "3.0",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32)",
+        "shortDescription" : "",
+        "shape" : "[]",
+        "name" : "log_probs",
+        "type" : "MultiArray"
+      }
+    ],
+    "version" : "pyannote-speaker-diarization-community-1",
+    "modelParameters" : [
+
+    ],
+    "author" : "Fluid Inference",
+    "specificationVersion" : 8,
+    "storagePrecision" : "Float32",
+    "license" : "CC-BY-4.0",
+    "mlProgramOperationTypeHistogram" : {
+      "Ios17.linear" : 3,
+      "Ios17.transpose" : 3,
+      "Ios17.conv" : 3,
+      "Ios17.log" : 1,
+      "Shape" : 1,
+      "Ios17.leakyRelu" : 5,
+      "Ios17.gather" : 1,
+      "Ios17.concat" : 9,
+      "Fill" : 1,
+      "Ios17.abs" : 1,
+      "Ios16.maxPool" : 3,
+      "Ios17.lstm" : 4,
+      "Ios16.softmax" : 1,
+      "Ios17.instanceNorm" : 4,
+      "Split" : 10,
+      "Ios17.squeeze" : 8
+    },
+    "computePrecision" : "Mixed (Float32, Int32)",
+    "stateSchema" : [
+
+    ],
+    "isUpdatable" : "0",
+    "availability" : {
+      "macOS" : "14.0",
+      "tvOS" : "17.0",
+      "visionOS" : "1.0",
+      "watchOS" : "10.0",
+      "iOS" : "17.0",
+      "macCatalyst" : "17.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "inputSchema" : [
+      {
+        "shortDescription" : "",
+        "dataType" : "Float32",
+        "hasShapeFlexibility" : "1",
+        "isOptional" : "0",
+        "shapeFlexibility" : "32 × 1 × 160000 | 1 × 1 × 160000 | 2 × 1 × 160000 | 3 × 1 × 160000 | 4 × 1 × 160000 | 5 × 1 × 160000 | 6 × 1 × 160000 | 7 × 1 × 160000 | 8 × 1 × 160000 | 9 × 1 × 160000 | 10 × 1 × 160000 | 11 × 1 × 160000 | 12 × 1 × 160000 | 13 × 1 × 160000 | 14 × 1 × 160000 | 15 × 1 × 160000 | 16 × 1 × 160000 | 17 × 1 × 160000 | 18 × 1 × 160000 | 19 × 1 × 160000 | 20 × 1 × 160000 | 21 × 1 × 160000 | 22 × 1 × 160000 | 23 × 1 × 160000 | 24 × 1 × 160000 | 25 × 1 × 160000 | 26 × 1 × 160000 | 27 × 1 × 160000 | 28 × 1 × 160000 | 29 × 1 × 160000 | 30 × 1 × 160000 | 31 × 1 × 160000",
+        "formattedType" : "MultiArray (Float32 32 × 1 × 160000)",
+        "type" : "MultiArray",
+        "shape" : "[32, 1, 160000]",
+        "name" : "audio",
+        "enumeratedShapes" : "[[32, 1, 160000], [1, 1, 160000], [2, 1, 160000], [3, 1, 160000], [4, 1, 160000], [5, 1, 160000], [6, 1, 160000], [7, 1, 160000], [8, 1, 160000], [9, 1, 160000], [10, 1, 160000], [11, 1, 160000], [12, 1, 160000], [13, 1, 160000], [14, 1, 160000], [15, 1, 160000], [16, 1, 160000], [17, 1, 160000], [18, 1, 160000], [19, 1, 160000], [20, 1, 160000], [21, 1, 160000], [22, 1, 160000], [23, 1, 160000], [24, 1, 160000], [25, 1, 160000], [26, 1, 160000], [27, 1, 160000], [28, 1, 160000], [29, 1, 160000], [30, 1, 160000], [31, 1, 160000]]"
+      }
+    ],
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2025-10-13",
+      "com.github.apple.coremltools.source" : "torch==2.8.0",
+      "com.github.apple.coremltools.version" : "9.0b1",
+      "com.github.apple.coremltools.source_dialect" : "TorchScript"
+    },
+    "generatedClassName" : "Segmentation",
+    "method" : "predict"
+  }
+]

Segmentation.mlmodelc/model.mil

@@ -0,0 +1,219 @@
+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3500.14.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.8.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0b1"}})]
+{
+    func main<ios17>(tensor<fp32, [?, 1, 160000]> audio) [FlexibleShapeInformation = tuple<tuple<tensor<string, []>, dict<tensor<string, []>, tensor<int32, [?]>>>, tuple<tensor<string, []>, dict<tensor<string, []>, dict<tensor<string, []>, tensor<int32, [?]>>>>>((("DefaultShapes", {{"audio", [32, 1, 160000]}}), ("EnumeratedShapes", {{"audio_1_1_10_1_160000_", {{"audio", [10, 1, 160000]}}}, {"audio_1_1_11_1_160000_", {{"audio", [11, 1, 160000]}}}, {"audio_1_1_12_1_160000_", {{"audio", [12, 1, 160000]}}}, {"audio_1_1_13_1_160000_", {{"audio", [13, 1, 160000]}}}, {"audio_1_1_14_1_160000_", {{"audio", [14, 1, 160000]}}}, {"audio_1_1_15_1_160000_", {{"audio", [15, 1, 160000]}}}, {"audio_1_1_16_1_160000_", {{"audio", [16, 1, 160000]}}}, {"audio_1_1_17_1_160000_", {{"audio", [17, 1, 160000]}}}, {"audio_1_1_18_1_160000_", {{"audio", [18, 1, 160000]}}}, {"audio_1_1_19_1_160000_", {{"audio", [19, 1, 160000]}}}, {"audio_1_1_1_1_160000_", {{"audio", [1, 1, 160000]}}}, {"audio_1_1_20_1_160000_", {{"audio", [20, 1, 160000]}}}, {"audio_1_1_21_1_160000_", {{"audio", [21, 1, 160000]}}}, {"audio_1_1_22_1_160000_", {{"audio", [22, 1, 160000]}}}, {"audio_1_1_23_1_160000_", {{"audio", [23, 1, 160000]}}}, {"audio_1_1_24_1_160000_", {{"audio", [24, 1, 160000]}}}, {"audio_1_1_25_1_160000_", {{"audio", [25, 1, 160000]}}}, {"audio_1_1_26_1_160000_", {{"audio", [26, 1, 160000]}}}, {"audio_1_1_27_1_160000_", {{"audio", [27, 1, 160000]}}}, {"audio_1_1_28_1_160000_", {{"audio", [28, 1, 160000]}}}, {"audio_1_1_29_1_160000_", {{"audio", [29, 1, 160000]}}}, {"audio_1_1_2_1_160000_", {{"audio", [2, 1, 160000]}}}, {"audio_1_1_30_1_160000_", {{"audio", [30, 1, 160000]}}}, {"audio_1_1_31_1_160000_", {{"audio", [31, 1, 160000]}}}, {"audio_1_1_32_1_160000_", {{"audio", [32, 1, 160000]}}}, {"audio_1_1_3_1_160000_", {{"audio", [3, 1, 160000]}}}, {"audio_1_1_4_1_160000_", {{"audio", [4, 1, 160000]}}}, {"audio_1_1_5_1_160000_", {{"audio", [5, 1, 160000]}}}, {"audio_1_1_6_1_160000_", {{"audio", [6, 1, 160000]}}}, {"audio_1_1_7_1_160000_", {{"audio", [7, 1, 160000]}}}, {"audio_1_1_8_1_160000_", {{"audio", [8, 1, 160000]}}}, {"audio_1_1_9_1_160000_", {{"audio", [9, 1, 160000]}}}})))] {
