Upload nvidia_parakeet-ja

nvidia_parakeet-ja/AudioEncoder.mlmodelc/analytics/coremldata.bin

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nvidia_parakeet-ja/AudioEncoder.mlmodelc/coremldata.bin

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nvidia_parakeet-ja/AudioEncoder.mlmodelc/metadata.json

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+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "storagePrecision" : "Float16",
+    "outputSchema" : [
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+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16 1 × 1024 × 1 × 188)",
+        "shortDescription" : "",
+        "shape" : "[1, 1024, 1, 188]",
+        "name" : "encoder_output_embeds",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16 1 × 640 × 1 × 188)",
+        "shortDescription" : "",
+        "shape" : "[1, 640, 1, 188]",
+        "name" : "joint_projected_encoder_output_embeds",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16 1 × 3073 × 1 × 188)",
+        "shortDescription" : "",
+        "shape" : "[1, 3073, 1, 188]",
+        "name" : "ctc_head_raw_output",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16 1 × 3073 × 1 × 188)",
+        "shortDescription" : "",
+        "shape" : "[1, 3073, 1, 188]",
+        "name" : "ctc_head_output",
+        "type" : "MultiArray"
+      }
+    ],
+    "modelParameters" : [
+
+    ],
+    "specificationVersion" : 8,
+    "mlProgramOperationTypeHistogram" : {
+      "Ios16.silu" : 72,
+      "Ios17.mul" : 73,
+      "Split" : 24,
+      "Ios17.transpose" : 1,
+      "Ios17.sub" : 1,
+      "Ios17.matmul" : 72,
+      "Ios17.conv" : 296,
+      "Ios17.log" : 1,
+      "Ios16.sigmoid" : 24,
+      "Ios17.add" : 168,
+      "Ios17.sliceByIndex" : 48,
+      "Ios17.batchNorm" : 120,
+      "Ios16.relu" : 3,
+      "Ios16.softmax" : 25,
+      "Ios17.reshape" : 193,
+      "Ios17.layerNorm" : 120,
+      "Pad" : 24
+    },
+    "computePrecision" : "Mixed (Float16, Float32, Int32)",
+    "isUpdatable" : "0",
+    "stateSchema" : [
+
+    ],
+    "availability" : {
+      "macOS" : "14.0",
+      "tvOS" : "17.0",
+      "visionOS" : "1.0",
+      "watchOS" : "10.0",
+      "iOS" : "17.0",
+      "macCatalyst" : "17.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2026-04-09",
+      "com.github.apple.coremltools.source" : "torch==2.11.0",
+      "com.github.apple.coremltools.version" : "9.0",
+      "com.github.apple.coremltools.source_dialect" : "TorchScript"
+    },
+    "inputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16 1 × 1 × 1501 × 80)",
+        "shortDescription" : "",
+        "shape" : "[1, 1, 1501, 80]",
+        "name" : "melspectrogram_features",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16 1 × 1 × 1 × 1)",
+        "shortDescription" : "",
+        "shape" : "[1, 1, 1, 1]",
+        "name" : "input_1",
+        "type" : "MultiArray"
+      }
+    ],
+    "generatedClassName" : "AudioEncoder",
+    "method" : "predict"
+  }
+]

nvidia_parakeet-ja/AudioEncoder.mlmodelc/weights/weight.bin

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+size 1223788034

nvidia_parakeet-ja/LICENSE_NOTICE.txt

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+Argmax proprietary and confidential. Under NDA.
+
+Copyright 2024 Argmax, Inc. All rights reserved.
+
+Unauthorized access, copying, use, distribution, and or commercialization of this file, via any medium or means is strictly prohibited.
+
+Please contact Argmax for licensing information at info@argmaxinc.com.

nvidia_parakeet-ja/MelSpectrogram.mlmodelc/analytics/coremldata.bin

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nvidia_parakeet-ja/MelSpectrogram.mlmodelc/coremldata.bin

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nvidia_parakeet-ja/MelSpectrogram.mlmodelc/metadata.json

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+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "storagePrecision" : "Float16",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16 1 × 1 × 1501 × 80)",
+        "shortDescription" : "",
+        "shape" : "[1, 1, 1501, 80]",
+        "name" : "melspectrogram_features",
+        "type" : "MultiArray"
+      }
+    ],
+    "modelParameters" : [
+
+    ],
+    "specificationVersion" : 8,
+    "mlProgramOperationTypeHistogram" : {
+      "Ios17.mul" : 2,
+      "Ios17.sqrt" : 1,
+      "Ios17.square" : 3,
+      "Ios17.transpose" : 1,
+      "Ios17.sub" : 2,
+      "Ios17.matmul" : 1,
+      "Ios17.conv" : 2,
+      "Ios17.log" : 1,
+      "Ios17.concat" : 1,
+      "Ios17.sliceByIndex" : 3,
+      "Ios17.add" : 3,
+      "Ios16.reduceMean" : 2,
+      "Ios17.realDiv" : 1,
+      "Ios17.expandDims" : 5,
+      "Ios17.squeeze" : 2,
+      "Ios17.reshape" : 2,
+      "Identity" : 1,
+      "Pad" : 1
+    },
+    "computePrecision" : "Mixed (Float16, Float32, Int32)",
+    "isUpdatable" : "0",
+    "stateSchema" : [
+
+    ],
+    "availability" : {
+      "macOS" : "14.0",
+      "tvOS" : "17.0",
+      "visionOS" : "1.0",
+      "watchOS" : "10.0",
+      "iOS" : "17.0",
+      "macCatalyst" : "17.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2026-04-09",
+      "com.github.apple.coremltools.source" : "torch==2.11.0",
+      "com.github.apple.coremltools.version" : "9.0",
+      "com.github.apple.coremltools.source_dialect" : "TorchScript"
+    },
+    "inputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16 240000)",
+        "shortDescription" : "",
+        "shape" : "[240000]",
+        "name" : "audio",
+        "type" : "MultiArray"
+      }
+    ],
+    "generatedClassName" : "MelSpectrogram",
+    "method" : "predict"
+  }
+]

