Upload 162 files

es/1120ms/decoder.mlmodelc/analytics/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d8c3744468694142efbb178729d4d6a56edc642464a04a884b938cfe27c4e094
+size 243

es/1120ms/decoder.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3c993f8b96ce22027cd2ed42d99b7e61f93a01197bb17cadada8eb989e946dec
+size 433

es/1120ms/decoder.mlmodelc/model.mil

@@ -0,0 +1,64 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.5.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})]
+{
+    func main<ios18>(tensor<fp32, [2, 1, 640]> c_in, tensor<fp32, [2, 1, 640]> h_in, tensor<int32, [1, 1]> token, tensor<int32, [1]> token_length) {
+            int32 y_axis_0 = const()[name = string("y_axis_0"), val = int32(0)];
+            int32 y_batch_dims_0 = const()[name = string("y_batch_dims_0"), val = int32(0)];
+            bool y_validate_indices_0 = const()[name = string("y_validate_indices_0"), val = bool(false)];
+            tensor<fp16, [832, 640]> module_prediction_embed_weight_to_fp16 = const()[name = string("module_prediction_embed_weight_to_fp16"), val = tensor<fp16, [832, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            string token_to_int16_dtype_0 = const()[name = string("token_to_int16_dtype_0"), val = string("int16")];
+            tensor<int16, [1, 1]> token_to_int16 = cast(dtype = token_to_int16_dtype_0, x = token)[name = string("cast_8")];
+            tensor<fp16, [1, 1, 640]> y_cast_fp16_cast_uint16 = gather(axis = y_axis_0, batch_dims = y_batch_dims_0, indices = token_to_int16, validate_indices = y_validate_indices_0, x = module_prediction_embed_weight_to_fp16)[name = string("y_cast_fp16_cast_uint16")];
+            tensor<int32, [3]> input_3_perm_0 = const()[name = string("input_3_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
+            int32 split_0_num_splits_0 = const()[name = string("split_0_num_splits_0"), val = int32(2)];
+            int32 split_0_axis_0 = const()[name = string("split_0_axis_0"), val = int32(0)];
+            string h_in_to_fp16_dtype_0 = const()[name = string("h_in_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [2, 1, 640]> h_in_to_fp16 = cast(dtype = h_in_to_fp16_dtype_0, x = h_in)[name = string("cast_7")];
+            tensor<fp16, [1, 1, 640]> split_0_cast_fp16_0, tensor<fp16, [1, 1, 640]> split_0_cast_fp16_1 = split(axis = split_0_axis_0, num_splits = split_0_num_splits_0, x = h_in_to_fp16)[name = string("split_0_cast_fp16")];
+            int32 split_1_num_splits_0 = const()[name = string("split_1_num_splits_0"), val = int32(2)];
+            int32 split_1_axis_0 = const()[name = string("split_1_axis_0"), val = int32(0)];
+            string c_in_to_fp16_dtype_0 = const()[name = string("c_in_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [2, 1, 640]> c_in_to_fp16 = cast(dtype = c_in_to_fp16_dtype_0, x = c_in)[name = string("cast_6")];
+            tensor<fp16, [1, 1, 640]> split_1_cast_fp16_0, tensor<fp16, [1, 1, 640]> split_1_cast_fp16_1 = split(axis = split_1_axis_0, num_splits = split_1_num_splits_0, x = c_in_to_fp16)[name = string("split_1_cast_fp16")];
+            tensor<int32, [1]> input_lstm_layer_0_lstm_h0_squeeze_axes_0 = const()[name = string("input_lstm_layer_0_lstm_h0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 640]> input_lstm_layer_0_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_lstm_layer_0_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_0)[name = string("input_lstm_layer_0_lstm_h0_squeeze_cast_fp16")];
+            tensor<int32, [1]> input_lstm_layer_0_lstm_c0_squeeze_axes_0 = const()[name = string("input_lstm_layer_0_lstm_c0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 640]> input_lstm_layer_0_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_lstm_layer_0_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_0)[name = string("input_lstm_layer_0_lstm_c0_squeeze_cast_fp16")];
+            string input_lstm_layer_0_direction_0 = const()[name = string("input_lstm_layer_0_direction_0"), val = string("forward")];
+            bool input_lstm_layer_0_output_sequence_0 = const()[name = string("input_lstm_layer_0_output_sequence_0"), val = bool(true)];
+            string input_lstm_layer_0_recurrent_activation_0 = const()[name = string("input_lstm_layer_0_recurrent_activation_0"), val = string("sigmoid")];
+            string input_lstm_layer_0_cell_activation_0 = const()[name = string("input_lstm_layer_0_cell_activation_0"), val = string("tanh")];
+            string input_lstm_layer_0_activation_0 = const()[name = string("input_lstm_layer_0_activation_0"), val = string("tanh")];
+            tensor<fp16, [2560, 640]> concat_1_to_fp16 = const()[name = string("concat_1_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1065088)))];
+            tensor<fp16, [2560, 640]> concat_2_to_fp16 = const()[name = string("concat_2_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4341952)))];
+            tensor<fp16, [2560]> concat_0_to_fp16 = const()[name = string("concat_0_to_fp16"), val = tensor<fp16, [2560]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7618816)))];
+            tensor<fp16, [1, 1, 640]> input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = y_cast_fp16_cast_uint16)[name = string("transpose_2")];
+            tensor<fp16, [1, 1, 640]> input_lstm_layer_0_cast_fp16_0, tensor<fp16, [1, 640]> input_lstm_layer_0_cast_fp16_1, tensor<fp16, [1, 640]> input_lstm_layer_0_cast_fp16_2 = lstm(activation = input_lstm_layer_0_activation_0, bias = concat_0_to_fp16, cell_activation = input_lstm_layer_0_cell_activation_0, direction = input_lstm_layer_0_direction_0, initial_c = input_lstm_layer_0_lstm_c0_squeeze_cast_fp16, initial_h = input_lstm_layer_0_lstm_h0_squeeze_cast_fp16, output_sequence = input_lstm_layer_0_output_sequence_0, recurrent_activation = input_lstm_layer_0_recurrent_activation_0, weight_hh = concat_2_to_fp16, weight_ih = concat_1_to_fp16, x = input_3_cast_fp16)[name = string("input_lstm_layer_0_cast_fp16")];
+            tensor<int32, [1]> input_lstm_h0_squeeze_axes_0 = const()[name = string("input_lstm_h0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 640]> input_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_1)[name = string("input_lstm_h0_squeeze_cast_fp16")];
+            tensor<int32, [1]> input_lstm_c0_squeeze_axes_0 = const()[name = string("input_lstm_c0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 640]> input_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_1)[name = string("input_lstm_c0_squeeze_cast_fp16")];
+            string input_direction_0 = const()[name = string("input_direction_0"), val = string("forward")];
+            bool input_output_sequence_0 = const()[name = string("input_output_sequence_0"), val = bool(true)];
+            string input_recurrent_activation_0 = const()[name = string("input_recurrent_activation_0"), val = string("sigmoid")];
+            string input_cell_activation_0 = const()[name = string("input_cell_activation_0"), val = string("tanh")];
+            string input_activation_0 = const()[name = string("input_activation_0"), val = string("tanh")];
+            tensor<fp16, [2560, 640]> concat_4_to_fp16 = const()[name = string("concat_4_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7624000)))];
+            tensor<fp16, [2560, 640]> concat_5_to_fp16 = const()[name = string("concat_5_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10900864)))];
+            tensor<fp16, [2560]> concat_3_to_fp16 = const()[name = string("concat_3_to_fp16"), val = tensor<fp16, [2560]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14177728)))];
+            tensor<fp16, [1, 1, 640]> input_cast_fp16_0, tensor<fp16, [1, 640]> input_cast_fp16_1, tensor<fp16, [1, 640]> input_cast_fp16_2 = lstm(activation = input_activation_0, bias = concat_3_to_fp16, cell_activation = input_cell_activation_0, direction = input_direction_0, initial_c = input_lstm_c0_squeeze_cast_fp16, initial_h = input_lstm_h0_squeeze_cast_fp16, output_sequence = input_output_sequence_0, recurrent_activation = input_recurrent_activation_0, weight_hh = concat_5_to_fp16, weight_ih = concat_4_to_fp16, x = input_lstm_layer_0_cast_fp16_0)[name = string("input_cast_fp16")];
+            int32 obj_3_axis_0 = const()[name = string("obj_3_axis_0"), val = int32(0)];
+            tensor<fp16, [2, 1, 640]> obj_3_cast_fp16 = stack(axis = obj_3_axis_0, values = (input_lstm_layer_0_cast_fp16_1, input_cast_fp16_1))[name = string("obj_3_cast_fp16")];
+            string obj_3_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_3_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
+            int32 obj_axis_0 = const()[name = string("obj_axis_0"), val = int32(0)];
+            tensor<fp16, [2, 1, 640]> obj_cast_fp16 = stack(axis = obj_axis_0, values = (input_lstm_layer_0_cast_fp16_2, input_cast_fp16_2))[name = string("obj_cast_fp16")];
+            string obj_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
+            tensor<int32, [3]> transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor<int32, [3]>([1, 2, 0])];
+            string transpose_0_cast_fp16_to_fp32_dtype_0 = const()[name = string("transpose_0_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
+            tensor<fp16, [1, 640, 1]> transpose_0_cast_fp16 = transpose(perm = transpose_0_perm_0, x = input_cast_fp16_0)[name = string("transpose_1")];
+            tensor<fp32, [1, 640, 1]> decoder_out = cast(dtype = transpose_0_cast_fp16_to_fp32_dtype_0, x = transpose_0_cast_fp16)[name = string("cast_3")];
+            tensor<fp32, [2, 1, 640]> c_out = cast(dtype = obj_cast_fp16_to_fp32_dtype_0, x = obj_cast_fp16)[name = string("cast_4")];
+            tensor<fp32, [2, 1, 640]> h_out = cast(dtype = obj_3_cast_fp16_to_fp32_dtype_0, x = obj_3_cast_fp16)[name = string("cast_5")];
+            tensor<int32, [1]> token_length_tmp = identity(x = token_length)[name = string("token_length_tmp")];
+        } -> (decoder_out, h_out, c_out);
+}

es/1120ms/decoder.mlmodelc/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a6fd0dd21233238b9d55296b6c537f0057b621e75f2f93a4bbbe12fe1f00e99e
+size 14182912

es/1120ms/decoder.mlpackage/Data/com.apple.CoreML/model.mlmodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d5e3eed357e13b333dc78970ea0303fbe89c98df50e4f6be350345ed506bf5e3
+size 10359

es/1120ms/decoder.mlpackage/Data/com.apple.CoreML/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a6fd0dd21233238b9d55296b6c537f0057b621e75f2f93a4bbbe12fe1f00e99e
+size 14182912

es/1120ms/decoder.mlpackage/Manifest.json

@@ -0,0 +1,18 @@
+{
+    "fileFormatVersion": "1.0.0",
+    "itemInfoEntries": {
+        "1B1CD491-99B7-42CD-9B23-56E207B4734F": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Weights",
+            "name": "weights",
+            "path": "com.apple.CoreML/weights"
+        },
+        "68C4577A-86E9-400F-89F4-F791D33C5BC8": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Specification",
+            "name": "model.mlmodel",
+            "path": "com.apple.CoreML/model.mlmodel"
+        }
+    },
+    "rootModelIdentifier": "68C4577A-86E9-400F-89F4-F791D33C5BC8"
+}

es/1120ms/decoder_joint.mlmodelc/analytics/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9229d9468fb1b6f8bf15b9a985eb2ea1bb2ca8aaf104768f3912656e4aec4364
+size 243

es/1120ms/decoder_joint.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:69ceb1d95a0e71aee083126abf2c95089e7eb692a50796b10f45996238a033ce
+size 454