+            tensor<fp32, [1]> sincnet_wav_norm1d_bias = const()[name = tensor<string, []>("sincnet_wav_norm1d_bias"), val = tensor<fp32, [1]>([0x1.73505ep-5])];
+            tensor<fp32, [1]> sincnet_wav_norm1d_weight = const()[name = tensor<string, []>("sincnet_wav_norm1d_weight"), val = tensor<fp32, [1]>([0x1.43f862p-7])];
+            tensor<fp32, [80]> sincnet_norm1d_0_bias = const()[name = tensor<string, []>("sincnet_norm1d_0_bias"), val = tensor<fp32, [80]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
+            tensor<fp32, [80]> sincnet_norm1d_0_weight = const()[name = tensor<string, []>("sincnet_norm1d_0_weight"), val = tensor<fp32, [80]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448)))];
+            tensor<fp32, [60]> sincnet_conv1d_1_bias = const()[name = tensor<string, []>("sincnet_conv1d_1_bias"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(832)))];
+            tensor<fp32, [60, 80, 5]> sincnet_conv1d_1_weight = const()[name = tensor<string, []>("sincnet_conv1d_1_weight"), val = tensor<fp32, [60, 80, 5]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1152)))];
+            tensor<fp32, [60]> sincnet_norm1d_1_bias = const()[name = tensor<string, []>("sincnet_norm1d_1_bias"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97216)))];
+            tensor<fp32, [60]> sincnet_norm1d_1_weight = const()[name = tensor<string, []>("sincnet_norm1d_1_weight"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97536)))];
+            tensor<fp32, [60]> sincnet_conv1d_2_bias = const()[name = tensor<string, []>("sincnet_conv1d_2_bias"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97856)))];
+            tensor<fp32, [60, 60, 5]> sincnet_conv1d_2_weight = const()[name = tensor<string, []>("sincnet_conv1d_2_weight"), val = tensor<fp32, [60, 60, 5]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(98176)))];
+            tensor<fp32, [60]> sincnet_norm1d_2_bias = const()[name = tensor<string, []>("sincnet_norm1d_2_bias"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(170240)))];
+            tensor<fp32, [60]> sincnet_norm1d_2_weight = const()[name = tensor<string, []>("sincnet_norm1d_2_weight"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(170560)))];
+            tensor<fp32, [128]> linear_0_bias = const()[name = tensor<string, []>("linear_0_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(170880)))];
+            tensor<fp32, [128, 256]> linear_0_weight = const()[name = tensor<string, []>("linear_0_weight"), val = tensor<fp32, [128, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(171456)))];
+            tensor<fp32, [128]> linear_1_bias = const()[name = tensor<string, []>("linear_1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(302592)))];
+            tensor<fp32, [128, 128]> linear_1_weight = const()[name = tensor<string, []>("linear_1_weight"), val = tensor<fp32, [128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(303168)))];
+            tensor<fp32, [7]> classifier_bias = const()[name = tensor<string, []>("classifier_bias"), val = tensor<fp32, [7]>([-0x1.00e888p+0, 0x1.67cb52p-2, 0x1.3d87fp-1, 0x1.c8aa8p-2, -0x1.445f5ep-2, -0x1.591274p-1, -0x1.8fb70ep-2])];
+            tensor<fp32, [7, 128]> classifier_weight = const()[name = tensor<string, []>("classifier_weight"), val = tensor<fp32, [7, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(368768)))];
+            tensor<fp32, []> var_9 = const()[name = tensor<string, []>("op_9"), val = tensor<fp32, []>(0x1.47ae14p-7)];
+            tensor<fp32, []> var_24 = const()[name = tensor<string, []>("op_24"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
+            tensor<fp32, [?, 1, 160000]> waveform = instance_norm(beta = sincnet_wav_norm1d_bias, epsilon = var_24, gamma = sincnet_wav_norm1d_weight, x = audio)[name = tensor<string, []>("waveform")];
+            tensor<fp32, [80, 1, 251]> filters = const()[name = tensor<string, []>("filters"), val = tensor<fp32, [80, 1, 251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(372416)))];
+            tensor<string, []> outputs_pad_type_0 = const()[name = tensor<string, []>("outputs_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> outputs_strides_0 = const()[name = tensor<string, []>("outputs_strides_0"), val = tensor<int32, [1]>([10])];
+            tensor<int32, [2]> outputs_pad_0 = const()[name = tensor<string, []>("outputs_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> outputs_dilations_0 = const()[name = tensor<string, []>("outputs_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> outputs_groups_0 = const()[name = tensor<string, []>("outputs_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [?, 80, 15975]> outputs = conv(dilations = outputs_dilations_0, groups = outputs_groups_0, pad = outputs_pad_0, pad_type = outputs_pad_type_0, strides = outputs_strides_0, weight = filters, x = waveform)[name = tensor<string, []>("outputs")];