nvidia_parakeet-ja/MelSpectrogram.mlmodelc/model.mil

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+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3404.16.1"}, {"coremlc-version", "3404.23.1"}, {"coremltools-component-torch", "2.11.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
+{
+    func main<ios17>(tensor<fp16, [240000]> audio) {
+            tensor<int32, [1]> var_6_begin_0 = const()[name = tensor<string, []>("op_6_begin_0"), val = tensor<int32, [1]>([0])];
+            tensor<int32, [1]> var_6_end_0 = const()[name = tensor<string, []>("op_6_end_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1]> var_6_end_mask_0 = const()[name = tensor<string, []>("op_6_end_mask_0"), val = tensor<bool, [1]>([false])];
+            tensor<bool, [1]> var_6_squeeze_mask_0 = const()[name = tensor<string, []>("op_6_squeeze_mask_0"), val = tensor<bool, [1]>([true])];
+            tensor<fp16, []> var_6_cast_fp16 = slice_by_index(begin = var_6_begin_0, end = var_6_end_0, end_mask = var_6_end_mask_0, squeeze_mask = var_6_squeeze_mask_0, x = audio)[name = tensor<string, []>("op_6_cast_fp16")];
+            tensor<int32, [1]> var_8_axes_0 = const()[name = tensor<string, []>("op_8_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1]> var_8_cast_fp16 = expand_dims(axes = var_8_axes_0, x = var_6_cast_fp16)[name = tensor<string, []>("op_8_cast_fp16")];
+            tensor<int32, [1]> var_13_begin_0 = const()[name = tensor<string, []>("op_13_begin_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> var_13_end_0 = const()[name = tensor<string, []>("op_13_end_0"), val = tensor<int32, [1]>([240000])];
+            tensor<bool, [1]> var_13_end_mask_0 = const()[name = tensor<string, []>("op_13_end_mask_0"), val = tensor<bool, [1]>([true])];
+            tensor<fp16, [239999]> var_13_cast_fp16 = slice_by_index(begin = var_13_begin_0, end = var_13_end_0, end_mask = var_13_end_mask_0, x = audio)[name = tensor<string, []>("op_13_cast_fp16")];
+            tensor<int32, [1]> var_18_begin_0 = const()[name = tensor<string, []>("op_18_begin_0"), val = tensor<int32, [1]>([0])];
+            tensor<int32, [1]> var_18_end_0 = const()[name = tensor<string, []>("op_18_end_0"), val = tensor<int32, [1]>([239999])];
+            tensor<bool, [1]> var_18_end_mask_0 = const()[name = tensor<string, []>("op_18_end_mask_0"), val = tensor<bool, [1]>([false])];
+            tensor<fp16, [239999]> var_18_cast_fp16 = slice_by_index(begin = var_18_begin_0, end = var_18_end_0, end_mask = var_18_end_mask_0, x = audio)[name = tensor<string, []>("op_18_cast_fp16")];
+            tensor<fp16, []> var_19_to_fp16 = const()[name = tensor<string, []>("op_19_to_fp16"), val = tensor<fp16, []>(0x1.f0cp-1)];
+            tensor<fp16, [239999]> var_20_cast_fp16 = mul(x = var_18_cast_fp16, y = var_19_to_fp16)[name = tensor<string, []>("op_20_cast_fp16")];
+            tensor<fp16, [239999]> var_22_cast_fp16 = sub(x = var_13_cast_fp16, y = var_20_cast_fp16)[name = tensor<string, []>("op_22_cast_fp16")];
+            tensor<int32, []> var_24 = const()[name = tensor<string, []>("op_24"), val = tensor<int32, []>(0)];
+            tensor<bool, []> input_1_interleave_0 = const()[name = tensor<string, []>("input_1_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [240000]> input_1_cast_fp16 = concat(axis = var_24, interleave = input_1_interleave_0, values = (var_8_cast_fp16, var_22_cast_fp16))[name = tensor<string, []>("input_1_cast_fp16")];
+            tensor<int32, [3]> var_32 = const()[name = tensor<string, []>("op_32"), val = tensor<int32, [3]>([1, 1, 240000])];
+            tensor<fp16, [1, 1, 240000]> input_3_cast_fp16 = reshape(shape = var_32, x = input_1_cast_fp16)[name = tensor<string, []>("input_3_cast_fp16")];
+            tensor<int32, [6]> input_5_pad_0 = const()[name = tensor<string, []>("input_5_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 256, 256])];
+            tensor<string, []> input_5_mode_0 = const()[name = tensor<string, []>("input_5_mode_0"), val = tensor<string, []>("reflect")];
+            tensor<fp16, []> const_1_to_fp16 = const()[name = tensor<string, []>("const_1_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 1, 240512]> input_5_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = input_5_mode_0, pad = input_5_pad_0, x = input_3_cast_fp16)[name = tensor<string, []>("input_5_cast_fp16")];
+            tensor<int32, [1]> var_44 = const()[name = tensor<string, []>("op_44"), val = tensor<int32, [1]>([240512])];
+            tensor<fp16, [240512]> input_cast_fp16 = reshape(shape = var_44, x = input_5_cast_fp16)[name = tensor<string, []>("input_cast_fp16")];
+            tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = tensor<string, []>("expand_dims_0_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 240512]> expand_dims_0_cast_fp16 = expand_dims(axes = expand_dims_0_axes_0, x = input_cast_fp16)[name = tensor<string, []>("expand_dims_0_cast_fp16")];
+            tensor<int32, [1]> expand_dims_3 = const()[name = tensor<string, []>("expand_dims_3"), val = tensor<int32, [1]>([160])];
+            tensor<int32, [1]> expand_dims_4_axes_0 = const()[name = tensor<string, []>("expand_dims_4_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1, 240512]> expand_dims_4_cast_fp16 = expand_dims(axes = expand_dims_4_axes_0, x = expand_dims_0_cast_fp16)[name = tensor<string, []>("expand_dims_4_cast_fp16")];
+            tensor<string, []> conv_0_pad_type_0 = const()[name = tensor<string, []>("conv_0_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> conv_0_pad_0 = const()[name = tensor<string, []>("conv_0_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> conv_0_dilations_0 = const()[name = tensor<string, []>("conv_0_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> conv_0_groups_0 = const()[name = tensor<string, []>("conv_0_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [257, 1, 512]> expand_dims_1_to_fp16 = const()[name = tensor<string, []>("expand_dims_1_to_fp16"), val = tensor<fp16, [257, 1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
+            tensor<fp16, [1, 257, 1501]> 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 = tensor<string, []>("conv_0_cast_fp16")];
+            tensor<string, []> conv_1_pad_type_0 = const()[name = tensor<string, []>("conv_1_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> conv_1_pad_0 = const()[name = tensor<string, []>("conv_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> conv_1_dilations_0 = const()[name = tensor<string, []>("conv_1_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> conv_1_groups_0 = const()[name = tensor<string, []>("conv_1_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [257, 1, 512]> expand_dims_2_to_fp16 = const()[name = tensor<string, []>("expand_dims_2_to_fp16"), val = tensor<fp16, [257, 1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(263296)))];
+            tensor<fp16, [1, 257, 1501]> 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 = tensor<string, []>("conv_1_cast_fp16")];
+            tensor<int32, [1]> squeeze_0_axes_0 = const()[name = tensor<string, []>("squeeze_0_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [257, 1501]> squeeze_0_cast_fp16 = squeeze(axes = squeeze_0_axes_0, x = conv_0_cast_fp16)[name = tensor<string, []>("squeeze_0_cast_fp16")];
+            tensor<int32, [1]> squeeze_1_axes_0 = const()[name = tensor<string, []>("squeeze_1_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [257, 1501]> squeeze_1_cast_fp16 = squeeze(axes = squeeze_1_axes_0, x = conv_1_cast_fp16)[name = tensor<string, []>("squeeze_1_cast_fp16")];
+            tensor<fp16, [257, 1501]> square_1_cast_fp16 = square(x = squeeze_0_cast_fp16)[name = tensor<string, []>("square_1_cast_fp16")];
+            tensor<fp16, [257, 1501]> square_2_cast_fp16 = square(x = squeeze_1_cast_fp16)[name = tensor<string, []>("square_2_cast_fp16")];
+            tensor<fp16, [257, 1501]> add_1_cast_fp16 = add(x = square_1_cast_fp16, y = square_2_cast_fp16)[name = tensor<string, []>("add_1_cast_fp16")];
+            tensor<fp16, [257, 1501]> magnitudes_cast_fp16 = identity(x = add_1_cast_fp16)[name = tensor<string, []>("magnitudes_cast_fp16")];
+            tensor<bool, []> mel_spec_1_transpose_x_0 = const()[name = tensor<string, []>("mel_spec_1_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> mel_spec_1_transpose_y_0 = const()[name = tensor<string, []>("mel_spec_1_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [80, 257]> mel_filters_to_fp16 = const()[name = tensor<string, []>("mel_filters_to_fp16"), val = tensor<fp16, [80, 257]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(526528)))];
+            tensor<fp16, [80, 1501]> mel_spec_1_cast_fp16 = matmul(transpose_x = mel_spec_1_transpose_x_0, transpose_y = mel_spec_1_transpose_y_0, x = mel_filters_to_fp16, y = magnitudes_cast_fp16)[name = tensor<string, []>("mel_spec_1_cast_fp16")];
+            tensor<fp16, []> var_59_to_fp16 = const()[name = tensor<string, []>("op_59_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
+            tensor<fp16, [80, 1501]> mel_spec_3_cast_fp16 = add(x = mel_spec_1_cast_fp16, y = var_59_to_fp16)[name = tensor<string, []>("mel_spec_3_cast_fp16")];
+            tensor<fp32, []> mel_spec_5_epsilon_0 = const()[name = tensor<string, []>("mel_spec_5_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
+            tensor<fp16, [80, 1501]> mel_spec_5_cast_fp16 = log(epsilon = mel_spec_5_epsilon_0, x = mel_spec_3_cast_fp16)[name = tensor<string, []>("mel_spec_5_cast_fp16")];
+            tensor<int32, [1]> per_feature_mean_axes_0 = const()[name = tensor<string, []>("per_feature_mean_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, []> per_feature_mean_keep_dims_0 = const()[name = tensor<string, []>("per_feature_mean_keep_dims_0"), val = tensor<bool, []>(true)];
+            tensor<fp16, [80, 1]> per_feature_mean_cast_fp16 = reduce_mean(axes = per_feature_mean_axes_0, keep_dims = per_feature_mean_keep_dims_0, x = mel_spec_5_cast_fp16)[name = tensor<string, []>("per_feature_mean_cast_fp16")];
+            tensor<fp16, [80, 1501]> sub_0_cast_fp16 = sub(x = mel_spec_5_cast_fp16, y = per_feature_mean_cast_fp16)[name = tensor<string, []>("sub_0_cast_fp16")];
+            tensor<fp16, [80, 1501]> square_0_cast_fp16 = square(x = sub_0_cast_fp16)[name = tensor<string, []>("square_0_cast_fp16")];
+            tensor<int32, [1]> reduce_mean_1_axes_0 = const()[name = tensor<string, []>("reduce_mean_1_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, []> reduce_mean_1_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_1_keep_dims_0"), val = tensor<bool, []>(true)];
+            tensor<fp16, [80, 1]> reduce_mean_1_cast_fp16 = reduce_mean(axes = reduce_mean_1_axes_0, keep_dims = reduce_mean_1_keep_dims_0, x = square_0_cast_fp16)[name = tensor<string, []>("reduce_mean_1_cast_fp16")];
+            tensor<fp16, []> real_div_0_to_fp16 = const()[name = tensor<string, []>("real_div_0_to_fp16"), val = tensor<fp16, []>(0x1.004p+0)];
+            tensor<fp16, [80, 1]> mul_0_cast_fp16 = mul(x = reduce_mean_1_cast_fp16, y = real_div_0_to_fp16)[name = tensor<string, []>("mul_0_cast_fp16")];
+            tensor<fp16, [80, 1]> sqrt_0_cast_fp16 = sqrt(x = mul_0_cast_fp16)[name = tensor<string, []>("sqrt_0_cast_fp16")];
+            tensor<fp16, []> var_73_to_fp16 = const()[name = tensor<string, []>("op_73_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
+            tensor<fp16, [80, 1]> per_feature_std_cast_fp16 = add(x = sqrt_0_cast_fp16, y = var_73_to_fp16)[name = tensor<string, []>("per_feature_std_cast_fp16")];
+            tensor<fp16, [80, 1501]> mel_spec_cast_fp16 = real_div(x = sub_0_cast_fp16, y = per_feature_std_cast_fp16)[name = tensor<string, []>("mel_spec_cast_fp16")];
+            tensor<int32, [2]> var_78_perm_0 = const()[name = tensor<string, []>("op_78_perm_0"), val = tensor<int32, [2]>([1, 0])];
+            tensor<int32, [1]> var_80_axes_0 = const()[name = tensor<string, []>("op_80_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1501, 80]> var_78_cast_fp16 = transpose(perm = var_78_perm_0, x = mel_spec_cast_fp16)[name = tensor<string, []>("transpose_0")];
+            tensor<fp16, [1, 1501, 80]> var_80_cast_fp16 = expand_dims(axes = var_80_axes_0, x = var_78_cast_fp16)[name = tensor<string, []>("op_80_cast_fp16")];
+            tensor<int32, [1]> var_82_axes_0 = const()[name = tensor<string, []>("op_82_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1, 1501, 80]> melspectrogram_features = expand_dims(axes = var_82_axes_0, x = var_80_cast_fp16)[name = tensor<string, []>("op_82_cast_fp16")];
+        } -> (melspectrogram_features);
+}