es/1120ms/decoder_joint.mlmodelc/model.mil

@@ -0,0 +1,83 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.5.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})]
+{
+    func main<ios18>(tensor<fp32, [2, 1, 640]> c_in, tensor<fp32, [1, 1024, 1]> encoder, tensor<fp32, [2, 1, 640]> h_in, tensor<int32, [1, 1]> token, tensor<int32, [1]> token_length) {
+            int32 y_axis_0 = const()[name = string("y_axis_0"), val = int32(0)];
+            int32 y_batch_dims_0 = const()[name = string("y_batch_dims_0"), val = int32(0)];
+            bool y_validate_indices_0 = const()[name = string("y_validate_indices_0"), val = bool(false)];
+            tensor<fp16, [832, 640]> decoder_module_prediction_embed_weight_to_fp16 = const()[name = string("decoder_module_prediction_embed_weight_to_fp16"), val = tensor<fp16, [832, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            string token_to_int16_dtype_0 = const()[name = string("token_to_int16_dtype_0"), val = string("int16")];
+            tensor<int16, [1, 1]> token_to_int16 = cast(dtype = token_to_int16_dtype_0, x = token)[name = string("cast_9")];
+            tensor<fp16, [1, 1, 640]> y_cast_fp16_cast_uint16 = gather(axis = y_axis_0, batch_dims = y_batch_dims_0, indices = token_to_int16, validate_indices = y_validate_indices_0, x = decoder_module_prediction_embed_weight_to_fp16)[name = string("y_cast_fp16_cast_uint16")];
+            tensor<int32, [3]> input_3_perm_0 = const()[name = string("input_3_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
+            int32 split_0_num_splits_0 = const()[name = string("split_0_num_splits_0"), val = int32(2)];
+            int32 split_0_axis_0 = const()[name = string("split_0_axis_0"), val = int32(0)];
+            string h_in_to_fp16_dtype_0 = const()[name = string("h_in_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [2, 1, 640]> h_in_to_fp16 = cast(dtype = h_in_to_fp16_dtype_0, x = h_in)[name = string("cast_8")];
+            tensor<fp16, [1, 1, 640]> split_0_cast_fp16_0, tensor<fp16, [1, 1, 640]> split_0_cast_fp16_1 = split(axis = split_0_axis_0, num_splits = split_0_num_splits_0, x = h_in_to_fp16)[name = string("split_0_cast_fp16")];
+            int32 split_1_num_splits_0 = const()[name = string("split_1_num_splits_0"), val = int32(2)];
+            int32 split_1_axis_0 = const()[name = string("split_1_axis_0"), val = int32(0)];
+            string c_in_to_fp16_dtype_0 = const()[name = string("c_in_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [2, 1, 640]> c_in_to_fp16 = cast(dtype = c_in_to_fp16_dtype_0, x = c_in)[name = string("cast_7")];
+            tensor<fp16, [1, 1, 640]> split_1_cast_fp16_0, tensor<fp16, [1, 1, 640]> split_1_cast_fp16_1 = split(axis = split_1_axis_0, num_splits = split_1_num_splits_0, x = c_in_to_fp16)[name = string("split_1_cast_fp16")];
+            tensor<int32, [1]> input_5_lstm_layer_0_lstm_h0_squeeze_axes_0 = const()[name = string("input_5_lstm_layer_0_lstm_h0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 640]> input_5_lstm_layer_0_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_layer_0_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_0)[name = string("input_5_lstm_layer_0_lstm_h0_squeeze_cast_fp16")];
+            tensor<int32, [1]> input_5_lstm_layer_0_lstm_c0_squeeze_axes_0 = const()[name = string("input_5_lstm_layer_0_lstm_c0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 640]> input_5_lstm_layer_0_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_layer_0_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_0)[name = string("input_5_lstm_layer_0_lstm_c0_squeeze_cast_fp16")];
+            string input_5_lstm_layer_0_direction_0 = const()[name = string("input_5_lstm_layer_0_direction_0"), val = string("forward")];
+            bool input_5_lstm_layer_0_output_sequence_0 = const()[name = string("input_5_lstm_layer_0_output_sequence_0"), val = bool(true)];
+            string input_5_lstm_layer_0_recurrent_activation_0 = const()[name = string("input_5_lstm_layer_0_recurrent_activation_0"), val = string("sigmoid")];
+            string input_5_lstm_layer_0_cell_activation_0 = const()[name = string("input_5_lstm_layer_0_cell_activation_0"), val = string("tanh")];
+            string input_5_lstm_layer_0_activation_0 = const()[name = string("input_5_lstm_layer_0_activation_0"), val = string("tanh")];
+            tensor<fp16, [2560, 640]> concat_1_to_fp16 = const()[name = string("concat_1_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1065088)))];
+            tensor<fp16, [2560, 640]> concat_2_to_fp16 = const()[name = string("concat_2_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4341952)))];
+            tensor<fp16, [2560]> concat_0_to_fp16 = const()[name = string("concat_0_to_fp16"), val = tensor<fp16, [2560]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7618816)))];
+            tensor<fp16, [1, 1, 640]> input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = y_cast_fp16_cast_uint16)[name = string("transpose_4")];
+            tensor<fp16, [1, 1, 640]> input_5_lstm_layer_0_cast_fp16_0, tensor<fp16, [1, 640]> input_5_lstm_layer_0_cast_fp16_1, tensor<fp16, [1, 640]> input_5_lstm_layer_0_cast_fp16_2 = lstm(activation = input_5_lstm_layer_0_activation_0, bias = concat_0_to_fp16, cell_activation = input_5_lstm_layer_0_cell_activation_0, direction = input_5_lstm_layer_0_direction_0, initial_c = input_5_lstm_layer_0_lstm_c0_squeeze_cast_fp16, initial_h = input_5_lstm_layer_0_lstm_h0_squeeze_cast_fp16, output_sequence = input_5_lstm_layer_0_output_sequence_0, recurrent_activation = input_5_lstm_layer_0_recurrent_activation_0, weight_hh = concat_2_to_fp16, weight_ih = concat_1_to_fp16, x = input_3_cast_fp16)[name = string("input_5_lstm_layer_0_cast_fp16")];
+            tensor<int32, [1]> input_5_lstm_h0_squeeze_axes_0 = const()[name = string("input_5_lstm_h0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 640]> input_5_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_1)[name = string("input_5_lstm_h0_squeeze_cast_fp16")];
+            tensor<int32, [1]> input_5_lstm_c0_squeeze_axes_0 = const()[name = string("input_5_lstm_c0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 640]> input_5_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_1)[name = string("input_5_lstm_c0_squeeze_cast_fp16")];
+            string input_5_direction_0 = const()[name = string("input_5_direction_0"), val = string("forward")];
+            bool input_5_output_sequence_0 = const()[name = string("input_5_output_sequence_0"), val = bool(true)];
+            string input_5_recurrent_activation_0 = const()[name = string("input_5_recurrent_activation_0"), val = string("sigmoid")];
+            string input_5_cell_activation_0 = const()[name = string("input_5_cell_activation_0"), val = string("tanh")];
+            string input_5_activation_0 = const()[name = string("input_5_activation_0"), val = string("tanh")];
+            tensor<fp16, [2560, 640]> concat_4_to_fp16 = const()[name = string("concat_4_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7624000)))];
+            tensor<fp16, [2560, 640]> concat_5_to_fp16 = const()[name = string("concat_5_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10900864)))];
+            tensor<fp16, [2560]> concat_3_to_fp16 = const()[name = string("concat_3_to_fp16"), val = tensor<fp16, [2560]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14177728)))];
+            tensor<fp16, [1, 1, 640]> input_5_cast_fp16_0, tensor<fp16, [1, 640]> input_5_cast_fp16_1, tensor<fp16, [1, 640]> input_5_cast_fp16_2 = lstm(activation = input_5_activation_0, bias = concat_3_to_fp16, cell_activation = input_5_cell_activation_0, direction = input_5_direction_0, initial_c = input_5_lstm_c0_squeeze_cast_fp16, initial_h = input_5_lstm_h0_squeeze_cast_fp16, output_sequence = input_5_output_sequence_0, recurrent_activation = input_5_recurrent_activation_0, weight_hh = concat_5_to_fp16, weight_ih = concat_4_to_fp16, x = input_5_lstm_layer_0_cast_fp16_0)[name = string("input_5_cast_fp16")];
+            int32 obj_3_axis_0 = const()[name = string("obj_3_axis_0"), val = int32(0)];
+            tensor<fp16, [2, 1, 640]> obj_3_cast_fp16 = stack(axis = obj_3_axis_0, values = (input_5_lstm_layer_0_cast_fp16_1, input_5_cast_fp16_1))[name = string("obj_3_cast_fp16")];
+            string obj_3_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_3_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
+            int32 obj_axis_0 = const()[name = string("obj_axis_0"), val = int32(0)];
+            tensor<fp16, [2, 1, 640]> obj_cast_fp16 = stack(axis = obj_axis_0, values = (input_5_lstm_layer_0_cast_fp16_2, input_5_cast_fp16_2))[name = string("obj_cast_fp16")];
+            string obj_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
+            tensor<int32, [3]> transpose_1_perm_0 = const()[name = string("transpose_1_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
+            tensor<int32, [3]> input_7_perm_0 = const()[name = string("input_7_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string encoder_to_fp16_dtype_0 = const()[name = string("encoder_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [640, 1024]> joint_module_enc_weight_to_fp16 = const()[name = string("joint_module_enc_weight_to_fp16"), val = tensor<fp16, [640, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14182912)))];
+            tensor<fp16, [640]> joint_module_enc_bias_to_fp16 = const()[name = string("joint_module_enc_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15493696)))];
+            tensor<fp16, [1, 1024, 1]> encoder_to_fp16 = cast(dtype = encoder_to_fp16_dtype_0, x = encoder)[name = string("cast_4")];
+            tensor<fp16, [1, 1, 1024]> input_7_cast_fp16 = transpose(perm = input_7_perm_0, x = encoder_to_fp16)[name = string("transpose_2")];
+            tensor<fp16, [1, 1, 640]> linear_0_cast_fp16 = linear(bias = joint_module_enc_bias_to_fp16, weight = joint_module_enc_weight_to_fp16, x = input_7_cast_fp16)[name = string("linear_0_cast_fp16")];
+            tensor<fp16, [640, 640]> joint_module_pred_weight_to_fp16 = const()[name = string("joint_module_pred_weight_to_fp16"), val = tensor<fp16, [640, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15495040)))];
+            tensor<fp16, [640]> joint_module_pred_bias_to_fp16 = const()[name = string("joint_module_pred_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16314304)))];
+            tensor<fp16, [1, 1, 640]> transpose_1_cast_fp16 = transpose(perm = transpose_1_perm_0, x = input_5_cast_fp16_0)[name = string("transpose_3")];
+            tensor<fp16, [1, 1, 640]> linear_1_cast_fp16 = linear(bias = joint_module_pred_bias_to_fp16, weight = joint_module_pred_weight_to_fp16, x = transpose_1_cast_fp16)[name = string("linear_1_cast_fp16")];
+            tensor<int32, [1]> var_79_axes_0 = const()[name = string("op_79_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp16, [1, 1, 1, 640]> var_79_cast_fp16 = expand_dims(axes = var_79_axes_0, x = linear_0_cast_fp16)[name = string("op_79_cast_fp16")];
+            tensor<int32, [1]> var_80_axes_0 = const()[name = string("op_80_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1, 1, 640]> var_80_cast_fp16 = expand_dims(axes = var_80_axes_0, x = linear_1_cast_fp16)[name = string("op_80_cast_fp16")];
+            tensor<fp16, [1, 1, 1, 640]> input_11_cast_fp16 = add(x = var_79_cast_fp16, y = var_80_cast_fp16)[name = string("input_11_cast_fp16")];
+            tensor<fp16, [1, 1, 1, 640]> input_13_cast_fp16 = relu(x = input_11_cast_fp16)[name = string("input_13_cast_fp16")];
+            tensor<fp16, [832, 640]> joint_module_joint_net_2_weight_to_fp16 = const()[name = string("joint_module_joint_net_2_weight_to_fp16"), val = tensor<fp16, [832, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16315648)))];
+            tensor<fp16, [832]> joint_module_joint_net_2_bias_to_fp16 = const()[name = string("joint_module_joint_net_2_bias_to_fp16"), val = tensor<fp16, [832]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17380672)))];
+            tensor<fp16, [1, 1, 1, 832]> linear_2_cast_fp16 = linear(bias = joint_module_joint_net_2_bias_to_fp16, weight = joint_module_joint_net_2_weight_to_fp16, x = input_13_cast_fp16)[name = string("linear_2_cast_fp16")];
+            string linear_2_cast_fp16_to_fp32_dtype_0 = const()[name = string("linear_2_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
+            tensor<fp32, [1, 1, 1, 832]> logits = cast(dtype = linear_2_cast_fp16_to_fp32_dtype_0, x = linear_2_cast_fp16)[name = string("cast_3")];
+            tensor<fp32, [2, 1, 640]> c_out = cast(dtype = obj_cast_fp16_to_fp32_dtype_0, x = obj_cast_fp16)[name = string("cast_5")];
+            tensor<fp32, [2, 1, 640]> h_out = cast(dtype = obj_3_cast_fp16_to_fp32_dtype_0, x = obj_3_cast_fp16)[name = string("cast_6")];
+            tensor<int32, [1]> token_length_tmp = identity(x = token_length)[name = string("token_length_tmp")];
+        } -> (logits, h_out, c_out);
+}

es/1120ms/decoder_joint.mlmodelc/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:87b9845bb151ff3a361fc70b09adbc8cd194e4db3c264ad930dac0e10bd49929
+size 17382400

es/1120ms/decoder_joint.mlpackage/Data/com.apple.CoreML/model.mlmodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0e1d2a4cd1217384e38934290126fec1aeec3220739a5bdbcc20f742b2768860
+size 13745

es/1120ms/decoder_joint.mlpackage/Data/com.apple.CoreML/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:87b9845bb151ff3a361fc70b09adbc8cd194e4db3c264ad930dac0e10bd49929
+size 17382400