+            tensor<fp32, [?, 80, 15975]> input_1 = abs(x = outputs)[name = tensor<string, []>("input_1")];
+            tensor<int32, [1]> var_119 = const()[name = tensor<string, []>("op_119"), val = tensor<int32, [1]>([3])];
+            tensor<int32, [1]> var_120 = const()[name = tensor<string, []>("op_120"), val = tensor<int32, [1]>([3])];
+            tensor<string, []> input_3_pad_type_0 = const()[name = tensor<string, []>("input_3_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_3_pad_0 = const()[name = tensor<string, []>("input_3_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<bool, []> input_3_ceil_mode_0 = const()[name = tensor<string, []>("input_3_ceil_mode_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [?, 80, 5325]> input_3 = max_pool(ceil_mode = input_3_ceil_mode_0, kernel_sizes = var_119, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_120, x = input_1)[name = tensor<string, []>("input_3")];
+            tensor<fp32, [?, 80, 5325]> input_5 = instance_norm(beta = sincnet_norm1d_0_bias, epsilon = var_24, gamma = sincnet_norm1d_0_weight, x = input_3)[name = tensor<string, []>("input_5")];
+            tensor<fp32, [?, 80, 5325]> input_7 = leaky_relu(alpha = var_9, x = input_5)[name = tensor<string, []>("input_7")];
+            tensor<string, []> input_9_pad_type_0 = const()[name = tensor<string, []>("input_9_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_9_strides_0 = const()[name = tensor<string, []>("input_9_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_9_pad_0 = const()[name = tensor<string, []>("input_9_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_9_dilations_0 = const()[name = tensor<string, []>("input_9_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_9_groups_0 = const()[name = tensor<string, []>("input_9_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [?, 60, 5321]> input_9 = conv(bias = sincnet_conv1d_1_bias, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = sincnet_conv1d_1_weight, x = input_7)[name = tensor<string, []>("input_9")];
+            tensor<int32, [1]> var_135 = const()[name = tensor<string, []>("op_135"), val = tensor<int32, [1]>([3])];
+            tensor<int32, [1]> var_136 = const()[name = tensor<string, []>("op_136"), val = tensor<int32, [1]>([3])];
+            tensor<string, []> input_11_pad_type_0 = const()[name = tensor<string, []>("input_11_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_11_pad_0 = const()[name = tensor<string, []>("input_11_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<bool, []> input_11_ceil_mode_0 = const()[name = tensor<string, []>("input_11_ceil_mode_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [?, 60, 1773]> input_11 = max_pool(ceil_mode = input_11_ceil_mode_0, kernel_sizes = var_135, pad = input_11_pad_0, pad_type = input_11_pad_type_0, strides = var_136, x = input_9)[name = tensor<string, []>("input_11")];
+            tensor<fp32, [?, 60, 1773]> input_13 = instance_norm(beta = sincnet_norm1d_1_bias, epsilon = var_24, gamma = sincnet_norm1d_1_weight, x = input_11)[name = tensor<string, []>("input_13")];
+            tensor<fp32, [?, 60, 1773]> input_15 = leaky_relu(alpha = var_9, x = input_13)[name = tensor<string, []>("input_15")];
+            tensor<string, []> input_17_pad_type_0 = const()[name = tensor<string, []>("input_17_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_17_strides_0 = const()[name = tensor<string, []>("input_17_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_17_pad_0 = const()[name = tensor<string, []>("input_17_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_17_dilations_0 = const()[name = tensor<string, []>("input_17_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_17_groups_0 = const()[name = tensor<string, []>("input_17_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [?, 60, 1769]> input_17 = conv(bias = sincnet_conv1d_2_bias, dilations = input_17_dilations_0, groups = input_17_groups_0, pad = input_17_pad_0, pad_type = input_17_pad_type_0, strides = input_17_strides_0, weight = sincnet_conv1d_2_weight, x = input_15)[name = tensor<string, []>("input_17")];
+            tensor<int32, [1]> var_151 = const()[name = tensor<string, []>("op_151"), val = tensor<int32, [1]>([3])];
+            tensor<int32, [1]> var_152 = const()[name = tensor<string, []>("op_152"), val = tensor<int32, [1]>([3])];
+            tensor<string, []> input_19_pad_type_0 = const()[name = tensor<string, []>("input_19_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_19_pad_0 = const()[name = tensor<string, []>("input_19_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<bool, []> input_19_ceil_mode_0 = const()[name = tensor<string, []>("input_19_ceil_mode_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [?, 60, 589]> input_19 = max_pool(ceil_mode = input_19_ceil_mode_0, kernel_sizes = var_151, pad = input_19_pad_0, pad_type = input_19_pad_type_0, strides = var_152, x = input_17)[name = tensor<string, []>("input_19")];
+            tensor<fp32, [?, 60, 589]> input_21 = instance_norm(beta = sincnet_norm1d_2_bias, epsilon = var_24, gamma = sincnet_norm1d_2_weight, x = input_19)[name = tensor<string, []>("input_21")];
