1..32

PldaRho.mlmodelc/analytics/coremldata.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e16fefb0b66565e7883ada5be64887942904d173684d8912ac3f610d9487faa7
+oid sha256:8940ea6044dbcbefa22da8cc41e0b485e1fb5ed89aecaf37c6e0c483a97ddcd7
 size 243

PldaRho.mlmodelc/coremldata.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7dadf982dbddb8c2ae6a787edd156973e260bd0f42248fb68635638371024ba0
-size 535
+oid sha256:4d9741477f721c79b09fcdfe455110c4b7d4272e2de3496bf1729d966d3ee418
+size 763

PldaRho.mlmodelc/metadata.json

@@ -1,15 +1,15 @@
 [
   {
-    "shortDescription" : "pyannote community-1 PLDA rho (features scaled by sqrt(phi) for VBx clustering, batch=32)",
+    "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 32 × 128)",
+        "formattedType" : "MultiArray (Float32)",
         "shortDescription" : "",
-        "shape" : "[32, 128]",
+        "shape" : "[]",
         "name" : "rho",
         "type" : "MultiArray"
       }
@@ -49,18 +49,20 @@
     },
     "inputSchema" : [
       {
-        "hasShapeFlexibility" : "0",
-        "isOptional" : "0",
+        "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)",
-        "shortDescription" : "",
+        "type" : "MultiArray",
         "shape" : "[32, 256]",
         "name" : "embeddings",
-        "type" : "MultiArray"
+        "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" : "2025-10-03",
+      "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"

PldaRho.mlmodelc/model.mil

@@ -1,7 +1,7 @@
 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, [32, 256]> embeddings) {
+    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)))];
@@ -10,31 +10,31 @@ program(1.0)
             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, [32, 256]> x_1 = sub(x = embeddings, y = transform_mean1)[name = tensor<string, []>("x_1")];
-            tensor<fp32, [32, 256]> var_17 = mul(x = x_1, y = x_1)[name = tensor<string, []>("op_17")];
+            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, [32, 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, [?, 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, [32, 1]> clip_0 = clip(alpha = var_4, beta = const_0, x = var_19)[name = tensor<string, []>("clip_0")];
-            tensor<fp32, [32, 1]> norm_1 = sqrt(x = clip_0)[name = tensor<string, []>("norm_1")];
-            tensor<fp32, [32, 256]> normalized1 = real_div(x = x_1, y = norm_1)[name = tensor<string, []>("normalized1")];
+            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, [32, 128]> var_23 = linear(bias = var_23_bias_0, weight = transpose_0, x = normalized1)[name = tensor<string, []>("op_23")];
-            tensor<fp32, [32, 128]> projected = mul(x = var_23, y = transform_lda_scale)[name = tensor<string, []>("projected")];
-            tensor<fp32, [32, 128]> x = sub(x = projected, y = transform_mean2)[name = tensor<string, []>("x")];
-            tensor<fp32, [32, 128]> var_26 = mul(x = x, y = x)[name = tensor<string, []>("op_26")];
+            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, [32, 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, [?, 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, [32, 1]> clip_1 = clip(alpha = var_4, beta = const_1, x = var_28)[name = tensor<string, []>("clip_1")];
-            tensor<fp32, [32, 1]> norm = sqrt(x = clip_1)[name = tensor<string, []>("norm")];
-            tensor<fp32, [32, 128]> var_31 = real_div(x = x, y = norm)[name = tensor<string, []>("op_31")];
-            tensor<fp32, [32, 128]> normalized2 = mul(x = var_31, y = transform_lda_dim_scale)[name = tensor<string, []>("normalized2")];
-            tensor<fp32, [32, 128]> plda_centered = sub(x = normalized2, y = transform_mu)[name = tensor<string, []>("plda_centered")];
-            tensor<fp32, [32, 128]> features = linear(bias = var_23_bias_0, weight = transform_plda_tr, x = plda_centered)[name = tensor<string, []>("features")];
-            tensor<fp32, [32, 128]> rho = mul(x = features, y = sqrt_phi)[name = tensor<string, []>("op_36")];
+            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);
 }