es/1120ms/decoder_joint.mlpackage/Manifest.json

@@ -0,0 +1,18 @@
+{
+    "fileFormatVersion": "1.0.0",
+    "itemInfoEntries": {
+        "697CAFDC-9AF0-4BDE-864E-5347D1B6BE21": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Specification",
+            "name": "model.mlmodel",
+            "path": "com.apple.CoreML/model.mlmodel"
+        },
+        "70D75E2B-39B1-4F21-AC9F-57B31E52DDEF": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Weights",
+            "name": "weights",
+            "path": "com.apple.CoreML/weights"
+        }
+    },
+    "rootModelIdentifier": "697CAFDC-9AF0-4BDE-864E-5347D1B6BE21"
+}

es/1120ms/encoder.mlmodelc/analytics/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0638780288ed25026170632a153dbed5d798954deac843fa58ddaae3190914d4
+size 243

es/1120ms/encoder.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f68f09c6358cf29875a774c8dee3a5f2b79bf9f85bca6404f10e34a40ac28248
+size 662

es/1120ms/encoder.mlmodelc/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:011a5342cc1e2901633a8f0468be561f50dfc289ebe51851cddb4831f9a6a23f
+size 565952640

es/1120ms/encoder.mlpackage/Data/com.apple.CoreML/model.mlmodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:14655ea0dbccc666356247f05afc87bbea77ff469344bfb084526e00c0ce6d15
+size 804512

es/1120ms/encoder.mlpackage/Data/com.apple.CoreML/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:011a5342cc1e2901633a8f0468be561f50dfc289ebe51851cddb4831f9a6a23f
+size 565952640

es/1120ms/encoder.mlpackage/Manifest.json

@@ -0,0 +1,18 @@
+{
+    "fileFormatVersion": "1.0.0",
+    "itemInfoEntries": {
+        "5A1F3899-93C0-4456-9DE6-B2D78CDDE258": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Weights",
+            "name": "weights",
+            "path": "com.apple.CoreML/weights"
+        },
+        "E2FF78FE-1F88-4C4D-820E-E59B536101F4": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Specification",
+            "name": "model.mlmodel",
+            "path": "com.apple.CoreML/model.mlmodel"
+        }
+    },
+    "rootModelIdentifier": "E2FF78FE-1F88-4C4D-820E-E59B536101F4"
+}

es/1120ms/joint.mlmodelc/analytics/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9f887b7ea560fb7d01eb634d18b5785cf01d846c22dbdd31183155f4b2f64a39
+size 243

es/1120ms/joint.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9374f72f67b3de1571570c8fad01a52e2003755b10f0e0b8799623e2c5ff114f
+size 341

es/1120ms/joint.mlmodelc/model.mil

@@ -0,0 +1,31 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.5.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})]
+{
+    func main<ios18>(tensor<fp32, [1, 640, 1]> decoder, tensor<fp32, [1, 1024, 1]> encoder) {
+            tensor<int32, [3]> input_1_perm_0 = const()[name = string("input_1_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string encoder_to_fp16_dtype_0 = const()[name = string("encoder_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<int32, [3]> input_3_perm_0 = const()[name = string("input_3_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string decoder_to_fp16_dtype_0 = const()[name = string("decoder_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [640, 1024]> module_enc_weight_to_fp16 = const()[name = string("module_enc_weight_to_fp16"), val = tensor<fp16, [640, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<fp16, [640]> module_enc_bias_to_fp16 = const()[name = string("module_enc_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1310848)))];
+            tensor<fp16, [1, 1024, 1]> encoder_to_fp16 = cast(dtype = encoder_to_fp16_dtype_0, x = encoder)[name = string("cast_2")];
+            tensor<fp16, [1, 1, 1024]> input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = encoder_to_fp16)[name = string("transpose_1")];
+            tensor<fp16, [1, 1, 640]> linear_0_cast_fp16 = linear(bias = module_enc_bias_to_fp16, weight = module_enc_weight_to_fp16, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")];
+            tensor<fp16, [640, 640]> module_pred_weight_to_fp16 = const()[name = string("module_pred_weight_to_fp16"), val = tensor<fp16, [640, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1312192)))];
+            tensor<fp16, [640]> module_pred_bias_to_fp16 = const()[name = string("module_pred_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2131456)))];
+            tensor<fp16, [1, 640, 1]> decoder_to_fp16 = cast(dtype = decoder_to_fp16_dtype_0, x = decoder)[name = string("cast_1")];
+            tensor<fp16, [1, 1, 640]> input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = decoder_to_fp16)[name = string("transpose_0")];
+            tensor<fp16, [1, 1, 640]> linear_1_cast_fp16 = linear(bias = module_pred_bias_to_fp16, weight = module_pred_weight_to_fp16, x = input_3_cast_fp16)[name = string("linear_1_cast_fp16")];
+            tensor<int32, [1]> var_23_axes_0 = const()[name = string("op_23_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp16, [1, 1, 1, 640]> var_23_cast_fp16 = expand_dims(axes = var_23_axes_0, x = linear_0_cast_fp16)[name = string("op_23_cast_fp16")];
+            tensor<int32, [1]> var_25_axes_0 = const()[name = string("op_25_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1, 1, 640]> var_25_cast_fp16 = expand_dims(axes = var_25_axes_0, x = linear_1_cast_fp16)[name = string("op_25_cast_fp16")];
+            tensor<fp16, [1, 1, 1, 640]> input_5_cast_fp16 = add(x = var_23_cast_fp16, y = var_25_cast_fp16)[name = string("input_5_cast_fp16")];
+            tensor<fp16, [1, 1, 1, 640]> input_7_cast_fp16 = relu(x = input_5_cast_fp16)[name = string("input_7_cast_fp16")];
+            tensor<fp16, [832, 640]> module_joint_net_2_weight_to_fp16 = const()[name = string("module_joint_net_2_weight_to_fp16"), val = tensor<fp16, [832, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2132800)))];
+            tensor<fp16, [832]> module_joint_net_2_bias_to_fp16 = const()[name = string("module_joint_net_2_bias_to_fp16"), val = tensor<fp16, [832]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3197824)))];
+            tensor<fp16, [1, 1, 1, 832]> linear_2_cast_fp16 = linear(bias = module_joint_net_2_bias_to_fp16, weight = module_joint_net_2_weight_to_fp16, x = input_7_cast_fp16)[name = string("linear_2_cast_fp16")];
+            string linear_2_cast_fp16_to_fp32_dtype_0 = const()[name = string("linear_2_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
+            tensor<fp32, [1, 1, 1, 832]> logits = cast(dtype = linear_2_cast_fp16_to_fp32_dtype_0, x = linear_2_cast_fp16)[name = string("cast_0")];
+        } -> (logits);
+}

es/1120ms/joint.mlmodelc/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fab26b1275d86fce5d44c967296fa6a27309cd235a8cb5b45dde07150076b5b4
+size 3199552

es/1120ms/joint.mlpackage/Data/com.apple.CoreML/model.mlmodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:548c27ebedec8d425f6a455597ac17e6eea792556b7855889d1828155a9d6e33
+size 4486

es/1120ms/joint.mlpackage/Data/com.apple.CoreML/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fab26b1275d86fce5d44c967296fa6a27309cd235a8cb5b45dde07150076b5b4
+size 3199552

es/1120ms/joint.mlpackage/Manifest.json

@@ -0,0 +1,18 @@
+{
+    "fileFormatVersion": "1.0.0",
+    "itemInfoEntries": {
+        "229533BF-AD08-49EA-A7C8-2D97F9533D31": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Weights",
+            "name": "weights",
+            "path": "com.apple.CoreML/weights"
+        },
+        "CC897513-5590-4EEB-9563-434963785E7B": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Specification",
+            "name": "model.mlmodel",
+            "path": "com.apple.CoreML/model.mlmodel"
+        }
+    },
+    "rootModelIdentifier": "CC897513-5590-4EEB-9563-434963785E7B"
+}

es/1120ms/metadata.json

@@ -0,0 +1,198 @@
+{
+  "model": "nvidia/nemotron-asr-streaming-multilingual-0.6b",
+  "model_class": "nemo.collections.asr.models.rnnt_bpe_models_prompt.EncDecRNNTBPEModelWithPrompt",
+  "sample_rate": 16000,
+  "mel_features": 128,
+  "chunk_mel_frames": 112,
+  "pre_encode_cache": 9,
+  "total_mel_frames": 121,
+  "att_context_size": [
+    42,
+    13
+  ],
+  "vocab_size": 831,
+  "blank_idx": 831,
+  "vocab_pruned": true,
+  "vocab_pruned_original_size": 13087,
+  "cache_channel_shape": [
+    1,
+    24,
+    42,
+    1024
+  ],
+  "cache_time_shape": [
+    1,
+    24,
+    1024,
+    8
+  ],
+  "decoder_hidden": 640,
+  "decoder_layers": 2,
+  "encoder_dim": 1024,
+  "num_prompts": 128,
+  "prompt_dictionary": {
+    "af-ZA": 54,
+    "am-ET": 49,
+    "ar": 7,
+    "ar-AR": 7,
+    "auto": 101,
+    "ay-BO": 81,
+    "az-AZ": 66,
+    "bg": 30,
+    "bg-BG": 30,
+    "bn-IN": 36,
+    "cs": 22,
+    "cs-CZ": 22,
+    "da": 25,
+    "da-DK": 25,
+    "de": 9,
+    "de-DE": 9,
+    "el": 21,
+    "el-GR": 21,
+    "en": 0,
+    "en-GB": 1,
+    "en-US": 0,
+    "enGB": 1,
+    "es": 3,
+    "es-ES": 2,
+    "es-US": 3,
+    "esES": 2,
+    "et": 60,
+    "et-EE": 60,
+    "fa-IR": 38,
+    "fi": 26,
+    "fi-FI": 26,
+    "fr": 8,
+    "fr-CA": 100,
+    "fr-FR": 8,
+    "gn-PY": 82,
+    "gu-IN": 42,
+    "ha-NG": 50,
+    "haw-US": 97,
+    "he-IL": 64,
+    "hi": 6,
+    "hi-HI": 6,
+    "hi-IN": 6,
+    "hr": 29,
+    "hr-HR": 29,
+    "hu": 23,
+    "hu-HU": 23,
+    "hy-AM": 68,
+    "id-ID": 34,
+    "ig-NG": 53,
+    "it": 15,
+    "it-IT": 15,
+    "ja-JA": 10,
+    "ja-JP": 10,
+    "ka-GE": 67,
+    "km-KH": 47,
+    "kn-IN": 43,
+    "ko": 14,
+    "ko-KO": 14,
+    "ko-KR": 14,
+    "ku-TR": 65,
+    "ky-KG": 71,
+    "ln-CD": 58,
+    "lt": 31,
+    "lt-LT": 31,
+    "lv": 61,
+    "lv-LV": 61,
+    "mi-NZ": 96,
+    "ml-IN": 44,
+    "mr-IN": 41,
+    "ms-MY": 35,
+    "mt-MT": 102,
+    "nah-MX": 83,
+    "nb": 103,
+    "nb-NO": 103,
+    "ne-NP": 46,
+    "nl": 16,
+    "nl-NL": 16,
+    "nn": 104,
+    "nn-NO": 104,
+    "no": 27,
+    "no-NO": 27,
+    "ny-MW": 57,
+    "or-KE": 59,
+    "pl": 17,
+    "pl-PL": 17,
+    "pt": 13,
+    "pt-BR": 12,
+    "pt-PT": 13,
+    "qu-PE": 80,
+    "ro": 20,
+    "ro-RO": 20,
+    "ru": 11,
+    "ru-RU": 11,
+    "rw-RW": 55,
+    "si-LK": 45,
+    "sk": 28,
+    "sk-SK": 28,
+    "sl": 62,
+    "sl-SI": 62,
+    "sm-WS": 98,
+    "so-SO": 56,
+    "sv": 24,
+    "sv-SE": 24,
+    "sw-KE": 48,
+    "ta-IN": 39,
+    "te-IN": 40,
+    "tg-TJ": 70,
+    "th-TH": 32,
+    "to-TO": 99,
+    "tr": 18,
+    "tr-TR": 18,
+    "uk": 19,
+    "uk-UA": 19,
+    "ur-PK": 37,
+    "uz-UZ": 69,
+    "vi-VN": 33,
+    "yo-NG": 52,
+    "zh-CN": 4,
+    "zh-TW": 5,
+    "zh-ZH": 4,
+    "zu-ZA": 51
+  },
+  "default_prompt_id": 101,
+  "lang_tag_token_ids": [
+    1,
+    59,
+    117,
+    165,
+    226,
+    227,
+    260,
+    275,
+    332,
+    364,
+    379,
+    429,
+    448,
+    470,
+    493,
+    523,
+    559,
+    583,
+    584,
+    596,
+    607,
+    628,
+    629,
+    647,
+    669,
+    681,
+    740,
+    750,
+    769,
+    770,
+    772,
+    773,
+    774,
+    778,
+    794,
+    812,
+    813,
+    827,
+    828
+  ]
+}

es/1120ms/preprocessor.mlmodelc/analytics/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:422a77fc3b64b27260a8ae2031d287abaa1630d1ebc70343e9dcd280dd4c7e5c
+size 243

es/1120ms/preprocessor.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:41ec4b4c1059ff1f2ac7c71d90b1da0caf9244499d06f6c9de175a1ef992bec1
+size 371