+            tensor<fp32, [?, 60, 589]> x = leaky_relu(alpha = var_9, x = input_21)[name = tensor<string, []>("x")];
+            tensor<int32, [3]> var_163 = const()[name = tensor<string, []>("op_163"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, []> var_172 = const()[name = tensor<string, []>("op_172"), val = tensor<int32, []>(128)];
+            tensor<int32, []> var_173 = const()[name = tensor<string, []>("op_173"), val = tensor<int32, []>(8)];
+            tensor<fp32, [?, 589, 60]> input_23 = transpose(perm = var_163, x = x)[name = tensor<string, []>("transpose_6")];
+            tensor<int32, [3]> var_207_shape = shape(x = input_23)[name = tensor<string, []>("op_207_shape")];
+            tensor<int32, []> gather_0_batch_dims_0 = const()[name = tensor<string, []>("gather_0_batch_dims_0"), val = tensor<int32, []>(0)];
+            tensor<bool, []> gather_0_validate_indices_0 = const()[name = tensor<string, []>("gather_0_validate_indices_0"), val = tensor<bool, []>(false)];
+            tensor<int32, []> select_0 = const()[name = tensor<string, []>("select_0"), val = tensor<int32, []>(0)];
+            tensor<int32, []> gather_0_axis_1 = const()[name = tensor<string, []>("gather_0_axis_1"), val = tensor<int32, []>(0)];
+            tensor<int32, []> gather_0 = gather(axis = gather_0_axis_1, batch_dims = gather_0_batch_dims_0, indices = select_0, validate_indices = gather_0_validate_indices_0, x = var_207_shape)[name = tensor<string, []>("gather_0")];
+            tensor<int32, []> concat_0_axis_0 = const()[name = tensor<string, []>("concat_0_axis_0"), val = tensor<int32, []>(0)];
+            tensor<bool, []> concat_0_interleave_0 = const()[name = tensor<string, []>("concat_0_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [3]> concat_0 = concat(axis = concat_0_axis_0, interleave = concat_0_interleave_0, values = (var_173, gather_0, var_172))[name = tensor<string, []>("concat_0")];
+            tensor<fp32, []> hx_1_value_0 = const()[name = tensor<string, []>("hx_1_value_0"), val = tensor<fp32, []>(0x0p+0)];
+            tensor<fp32, [8, ?, 128]> hx_1 = fill(shape = concat_0, value = hx_1_value_0)[name = tensor<string, []>("hx_1")];
+            tensor<int32, [3]> input_23_batch_first_transpose_perm_0 = const()[name = tensor<string, []>("input_23_batch_first_transpose_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
+            tensor<int32, []> split_0_num_splits_0 = const()[name = tensor<string, []>("split_0_num_splits_0"), val = tensor<int32, []>(4)];
+            tensor<int32, []> split_0_axis_0 = const()[name = tensor<string, []>("split_0_axis_0"), val = tensor<int32, []>(0)];
+            tensor<fp32, [2, ?, 128]> split_0_0, tensor<fp32, [2, ?, 128]> split_0_1, tensor<fp32, [2, ?, 128]> split_0_2, tensor<fp32, [2, ?, 128]> split_0_3 = split(axis = split_0_axis_0, num_splits = split_0_num_splits_0, x = hx_1)[name = tensor<string, []>("split_0")];
+            tensor<int32, []> split_1_num_splits_0 = const()[name = tensor<string, []>("split_1_num_splits_0"), val = tensor<int32, []>(4)];
+            tensor<int32, []> split_1_axis_0 = const()[name = tensor<string, []>("split_1_axis_0"), val = tensor<int32, []>(0)];
+            tensor<fp32, [2, ?, 128]> split_1_0, tensor<fp32, [2, ?, 128]> split_1_1, tensor<fp32, [2, ?, 128]> split_1_2, tensor<fp32, [2, ?, 128]> split_1_3 = split(axis = split_1_axis_0, num_splits = split_1_num_splits_0, x = hx_1)[name = tensor<string, []>("split_1")];
+            tensor<fp32, [512]> add_0 = const()[name = tensor<string, []>("add_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(452800)))];
+            tensor<fp32, [512]> add_1 = const()[name = tensor<string, []>("add_1"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(454912)))];
+            tensor<fp32, [512, 60]> concat_6 = const()[name = tensor<string, []>("concat_6"), val = tensor<fp32, [512, 60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(457024)))];
+            tensor<fp32, [512, 128]> concat_7 = const()[name = tensor<string, []>("concat_7"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(579968)))];
+            tensor<fp32, [512, 60]> concat_8 = const()[name = tensor<string, []>("concat_8"), val = tensor<fp32, [512, 60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(842176)))];
+            tensor<fp32, [512, 128]> concat_9 = const()[name = tensor<string, []>("concat_9"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(965120)))];
+            tensor<int32, [2]> split_10_split_sizes_0 = const()[name = tensor<string, []>("split_10_split_sizes_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> split_10_axis_0 = const()[name = tensor<string, []>("split_10_axis_0"), val = tensor<int32, []>(0)];
+            tensor<fp32, [1, ?, 128]> split_10_0, tensor<fp32, [1, ?, 128]> split_10_1 = split(axis = split_10_axis_0, split_sizes = split_10_split_sizes_0, x = split_0_0)[name = tensor<string, []>("split_10")];
+            tensor<int32, []> concat_10_axis_0 = const()[name = tensor<string, []>("concat_10_axis_0"), val = tensor<int32, []>(2)];
+            tensor<bool, []> concat_10_interleave_0 = const()[name = tensor<string, []>("concat_10_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [1, ?, 256]> concat_10 = concat(axis = concat_10_axis_0, interleave = concat_10_interleave_0, values = (split_10_0, split_10_1))[name = tensor<string, []>("concat_10")];