nvidia_parakeet-ja/MelSpectrogram.mlmodelc/weights/weight.bin

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nvidia_parakeet-ja/MultimodalLogits.mlmodelc/metadata.json

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+  {
+    "metadataOutputVersion" : "3.0",
+    "storagePrecision" : "Float16",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
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+        "formattedType" : "MultiArray (Float16 1 × 3078)",
+        "shortDescription" : "",
+        "shape" : "[1, 3078]",
+        "name" : "raw_logits",
+        "type" : "MultiArray"
+      },
+      {
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+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16 1 × 3078)",
+        "shortDescription" : "",
+        "shape" : "[1, 3078]",
+        "name" : "logits",
+        "type" : "MultiArray"
+      }
+    ],
+    "modelParameters" : [
+
+    ],
+    "specificationVersion" : 8,
+    "mlProgramOperationTypeHistogram" : {
+      "Ios16.softmax" : 1,
+      "Ios17.log" : 1,
+      "Ios17.linear" : 1,
+      "Ios17.add" : 1,
+      "Ios16.relu" : 1
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+    "computePrecision" : "Mixed (Float16, Float32, Int32)",
+    "isUpdatable" : "0",
+    "stateSchema" : [
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+    ],
+    "availability" : {
+      "macOS" : "14.0",
+      "tvOS" : "17.0",
+      "visionOS" : "1.0",
+      "watchOS" : "10.0",
+      "iOS" : "17.0",
+      "macCatalyst" : "17.0"
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+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2026-04-10",
+      "com.github.apple.coremltools.source" : "torch==2.11.0",
+      "com.github.apple.coremltools.version" : "9.0",
+      "com.github.apple.coremltools.source_dialect" : "TorchScript"
+    },
+    "inputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16 1 × 640)",
+        "shortDescription" : "",
+        "shape" : "[1, 640]",
+        "name" : "encoder_output_projected",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16 1 × 640)",
+        "shortDescription" : "",
+        "shape" : "[1, 640]",
+        "name" : "decoder_output_projected",
+        "type" : "MultiArray"
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+    "generatedClassName" : "MultimodalLogits",
+    "method" : "predict"
+  }
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nvidia_parakeet-ja/MultimodalLogits.mlmodelc/model.mil

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+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3404.16.1"}, {"coremlc-version", "3404.23.1"}, {"coremltools-component-torch", "2.11.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
+{
+    func main<ios17>(tensor<fp16, [1, 640]> decoder_output_projected, tensor<fp16, [1, 640]> encoder_output_projected) {
+            tensor<fp16, [1, 640]> input_1_cast_fp16 = add(x = decoder_output_projected, y = encoder_output_projected)[name = tensor<string, []>("input_1_cast_fp16")];
+            tensor<fp16, [1, 640]> input_3_cast_fp16 = relu(x = input_1_cast_fp16)[name = tensor<string, []>("input_3_cast_fp16")];
+            tensor<fp16, [3078, 640]> joint_net_1_weight_to_fp16 = const()[name = tensor<string, []>("joint_net_1_weight_to_fp16"), val = tensor<fp16, [3078, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
+            tensor<fp16, [3078]> joint_net_1_bias_to_fp16 = const()[name = tensor<string, []>("joint_net_1_bias_to_fp16"), val = tensor<fp16, [3078]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3939968)))];
+            tensor<fp16, [1, 3078]> raw_logits = linear(bias = joint_net_1_bias_to_fp16, weight = joint_net_1_weight_to_fp16, x = input_3_cast_fp16)[name = tensor<string, []>("linear_0_cast_fp16")];
+            tensor<int32, []> var_11 = const()[name = tensor<string, []>("op_11"), val = tensor<int32, []>(-1)];
+            tensor<fp16, [1, 3078]> var_13_softmax_cast_fp16 = softmax(axis = var_11, x = raw_logits)[name = tensor<string, []>("op_13_softmax_cast_fp16")];
+            tensor<fp32, []> var_13_epsilon_0 = const()[name = tensor<string, []>("op_13_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
+            tensor<fp16, [1, 3078]> logits = log(epsilon = var_13_epsilon_0, x = var_13_softmax_cast_fp16)[name = tensor<string, []>("op_13_cast_fp16")];
+        } -> (raw_logits, logits);
+}

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nvidia_parakeet-ja/TextDecoder.mlmodelc/coremldata.bin

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nvidia_parakeet-ja/TextDecoder.mlmodelc/metadata.json

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+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "storagePrecision" : "Float16",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16 1 × 640)",
+        "shortDescription" : "",
+        "shape" : "[1, 640]",
+        "name" : "decoder_output_projected",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16 2 × 640)",
+        "shortDescription" : "",
+        "shape" : "[2, 640]",
+        "name" : "new_state_1",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16 2 × 640)",
+        "shortDescription" : "",
+        "shape" : "[2, 640]",
+        "name" : "new_state_2",
+        "type" : "MultiArray"
+      }
+    ],
+    "modelParameters" : [
+
+    ],
+    "specificationVersion" : 8,
+    "mlProgramOperationTypeHistogram" : {
+      "Select" : 1,
+      "Ios17.squeeze" : 7,
+      "Ios17.gather" : 1,
+      "Ios17.cast" : 3,
+      "Ios17.lstm" : 2,
+      "Split" : 2,
+      "Ios17.add" : 1,
+      "Ios17.linear" : 1,
+      "Ios17.greaterEqual" : 1,
+      "Stack" : 2,
+      "Ios17.expandDims" : 3
+    },
+    "computePrecision" : "Mixed (Float16, Int16, Int32)",
+    "isUpdatable" : "0",
+    "stateSchema" : [
+
+    ],
+    "availability" : {
+      "macOS" : "14.0",
+      "tvOS" : "17.0",
+      "visionOS" : "1.0",
+      "watchOS" : "10.0",
+      "iOS" : "17.0",
+      "macCatalyst" : "17.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2026-04-10",
+      "com.github.apple.coremltools.source" : "torch==2.11.0",
+      "com.github.apple.coremltools.version" : "9.0",
+      "com.github.apple.coremltools.source_dialect" : "TorchScript"
+    },
+    "inputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Int32",
+        "formattedType" : "MultiArray (Int32 1)",
+        "shortDescription" : "",
+        "shape" : "[1]",
+        "name" : "decoder_input_ids",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16 2 × 640)",
+        "shortDescription" : "",
+        "shape" : "[2, 640]",
+        "name" : "state_1",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16 2 × 640)",
+        "shortDescription" : "",
+        "shape" : "[2, 640]",
+        "name" : "state_2",
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+    ],
+    "generatedClassName" : "TextDecoder",
+    "method" : "predict"
+  }
+]