es/1120ms/preprocessor.mlmodelc/model.mil

@@ -0,0 +1,122 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.5.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})]
+{
+    func main<ios18>(tensor<fp32, [1, ?]> audio, tensor<int32, [1]> audio_length) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"audio", [1, 1]}}), ("RangeDims", {{"audio", [[1, 1], [1, 480000]]}})))] {
+            int32 var_9 = const()[name = string("op_9"), val = int32(1)];
+            int32 var_10 = const()[name = string("op_10"), val = int32(160)];
+            int32 var_12 = const()[name = string("op_12"), val = int32(0)];
+            int32 var_33 = const()[name = string("op_33"), val = int32(512)];
+            tensor<int32, [1]> var_34 = add(x = audio_length, y = var_33)[name = string("op_34")];
+            int32 var_35 = const()[name = string("op_35"), val = int32(512)];
+            tensor<int32, [1]> var_36 = sub(x = var_34, y = var_35)[name = string("op_36")];
+            tensor<int32, [1]> floor_div_0 = floor_div(x = var_36, y = var_10)[name = string("floor_div_0")];
+            tensor<bool, [1]> var_39 = equal(x = audio_length, y = var_12)[name = string("op_39")];
+            tensor<int32, [1]> var_40 = const()[name = string("op_40"), val = tensor<int32, [1]>([0])];
+            tensor<int32, [1]> mel_length = select(a = var_40, b = floor_div_0, cond = var_39)[name = string("seq_len")];
+            string audio_to_fp16_dtype_0 = const()[name = string("audio_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, ?]> audio_to_fp16 = cast(dtype = audio_to_fp16_dtype_0, x = audio)[name = string("cast_14")];
+            tensor<int32, [2]> var_42_shape_cast_fp16 = shape(x = audio_to_fp16)[name = string("op_42_shape_cast_fp16")];
+            int32 gather_0_axis_0 = const()[name = string("gather_0_axis_0"), val = int32(0)];
+            int32 gather_0_batch_dims_0 = const()[name = string("gather_0_batch_dims_0"), val = int32(0)];
+            bool gather_0_validate_indices_0 = const()[name = string("gather_0_validate_indices_0"), val = bool(false)];
+            string var_42_shape_cast_fp16_to_int16_dtype_0 = const()[name = string("op_42_shape_cast_fp16_to_int16_dtype_0"), val = string("int16")];
+            uint16 select_0_to_uint16 = const()[name = string("select_0_to_uint16"), val = uint16(1)];
+            tensor<int16, [2]> var_42_shape_cast_fp16_to_int16 = cast(dtype = var_42_shape_cast_fp16_to_int16_dtype_0, x = var_42_shape_cast_fp16)[name = string("cast_13")];
+            int16 gather_0_cast_uint16 = gather(axis = gather_0_axis_0, batch_dims = gather_0_batch_dims_0, indices = select_0_to_uint16, validate_indices = gather_0_validate_indices_0, x = var_42_shape_cast_fp16_to_int16)[name = string("gather_0_cast_uint16")];
+            string gather_0_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_0_cast_uint16_to_int32_dtype_0"), val = string("int32")];
+            int32 const_0 = const()[name = string("const_0"), val = int32(0)];
+            int32 const_1 = const()[name = string("const_1"), val = int32(1)];
+            int32 gather_0_cast_uint16_to_int32 = cast(dtype = gather_0_cast_uint16_to_int32_dtype_0, x = gather_0_cast_uint16)[name = string("cast_12")];
+            tensor<int32, [?]> var_43 = range_1d(end = gather_0_cast_uint16_to_int32, start = const_0, step = const_1)[name = string("op_43")];
+            tensor<int32, [1]> var_44_axes_0 = const()[name = string("op_44_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<int32, [1, ?]> var_44 = expand_dims(axes = var_44_axes_0, x = var_43)[name = string("op_44")];
+            tensor<int32, [1]> var_45_axes_0 = const()[name = string("op_45_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1, 1]> var_45 = expand_dims(axes = var_45_axes_0, x = audio_length)[name = string("op_45")];
+            tensor<bool, [1, ?]> timemask = less(x = var_44, y = var_45)[name = string("timemask")];
+            tensor<int32, [2]> var_48_begin_0 = const()[name = string("op_48_begin_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [2]> var_48_end_0 = const()[name = string("op_48_end_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<bool, [2]> var_48_end_mask_0 = const()[name = string("op_48_end_mask_0"), val = tensor<bool, [2]>([true, false])];
+            tensor<bool, [2]> var_48_squeeze_mask_0 = const()[name = string("op_48_squeeze_mask_0"), val = tensor<bool, [2]>([false, true])];
+            tensor<fp16, [1]> var_48_cast_fp16 = slice_by_index(begin = var_48_begin_0, end = var_48_end_0, end_mask = var_48_end_mask_0, squeeze_mask = var_48_squeeze_mask_0, x = audio_to_fp16)[name = string("op_48_cast_fp16")];
+            tensor<int32, [1]> var_49_axes_0 = const()[name = string("op_49_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1]> var_49_cast_fp16 = expand_dims(axes = var_49_axes_0, x = var_48_cast_fp16)[name = string("op_49_cast_fp16")];
+            tensor<int32, [2]> var_51_begin_0 = const()[name = string("op_51_begin_0"), val = tensor<int32, [2]>([0, 1])];
+            tensor<int32, [2]> var_51_end_0 = const()[name = string("op_51_end_0"), val = tensor<int32, [2]>([1, 0])];
+            tensor<bool, [2]> var_51_end_mask_0 = const()[name = string("op_51_end_mask_0"), val = tensor<bool, [2]>([true, true])];
+            tensor<fp16, [1, ?]> var_51_cast_fp16 = slice_by_index(begin = var_51_begin_0, end = var_51_end_0, end_mask = var_51_end_mask_0, x = audio_to_fp16)[name = string("op_51_cast_fp16")];
+            tensor<int32, [2]> var_53_begin_0 = const()[name = string("op_53_begin_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [2]> var_53_end_0 = const()[name = string("op_53_end_0"), val = tensor<int32, [2]>([1, -1])];
+            tensor<bool, [2]> var_53_end_mask_0 = const()[name = string("op_53_end_mask_0"), val = tensor<bool, [2]>([true, false])];
+            tensor<fp16, [1, ?]> var_53_cast_fp16 = slice_by_index(begin = var_53_begin_0, end = var_53_end_0, end_mask = var_53_end_mask_0, x = audio_to_fp16)[name = string("op_53_cast_fp16")];
+            fp16 var_54_to_fp16 = const()[name = string("op_54_to_fp16"), val = fp16(0x1.f0cp-1)];
+            tensor<fp16, [1, ?]> var_55_cast_fp16 = mul(x = var_53_cast_fp16, y = var_54_to_fp16)[name = string("op_55_cast_fp16")];
+            tensor<fp16, [1, ?]> var_56_cast_fp16 = sub(x = var_51_cast_fp16, y = var_55_cast_fp16)[name = string("op_56_cast_fp16")];
+            bool x_3_interleave_0 = const()[name = string("x_3_interleave_0"), val = bool(false)];
+            tensor<fp16, [1, ?]> x_3_cast_fp16 = concat(axis = var_9, interleave = x_3_interleave_0, values = (var_49_cast_fp16, var_56_cast_fp16))[name = string("x_3_cast_fp16")];
+            tensor<bool, [1, ?]> var_59 = logical_not(x = timemask)[name = string("op_59")];
+            fp16 var_16_to_fp16 = const()[name = string("op_16_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, ?]> input_1_cast_fp16 = select(a = var_16_to_fp16, b = x_3_cast_fp16, cond = var_59)[name = string("input_1_cast_fp16")];
+            tensor<int32, [3]> concat_1x = const()[name = string("concat_1x"), val = tensor<int32, [3]>([1, 1, -1])];
+            tensor<fp16, [1, 1, ?]> input_3_cast_fp16 = reshape(shape = concat_1x, x = input_1_cast_fp16)[name = string("input_3_cast_fp16")];
+            tensor<int32, [6]> input_5_pad_0 = const()[name = string("input_5_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 256, 256])];
+            string input_5_mode_0 = const()[name = string("input_5_mode_0"), val = string("constant")];
+            fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1, ?]> input_5_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = input_5_mode_0, pad = input_5_pad_0, x = input_3_cast_fp16)[name = string("input_5_cast_fp16")];
+            tensor<int32, [2]> concat_2x = const()[name = string("concat_2x"), val = tensor<int32, [2]>([1, -1])];
+            tensor<fp16, [1, ?]> input_cast_fp16 = reshape(shape = concat_2x, x = input_5_cast_fp16)[name = string("input_cast_fp16")];
+            tensor<int32, [1]> expand_dims_3 = const()[name = string("expand_dims_3"), val = tensor<int32, [1]>([160])];
+            tensor<int32, [1]> expand_dims_4_axes_0 = const()[name = string("expand_dims_4_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1, ?]> expand_dims_4_cast_fp16 = expand_dims(axes = expand_dims_4_axes_0, x = input_cast_fp16)[name = string("expand_dims_4_cast_fp16")];
+            string conv_0_pad_type_0 = const()[name = string("conv_0_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> conv_0_pad_0 = const()[name = string("conv_0_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> conv_0_dilations_0 = const()[name = string("conv_0_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 conv_0_groups_0 = const()[name = string("conv_0_groups_0"), val = int32(1)];
+            tensor<fp16, [257, 1, 512]> expand_dims_1_to_fp16 = const()[name = string("expand_dims_1_to_fp16"), val = tensor<fp16, [257, 1, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<fp16, [1, 257, ?]> conv_0_cast_fp16 = conv(dilations = conv_0_dilations_0, groups = conv_0_groups_0, pad = conv_0_pad_0, pad_type = conv_0_pad_type_0, strides = expand_dims_3, weight = expand_dims_1_to_fp16, x = expand_dims_4_cast_fp16)[name = string("conv_0_cast_fp16")];
+            string conv_1_pad_type_0 = const()[name = string("conv_1_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> conv_1_pad_0 = const()[name = string("conv_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> conv_1_dilations_0 = const()[name = string("conv_1_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 conv_1_groups_0 = const()[name = string("conv_1_groups_0"), val = int32(1)];
+            tensor<fp16, [257, 1, 512]> expand_dims_2_to_fp16 = const()[name = string("expand_dims_2_to_fp16"), val = tensor<fp16, [257, 1, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263296)))];
+            tensor<fp16, [1, 257, ?]> conv_1_cast_fp16 = conv(dilations = conv_1_dilations_0, groups = conv_1_groups_0, pad = conv_1_pad_0, pad_type = conv_1_pad_type_0, strides = expand_dims_3, weight = expand_dims_2_to_fp16, x = expand_dims_4_cast_fp16)[name = string("conv_1_cast_fp16")];
+            int32 stack_0_axis_0 = const()[name = string("stack_0_axis_0"), val = int32(-1)];
+            tensor<fp16, [1, 257, ?, 2]> stack_0_cast_fp16 = stack(axis = stack_0_axis_0, values = (conv_0_cast_fp16, conv_1_cast_fp16))[name = string("stack_0_cast_fp16")];
+            fp16 var_19_promoted_to_fp16 = const()[name = string("op_19_promoted_to_fp16"), val = fp16(0x1p+1)];
+            tensor<fp16, [1, 257, ?, 2]> var_74_cast_fp16 = pow(x = stack_0_cast_fp16, y = var_19_promoted_to_fp16)[name = string("op_74_cast_fp16")];
+            tensor<int32, [1]> var_76_axes_0 = const()[name = string("op_76_axes_0"), val = tensor<int32, [1]>([-1])];
+            bool var_76_keep_dims_0 = const()[name = string("op_76_keep_dims_0"), val = bool(false)];
+            tensor<fp16, [1, 257, ?]> var_76_cast_fp16 = reduce_sum(axes = var_76_axes_0, keep_dims = var_76_keep_dims_0, x = var_74_cast_fp16)[name = string("op_76_cast_fp16")];
+            tensor<fp16, [1, 257, ?]> x_11_cast_fp16 = identity(x = var_76_cast_fp16)[name = string("x_11_cast_fp16")];
+            bool x_13_transpose_x_0 = const()[name = string("x_13_transpose_x_0"), val = bool(false)];
+            bool x_13_transpose_y_0 = const()[name = string("x_13_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 128, 257]> const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = tensor<fp16, [1, 128, 257]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526528)))];
+            tensor<fp16, [1, 128, ?]> x_13_cast_fp16 = matmul(transpose_x = x_13_transpose_x_0, transpose_y = x_13_transpose_y_0, x = const_4_to_fp16, y = x_11_cast_fp16)[name = string("x_13_cast_fp16")];
+            fp16 var_83_to_fp16 = const()[name = string("op_83_to_fp16"), val = fp16(0x1p-24)];
+            tensor<fp16, [1, 128, ?]> var_84_cast_fp16 = add(x = x_13_cast_fp16, y = var_83_to_fp16)[name = string("op_84_cast_fp16")];
+            fp32 x_epsilon_0 = const()[name = string("x_epsilon_0"), val = fp32(0x1p-149)];
+            tensor<fp16, [1, 128, ?]> x_cast_fp16 = log(epsilon = x_epsilon_0, x = var_84_cast_fp16)[name = string("x_cast_fp16")];
+            tensor<int32, [3]> var_86_shape_cast_fp16 = shape(x = x_cast_fp16)[name = string("op_86_shape_cast_fp16")];
+            int32 gather_5_axis_0 = const()[name = string("gather_5_axis_0"), val = int32(0)];
+            int32 gather_5_batch_dims_0 = const()[name = string("gather_5_batch_dims_0"), val = int32(0)];
+            bool gather_5_validate_indices_0 = const()[name = string("gather_5_validate_indices_0"), val = bool(false)];
+            string var_86_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_86_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")];
+            uint16 select_5_to_uint16 = const()[name = string("select_5_to_uint16"), val = uint16(2)];
+            tensor<uint16, [3]> var_86_shape_cast_fp16_to_uint16 = cast(dtype = var_86_shape_cast_fp16_to_uint16_dtype_0, x = var_86_shape_cast_fp16)[name = string("cast_11")];
+            uint16 gather_5_cast_uint16 = gather(axis = gather_5_axis_0, batch_dims = gather_5_batch_dims_0, indices = select_5_to_uint16, validate_indices = gather_5_validate_indices_0, x = var_86_shape_cast_fp16_to_uint16)[name = string("gather_5_cast_uint16")];
+            string gather_5_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_5_cast_uint16_to_int32_dtype_0"), val = string("int32")];
+            int32 const_5 = const()[name = string("const_5"), val = int32(0)];
+            int32 const_6 = const()[name = string("const_6"), val = int32(1)];
+            int32 gather_5_cast_uint16_to_int32 = cast(dtype = gather_5_cast_uint16_to_int32_dtype_0, x = gather_5_cast_uint16)[name = string("cast_10")];
+            tensor<int32, [?]> mask_1 = range_1d(end = gather_5_cast_uint16_to_int32, start = const_5, step = const_6)[name = string("mask_1")];
+            tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<int32, [1, ?]> expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = mask_1)[name = string("expand_dims_0")];
+            tensor<int32, [1]> var_91_axes_0 = const()[name = string("op_91_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1, 1]> var_91 = expand_dims(axes = var_91_axes_0, x = mel_length)[name = string("op_91")];
+            tensor<bool, [1, ?]> mask = greater_equal(x = expand_dims_0, y = var_91)[name = string("mask")];
+            tensor<int32, [1]> var_93_axes_0 = const()[name = string("op_93_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, ?]> var_93 = expand_dims(axes = var_93_axes_0, x = mask)[name = string("op_93")];
+            tensor<fp16, [1, 128, ?]> processed_signal_cast_fp16 = select(a = var_16_to_fp16, b = x_cast_fp16, cond = var_93)[name = string("processed_signal_cast_fp16")];
+            string processed_signal_cast_fp16_to_fp32_dtype_0 = const()[name = string("processed_signal_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
+            tensor<fp32, [1, 128, ?]> mel = cast(dtype = processed_signal_cast_fp16_to_fp32_dtype_0, x = processed_signal_cast_fp16)[name = string("cast_9")];
+        } -> (mel, mel_length);
+}