+            tensor<int32, [1]> input_25_lstm_layer_0_lstm_h0_reshaped_axes_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_lstm_h0_reshaped_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp32, [?, 256]> input_25_lstm_layer_0_lstm_h0_reshaped = squeeze(axes = input_25_lstm_layer_0_lstm_h0_reshaped_axes_0, x = concat_10)[name = tensor<string, []>("input_25_lstm_layer_0_lstm_h0_reshaped")];
+            tensor<int32, [2]> split_11_split_sizes_0 = const()[name = tensor<string, []>("split_11_split_sizes_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> split_11_axis_0 = const()[name = tensor<string, []>("split_11_axis_0"), val = tensor<int32, []>(0)];
+            tensor<fp32, [1, ?, 128]> split_11_0, tensor<fp32, [1, ?, 128]> split_11_1 = split(axis = split_11_axis_0, split_sizes = split_11_split_sizes_0, x = split_1_0)[name = tensor<string, []>("split_11")];
+            tensor<int32, []> concat_11_axis_0 = const()[name = tensor<string, []>("concat_11_axis_0"), val = tensor<int32, []>(2)];
+            tensor<bool, []> concat_11_interleave_0 = const()[name = tensor<string, []>("concat_11_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [1, ?, 256]> concat_11 = concat(axis = concat_11_axis_0, interleave = concat_11_interleave_0, values = (split_11_0, split_11_1))[name = tensor<string, []>("concat_11")];
+            tensor<int32, [1]> input_25_lstm_layer_0_lstm_c0_reshaped_axes_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_lstm_c0_reshaped_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp32, [?, 256]> input_25_lstm_layer_0_lstm_c0_reshaped = squeeze(axes = input_25_lstm_layer_0_lstm_c0_reshaped_axes_0, x = concat_11)[name = tensor<string, []>("input_25_lstm_layer_0_lstm_c0_reshaped")];
+            tensor<string, []> input_25_lstm_layer_0_direction_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_direction_0"), val = tensor<string, []>("bidirectional")];
+            tensor<bool, []> input_25_lstm_layer_0_output_sequence_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_output_sequence_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> input_25_lstm_layer_0_recurrent_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
+            tensor<string, []> input_25_lstm_layer_0_cell_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_cell_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<string, []> input_25_lstm_layer_0_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<fp32, [589, ?, 60]> input_23_batch_first_transpose = transpose(perm = input_23_batch_first_transpose_perm_0, x = input_23)[name = tensor<string, []>("transpose_5")];
+            tensor<fp32, [589, ?, 256]> input_25_lstm_layer_0_0, tensor<fp32, [?, 256]> input_25_lstm_layer_0_1, tensor<fp32, [?, 256]> input_25_lstm_layer_0_2 = lstm(activation = input_25_lstm_layer_0_activation_0, bias = add_0, bias_back = add_1, cell_activation = input_25_lstm_layer_0_cell_activation_0, direction = input_25_lstm_layer_0_direction_0, initial_c = input_25_lstm_layer_0_lstm_c0_reshaped, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped, output_sequence = input_25_lstm_layer_0_output_sequence_0, recurrent_activation = input_25_lstm_layer_0_recurrent_activation_0, weight_hh = concat_7, weight_hh_back = concat_9, weight_ih = concat_6, weight_ih_back = concat_8, x = input_23_batch_first_transpose)[name = tensor<string, []>("input_25_lstm_layer_0")];
+            tensor<fp32, [512]> add_2 = const()[name = tensor<string, []>("add_2"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1227328)))];
+            tensor<fp32, [512]> add_3 = const()[name = tensor<string, []>("add_3"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1229440)))];
+            tensor<fp32, [512, 256]> concat_16 = const()[name = tensor<string, []>("concat_16"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1231552)))];
+            tensor<fp32, [512, 128]> concat_17 = const()[name = tensor<string, []>("concat_17"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1755904)))];
+            tensor<fp32, [512, 256]> concat_18 = const()[name = tensor<string, []>("concat_18"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2018112)))];
+            tensor<fp32, [512, 128]> concat_19 = const()[name = tensor<string, []>("concat_19"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2542464)))];
+            tensor<int32, [2]> split_20_split_sizes_0 = const()[name = tensor<string, []>("split_20_split_sizes_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> split_20_axis_0 = const()[name = tensor<string, []>("split_20_axis_0"), val = tensor<int32, []>(0)];
+            tensor<fp32, [1, ?, 128]> split_20_0, tensor<fp32, [1, ?, 128]> split_20_1 = split(axis = split_20_axis_0, split_sizes = split_20_split_sizes_0, x = split_0_1)[name = tensor<string, []>("split_20")];
+            tensor<int32, []> concat_20_axis_0 = const()[name = tensor<string, []>("concat_20_axis_0"), val = tensor<int32, []>(2)];