nvidia_parakeet-ja/TextDecoder.mlmodelc/model.mil

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+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3404.16.1"}, {"coremlc-version", "3404.23.1"}, {"coremltools-component-torch", "2.11.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
+{
+    func main<ios17>(tensor<int32, [1]> decoder_input_ids, tensor<fp16, [2, 640]> state_1, tensor<fp16, [2, 640]> state_2) {
+            tensor<int32, []> input_1_batch_dims_0 = const()[name = tensor<string, []>("input_1_batch_dims_0"), val = tensor<int32, []>(0)];
+            tensor<bool, []> input_1_validate_indices_0 = const()[name = tensor<string, []>("input_1_validate_indices_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [3073, 640]> prediction_embed_weight_to_fp16 = const()[name = tensor<string, []>("prediction_embed_weight_to_fp16"), val = tensor<fp16, [3073, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
+            tensor<string, []> decoder_input_ids_to_int16_dtype_0 = const()[name = tensor<string, []>("decoder_input_ids_to_int16_dtype_0"), val = tensor<string, []>("int16")];
+            tensor<string, []> cast_6_dtype_0 = const()[name = tensor<string, []>("cast_6_dtype_0"), val = tensor<string, []>("int32")];
+            tensor<int32, []> greater_equal_0_y_0 = const()[name = tensor<string, []>("greater_equal_0_y_0"), val = tensor<int32, []>(0)];
+            tensor<int16, [1]> decoder_input_ids_to_int16 = cast(dtype = decoder_input_ids_to_int16_dtype_0, x = decoder_input_ids)[name = tensor<string, []>("cast_9")];
+            tensor<int32, [1]> cast_6 = cast(dtype = cast_6_dtype_0, x = decoder_input_ids_to_int16)[name = tensor<string, []>("cast_8")];
+            tensor<bool, [1]> greater_equal_0 = greater_equal(x = cast_6, y = greater_equal_0_y_0)[name = tensor<string, []>("greater_equal_0")];
+            tensor<int32, []> slice_by_index_0 = const()[name = tensor<string, []>("slice_by_index_0"), val = tensor<int32, []>(3073)];
+            tensor<int32, [1]> add_2 = add(x = cast_6, y = slice_by_index_0)[name = tensor<string, []>("add_2")];
+            tensor<int32, [1]> select_0 = select(a = cast_6, b = add_2, cond = greater_equal_0)[name = tensor<string, []>("select_0")];
+            tensor<int32, []> input_1_cast_fp16_cast_uint16_axis_0 = const()[name = tensor<string, []>("input_1_cast_fp16_cast_uint16_axis_0"), val = tensor<int32, []>(0)];
+            tensor<string, []> select_0_to_int16_dtype_0 = const()[name = tensor<string, []>("select_0_to_int16_dtype_0"), val = tensor<string, []>("int16")];
+            tensor<int16, [1]> select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = tensor<string, []>("cast_7")];
+            tensor<fp16, [1, 640]> input_1_cast_fp16_cast_uint16_cast_uint16 = gather(axis = input_1_cast_fp16_cast_uint16_axis_0, batch_dims = input_1_batch_dims_0, indices = select_0_to_int16, validate_indices = input_1_validate_indices_0, x = prediction_embed_weight_to_fp16)[name = tensor<string, []>("input_1_cast_fp16_cast_uint16_cast_uint16")];
+            tensor<int32, [1]> input_3_axes_0 = const()[name = tensor<string, []>("input_3_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1, 640]> input_3_cast_fp16 = expand_dims(axes = input_3_axes_0, x = input_1_cast_fp16_cast_uint16_cast_uint16)[name = tensor<string, []>("input_3_cast_fp16")];
+            tensor<int32, [1]> hx_1_axes_0 = const()[name = tensor<string, []>("hx_1_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [2, 1, 640]> hx_1_cast_fp16 = expand_dims(axes = hx_1_axes_0, x = state_1)[name = tensor<string, []>("hx_1_cast_fp16")];
+            tensor<int32, [1]> hx_axes_0 = const()[name = tensor<string, []>("hx_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [2, 1, 640]> hx_cast_fp16 = expand_dims(axes = hx_axes_0, x = state_2)[name = tensor<string, []>("hx_cast_fp16")];
+            tensor<int32, []> split_0_num_splits_0 = const()[name = tensor<string, []>("split_0_num_splits_0"), val = tensor<int32, []>(2)];
+            tensor<int32, []> split_0_axis_0 = const()[name = tensor<string, []>("split_0_axis_0"), val = tensor<int32, []>(0)];
+            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 = hx_1_cast_fp16)[name = tensor<string, []>("split_0_cast_fp16")];
+            tensor<int32, []> split_1_num_splits_0 = const()[name = tensor<string, []>("split_1_num_splits_0"), val = tensor<int32, []>(2)];
+            tensor<int32, []> split_1_axis_0 = const()[name = tensor<string, []>("split_1_axis_0"), val = tensor<int32, []>(0)];
+            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 = hx_cast_fp16)[name = tensor<string, []>("split_1_cast_fp16")];
+            tensor<int32, [1]> output_lstm_layer_0_lstm_h0_squeeze_axes_0 = const()[name = tensor<string, []>("output_lstm_layer_0_lstm_h0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 640]> output_lstm_layer_0_lstm_h0_squeeze_cast_fp16 = squeeze(axes = output_lstm_layer_0_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_0)[name = tensor<string, []>("output_lstm_layer_0_lstm_h0_squeeze_cast_fp16")];
+            tensor<int32, [1]> output_lstm_layer_0_lstm_c0_squeeze_axes_0 = const()[name = tensor<string, []>("output_lstm_layer_0_lstm_c0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 640]> output_lstm_layer_0_lstm_c0_squeeze_cast_fp16 = squeeze(axes = output_lstm_layer_0_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_0)[name = tensor<string, []>("output_lstm_layer_0_lstm_c0_squeeze_cast_fp16")];
+            tensor<string, []> output_lstm_layer_0_direction_0 = const()[name = tensor<string, []>("output_lstm_layer_0_direction_0"), val = tensor<string, []>("forward")];
+            tensor<bool, []> output_lstm_layer_0_output_sequence_0 = const()[name = tensor<string, []>("output_lstm_layer_0_output_sequence_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> output_lstm_layer_0_recurrent_activation_0 = const()[name = tensor<string, []>("output_lstm_layer_0_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
+            tensor<string, []> output_lstm_layer_0_cell_activation_0 = const()[name = tensor<string, []>("output_lstm_layer_0_cell_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<string, []> output_lstm_layer_0_activation_0 = const()[name = tensor<string, []>("output_lstm_layer_0_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<fp16, [2560, 640]> concat_1_to_fp16 = const()[name = tensor<string, []>("concat_1_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3933568)))];
+            tensor<fp16, [2560, 640]> concat_2_to_fp16 = const()[name = tensor<string, []>("concat_2_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7210432)))];
+            tensor<fp16, [2560]> concat_0_to_fp16 = const()[name = tensor<string, []>("concat_0_to_fp16"), val = tensor<fp16, [2560]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10487296)))];
+            tensor<fp16, [1, 1, 640]> output_lstm_layer_0_cast_fp16_0, tensor<fp16, [1, 640]> output_lstm_layer_0_cast_fp16_1, tensor<fp16, [1, 640]> output_lstm_layer_0_cast_fp16_2 = lstm(activation = output_lstm_layer_0_activation_0, bias = concat_0_to_fp16, cell_activation = output_lstm_layer_0_cell_activation_0, direction = output_lstm_layer_0_direction_0, initial_c = output_lstm_layer_0_lstm_c0_squeeze_cast_fp16, initial_h = output_lstm_layer_0_lstm_h0_squeeze_cast_fp16, output_sequence = output_lstm_layer_0_output_sequence_0, recurrent_activation = output_lstm_layer_0_recurrent_activation_0, weight_hh = concat_2_to_fp16, weight_ih = concat_1_to_fp16, x = input_3_cast_fp16)[name = tensor<string, []>("output_lstm_layer_0_cast_fp16")];
+            tensor<int32, [1]> output_lstm_h0_squeeze_axes_0 = const()[name = tensor<string, []>("output_lstm_h0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 640]> output_lstm_h0_squeeze_cast_fp16 = squeeze(axes = output_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_1)[name = tensor<string, []>("output_lstm_h0_squeeze_cast_fp16")];
+            tensor<int32, [1]> output_lstm_c0_squeeze_axes_0 = const()[name = tensor<string, []>("output_lstm_c0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 640]> output_lstm_c0_squeeze_cast_fp16 = squeeze(axes = output_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_1)[name = tensor<string, []>("output_lstm_c0_squeeze_cast_fp16")];
+            tensor<string, []> output_direction_0 = const()[name = tensor<string, []>("output_direction_0"), val = tensor<string, []>("forward")];
+            tensor<bool, []> output_output_sequence_0 = const()[name = tensor<string, []>("output_output_sequence_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> output_recurrent_activation_0 = const()[name = tensor<string, []>("output_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
+            tensor<string, []> output_cell_activation_0 = const()[name = tensor<string, []>("output_cell_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<string, []> output_activation_0 = const()[name = tensor<string, []>("output_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<fp16, [2560, 640]> concat_4_to_fp16 = const()[name = tensor<string, []>("concat_4_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10492480)))];
+            tensor<fp16, [2560, 640]> concat_5_to_fp16 = const()[name = tensor<string, []>("concat_5_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13769344)))];
+            tensor<fp16, [2560]> concat_3_to_fp16 = const()[name = tensor<string, []>("concat_3_to_fp16"), val = tensor<fp16, [2560]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17046208)))];
+            tensor<fp16, [1, 1, 640]> output_cast_fp16_0, tensor<fp16, [1, 640]> output_cast_fp16_1, tensor<fp16, [1, 640]> output_cast_fp16_2 = lstm(activation = output_activation_0, bias = concat_3_to_fp16, cell_activation = output_cell_activation_0, direction = output_direction_0, initial_c = output_lstm_c0_squeeze_cast_fp16, initial_h = output_lstm_h0_squeeze_cast_fp16, output_sequence = output_output_sequence_0, recurrent_activation = output_recurrent_activation_0, weight_hh = concat_5_to_fp16, weight_ih = concat_4_to_fp16, x = output_lstm_layer_0_cast_fp16_0)[name = tensor<string, []>("output_cast_fp16")];
+            tensor<int32, []> var_32_axis_0 = const()[name = tensor<string, []>("op_32_axis_0"), val = tensor<int32, []>(0)];
+            tensor<fp16, [2, 1, 640]> var_32_cast_fp16 = stack(axis = var_32_axis_0, values = (output_lstm_layer_0_cast_fp16_1, output_cast_fp16_1))[name = tensor<string, []>("op_32_cast_fp16")];
+            tensor<int32, []> var_33_axis_0 = const()[name = tensor<string, []>("op_33_axis_0"), val = tensor<int32, []>(0)];
+            tensor<fp16, [2, 1, 640]> var_33_cast_fp16 = stack(axis = var_33_axis_0, values = (output_lstm_layer_0_cast_fp16_2, output_cast_fp16_2))[name = tensor<string, []>("op_33_cast_fp16")];
+            tensor<int32, [1]> input_axes_0 = const()[name = tensor<string, []>("input_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 640]> input_cast_fp16 = squeeze(axes = input_axes_0, x = output_cast_fp16_0)[name = tensor<string, []>("input_cast_fp16")];
+            tensor<int32, [1]> var_35_axes_0 = const()[name = tensor<string, []>("op_35_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [2, 640]> new_state_1 = squeeze(axes = var_35_axes_0, x = var_32_cast_fp16)[name = tensor<string, []>("op_35_cast_fp16")];
+            tensor<int32, [1]> var_36_axes_0 = const()[name = tensor<string, []>("op_36_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [2, 640]> new_state_2 = squeeze(axes = var_36_axes_0, x = var_33_cast_fp16)[name = tensor<string, []>("op_36_cast_fp16")];
+            tensor<fp16, [640, 640]> joint_projection_weight_to_fp16 = const()[name = tensor<string, []>("joint_projection_weight_to_fp16"), val = tensor<fp16, [640, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17051392)))];
+            tensor<fp16, [640]> joint_projection_bias_to_fp16 = const()[name = tensor<string, []>("joint_projection_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17870656)))];
+            tensor<fp16, [1, 640]> decoder_output_projected = linear(bias = joint_projection_bias_to_fp16, weight = joint_projection_weight_to_fp16, x = input_cast_fp16)[name = tensor<string, []>("linear_0_cast_fp16")];
+        } -> (decoder_output_projected, new_state_1, new_state_2);
+}