es/1120ms/preprocessor.mlmodelc/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:297514e2b211d14b0e53cb97193d679bb89ead98d28e578f3f1d049ddbcc36b3
+size 592384

es/1120ms/preprocessor.mlpackage/Data/com.apple.CoreML/model.mlmodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:912e39c12c0ddeb4d3716693621d0140bb7de556056ab8dc9e5cd9336ebf1baa
+size 15878

es/1120ms/preprocessor.mlpackage/Data/com.apple.CoreML/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:297514e2b211d14b0e53cb97193d679bb89ead98d28e578f3f1d049ddbcc36b3
+size 592384

es/1120ms/preprocessor.mlpackage/Manifest.json

@@ -0,0 +1,18 @@
+{
+    "fileFormatVersion": "1.0.0",
+    "itemInfoEntries": {
+        "470B2EDA-A2F1-4777-8F1D-55E0D6A275A8": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Specification",
+            "name": "model.mlmodel",
+            "path": "com.apple.CoreML/model.mlmodel"
+        },
+        "D83A1966-0388-45B9-9CA7-1DACB258C26C": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Weights",
+            "name": "weights",
+            "path": "com.apple.CoreML/weights"
+        }
+    },
+    "rootModelIdentifier": "470B2EDA-A2F1-4777-8F1D-55E0D6A275A8"
+}