+            tensor<bool, []> concat_20_interleave_0 = const()[name = tensor<string, []>("concat_20_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [1, ?, 256]> concat_20 = concat(axis = concat_20_axis_0, interleave = concat_20_interleave_0, values = (split_20_0, split_20_1))[name = tensor<string, []>("concat_20")];
+            tensor<int32, [1]> input_25_lstm_layer_1_lstm_h0_reshaped_axes_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_lstm_h0_reshaped_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp32, [?, 256]> input_25_lstm_layer_1_lstm_h0_reshaped = squeeze(axes = input_25_lstm_layer_1_lstm_h0_reshaped_axes_0, x = concat_20)[name = tensor<string, []>("input_25_lstm_layer_1_lstm_h0_reshaped")];
+            tensor<int32, [2]> split_21_split_sizes_0 = const()[name = tensor<string, []>("split_21_split_sizes_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> split_21_axis_0 = const()[name = tensor<string, []>("split_21_axis_0"), val = tensor<int32, []>(0)];
+            tensor<fp32, [1, ?, 128]> split_21_0, tensor<fp32, [1, ?, 128]> split_21_1 = split(axis = split_21_axis_0, split_sizes = split_21_split_sizes_0, x = split_1_1)[name = tensor<string, []>("split_21")];
+            tensor<int32, []> concat_21_axis_0 = const()[name = tensor<string, []>("concat_21_axis_0"), val = tensor<int32, []>(2)];
+            tensor<bool, []> concat_21_interleave_0 = const()[name = tensor<string, []>("concat_21_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [1, ?, 256]> concat_21 = concat(axis = concat_21_axis_0, interleave = concat_21_interleave_0, values = (split_21_0, split_21_1))[name = tensor<string, []>("concat_21")];
+            tensor<int32, [1]> input_25_lstm_layer_1_lstm_c0_reshaped_axes_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_lstm_c0_reshaped_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp32, [?, 256]> input_25_lstm_layer_1_lstm_c0_reshaped = squeeze(axes = input_25_lstm_layer_1_lstm_c0_reshaped_axes_0, x = concat_21)[name = tensor<string, []>("input_25_lstm_layer_1_lstm_c0_reshaped")];
+            tensor<string, []> input_25_lstm_layer_1_direction_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_direction_0"), val = tensor<string, []>("bidirectional")];
+            tensor<bool, []> input_25_lstm_layer_1_output_sequence_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_output_sequence_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> input_25_lstm_layer_1_recurrent_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
+            tensor<string, []> input_25_lstm_layer_1_cell_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_cell_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<string, []> input_25_lstm_layer_1_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<fp32, [589, ?, 256]> input_25_lstm_layer_1_0, tensor<fp32, [?, 256]> input_25_lstm_layer_1_1, tensor<fp32, [?, 256]> input_25_lstm_layer_1_2 = lstm(activation = input_25_lstm_layer_1_activation_0, bias = add_2, bias_back = add_3, cell_activation = input_25_lstm_layer_1_cell_activation_0, direction = input_25_lstm_layer_1_direction_0, initial_c = input_25_lstm_layer_1_lstm_c0_reshaped, initial_h = input_25_lstm_layer_1_lstm_h0_reshaped, output_sequence = input_25_lstm_layer_1_output_sequence_0, recurrent_activation = input_25_lstm_layer_1_recurrent_activation_0, weight_hh = concat_17, weight_hh_back = concat_19, weight_ih = concat_16, weight_ih_back = concat_18, x = input_25_lstm_layer_0_0)[name = tensor<string, []>("input_25_lstm_layer_1")];
+            tensor<fp32, [512]> add_4 = const()[name = tensor<string, []>("add_4"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2804672)))];
+            tensor<fp32, [512]> add_5 = const()[name = tensor<string, []>("add_5"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2806784)))];
+            tensor<fp32, [512, 256]> concat_26 = const()[name = tensor<string, []>("concat_26"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2808896)))];
+            tensor<fp32, [512, 128]> concat_27 = const()[name = tensor<string, []>("concat_27"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3333248)))];
+            tensor<fp32, [512, 256]> concat_28 = const()[name = tensor<string, []>("concat_28"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3595456)))];
+            tensor<fp32, [512, 128]> concat_29 = const()[name = tensor<string, []>("concat_29"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4119808)))];
+            tensor<int32, [2]> split_30_split_sizes_0 = const()[name = tensor<string, []>("split_30_split_sizes_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> split_30_axis_0 = const()[name = tensor<string, []>("split_30_axis_0"), val = tensor<int32, []>(0)];
+            tensor<fp32, [1, ?, 128]> split_30_0, tensor<fp32, [1, ?, 128]> split_30_1 = split(axis = split_30_axis_0, split_sizes = split_30_split_sizes_0, x = split_0_2)[name = tensor<string, []>("split_30")];
+            tensor<int32, []> concat_30_axis_0 = const()[name = tensor<string, []>("concat_30_axis_0"), val = tensor<int32, []>(2)];
+            tensor<bool, []> concat_30_interleave_0 = const()[name = tensor<string, []>("concat_30_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [1, ?, 256]> concat_30 = concat(axis = concat_30_axis_0, interleave = concat_30_interleave_0, values = (split_30_0, split_30_1))[name = tensor<string, []>("concat_30")];