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nvidia_parakeet-ja_483MB/LICENSE_NOTICE.txt

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+Argmax proprietary and confidential. Under NDA.
+
+Copyright 2024 Argmax, Inc. All rights reserved.
+
+Unauthorized access, copying, use, distribution, and or commercialization of this file, via any medium or means is strictly prohibited.
+
+Please contact Argmax for licensing information at info@argmaxinc.com.

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nvidia_parakeet-ja_483MB/MelSpectrogram.mlmodelc/model.mil

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+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3404.16.1"}, {"coremlc-version", "3404.23.1"}})]
+{
+    func main<ios17>(tensor<fp16, [240000]> audio) {
+            tensor<int32, [1]> var_6_begin_0 = const()[name = tensor<string, []>("op_6_begin_0"), val = tensor<int32, [1]>([0])];
+            tensor<int32, [1]> var_6_end_0 = const()[name = tensor<string, []>("op_6_end_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1]> var_6_end_mask_0 = const()[name = tensor<string, []>("op_6_end_mask_0"), val = tensor<bool, [1]>([false])];
+            tensor<bool, [1]> var_6_squeeze_mask_0 = const()[name = tensor<string, []>("op_6_squeeze_mask_0"), val = tensor<bool, [1]>([true])];
+            tensor<fp16, []> var_6_cast_fp16 = slice_by_index(begin = var_6_begin_0, end = var_6_end_0, end_mask = var_6_end_mask_0, squeeze_mask = var_6_squeeze_mask_0, x = audio)[name = tensor<string, []>("op_6_cast_fp16")];
+            tensor<int32, [1]> var_8_axes_0 = const()[name = tensor<string, []>("op_8_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1]> var_8_cast_fp16 = expand_dims(axes = var_8_axes_0, x = var_6_cast_fp16)[name = tensor<string, []>("op_8_cast_fp16")];
+            tensor<int32, [1]> var_13_begin_0 = const()[name = tensor<string, []>("op_13_begin_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> var_13_end_0 = const()[name = tensor<string, []>("op_13_end_0"), val = tensor<int32, [1]>([240000])];
+            tensor<bool, [1]> var_13_end_mask_0 = const()[name = tensor<string, []>("op_13_end_mask_0"), val = tensor<bool, [1]>([true])];
+            tensor<fp16, [239999]> var_13_cast_fp16 = slice_by_index(begin = var_13_begin_0, end = var_13_end_0, end_mask = var_13_end_mask_0, x = audio)[name = tensor<string, []>("op_13_cast_fp16")];
+            tensor<int32, [1]> var_18_begin_0 = const()[name = tensor<string, []>("op_18_begin_0"), val = tensor<int32, [1]>([0])];
+            tensor<int32, [1]> var_18_end_0 = const()[name = tensor<string, []>("op_18_end_0"), val = tensor<int32, [1]>([239999])];
+            tensor<bool, [1]> var_18_end_mask_0 = const()[name = tensor<string, []>("op_18_end_mask_0"), val = tensor<bool, [1]>([false])];
+            tensor<fp16, [239999]> var_18_cast_fp16 = slice_by_index(begin = var_18_begin_0, end = var_18_end_0, end_mask = var_18_end_mask_0, x = audio)[name = tensor<string, []>("op_18_cast_fp16")];
+            tensor<fp16, []> var_19_to_fp16 = const()[name = tensor<string, []>("op_19_to_fp16"), val = tensor<fp16, []>(0x1.f0cp-1)];
+            tensor<fp16, [239999]> var_20_cast_fp16 = mul(x = var_18_cast_fp16, y = var_19_to_fp16)[name = tensor<string, []>("op_20_cast_fp16")];
+            tensor<fp16, [239999]> var_22_cast_fp16 = sub(x = var_13_cast_fp16, y = var_20_cast_fp16)[name = tensor<string, []>("op_22_cast_fp16")];
+            tensor<int32, []> var_24 = const()[name = tensor<string, []>("op_24"), val = tensor<int32, []>(0)];
+            tensor<bool, []> input_1_interleave_0 = const()[name = tensor<string, []>("input_1_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [240000]> input_1_cast_fp16 = concat(axis = var_24, interleave = input_1_interleave_0, values = (var_8_cast_fp16, var_22_cast_fp16))[name = tensor<string, []>("input_1_cast_fp16")];
+            tensor<int32, [3]> var_32 = const()[name = tensor<string, []>("op_32"), val = tensor<int32, [3]>([1, 1, 240000])];
+            tensor<fp16, [1, 1, 240000]> input_3_cast_fp16 = reshape(shape = var_32, x = input_1_cast_fp16)[name = tensor<string, []>("input_3_cast_fp16")];
+            tensor<int32, [6]> input_5_pad_0 = const()[name = tensor<string, []>("input_5_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 256, 256])];
+            tensor<string, []> input_5_mode_0 = const()[name = tensor<string, []>("input_5_mode_0"), val = tensor<string, []>("reflect")];
+            tensor<fp16, []> const_1_to_fp16 = const()[name = tensor<string, []>("const_1_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 1, 240512]> input_5_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = input_5_mode_0, pad = input_5_pad_0, x = input_3_cast_fp16)[name = tensor<string, []>("input_5_cast_fp16")];
+            tensor<int32, [1]> var_44 = const()[name = tensor<string, []>("op_44"), val = tensor<int32, [1]>([240512])];
+            tensor<fp16, [240512]> input_cast_fp16 = reshape(shape = var_44, x = input_5_cast_fp16)[name = tensor<string, []>("input_cast_fp16")];
+            tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = tensor<string, []>("expand_dims_0_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 240512]> expand_dims_0_cast_fp16 = expand_dims(axes = expand_dims_0_axes_0, x = input_cast_fp16)[name = tensor<string, []>("expand_dims_0_cast_fp16")];
+            tensor<int32, [1]> expand_dims_3 = const()[name = tensor<string, []>("expand_dims_3"), val = tensor<int32, [1]>([160])];
+            tensor<int32, [1]> expand_dims_4_axes_0 = const()[name = tensor<string, []>("expand_dims_4_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1, 240512]> expand_dims_4_cast_fp16 = expand_dims(axes = expand_dims_4_axes_0, x = expand_dims_0_cast_fp16)[name = tensor<string, []>("expand_dims_4_cast_fp16")];
+            tensor<string, []> conv_0_pad_type_0 = const()[name = tensor<string, []>("conv_0_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> conv_0_pad_0 = const()[name = tensor<string, []>("conv_0_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> conv_0_dilations_0 = const()[name = tensor<string, []>("conv_0_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> conv_0_groups_0 = const()[name = tensor<string, []>("conv_0_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [257, 1, 512]> expand_dims_1_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [98688]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(98816))), name = tensor<string, []>("expand_dims_1_to_fp16_palettized"), shape = tensor<uint32, [3]>([257, 1, 512])];
+            tensor<fp16, [1, 257, 1501]> 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_palettized, x = expand_dims_4_cast_fp16)[name = tensor<string, []>("conv_0_cast_fp16")];