es/1120ms/tokenizer.json

@@ -0,0 +1,834 @@
+{
+  "0": "<unk>",
+  "1": "<bg-BG>",
+  "2": "▁",
+  "3": ".",
+  "4": ",",
+  "5": "e",
+  "6": "t",
+  "7": "a",
+  "8": "s",
+  "9": "o",
+  "10": "i",
+  "11": "r",
+  "12": "l",
+  "13": "u",
+  "14": "d",
+  "15": "c",
+  "16": "h",
+  "17": "m",
+  "18": "p",
+  "19": "n",
+  "20": "g",
+  "21": "f",
+  "22": "en",
+  "23": "in",
+  "24": "on",
+  "25": "y",
+  "26": "er",
+  "27": "an",
+  "28": "w",
+  "29": "0",
+  "30": "b",
+  "31": "v",
+  "32": "2",
+  "33": "1",
+  "34": "k",
+  "35": "?",
+  "36": "3",
+  "37": "5",
+  "38": "I",
+  "39": "é",
+  "40": "4",
+  "41": "z",
+  "42": "6",
+  "43": "j",
+  "44": "7",
+  "45": "8",
+  "46": "9",
+  "47": "S",
+  "48": "x",
+  "49": "P",
+  "50": "U",
+  "51": "L",
+  "52": "V",
+  "53": "R",
+  "54": "ó",
+  "55": "ü",
+  "56": "J",
+  "57": "í",
+  "58": "á",
+  "59": "<cs-CZ>",
+  "60": "st",
+  "61": "ch",
+  "62": "ní",
+  "63": "le",
+  "64": "li",
+  "65": "▁po",
+  "66": "no",
+  "67": "to",
+  "68": "me",
+  "69": "te",
+  "70": "ho",
+  "71": "▁pro",
+  "72": "ro",
+  "73": "▁na",
+  "74": "ce",
+  "75": "la",
+  "76": "ni",
+  "77": "ra",
+  "78": "ti",
+  "79": "lo",
+  "80": "ko",
+  "81": "ná",
+  "82": "po",
+  "83": "je",
+  "84": "de",
+  "85": "na",
+  "86": "mi",
+  "87": "ci",
+  "88": "▁by",
+  "89": "ve",
+  "90": "▁za",
+  "91": "▁A",
+  "92": "re",
+  "93": "ou",
+  "94": "vo",
+  "95": "né",
+  "96": "va",
+  "97": "mo",
+  "98": "ne",
+  "99": "ka",
+  "100": "ji",
+  "101": "rá",
+  "102": "cí",
+  "103": "▁jak",
+  "104": "ny",
+  "105": "ví",
+  "106": "vi",
+  "107": "ú",
+  "108": "ent",
+  "109": "▁pan",
+  "110": "Z",
+  "111": "dob",
+  "112": "▁To",
+  "113": "▁Je",
+  "114": "ă",
+  "115": "X",
+  "116": "Ú",
+  "117": "<da-DK>",
+  "118": "▁for",
+  "119": "▁det",
+  "120": "▁at",
+  "121": "et",
+  "122": "▁vi",
+  "123": "al",
+  "124": "▁de",
+  "125": "▁der",
+  "126": "or",
+  "127": "om",
+  "128": "and",
+  "129": "▁har",
+  "130": "at",
+  "131": "▁af",
+  "132": "ge",
+  "133": "ar",
+  "134": "is",
+  "135": "▁med",
+  "136": "▁be",
+  "137": "un",
+  "138": "lig",
+  "139": "▁man",
+  "140": "ig",
+  "141": "▁som",
+  "142": "el",
+  "143": "ag",
+  "144": "erne",
+  "145": "▁den",
+  "146": "ste",
+  "147": "id",
+  "148": "▁kan",
+  "149": "iv",
+  "150": "▁her",
+  "151": "ion",
+  "152": "am",
+  "153": "ur",
+  "154": "for",
+  "155": "▁pr",
+  "156": "▁sig",
+  "157": "▁men",
+  "158": "▁ind",
+  "159": "ende",
+  "160": "▁Vi",
+  "161": "▁op",
+  "162": "▁fra",
+  "163": "▁Men",
+  "164": "▁var",
+  "165": "<de-DE>",
+  "166": "▁die",
+  "167": "▁und",
+  "168": "sch",
+  "169": "it",
+  "170": "gen",
+  "171": "▁W",
+  "172": "▁B",
+  "173": "▁E",
+  "174": "▁F",
+  "175": "ll",
+  "176": "▁es",
+  "177": "▁K",
+  "178": "ie",
+  "179": "au",
+  "180": "ich",
+  "181": "ck",
+  "182": "ten",
+  "183": "mal",
+  "184": "ein",
+  "185": "▁T",
+  "186": "▁mit",
+  "187": "ter",
+  "188": "tz",
+  "189": "▁G",
+  "190": "ben",
+  "191": "um",
+  "192": "us",
+  "193": "il",
+  "194": "▁noch",
+  "195": "▁ver",
+  "196": "▁hier",
+  "197": "ri",
+  "198": "ach",
+  "199": "ol",
+  "200": "▁Da",
+  "201": "sp",
+  "202": "ell",
+  "203": "▁was",
+  "204": "▁ja",
+  "205": "uch",
+  "206": "wi",
+  "207": "rei",
+  "208": "▁Ge",
+  "209": "und",
+  "210": "▁sie",
+  "211": "▁Ja",
+  "212": "▁du",
+  "213": "▁dem",
+  "214": "▁Sch",
+  "215": "▁habe",
+  "216": "▁Ma",
+  "217": "▁De",
+  "218": "iert",
+  "219": "▁Le",
+  "220": "▁In",
+  "221": "▁Ver",
+  "222": "▁Re",
+  "223": "ieren",
+  "224": "▁Mi",
+  "225": "▁Ha",
+  "226": "<el-GR>",
+  "227": "<et-EE>",
+  "228": "ma",
+  "229": "ta",
+  "230": "se",
+  "231": "da",
+  "232": "si",
+  "233": "ks",
+  "234": "ga",
+  "235": "he",
+  "236": "mu",
+  "237": "tu",
+  "238": "ha",
+  "239": "ja",
+  "240": "<blank>",
+  "241": "▁ole",
+  "242": "gu",
+  "243": "ju",
+  "244": "est",
+  "245": "▁pa",
+  "246": "tud",
+  "247": "nda",
+  "248": "vad",
+  "249": "ke",
+  "250": "sta",
+  "251": "sed",
+  "252": "di",
+  "253": "▁su",
+  "254": "ide",
+  "255": "val",
+  "256": "▁Me",
+  "257": "ment",
+  "258": "Q",
+  "259": "ñ",
+  "260": "<fi-FI>",
+  "261": "lla",
+  "262": "ki",
+  "263": "pa",
+  "264": "lle",
+  "265": "lu",
+  "266": "tta",
+  "267": "isi",
+  "268": "ise",
+  "269": "tte",
+  "270": "ista",
+  "271": "inen",
+  "272": "llis",
+  "273": "vu",
+  "274": "iden",
+  "275": "<fr-FR>",
+  "276": "▁est",
+  "277": "▁c",
+  "278": "▁que",
+  "279": "es",
+  "280": "▁un",
+  "281": "▁pas",
+  "282": "▁les",
+  "283": "▁qui",
+  "284": "▁il",
+  "285": "▁des",
+  "286": "▁par",
+  "287": "▁une",
+  "288": "ant",
+  "289": "ais",
+  "290": "ez",
+  "291": "▁C",
+  "292": "tre",
+  "293": "ir",
+  "294": "elle",
+  "295": "eur",
+  "296": "▁sur",
+  "297": "▁con",
+  "298": "tion",
+  "299": "té",
+  "300": "mp",
+  "301": "ique",
+  "302": "▁dé",
+  "303": "qu",
+  "304": "ac",
+  "305": "ait",
+  "306": "che",
+  "307": "que",
+  "308": "ul",
+  "309": "▁bien",
+  "310": "age",
+  "311": "▁mon",
+  "312": "end",
+  "313": "ver",
+  "314": "tra",
+  "315": "cha",
+  "316": "ille",
+  "317": "ff",
+  "318": "▁ex",
+  "319": "▁Il",
+  "320": "im",
+  "321": "ité",
+  "322": "▁dire",
+  "323": "ance",
+  "324": "aire",
+  "325": "mé",
+  "326": "▁app",
+  "327": "mb",
+  "328": "man",
+  "329": "port",
+  "330": "form",
+  "331": "ign",
+  "332": "<hu-HU>",
+  "333": "ás",
+  "334": "ok",
+  "335": "gy",
+  "336": "ek",
+  "337": "ál",
+  "338": "és",
+  "339": "em",
+  "340": "ár",
+  "341": "▁is",
+  "342": "os",
+  "343": "ak",
+  "344": "ban",
+  "345": "ít",
+  "346": "ik",
+  "347": "oz",
+  "348": "án",
+  "349": "át",
+  "350": "cs",
+  "351": "él",
+  "352": "ér",
+  "353": "nek",
+  "354": "én",
+  "355": "▁ha",
+  "356": "▁fel",
+  "357": "vé",
+  "358": "leg",
+  "359": "▁ami",
+  "360": "É",
+  "361": "rend",
+  "362": "Á",
+  "363": "Í",
+  "364": "<hr-HR>",
+  "365": "▁bi",
+  "366": "▁sa",
+  "367": "ru",
+  "368": "go",
+  "369": "nje",
+  "370": "sti",
+  "371": "▁pri",
+  "372": "ima",
+  "373": "nu",
+  "374": "▁pre",
+  "375": "zi",
+  "376": "ca",
+  "377": "ba",
+  "378": "▁raz",
+  "379": "<it-IT>",
+  "380": "▁di",
+  "381": "▁che",
+  "382": "co",
+  "383": "▁per",
+  "384": "do",
+  "385": "so",
+  "386": "amo",
+  "387": "sa",
+  "388": "ndo",
+  "389": "▁una",
+  "390": "fi",
+  "391": "pi",
+  "392": "nti",
+  "393": "tro",
+  "394": "▁fa",
+  "395": "chi",
+  "396": "bi",
+  "397": "▁del",
+  "398": "mente",
+  "399": "pe",
+  "400": "▁sono",
+  "401": "nte",
+  "402": "tti",
+  "403": "nta",
+  "404": "▁come",
+  "405": "cu",
+  "406": "nza",
+  "407": "sto",
+  "408": "gra",
+  "409": "spe",
+  "410": "nde",
+  "411": "mento",
+  "412": "fe",
+  "413": "gio",
+  "414": "pu",
+  "415": "sci",
+  "416": "sco",
+  "417": "stra",
+  "418": "qui",
+  "419": "▁cosa",
+  "420": "▁inter",
+  "421": "▁com",
+  "422": "▁comp",
+  "423": "▁prima",
+  "424": "▁parte",
+  "425": "▁video",
+  "426": "▁imp",
+  "427": "sione",
+  "428": "scri",
+  "429": "<lt-LT>",
+  "430": "▁ir",
+  "431": "as",
+  "432": "▁tai",
+  "433": "uo",
+  "434": "tin",
+  "435": "▁vis",
+  "436": "ly",
+  "437": "gal",
+  "438": "jo",
+  "439": "tar",
+  "440": "▁Ir",
+  "441": "ijos",
+  "442": "▁turi",
+  "443": "▁Tai",
+  "444": "▁nu",
+  "445": "▁mes",
+  "446": "imas",
+  "447": "darb",
+  "448": "<lv-LV>",
+  "449": "iem",
+  "450": "▁pie",
+  "451": "ies",
+  "452": "ot",
+  "453": "▁vien",
+  "454": "gad",
+  "455": "▁Un",
+  "456": "▁Ta",
+  "457": "dar",
+  "458": "ija",
+  "459": "sim",
+  "460": "dien",
+  "461": "gan",
+  "462": "▁ap",
+  "463": "▁nav",
+  "464": "▁lab",
+  "465": "aug",
+  "466": "▁Nu",
+  "467": "▁Ne",
+  "468": "zin",
+  "469": "▁20",
+  "470": "<nl-NL>",
+  "471": "▁dat",
+  "472": "▁we",
+  "473": "ij",
+  "474": "▁En",
+  "475": "▁dan",
+  "476": "▁zo",
+  "477": "▁met",
+  "478": "▁wat",
+  "479": "ken",
+  "480": "der",
+  "481": "ui",
+  "482": "den",
+  "483": "op",
+  "484": "oor",
+  "485": "▁of",
+  "486": "ven",
+  "487": "▁even",
+  "488": "ond",
+  "489": "▁wil",
+  "490": "vol",
+  "491": "▁Dan",
+  "492": "▁hem",
+  "493": "<pl-PL>",
+  "494": "nie",
+  "495": "wa",
+  "496": "cie",
+  "497": "nia",
+  "498": "wo",
+  "499": "rze",
+  "500": "ej",
+  "501": "by",
+  "502": "za",
+  "503": "dy",
+  "504": "ry",
+  "505": "ego",
+  "506": "mie",
+  "507": "rz",
+  "508": "pie",
+  "509": "▁pod",
+  "510": "▁ale",
+  "511": "uje",
+  "512": "▁bo",
+  "513": "bie",
+  "514": "▁Po",
+  "515": "ski",
+  "516": "nego",
+  "517": "▁Nie",
+  "518": "▁No",
+  "519": "▁Na",
+  "520": "▁prac",
+  "521": "▁Was",
+  "522": "▁musi",
+  "523": "<pt-BR>",
+  "524": "pt",
+  "525": "-",
+  "526": "▁para",
+  "527": "▁pe",
+  "528": "▁tem",
+  "529": "▁gente",
+  "530": "▁O",
+  "531": "▁ele",
+  "532": "pre",
+  "533": "ria",
+  "534": "▁fo",
+  "535": "mos",
+  "536": "bo",
+  "537": "nha",
+  "538": "▁por",
+  "539": "nto",
+  "540": "▁Eu",
+  "541": "▁está",
+  "542": "idade",
+  "543": "be",
+  "544": "▁pode",
+  "545": "▁como",
+  "546": "ente",
+  "547": "▁mas",
+  "548": "▁lá",
+  "549": "fica",
+  "550": "▁porque",
+  "551": "▁Se",
+  "552": "...",
+  "553": "▁só",
+  "554": "▁Por",
+  "555": "▁Co",
+  "556": "iza",
+  "557": "▁todo",
+  "558": "ciona",
+  "559": "<ro-RO>",
+  "560": "sc",
+  "561": "are",
+  "562": "▁din",
+  "563": "▁este",
+  "564": "rea",
+  "565": "ele",
+  "566": "du",
+  "567": "▁M",
+  "568": "▁fac",
+  "569": "lor",
+  "570": "▁mult",
+  "571": "per",
+  "572": "cur",
+  "573": "tor",
+  "574": "inte",
+  "575": "tat",
+  "576": "ori",
+  "577": "▁sub",
+  "578": "▁prin",
+  "579": "▁alt",
+  "580": "stru",
+  "581": "fer",
+  "582": "▁acum",
+  "583": "<ru-RU>",
+  "584": "<sk-SK>",
+  "585": "ob",
+  "586": "tá",
+  "587": "▁bol",
+  "588": "▁sú",
+  "589": "ali",
+  "590": "▁má",
+  "591": "nov",
+  "592": "nú",
+  "593": "rob",
+  "594": "enie",
+  "595": "osti",
+  "596": "<sl-SL>",
+  "597": "sl",
+  "598": "udi",
+  "599": "▁sem",
+  "600": "▁samo",
+  "601": "▁pred",
+  "602": "▁Pre",
+  "603": "▁prot",
+  "604": "▁pisa",
+  "605": "▁internet",
+  "606": "▁film",
+  "607": "<sv-SE>",
+  "608": "▁och",
+  "609": "▁inte",
+  "610": "▁av",
+  "611": "▁ut",
+  "612": "all",
+  "613": "era",
+  "614": "pp",
+  "615": "▁upp",
+  "616": "het",
+  "617": "▁vill",
+  "618": "erna",
+  "619": "ande",
+  "620": "ade",
+  "621": "▁hur",
+  "622": "bil",
+  "623": "▁bara",
+  "624": "▁min",
+  "625": "lev",
+  "626": "land",
+  "627": "text",
+  "628": "<uk-UA>",
+  "629": "<ar-AR>",
+  "630": "ft",
+  "631": "ved",
+  "632": "'",
+  "633": "▁H",
+  "634": "▁D",
+  "635": "aus",
+  "636": "▁N",
+  "637": "▁Be",
+  "638": "mm",
+  "639": "ab",
+  "640": "▁Er",
+  "641": "hn",
+  "642": "rie",
+  "643": "lei",
+  "644": "▁An",
+  "645": "▁So",
+  "646": "▁Aus",
+  "647": "<en-GB>",
+  "648": "▁and",
+  "649": "▁can",
+  "650": "ed",
+  "651": "▁just",
+  "652": "ay",
+  "653": "th",
+  "654": "ic",
+  "655": "▁much",
+  "656": "hi",
+  "657": "▁Oh",
+  "658": "ight",
+  "659": "ex",
+  "660": "▁great",
+  "661": "▁call",
+  "662": "ill",
+  "663": "▁don",
+  "664": "▁problem",
+  "665": "▁fine",
+  "666": "able",
+  "667": "ever",
+  "668": "▁send",
+  "669": "<en-US>",
+  "670": "ate",
+  "671": "ad",
+  "672": "ect",
+  "673": "ng",
+  "674": "ther",
+  "675": "act",
+  "676": "ist",
+  "677": "▁his",
+  "678": "▁He",
+  "679": "▁part",
+  "680": "side",
+  "681": "<es-ES>",
+  "682": "ción",
+  "683": "▁Es",
+  "684": "res",
+  "685": "▁los",
+  "686": "▁La",
+  "687": "dos",
+  "688": "ía",
+  "689": "▁El",
+  "690": "▁las",
+  "691": "▁más",
+  "692": "men",
+  "693": "ño",
+  "694": "▁esta",
+  "695": "idad",
+  "696": "par",
+  "697": "¿",
+  "698": "ría",
+  "699": "▁fue",
+  "700": "rio",
+  "701": "enta",
+  "702": "ón",
+  "703": "cho",
+  "704": "ciones",
+  "705": "ble",
+  "706": "▁Ca",
+  "707": "▁muy",
+  "708": "▁también",
+  "709": "▁tiene",
+  "710": "ña",
+  "711": "▁Su",
+  "712": "▁pero",
+  "713": "▁son",
+  "714": "encia",
+  "715": "sión",
+  "716": "▁hay",
+  "717": "▁puede",
+  "718": "ncia",
+  "719": "▁mucho",
+  "720": "▁pues",
+  "721": "miento",
+  "722": "▁Con",
+  "723": "ones",
+  "724": "ecto",
+  "725": "iendo",
+  "726": "▁día",
+  "727": "▁sobre",
+  "728": "▁primer",
+  "729": "▁qué",
+  "730": "▁San",
+  "731": "▁hacer",
+  "732": "cional",
+  "733": "▁persona",
+  "734": "▁pasa",
+  "735": "▁mejor",
+  "736": "quí",
+  "737": "▁Fue",
+  "738": "▁Com",
+  "739": "▁ciudad",
+  "740": "<es-US>",
+  "741": "cia",
+  "742": "▁Y",
+  "743": "ron",
+  "744": "les",
+  "745": "cio",
+  "746": "bu",
+  "747": "▁sí",
+  "748": "▁Pero",
+  "749": "▁así",
+  "750": "<fr-CA>",
+  "751": "ré",
+  "752": "our",
+  "753": "▁Ce",
+  "754": "com",
+  "755": "ale",
+  "756": "if",
+  "757": "iste",
+  "758": "▁parti",
+  "759": "avec",
+  "760": "app",
+  "761": "cul",
+  "762": "gue",
+  "763": "▁nombre",
+  "764": "Une",
+  "765": "pri",
+  "766": "sion",
+  "767": "ix",
+  "768": "ard",
+  "769": "<he-IL>",
+  "770": "<hi-IN>",
+  "771": "!",
+  "772": "<ja-JP>",
+  "773": "<ko-KR>",
+  "774": "<nb-NO>",
+  "775": "ene",
+  "776": "▁Han",
+  "777": "▁han",
+  "778": "<nn-NO>",
+  "779": "eg",
+  "780": "kk",
+  "781": "dig",
+  "782": "tid",
+  "783": "ord",
+  "784": "▁tru",
+  "785": "▁sei",
+  "786": "ller",
+  "787": "car",
+  "788": "ito",
+  "789": "ram",
+  "790": "fa",
+  "791": "▁compra",
+  "792": "▁mil",
+  "793": "▁casa",
+  "794": "<pt-PT>",
+  "795": "das",
+  "796": "▁Pa",
+  "797": "tura",
+  "798": "forma",
+  "799": "▁Esta",
+  "800": "▁pelo",
+  "801": "tua",
+  "802": "mar",
+  "803": "este",
+  "804": "▁entre",
+  "805": "fun",
+  "806": "gua",
+  "807": "▁grande",
+  "808": "icos",
+  "809": "▁Este",
+  "810": "▁encontra",
+  "811": "var",
+  "812": "<th-TH>",
+  "813": "<tr-TR>",
+  "814": "ş",
+  "815": "ğ",
+  "816": "ya",
+  "817": "▁ve",
+  "818": "lar",
+  "819": "ler",
+  "820": "ye",
+  "821": "▁bu",
+  "822": "lan",
+  "823": "ara",
+  "824": "▁Bu",
+  "825": "inde",
+  "826": "yo",
+  "827": "<zh-CN>",
+  "828": "<vi-VN>",
+  "829": "▁t",
+  "830": "nh",
+  "831": "<blank>"
+}