+            tensor<int32, [1]> input_25_lstm_layer_2_lstm_h0_reshaped_axes_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_lstm_h0_reshaped_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp32, [?, 256]> input_25_lstm_layer_2_lstm_h0_reshaped = squeeze(axes = input_25_lstm_layer_2_lstm_h0_reshaped_axes_0, x = concat_30)[name = tensor<string, []>("input_25_lstm_layer_2_lstm_h0_reshaped")];
+            tensor<int32, [2]> split_31_split_sizes_0 = const()[name = tensor<string, []>("split_31_split_sizes_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> split_31_axis_0 = const()[name = tensor<string, []>("split_31_axis_0"), val = tensor<int32, []>(0)];
+            tensor<fp32, [1, ?, 128]> split_31_0, tensor<fp32, [1, ?, 128]> split_31_1 = split(axis = split_31_axis_0, split_sizes = split_31_split_sizes_0, x = split_1_2)[name = tensor<string, []>("split_31")];
+            tensor<int32, []> concat_31_axis_0 = const()[name = tensor<string, []>("concat_31_axis_0"), val = tensor<int32, []>(2)];
+            tensor<bool, []> concat_31_interleave_0 = const()[name = tensor<string, []>("concat_31_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [1, ?, 256]> concat_31 = concat(axis = concat_31_axis_0, interleave = concat_31_interleave_0, values = (split_31_0, split_31_1))[name = tensor<string, []>("concat_31")];
+            tensor<int32, [1]> input_25_lstm_layer_2_lstm_c0_reshaped_axes_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_lstm_c0_reshaped_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp32, [?, 256]> input_25_lstm_layer_2_lstm_c0_reshaped = squeeze(axes = input_25_lstm_layer_2_lstm_c0_reshaped_axes_0, x = concat_31)[name = tensor<string, []>("input_25_lstm_layer_2_lstm_c0_reshaped")];
+            tensor<string, []> input_25_lstm_layer_2_direction_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_direction_0"), val = tensor<string, []>("bidirectional")];
+            tensor<bool, []> input_25_lstm_layer_2_output_sequence_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_output_sequence_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> input_25_lstm_layer_2_recurrent_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
+            tensor<string, []> input_25_lstm_layer_2_cell_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_cell_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<string, []> input_25_lstm_layer_2_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<fp32, [589, ?, 256]> input_25_lstm_layer_2_0, tensor<fp32, [?, 256]> input_25_lstm_layer_2_1, tensor<fp32, [?, 256]> input_25_lstm_layer_2_2 = lstm(activation = input_25_lstm_layer_2_activation_0, bias = add_4, bias_back = add_5, cell_activation = input_25_lstm_layer_2_cell_activation_0, direction = input_25_lstm_layer_2_direction_0, initial_c = input_25_lstm_layer_2_lstm_c0_reshaped, initial_h = input_25_lstm_layer_2_lstm_h0_reshaped, output_sequence = input_25_lstm_layer_2_output_sequence_0, recurrent_activation = input_25_lstm_layer_2_recurrent_activation_0, weight_hh = concat_27, weight_hh_back = concat_29, weight_ih = concat_26, weight_ih_back = concat_28, x = input_25_lstm_layer_1_0)[name = tensor<string, []>("input_25_lstm_layer_2")];
+            tensor<fp32, [512]> add_6 = const()[name = tensor<string, []>("add_6"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4382016)))];
+            tensor<fp32, [512]> add_7 = const()[name = tensor<string, []>("add_7"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4384128)))];
+            tensor<fp32, [512, 256]> concat_36 = const()[name = tensor<string, []>("concat_36"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4386240)))];
+            tensor<fp32, [512, 128]> concat_37 = const()[name = tensor<string, []>("concat_37"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4910592)))];
+            tensor<fp32, [512, 256]> concat_38 = const()[name = tensor<string, []>("concat_38"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5172800)))];
+            tensor<fp32, [512, 128]> concat_39 = const()[name = tensor<string, []>("concat_39"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5697152)))];
+            tensor<int32, [2]> split_40_split_sizes_0 = const()[name = tensor<string, []>("split_40_split_sizes_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> split_40_axis_0 = const()[name = tensor<string, []>("split_40_axis_0"), val = tensor<int32, []>(0)];
+            tensor<fp32, [1, ?, 128]> split_40_0, tensor<fp32, [1, ?, 128]> split_40_1 = split(axis = split_40_axis_0, split_sizes = split_40_split_sizes_0, x = split_0_3)[name = tensor<string, []>("split_40")];
+            tensor<int32, []> concat_40_axis_0 = const()[name = tensor<string, []>("concat_40_axis_0"), val = tensor<int32, []>(2)];
+            tensor<bool, []> concat_40_interleave_0 = const()[name = tensor<string, []>("concat_40_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [1, ?, 256]> concat_40 = concat(axis = concat_40_axis_0, interleave = concat_40_interleave_0, values = (split_40_0, split_40_1))[name = tensor<string, []>("concat_40")];