+            tensor<string, []> conv_1_pad_type_0 = const()[name = tensor<string, []>("conv_1_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> conv_1_pad_0 = const()[name = tensor<string, []>("conv_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> conv_1_dilations_0 = const()[name = tensor<string, []>("conv_1_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> conv_1_groups_0 = const()[name = tensor<string, []>("conv_1_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [257, 1, 512]> expand_dims_2_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [98688]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(99008))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197760))), name = tensor<string, []>("expand_dims_2_to_fp16_palettized"), shape = tensor<uint32, [3]>([257, 1, 512])];
+            tensor<fp16, [1, 257, 1501]> 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_palettized, x = expand_dims_4_cast_fp16)[name = tensor<string, []>("conv_1_cast_fp16")];
+            tensor<int32, [1]> squeeze_0_axes_0 = const()[name = tensor<string, []>("squeeze_0_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [257, 1501]> squeeze_0_cast_fp16 = squeeze(axes = squeeze_0_axes_0, x = conv_0_cast_fp16)[name = tensor<string, []>("squeeze_0_cast_fp16")];
+            tensor<int32, [1]> squeeze_1_axes_0 = const()[name = tensor<string, []>("squeeze_1_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [257, 1501]> squeeze_1_cast_fp16 = squeeze(axes = squeeze_1_axes_0, x = conv_1_cast_fp16)[name = tensor<string, []>("squeeze_1_cast_fp16")];
+            tensor<fp16, [257, 1501]> square_1_cast_fp16 = square(x = squeeze_0_cast_fp16)[name = tensor<string, []>("square_1_cast_fp16")];
+            tensor<fp16, [257, 1501]> square_2_cast_fp16 = square(x = squeeze_1_cast_fp16)[name = tensor<string, []>("square_2_cast_fp16")];
+            tensor<fp16, [257, 1501]> add_1_cast_fp16 = add(x = square_1_cast_fp16, y = square_2_cast_fp16)[name = tensor<string, []>("add_1_cast_fp16")];
+            tensor<bool, []> mel_spec_1_transpose_x_0 = const()[name = tensor<string, []>("mel_spec_1_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> mel_spec_1_transpose_y_0 = const()[name = tensor<string, []>("mel_spec_1_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [80, 257]> mel_filters_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [15420]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197952))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(213440))), name = tensor<string, []>("mel_filters_to_fp16_palettized"), shape = tensor<uint32, [2]>([80, 257])];
+            tensor<fp16, [80, 1501]> mel_spec_1_cast_fp16 = matmul(transpose_x = mel_spec_1_transpose_x_0, transpose_y = mel_spec_1_transpose_y_0, x = mel_filters_to_fp16_palettized, y = add_1_cast_fp16)[name = tensor<string, []>("mel_spec_1_cast_fp16")];
+            tensor<fp16, []> var_59_to_fp16 = const()[name = tensor<string, []>("op_59_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
+            tensor<fp16, [80, 1501]> mel_spec_3_cast_fp16 = add(x = mel_spec_1_cast_fp16, y = var_59_to_fp16)[name = tensor<string, []>("mel_spec_3_cast_fp16")];
+            tensor<fp32, []> mel_spec_5_epsilon_0 = const()[name = tensor<string, []>("mel_spec_5_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
+            tensor<fp16, [80, 1501]> mel_spec_5_cast_fp16 = log(epsilon = mel_spec_5_epsilon_0, x = mel_spec_3_cast_fp16)[name = tensor<string, []>("mel_spec_5_cast_fp16")];
+            tensor<int32, [1]> per_feature_mean_axes_0 = const()[name = tensor<string, []>("per_feature_mean_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, []> per_feature_mean_keep_dims_0 = const()[name = tensor<string, []>("per_feature_mean_keep_dims_0"), val = tensor<bool, []>(true)];
+            tensor<fp16, [80, 1]> per_feature_mean_cast_fp16 = reduce_mean(axes = per_feature_mean_axes_0, keep_dims = per_feature_mean_keep_dims_0, x = mel_spec_5_cast_fp16)[name = tensor<string, []>("per_feature_mean_cast_fp16")];
+            tensor<fp16, [80, 1501]> sub_0_cast_fp16 = sub(x = mel_spec_5_cast_fp16, y = per_feature_mean_cast_fp16)[name = tensor<string, []>("sub_0_cast_fp16")];
+            tensor<fp16, [80, 1501]> square_0_cast_fp16 = square(x = sub_0_cast_fp16)[name = tensor<string, []>("square_0_cast_fp16")];
+            tensor<int32, [1]> reduce_mean_1_axes_0 = const()[name = tensor<string, []>("reduce_mean_1_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, []> reduce_mean_1_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_1_keep_dims_0"), val = tensor<bool, []>(true)];
+            tensor<fp16, [80, 1]> reduce_mean_1_cast_fp16 = reduce_mean(axes = reduce_mean_1_axes_0, keep_dims = reduce_mean_1_keep_dims_0, x = square_0_cast_fp16)[name = tensor<string, []>("reduce_mean_1_cast_fp16")];
+            tensor<fp16, []> real_div_0_to_fp16 = const()[name = tensor<string, []>("real_div_0_to_fp16"), val = tensor<fp16, []>(0x1.004p+0)];
+            tensor<fp16, [80, 1]> mul_0_cast_fp16 = mul(x = reduce_mean_1_cast_fp16, y = real_div_0_to_fp16)[name = tensor<string, []>("mul_0_cast_fp16")];
+            tensor<fp16, [80, 1]> sqrt_0_cast_fp16 = sqrt(x = mul_0_cast_fp16)[name = tensor<string, []>("sqrt_0_cast_fp16")];
+            tensor<fp16, []> var_73_to_fp16 = const()[name = tensor<string, []>("op_73_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
+            tensor<fp16, [80, 1]> per_feature_std_cast_fp16 = add(x = sqrt_0_cast_fp16, y = var_73_to_fp16)[name = tensor<string, []>("per_feature_std_cast_fp16")];
+            tensor<fp16, [80, 1501]> mel_spec_cast_fp16 = real_div(x = sub_0_cast_fp16, y = per_feature_std_cast_fp16)[name = tensor<string, []>("mel_spec_cast_fp16")];
+            tensor<int32, [2]> var_78_perm_0 = const()[name = tensor<string, []>("op_78_perm_0"), val = tensor<int32, [2]>([1, 0])];
+            tensor<int32, [1]> var_80_axes_0 = const()[name = tensor<string, []>("op_80_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1501, 80]> var_78_cast_fp16 = transpose(perm = var_78_perm_0, x = mel_spec_cast_fp16)[name = tensor<string, []>("transpose_0")];
+            tensor<fp16, [1, 1501, 80]> var_80_cast_fp16 = expand_dims(axes = var_80_axes_0, x = var_78_cast_fp16)[name = tensor<string, []>("op_80_cast_fp16")];
+            tensor<int32, [1]> var_82_axes_0 = const()[name = tensor<string, []>("op_82_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1, 1501, 80]> melspectrogram_features = expand_dims(axes = var_82_axes_0, x = var_80_cast_fp16)[name = tensor<string, []>("op_82_cast_fp16")];
+        } -> (melspectrogram_features);
+}