es/2240ms/decoder.mlmodelc/analytics/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d8c3744468694142efbb178729d4d6a56edc642464a04a884b938cfe27c4e094
+size 243

es/2240ms/decoder.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3c993f8b96ce22027cd2ed42d99b7e61f93a01197bb17cadada8eb989e946dec
+size 433

es/2240ms/decoder.mlmodelc/model.mil

@@ -0,0 +1,64 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.5.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})]
+{
+    func main<ios18>(tensor<fp32, [2, 1, 640]> c_in, tensor<fp32, [2, 1, 640]> h_in, tensor<int32, [1, 1]> token, tensor<int32, [1]> token_length) {
+            int32 y_axis_0 = const()[name = string("y_axis_0"), val = int32(0)];
+            int32 y_batch_dims_0 = const()[name = string("y_batch_dims_0"), val = int32(0)];
+            bool y_validate_indices_0 = const()[name = string("y_validate_indices_0"), val = bool(false)];
+            tensor<fp16, [832, 640]> module_prediction_embed_weight_to_fp16 = const()[name = string("module_prediction_embed_weight_to_fp16"), val = tensor<fp16, [832, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            string token_to_int16_dtype_0 = const()[name = string("token_to_int16_dtype_0"), val = string("int16")];
+            tensor<int16, [1, 1]> token_to_int16 = cast(dtype = token_to_int16_dtype_0, x = token)[name = string("cast_8")];
+            tensor<fp16, [1, 1, 640]> y_cast_fp16_cast_uint16 = gather(axis = y_axis_0, batch_dims = y_batch_dims_0, indices = token_to_int16, validate_indices = y_validate_indices_0, x = module_prediction_embed_weight_to_fp16)[name = string("y_cast_fp16_cast_uint16")];
+            tensor<int32, [3]> input_3_perm_0 = const()[name = string("input_3_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
+            int32 split_0_num_splits_0 = const()[name = string("split_0_num_splits_0"), val = int32(2)];
+            int32 split_0_axis_0 = const()[name = string("split_0_axis_0"), val = int32(0)];
+            string h_in_to_fp16_dtype_0 = const()[name = string("h_in_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [2, 1, 640]> h_in_to_fp16 = cast(dtype = h_in_to_fp16_dtype_0, x = h_in)[name = string("cast_7")];
+            tensor<fp16, [1, 1, 640]> split_0_cast_fp16_0, tensor<fp16, [1, 1, 640]> split_0_cast_fp16_1 = split(axis = split_0_axis_0, num_splits = split_0_num_splits_0, x = h_in_to_fp16)[name = string("split_0_cast_fp16")];
+            int32 split_1_num_splits_0 = const()[name = string("split_1_num_splits_0"), val = int32(2)];
+            int32 split_1_axis_0 = const()[name = string("split_1_axis_0"), val = int32(0)];
+            string c_in_to_fp16_dtype_0 = const()[name = string("c_in_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [2, 1, 640]> c_in_to_fp16 = cast(dtype = c_in_to_fp16_dtype_0, x = c_in)[name = string("cast_6")];
+            tensor<fp16, [1, 1, 640]> split_1_cast_fp16_0, tensor<fp16, [1, 1, 640]> split_1_cast_fp16_1 = split(axis = split_1_axis_0, num_splits = split_1_num_splits_0, x = c_in_to_fp16)[name = string("split_1_cast_fp16")];
+            tensor<int32, [1]> input_lstm_layer_0_lstm_h0_squeeze_axes_0 = const()[name = string("input_lstm_layer_0_lstm_h0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 640]> input_lstm_layer_0_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_lstm_layer_0_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_0)[name = string("input_lstm_layer_0_lstm_h0_squeeze_cast_fp16")];
+            tensor<int32, [1]> input_lstm_layer_0_lstm_c0_squeeze_axes_0 = const()[name = string("input_lstm_layer_0_lstm_c0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 640]> input_lstm_layer_0_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_lstm_layer_0_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_0)[name = string("input_lstm_layer_0_lstm_c0_squeeze_cast_fp16")];
+            string input_lstm_layer_0_direction_0 = const()[name = string("input_lstm_layer_0_direction_0"), val = string("forward")];
+            bool input_lstm_layer_0_output_sequence_0 = const()[name = string("input_lstm_layer_0_output_sequence_0"), val = bool(true)];
+            string input_lstm_layer_0_recurrent_activation_0 = const()[name = string("input_lstm_layer_0_recurrent_activation_0"), val = string("sigmoid")];
+            string input_lstm_layer_0_cell_activation_0 = const()[name = string("input_lstm_layer_0_cell_activation_0"), val = string("tanh")];
+            string input_lstm_layer_0_activation_0 = const()[name = string("input_lstm_layer_0_activation_0"), val = string("tanh")];
+            tensor<fp16, [2560, 640]> concat_1_to_fp16 = const()[name = string("concat_1_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1065088)))];
+            tensor<fp16, [2560, 640]> concat_2_to_fp16 = const()[name = string("concat_2_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4341952)))];
+            tensor<fp16, [2560]> concat_0_to_fp16 = const()[name = string("concat_0_to_fp16"), val = tensor<fp16, [2560]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7618816)))];
+            tensor<fp16, [1, 1, 640]> input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = y_cast_fp16_cast_uint16)[name = string("transpose_2")];
+            tensor<fp16, [1, 1, 640]> input_lstm_layer_0_cast_fp16_0, tensor<fp16, [1, 640]> input_lstm_layer_0_cast_fp16_1, tensor<fp16, [1, 640]> input_lstm_layer_0_cast_fp16_2 = lstm(activation = input_lstm_layer_0_activation_0, bias = concat_0_to_fp16, cell_activation = input_lstm_layer_0_cell_activation_0, direction = input_lstm_layer_0_direction_0, initial_c = input_lstm_layer_0_lstm_c0_squeeze_cast_fp16, initial_h = input_lstm_layer_0_lstm_h0_squeeze_cast_fp16, output_sequence = input_lstm_layer_0_output_sequence_0, recurrent_activation = input_lstm_layer_0_recurrent_activation_0, weight_hh = concat_2_to_fp16, weight_ih = concat_1_to_fp16, x = input_3_cast_fp16)[name = string("input_lstm_layer_0_cast_fp16")];
+            tensor<int32, [1]> input_lstm_h0_squeeze_axes_0 = const()[name = string("input_lstm_h0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 640]> input_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_1)[name = string("input_lstm_h0_squeeze_cast_fp16")];
+            tensor<int32, [1]> input_lstm_c0_squeeze_axes_0 = const()[name = string("input_lstm_c0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 640]> input_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_1)[name = string("input_lstm_c0_squeeze_cast_fp16")];
+            string input_direction_0 = const()[name = string("input_direction_0"), val = string("forward")];
+            bool input_output_sequence_0 = const()[name = string("input_output_sequence_0"), val = bool(true)];
+            string input_recurrent_activation_0 = const()[name = string("input_recurrent_activation_0"), val = string("sigmoid")];
+            string input_cell_activation_0 = const()[name = string("input_cell_activation_0"), val = string("tanh")];
+            string input_activation_0 = const()[name = string("input_activation_0"), val = string("tanh")];
+            tensor<fp16, [2560, 640]> concat_4_to_fp16 = const()[name = string("concat_4_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7624000)))];
+            tensor<fp16, [2560, 640]> concat_5_to_fp16 = const()[name = string("concat_5_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10900864)))];
+            tensor<fp16, [2560]> concat_3_to_fp16 = const()[name = string("concat_3_to_fp16"), val = tensor<fp16, [2560]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14177728)))];
+            tensor<fp16, [1, 1, 640]> input_cast_fp16_0, tensor<fp16, [1, 640]> input_cast_fp16_1, tensor<fp16, [1, 640]> input_cast_fp16_2 = lstm(activation = input_activation_0, bias = concat_3_to_fp16, cell_activation = input_cell_activation_0, direction = input_direction_0, initial_c = input_lstm_c0_squeeze_cast_fp16, initial_h = input_lstm_h0_squeeze_cast_fp16, output_sequence = input_output_sequence_0, recurrent_activation = input_recurrent_activation_0, weight_hh = concat_5_to_fp16, weight_ih = concat_4_to_fp16, x = input_lstm_layer_0_cast_fp16_0)[name = string("input_cast_fp16")];
+            int32 obj_3_axis_0 = const()[name = string("obj_3_axis_0"), val = int32(0)];
+            tensor<fp16, [2, 1, 640]> obj_3_cast_fp16 = stack(axis = obj_3_axis_0, values = (input_lstm_layer_0_cast_fp16_1, input_cast_fp16_1))[name = string("obj_3_cast_fp16")];
+            string obj_3_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_3_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
+            int32 obj_axis_0 = const()[name = string("obj_axis_0"), val = int32(0)];
+            tensor<fp16, [2, 1, 640]> obj_cast_fp16 = stack(axis = obj_axis_0, values = (input_lstm_layer_0_cast_fp16_2, input_cast_fp16_2))[name = string("obj_cast_fp16")];
+            string obj_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
+            tensor<int32, [3]> transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor<int32, [3]>([1, 2, 0])];
+            string transpose_0_cast_fp16_to_fp32_dtype_0 = const()[name = string("transpose_0_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
+            tensor<fp16, [1, 640, 1]> transpose_0_cast_fp16 = transpose(perm = transpose_0_perm_0, x = input_cast_fp16_0)[name = string("transpose_1")];
+            tensor<fp32, [1, 640, 1]> decoder_out = cast(dtype = transpose_0_cast_fp16_to_fp32_dtype_0, x = transpose_0_cast_fp16)[name = string("cast_3")];
+            tensor<fp32, [2, 1, 640]> c_out = cast(dtype = obj_cast_fp16_to_fp32_dtype_0, x = obj_cast_fp16)[name = string("cast_4")];
+            tensor<fp32, [2, 1, 640]> h_out = cast(dtype = obj_3_cast_fp16_to_fp32_dtype_0, x = obj_3_cast_fp16)[name = string("cast_5")];
+            tensor<int32, [1]> token_length_tmp = identity(x = token_length)[name = string("token_length_tmp")];
+        } -> (decoder_out, h_out, c_out);
+}

es/2240ms/decoder.mlmodelc/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a6fd0dd21233238b9d55296b6c537f0057b621e75f2f93a4bbbe12fe1f00e99e
+size 14182912

es/2240ms/decoder.mlpackage/Data/com.apple.CoreML/model.mlmodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d5e3eed357e13b333dc78970ea0303fbe89c98df50e4f6be350345ed506bf5e3
+size 10359

es/2240ms/decoder.mlpackage/Data/com.apple.CoreML/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a6fd0dd21233238b9d55296b6c537f0057b621e75f2f93a4bbbe12fe1f00e99e
+size 14182912

es/2240ms/decoder.mlpackage/Manifest.json

@@ -0,0 +1,18 @@
+{
+    "fileFormatVersion": "1.0.0",
+    "itemInfoEntries": {
+        "1B1CD491-99B7-42CD-9B23-56E207B4734F": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Weights",
+            "name": "weights",
+            "path": "com.apple.CoreML/weights"
+        },
+        "68C4577A-86E9-400F-89F4-F791D33C5BC8": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Specification",
+            "name": "model.mlmodel",
+            "path": "com.apple.CoreML/model.mlmodel"
+        }
+    },
+    "rootModelIdentifier": "68C4577A-86E9-400F-89F4-F791D33C5BC8"
+}

es/2240ms/decoder_joint.mlmodelc/analytics/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9229d9468fb1b6f8bf15b9a985eb2ea1bb2ca8aaf104768f3912656e4aec4364
+size 243

es/2240ms/decoder_joint.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:69ceb1d95a0e71aee083126abf2c95089e7eb692a50796b10f45996238a033ce
+size 454