+            tensor<int32, [1]> input_25_batch_first_lstm_h0_reshaped_axes_0 = const()[name = tensor<string, []>("input_25_batch_first_lstm_h0_reshaped_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp32, [?, 256]> input_25_batch_first_lstm_h0_reshaped = squeeze(axes = input_25_batch_first_lstm_h0_reshaped_axes_0, x = concat_40)[name = tensor<string, []>("input_25_batch_first_lstm_h0_reshaped")];
+            tensor<int32, [2]> split_41_split_sizes_0 = const()[name = tensor<string, []>("split_41_split_sizes_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> split_41_axis_0 = const()[name = tensor<string, []>("split_41_axis_0"), val = tensor<int32, []>(0)];
+            tensor<fp32, [1, ?, 128]> split_41_0, tensor<fp32, [1, ?, 128]> split_41_1 = split(axis = split_41_axis_0, split_sizes = split_41_split_sizes_0, x = split_1_3)[name = tensor<string, []>("split_41")];
+            tensor<int32, []> concat_41_axis_0 = const()[name = tensor<string, []>("concat_41_axis_0"), val = tensor<int32, []>(2)];
+            tensor<bool, []> concat_41_interleave_0 = const()[name = tensor<string, []>("concat_41_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [1, ?, 256]> concat_41 = concat(axis = concat_41_axis_0, interleave = concat_41_interleave_0, values = (split_41_0, split_41_1))[name = tensor<string, []>("concat_41")];
+            tensor<int32, [1]> input_25_batch_first_lstm_c0_reshaped_axes_0 = const()[name = tensor<string, []>("input_25_batch_first_lstm_c0_reshaped_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp32, [?, 256]> input_25_batch_first_lstm_c0_reshaped = squeeze(axes = input_25_batch_first_lstm_c0_reshaped_axes_0, x = concat_41)[name = tensor<string, []>("input_25_batch_first_lstm_c0_reshaped")];
+            tensor<string, []> input_25_batch_first_direction_0 = const()[name = tensor<string, []>("input_25_batch_first_direction_0"), val = tensor<string, []>("bidirectional")];
+            tensor<bool, []> input_25_batch_first_output_sequence_0 = const()[name = tensor<string, []>("input_25_batch_first_output_sequence_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> input_25_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("input_25_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
+            tensor<string, []> input_25_batch_first_cell_activation_0 = const()[name = tensor<string, []>("input_25_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<string, []> input_25_batch_first_activation_0 = const()[name = tensor<string, []>("input_25_batch_first_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<fp32, [589, ?, 256]> input_25_batch_first_0, tensor<fp32, [?, 256]> input_25_batch_first_1, tensor<fp32, [?, 256]> input_25_batch_first_2 = lstm(activation = input_25_batch_first_activation_0, bias = add_6, bias_back = add_7, cell_activation = input_25_batch_first_cell_activation_0, direction = input_25_batch_first_direction_0, initial_c = input_25_batch_first_lstm_c0_reshaped, initial_h = input_25_batch_first_lstm_h0_reshaped, output_sequence = input_25_batch_first_output_sequence_0, recurrent_activation = input_25_batch_first_recurrent_activation_0, weight_hh = concat_37, weight_hh_back = concat_39, weight_ih = concat_36, weight_ih_back = concat_38, x = input_25_lstm_layer_2_0)[name = tensor<string, []>("input_25_batch_first")];
+            tensor<int32, [3]> input_25_perm_0 = const()[name = tensor<string, []>("input_25_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
+            tensor<fp32, [?, 589, 256]> input_25 = transpose(perm = input_25_perm_0, x = input_25_batch_first_0)[name = tensor<string, []>("transpose_4")];
+            tensor<fp32, [?, 589, 128]> input_27 = linear(bias = linear_0_bias, weight = linear_0_weight, x = input_25)[name = tensor<string, []>("linear_0")];
+            tensor<fp32, []> var_220 = const()[name = tensor<string, []>("op_220"), val = tensor<fp32, []>(0x1.47ae14p-7)];
+            tensor<fp32, [?, 589, 128]> input_29 = leaky_relu(alpha = var_220, x = input_27)[name = tensor<string, []>("input_29")];
+            tensor<fp32, [?, 589, 128]> input_31 = linear(bias = linear_1_bias, weight = linear_1_weight, x = input_29)[name = tensor<string, []>("linear_1")];
+            tensor<fp32, []> var_225 = const()[name = tensor<string, []>("op_225"), val = tensor<fp32, []>(0x1.47ae14p-7)];
+            tensor<fp32, [?, 589, 128]> input_33 = leaky_relu(alpha = var_225, x = input_31)[name = tensor<string, []>("input_33")];
+            tensor<fp32, [?, 589, 7]> input = linear(bias = classifier_bias, weight = classifier_weight, x = input_33)[name = tensor<string, []>("linear_2")];
+            tensor<int32, []> var_231 = const()[name = tensor<string, []>("op_231"), val = tensor<int32, []>(-1)];
+            tensor<fp32, [?, 589, 7]> var_232_softmax = softmax(axis = var_231, x = input)[name = tensor<string, []>("op_232_softmax")];
+            tensor<fp32, []> var_232_epsilon_0 = const()[name = tensor<string, []>("op_232_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
+            tensor<fp32, [?, 589, 7]> log_probs = log(epsilon = var_232_epsilon_0, x = var_232_softmax)[name = tensor<string, []>("op_232")];
+        } -> (log_probs);
+}

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