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+    "storagePrecision" : "Float16",
+    "outputSchema" : [
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+        "shortDescription" : "",
+        "shape" : "[1, 3078]",
+        "name" : "raw_logits",
+        "type" : "MultiArray"
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+        "shortDescription" : "",
+        "shape" : "[1, 3078]",
+        "name" : "logits",
+        "type" : "MultiArray"
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+    "mlProgramOperationTypeHistogram" : {
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+      "watchOS" : "10.0",
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+      "com.github.apple.coremltools.conversion_date" : "2026-04-10",
+      "com.github.apple.coremltools.source" : "torch==2.11.0",
+      "com.github.apple.coremltools.version" : "9.0",
+      "com.github.apple.coremltools.source_dialect" : "TorchScript"
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+        "shortDescription" : "",
+        "shape" : "[1, 640]",
+        "name" : "encoder_output_projected",
+        "type" : "MultiArray"
+      },
+      {
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nvidia_parakeet-ja_483MB/MultimodalLogits.mlmodelc/model.mil

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+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3404.16.1"}, {"coremlc-version", "3404.23.1"}, {"coremltools-component-torch", "2.11.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
+{
+    func main<ios17>(tensor<fp16, [1, 640]> decoder_output_projected, tensor<fp16, [1, 640]> encoder_output_projected) {
+            tensor<fp16, [1, 640]> input_1_cast_fp16 = add(x = decoder_output_projected, y = encoder_output_projected)[name = tensor<string, []>("input_1_cast_fp16")];
+            tensor<fp16, [1, 640]> input_3_cast_fp16 = relu(x = input_1_cast_fp16)[name = tensor<string, []>("input_3_cast_fp16")];
+            tensor<fp16, [3078, 640]> joint_net_1_weight_to_fp16 = const()[name = tensor<string, []>("joint_net_1_weight_to_fp16"), val = tensor<fp16, [3078, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
+            tensor<fp16, [3078]> joint_net_1_bias_to_fp16 = const()[name = tensor<string, []>("joint_net_1_bias_to_fp16"), val = tensor<fp16, [3078]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3939968)))];
+            tensor<fp16, [1, 3078]> raw_logits = linear(bias = joint_net_1_bias_to_fp16, weight = joint_net_1_weight_to_fp16, x = input_3_cast_fp16)[name = tensor<string, []>("linear_0_cast_fp16")];
+            tensor<int32, []> var_11 = const()[name = tensor<string, []>("op_11"), val = tensor<int32, []>(-1)];
+            tensor<fp16, [1, 3078]> var_13_softmax_cast_fp16 = softmax(axis = var_11, x = raw_logits)[name = tensor<string, []>("op_13_softmax_cast_fp16")];
+            tensor<fp32, []> var_13_epsilon_0 = const()[name = tensor<string, []>("op_13_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
+            tensor<fp16, [1, 3078]> logits = log(epsilon = var_13_epsilon_0, x = var_13_softmax_cast_fp16)[name = tensor<string, []>("op_13_cast_fp16")];
+        } -> (raw_logits, logits);
+}

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+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "storagePrecision" : "Float16",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16 1 × 640)",
+        "shortDescription" : "",
+        "shape" : "[1, 640]",
+        "name" : "decoder_output_projected",
+        "type" : "MultiArray"
+      },
+      {
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+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16 2 × 640)",
+        "shortDescription" : "",
+        "shape" : "[2, 640]",
+        "name" : "new_state_1",
+        "type" : "MultiArray"
+      },
+      {
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+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16 2 × 640)",
+        "shortDescription" : "",
+        "shape" : "[2, 640]",
+        "name" : "new_state_2",
+        "type" : "MultiArray"
+      }
+    ],
+    "modelParameters" : [
+
+    ],
+    "specificationVersion" : 8,
+    "mlProgramOperationTypeHistogram" : {
+      "Select" : 1,
+      "Ios17.squeeze" : 7,
+      "Ios17.gather" : 1,
+      "Ios17.cast" : 3,
+      "Ios17.lstm" : 2,
+      "Split" : 2,
+      "Ios17.add" : 1,
+      "Ios17.linear" : 1,
+      "Ios17.greaterEqual" : 1,
+      "Stack" : 2,
+      "Ios17.expandDims" : 3
+    },
+    "computePrecision" : "Mixed (Float16, Int16, Int32)",
+    "isUpdatable" : "0",
+    "stateSchema" : [
+
+    ],
+    "availability" : {
+      "macOS" : "14.0",
+      "tvOS" : "17.0",
+      "visionOS" : "1.0",
+      "watchOS" : "10.0",
+      "iOS" : "17.0",
+      "macCatalyst" : "17.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2026-04-10",
+      "com.github.apple.coremltools.source" : "torch==2.11.0",
+      "com.github.apple.coremltools.version" : "9.0",
+      "com.github.apple.coremltools.source_dialect" : "TorchScript"
+    },
+    "inputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Int32",
+        "formattedType" : "MultiArray (Int32 1)",
+        "shortDescription" : "",
+        "shape" : "[1]",
+        "name" : "decoder_input_ids",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16 2 × 640)",
+        "shortDescription" : "",
+        "shape" : "[2, 640]",
+        "name" : "state_1",
+        "type" : "MultiArray"
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+      {
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+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16 2 × 640)",
+        "shortDescription" : "",
+        "shape" : "[2, 640]",
+        "name" : "state_2",
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+    "method" : "predict"
+  }
+]