es/2240ms/decoder_joint.mlmodelc/model.mil

@@ -0,0 +1,83 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.5.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})]
+{
+    func main<ios18>(tensor<fp32, [2, 1, 640]> c_in, tensor<fp32, [1, 1024, 1]> encoder, tensor<fp32, [2, 1, 640]> h_in, tensor<int32, [1, 1]> token, tensor<int32, [1]> token_length) {
+            int32 y_axis_0 = const()[name = string("y_axis_0"), val = int32(0)];
+            int32 y_batch_dims_0 = const()[name = string("y_batch_dims_0"), val = int32(0)];
+            bool y_validate_indices_0 = const()[name = string("y_validate_indices_0"), val = bool(false)];
+            tensor<fp16, [832, 640]> decoder_module_prediction_embed_weight_to_fp16 = const()[name = string("decoder_module_prediction_embed_weight_to_fp16"), val = tensor<fp16, [832, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            string token_to_int16_dtype_0 = const()[name = string("token_to_int16_dtype_0"), val = string("int16")];
+            tensor<int16, [1, 1]> token_to_int16 = cast(dtype = token_to_int16_dtype_0, x = token)[name = string("cast_9")];
+            tensor<fp16, [1, 1, 640]> y_cast_fp16_cast_uint16 = gather(axis = y_axis_0, batch_dims = y_batch_dims_0, indices = token_to_int16, validate_indices = y_validate_indices_0, x = decoder_module_prediction_embed_weight_to_fp16)[name = string("y_cast_fp16_cast_uint16")];
+            tensor<int32, [3]> input_3_perm_0 = const()[name = string("input_3_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
+            int32 split_0_num_splits_0 = const()[name = string("split_0_num_splits_0"), val = int32(2)];
+            int32 split_0_axis_0 = const()[name = string("split_0_axis_0"), val = int32(0)];
+            string h_in_to_fp16_dtype_0 = const()[name = string("h_in_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [2, 1, 640]> h_in_to_fp16 = cast(dtype = h_in_to_fp16_dtype_0, x = h_in)[name = string("cast_8")];
+            tensor<fp16, [1, 1, 640]> split_0_cast_fp16_0, tensor<fp16, [1, 1, 640]> split_0_cast_fp16_1 = split(axis = split_0_axis_0, num_splits = split_0_num_splits_0, x = h_in_to_fp16)[name = string("split_0_cast_fp16")];
+            int32 split_1_num_splits_0 = const()[name = string("split_1_num_splits_0"), val = int32(2)];
+            int32 split_1_axis_0 = const()[name = string("split_1_axis_0"), val = int32(0)];
+            string c_in_to_fp16_dtype_0 = const()[name = string("c_in_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [2, 1, 640]> c_in_to_fp16 = cast(dtype = c_in_to_fp16_dtype_0, x = c_in)[name = string("cast_7")];
+            tensor<fp16, [1, 1, 640]> split_1_cast_fp16_0, tensor<fp16, [1, 1, 640]> split_1_cast_fp16_1 = split(axis = split_1_axis_0, num_splits = split_1_num_splits_0, x = c_in_to_fp16)[name = string("split_1_cast_fp16")];
+            tensor<int32, [1]> input_5_lstm_layer_0_lstm_h0_squeeze_axes_0 = const()[name = string("input_5_lstm_layer_0_lstm_h0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 640]> input_5_lstm_layer_0_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_layer_0_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_0)[name = string("input_5_lstm_layer_0_lstm_h0_squeeze_cast_fp16")];
+            tensor<int32, [1]> input_5_lstm_layer_0_lstm_c0_squeeze_axes_0 = const()[name = string("input_5_lstm_layer_0_lstm_c0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 640]> input_5_lstm_layer_0_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_layer_0_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_0)[name = string("input_5_lstm_layer_0_lstm_c0_squeeze_cast_fp16")];
+            string input_5_lstm_layer_0_direction_0 = const()[name = string("input_5_lstm_layer_0_direction_0"), val = string("forward")];
+            bool input_5_lstm_layer_0_output_sequence_0 = const()[name = string("input_5_lstm_layer_0_output_sequence_0"), val = bool(true)];
+            string input_5_lstm_layer_0_recurrent_activation_0 = const()[name = string("input_5_lstm_layer_0_recurrent_activation_0"), val = string("sigmoid")];
+            string input_5_lstm_layer_0_cell_activation_0 = const()[name = string("input_5_lstm_layer_0_cell_activation_0"), val = string("tanh")];
+            string input_5_lstm_layer_0_activation_0 = const()[name = string("input_5_lstm_layer_0_activation_0"), val = string("tanh")];
+            tensor<fp16, [2560, 640]> concat_1_to_fp16 = const()[name = string("concat_1_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1065088)))];
+            tensor<fp16, [2560, 640]> concat_2_to_fp16 = const()[name = string("concat_2_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4341952)))];
+            tensor<fp16, [2560]> concat_0_to_fp16 = const()[name = string("concat_0_to_fp16"), val = tensor<fp16, [2560]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7618816)))];
+            tensor<fp16, [1, 1, 640]> input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = y_cast_fp16_cast_uint16)[name = string("transpose_4")];
+            tensor<fp16, [1, 1, 640]> input_5_lstm_layer_0_cast_fp16_0, tensor<fp16, [1, 640]> input_5_lstm_layer_0_cast_fp16_1, tensor<fp16, [1, 640]> input_5_lstm_layer_0_cast_fp16_2 = lstm(activation = input_5_lstm_layer_0_activation_0, bias = concat_0_to_fp16, cell_activation = input_5_lstm_layer_0_cell_activation_0, direction = input_5_lstm_layer_0_direction_0, initial_c = input_5_lstm_layer_0_lstm_c0_squeeze_cast_fp16, initial_h = input_5_lstm_layer_0_lstm_h0_squeeze_cast_fp16, output_sequence = input_5_lstm_layer_0_output_sequence_0, recurrent_activation = input_5_lstm_layer_0_recurrent_activation_0, weight_hh = concat_2_to_fp16, weight_ih = concat_1_to_fp16, x = input_3_cast_fp16)[name = string("input_5_lstm_layer_0_cast_fp16")];
+            tensor<int32, [1]> input_5_lstm_h0_squeeze_axes_0 = const()[name = string("input_5_lstm_h0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 640]> input_5_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_1)[name = string("input_5_lstm_h0_squeeze_cast_fp16")];
+            tensor<int32, [1]> input_5_lstm_c0_squeeze_axes_0 = const()[name = string("input_5_lstm_c0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 640]> input_5_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_1)[name = string("input_5_lstm_c0_squeeze_cast_fp16")];
+            string input_5_direction_0 = const()[name = string("input_5_direction_0"), val = string("forward")];
+            bool input_5_output_sequence_0 = const()[name = string("input_5_output_sequence_0"), val = bool(true)];
+            string input_5_recurrent_activation_0 = const()[name = string("input_5_recurrent_activation_0"), val = string("sigmoid")];
+            string input_5_cell_activation_0 = const()[name = string("input_5_cell_activation_0"), val = string("tanh")];
+            string input_5_activation_0 = const()[name = string("input_5_activation_0"), val = string("tanh")];
+            tensor<fp16, [2560, 640]> concat_4_to_fp16 = const()[name = string("concat_4_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7624000)))];
+            tensor<fp16, [2560, 640]> concat_5_to_fp16 = const()[name = string("concat_5_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10900864)))];
+            tensor<fp16, [2560]> concat_3_to_fp16 = const()[name = string("concat_3_to_fp16"), val = tensor<fp16, [2560]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14177728)))];
+            tensor<fp16, [1, 1, 640]> input_5_cast_fp16_0, tensor<fp16, [1, 640]> input_5_cast_fp16_1, tensor<fp16, [1, 640]> input_5_cast_fp16_2 = lstm(activation = input_5_activation_0, bias = concat_3_to_fp16, cell_activation = input_5_cell_activation_0, direction = input_5_direction_0, initial_c = input_5_lstm_c0_squeeze_cast_fp16, initial_h = input_5_lstm_h0_squeeze_cast_fp16, output_sequence = input_5_output_sequence_0, recurrent_activation = input_5_recurrent_activation_0, weight_hh = concat_5_to_fp16, weight_ih = concat_4_to_fp16, x = input_5_lstm_layer_0_cast_fp16_0)[name = string("input_5_cast_fp16")];
+            int32 obj_3_axis_0 = const()[name = string("obj_3_axis_0"), val = int32(0)];
+            tensor<fp16, [2, 1, 640]> obj_3_cast_fp16 = stack(axis = obj_3_axis_0, values = (input_5_lstm_layer_0_cast_fp16_1, input_5_cast_fp16_1))[name = string("obj_3_cast_fp16")];
+            string obj_3_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_3_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
+            int32 obj_axis_0 = const()[name = string("obj_axis_0"), val = int32(0)];
+            tensor<fp16, [2, 1, 640]> obj_cast_fp16 = stack(axis = obj_axis_0, values = (input_5_lstm_layer_0_cast_fp16_2, input_5_cast_fp16_2))[name = string("obj_cast_fp16")];
+            string obj_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
+            tensor<int32, [3]> transpose_1_perm_0 = const()[name = string("transpose_1_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
+            tensor<int32, [3]> input_7_perm_0 = const()[name = string("input_7_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string encoder_to_fp16_dtype_0 = const()[name = string("encoder_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [640, 1024]> joint_module_enc_weight_to_fp16 = const()[name = string("joint_module_enc_weight_to_fp16"), val = tensor<fp16, [640, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14182912)))];
+            tensor<fp16, [640]> joint_module_enc_bias_to_fp16 = const()[name = string("joint_module_enc_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15493696)))];
+            tensor<fp16, [1, 1024, 1]> encoder_to_fp16 = cast(dtype = encoder_to_fp16_dtype_0, x = encoder)[name = string("cast_4")];
+            tensor<fp16, [1, 1, 1024]> input_7_cast_fp16 = transpose(perm = input_7_perm_0, x = encoder_to_fp16)[name = string("transpose_2")];
+            tensor<fp16, [1, 1, 640]> linear_0_cast_fp16 = linear(bias = joint_module_enc_bias_to_fp16, weight = joint_module_enc_weight_to_fp16, x = input_7_cast_fp16)[name = string("linear_0_cast_fp16")];
+            tensor<fp16, [640, 640]> joint_module_pred_weight_to_fp16 = const()[name = string("joint_module_pred_weight_to_fp16"), val = tensor<fp16, [640, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15495040)))];
+            tensor<fp16, [640]> joint_module_pred_bias_to_fp16 = const()[name = string("joint_module_pred_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16314304)))];
+            tensor<fp16, [1, 1, 640]> transpose_1_cast_fp16 = transpose(perm = transpose_1_perm_0, x = input_5_cast_fp16_0)[name = string("transpose_3")];
+            tensor<fp16, [1, 1, 640]> linear_1_cast_fp16 = linear(bias = joint_module_pred_bias_to_fp16, weight = joint_module_pred_weight_to_fp16, x = transpose_1_cast_fp16)[name = string("linear_1_cast_fp16")];
+            tensor<int32, [1]> var_79_axes_0 = const()[name = string("op_79_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp16, [1, 1, 1, 640]> var_79_cast_fp16 = expand_dims(axes = var_79_axes_0, x = linear_0_cast_fp16)[name = string("op_79_cast_fp16")];
+            tensor<int32, [1]> var_80_axes_0 = const()[name = string("op_80_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1, 1, 640]> var_80_cast_fp16 = expand_dims(axes = var_80_axes_0, x = linear_1_cast_fp16)[name = string("op_80_cast_fp16")];
+            tensor<fp16, [1, 1, 1, 640]> input_11_cast_fp16 = add(x = var_79_cast_fp16, y = var_80_cast_fp16)[name = string("input_11_cast_fp16")];
+            tensor<fp16, [1, 1, 1, 640]> input_13_cast_fp16 = relu(x = input_11_cast_fp16)[name = string("input_13_cast_fp16")];
+            tensor<fp16, [832, 640]> joint_module_joint_net_2_weight_to_fp16 = const()[name = string("joint_module_joint_net_2_weight_to_fp16"), val = tensor<fp16, [832, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16315648)))];
+            tensor<fp16, [832]> joint_module_joint_net_2_bias_to_fp16 = const()[name = string("joint_module_joint_net_2_bias_to_fp16"), val = tensor<fp16, [832]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17380672)))];
+            tensor<fp16, [1, 1, 1, 832]> linear_2_cast_fp16 = linear(bias = joint_module_joint_net_2_bias_to_fp16, weight = joint_module_joint_net_2_weight_to_fp16, x = input_13_cast_fp16)[name = string("linear_2_cast_fp16")];
+            string linear_2_cast_fp16_to_fp32_dtype_0 = const()[name = string("linear_2_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
+            tensor<fp32, [1, 1, 1, 832]> logits = cast(dtype = linear_2_cast_fp16_to_fp32_dtype_0, x = linear_2_cast_fp16)[name = string("cast_3")];
+            tensor<fp32, [2, 1, 640]> c_out = cast(dtype = obj_cast_fp16_to_fp32_dtype_0, x = obj_cast_fp16)[name = string("cast_5")];
+            tensor<fp32, [2, 1, 640]> h_out = cast(dtype = obj_3_cast_fp16_to_fp32_dtype_0, x = obj_3_cast_fp16)[name = string("cast_6")];
+            tensor<int32, [1]> token_length_tmp = identity(x = token_length)[name = string("token_length_tmp")];
+        } -> (logits, h_out, c_out);
+}

es/2240ms/decoder_joint.mlmodelc/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:87b9845bb151ff3a361fc70b09adbc8cd194e4db3c264ad930dac0e10bd49929
+size 17382400

es/2240ms/decoder_joint.mlpackage/Data/com.apple.CoreML/model.mlmodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0e1d2a4cd1217384e38934290126fec1aeec3220739a5bdbcc20f742b2768860
+size 13745

es/2240ms/decoder_joint.mlpackage/Data/com.apple.CoreML/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:87b9845bb151ff3a361fc70b09adbc8cd194e4db3c264ad930dac0e10bd49929
+size 17382400