nvidia_parakeet-ja_483MB/TextDecoder.mlmodelc/model.mil

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+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3404.16.1"}, {"coremlc-version", "3404.23.1"}, {"coremltools-component-torch", "2.11.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
+{
+    func main<ios17>(tensor<int32, [1]> decoder_input_ids, tensor<fp16, [2, 640]> state_1, tensor<fp16, [2, 640]> state_2) {
+            tensor<int32, []> input_1_batch_dims_0 = const()[name = tensor<string, []>("input_1_batch_dims_0"), val = tensor<int32, []>(0)];
+            tensor<bool, []> input_1_validate_indices_0 = const()[name = tensor<string, []>("input_1_validate_indices_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [3073, 640]> prediction_embed_weight_to_fp16 = const()[name = tensor<string, []>("prediction_embed_weight_to_fp16"), val = tensor<fp16, [3073, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
+            tensor<string, []> decoder_input_ids_to_int16_dtype_0 = const()[name = tensor<string, []>("decoder_input_ids_to_int16_dtype_0"), val = tensor<string, []>("int16")];
+            tensor<string, []> cast_6_dtype_0 = const()[name = tensor<string, []>("cast_6_dtype_0"), val = tensor<string, []>("int32")];
+            tensor<int32, []> greater_equal_0_y_0 = const()[name = tensor<string, []>("greater_equal_0_y_0"), val = tensor<int32, []>(0)];
+            tensor<int16, [1]> decoder_input_ids_to_int16 = cast(dtype = decoder_input_ids_to_int16_dtype_0, x = decoder_input_ids)[name = tensor<string, []>("cast_9")];
+            tensor<int32, [1]> cast_6 = cast(dtype = cast_6_dtype_0, x = decoder_input_ids_to_int16)[name = tensor<string, []>("cast_8")];
+            tensor<bool, [1]> greater_equal_0 = greater_equal(x = cast_6, y = greater_equal_0_y_0)[name = tensor<string, []>("greater_equal_0")];
+            tensor<int32, []> slice_by_index_0 = const()[name = tensor<string, []>("slice_by_index_0"), val = tensor<int32, []>(3073)];
+            tensor<int32, [1]> add_2 = add(x = cast_6, y = slice_by_index_0)[name = tensor<string, []>("add_2")];
+            tensor<int32, [1]> select_0 = select(a = cast_6, b = add_2, cond = greater_equal_0)[name = tensor<string, []>("select_0")];
+            tensor<int32, []> input_1_cast_fp16_cast_uint16_axis_0 = const()[name = tensor<string, []>("input_1_cast_fp16_cast_uint16_axis_0"), val = tensor<int32, []>(0)];
+            tensor<string, []> select_0_to_int16_dtype_0 = const()[name = tensor<string, []>("select_0_to_int16_dtype_0"), val = tensor<string, []>("int16")];
+            tensor<int16, [1]> select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = tensor<string, []>("cast_7")];
+            tensor<fp16, [1, 640]> input_1_cast_fp16_cast_uint16_cast_uint16 = gather(axis = input_1_cast_fp16_cast_uint16_axis_0, batch_dims = input_1_batch_dims_0, indices = select_0_to_int16, validate_indices = input_1_validate_indices_0, x = prediction_embed_weight_to_fp16)[name = tensor<string, []>("input_1_cast_fp16_cast_uint16_cast_uint16")];
+            tensor<int32, [1]> input_3_axes_0 = const()[name = tensor<string, []>("input_3_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1, 640]> input_3_cast_fp16 = expand_dims(axes = input_3_axes_0, x = input_1_cast_fp16_cast_uint16_cast_uint16)[name = tensor<string, []>("input_3_cast_fp16")];
+            tensor<int32, [1]> hx_1_axes_0 = const()[name = tensor<string, []>("hx_1_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [2, 1, 640]> hx_1_cast_fp16 = expand_dims(axes = hx_1_axes_0, x = state_1)[name = tensor<string, []>("hx_1_cast_fp16")];
+            tensor<int32, [1]> hx_axes_0 = const()[name = tensor<string, []>("hx_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [2, 1, 640]> hx_cast_fp16 = expand_dims(axes = hx_axes_0, x = state_2)[name = tensor<string, []>("hx_cast_fp16")];
+            tensor<int32, []> split_0_num_splits_0 = const()[name = tensor<string, []>("split_0_num_splits_0"), val = tensor<int32, []>(2)];
+            tensor<int32, []> split_0_axis_0 = const()[name = tensor<string, []>("split_0_axis_0"), val = tensor<int32, []>(0)];
+            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 = hx_1_cast_fp16)[name = tensor<string, []>("split_0_cast_fp16")];
+            tensor<int32, []> split_1_num_splits_0 = const()[name = tensor<string, []>("split_1_num_splits_0"), val = tensor<int32, []>(2)];
+            tensor<int32, []> split_1_axis_0 = const()[name = tensor<string, []>("split_1_axis_0"), val = tensor<int32, []>(0)];
+            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 = hx_cast_fp16)[name = tensor<string, []>("split_1_cast_fp16")];
+            tensor<int32, [1]> output_lstm_layer_0_lstm_h0_squeeze_axes_0 = const()[name = tensor<string, []>("output_lstm_layer_0_lstm_h0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 640]> output_lstm_layer_0_lstm_h0_squeeze_cast_fp16 = squeeze(axes = output_lstm_layer_0_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_0)[name = tensor<string, []>("output_lstm_layer_0_lstm_h0_squeeze_cast_fp16")];
+            tensor<int32, [1]> output_lstm_layer_0_lstm_c0_squeeze_axes_0 = const()[name = tensor<string, []>("output_lstm_layer_0_lstm_c0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 640]> output_lstm_layer_0_lstm_c0_squeeze_cast_fp16 = squeeze(axes = output_lstm_layer_0_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_0)[name = tensor<string, []>("output_lstm_layer_0_lstm_c0_squeeze_cast_fp16")];
+            tensor<string, []> output_lstm_layer_0_direction_0 = const()[name = tensor<string, []>("output_lstm_layer_0_direction_0"), val = tensor<string, []>("forward")];
+            tensor<bool, []> output_lstm_layer_0_output_sequence_0 = const()[name = tensor<string, []>("output_lstm_layer_0_output_sequence_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> output_lstm_layer_0_recurrent_activation_0 = const()[name = tensor<string, []>("output_lstm_layer_0_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
+            tensor<string, []> output_lstm_layer_0_cell_activation_0 = const()[name = tensor<string, []>("output_lstm_layer_0_cell_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<string, []> output_lstm_layer_0_activation_0 = const()[name = tensor<string, []>("output_lstm_layer_0_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<fp16, [2560, 640]> concat_1_to_fp16 = const()[name = tensor<string, []>("concat_1_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3933568)))];
+            tensor<fp16, [2560, 640]> concat_2_to_fp16 = const()[name = tensor<string, []>("concat_2_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7210432)))];
+            tensor<fp16, [2560]> concat_0_to_fp16 = const()[name = tensor<string, []>("concat_0_to_fp16"), val = tensor<fp16, [2560]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10487296)))];
+            tensor<fp16, [1, 1, 640]> output_lstm_layer_0_cast_fp16_0, tensor<fp16, [1, 640]> output_lstm_layer_0_cast_fp16_1, tensor<fp16, [1, 640]> output_lstm_layer_0_cast_fp16_2 = lstm(activation = output_lstm_layer_0_activation_0, bias = concat_0_to_fp16, cell_activation = output_lstm_layer_0_cell_activation_0, direction = output_lstm_layer_0_direction_0, initial_c = output_lstm_layer_0_lstm_c0_squeeze_cast_fp16, initial_h = output_lstm_layer_0_lstm_h0_squeeze_cast_fp16, output_sequence = output_lstm_layer_0_output_sequence_0, recurrent_activation = output_lstm_layer_0_recurrent_activation_0, weight_hh = concat_2_to_fp16, weight_ih = concat_1_to_fp16, x = input_3_cast_fp16)[name = tensor<string, []>("output_lstm_layer_0_cast_fp16")];
+            tensor<int32, [1]> output_lstm_h0_squeeze_axes_0 = const()[name = tensor<string, []>("output_lstm_h0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 640]> output_lstm_h0_squeeze_cast_fp16 = squeeze(axes = output_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_1)[name = tensor<string, []>("output_lstm_h0_squeeze_cast_fp16")];
+            tensor<int32, [1]> output_lstm_c0_squeeze_axes_0 = const()[name = tensor<string, []>("output_lstm_c0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 640]> output_lstm_c0_squeeze_cast_fp16 = squeeze(axes = output_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_1)[name = tensor<string, []>("output_lstm_c0_squeeze_cast_fp16")];
+            tensor<string, []> output_direction_0 = const()[name = tensor<string, []>("output_direction_0"), val = tensor<string, []>("forward")];
+            tensor<bool, []> output_output_sequence_0 = const()[name = tensor<string, []>("output_output_sequence_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> output_recurrent_activation_0 = const()[name = tensor<string, []>("output_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
+            tensor<string, []> output_cell_activation_0 = const()[name = tensor<string, []>("output_cell_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<string, []> output_activation_0 = const()[name = tensor<string, []>("output_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<fp16, [2560, 640]> concat_4_to_fp16 = const()[name = tensor<string, []>("concat_4_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10492480)))];
+            tensor<fp16, [2560, 640]> concat_5_to_fp16 = const()[name = tensor<string, []>("concat_5_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13769344)))];
+            tensor<fp16, [2560]> concat_3_to_fp16 = const()[name = tensor<string, []>("concat_3_to_fp16"), val = tensor<fp16, [2560]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17046208)))];
+            tensor<fp16, [1, 1, 640]> output_cast_fp16_0, tensor<fp16, [1, 640]> output_cast_fp16_1, tensor<fp16, [1, 640]> output_cast_fp16_2 = lstm(activation = output_activation_0, bias = concat_3_to_fp16, cell_activation = output_cell_activation_0, direction = output_direction_0, initial_c = output_lstm_c0_squeeze_cast_fp16, initial_h = output_lstm_h0_squeeze_cast_fp16, output_sequence = output_output_sequence_0, recurrent_activation = output_recurrent_activation_0, weight_hh = concat_5_to_fp16, weight_ih = concat_4_to_fp16, x = output_lstm_layer_0_cast_fp16_0)[name = tensor<string, []>("output_cast_fp16")];
+            tensor<int32, []> var_32_axis_0 = const()[name = tensor<string, []>("op_32_axis_0"), val = tensor<int32, []>(0)];
+            tensor<fp16, [2, 1, 640]> var_32_cast_fp16 = stack(axis = var_32_axis_0, values = (output_lstm_layer_0_cast_fp16_1, output_cast_fp16_1))[name = tensor<string, []>("op_32_cast_fp16")];
+            tensor<int32, []> var_33_axis_0 = const()[name = tensor<string, []>("op_33_axis_0"), val = tensor<int32, []>(0)];
+            tensor<fp16, [2, 1, 640]> var_33_cast_fp16 = stack(axis = var_33_axis_0, values = (output_lstm_layer_0_cast_fp16_2, output_cast_fp16_2))[name = tensor<string, []>("op_33_cast_fp16")];
+            tensor<int32, [1]> input_axes_0 = const()[name = tensor<string, []>("input_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 640]> input_cast_fp16 = squeeze(axes = input_axes_0, x = output_cast_fp16_0)[name = tensor<string, []>("input_cast_fp16")];
+            tensor<int32, [1]> var_35_axes_0 = const()[name = tensor<string, []>("op_35_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [2, 640]> new_state_1 = squeeze(axes = var_35_axes_0, x = var_32_cast_fp16)[name = tensor<string, []>("op_35_cast_fp16")];
+            tensor<int32, [1]> var_36_axes_0 = const()[name = tensor<string, []>("op_36_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [2, 640]> new_state_2 = squeeze(axes = var_36_axes_0, x = var_33_cast_fp16)[name = tensor<string, []>("op_36_cast_fp16")];
+            tensor<fp16, [640, 640]> joint_projection_weight_to_fp16 = const()[name = tensor<string, []>("joint_projection_weight_to_fp16"), val = tensor<fp16, [640, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17051392)))];
+            tensor<fp16, [640]> joint_projection_bias_to_fp16 = const()[name = tensor<string, []>("joint_projection_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17870656)))];
+            tensor<fp16, [1, 640]> decoder_output_projected = linear(bias = joint_projection_bias_to_fp16, weight = joint_projection_weight_to_fp16, x = input_cast_fp16)[name = tensor<string, []>("linear_0_cast_fp16")];
+        } -> (decoder_output_projected, new_state_1, new_state_2);
+}

nvidia_parakeet-ja_483MB/TextDecoder.mlmodelc/weights/weight.bin

@@ -0,0 +1,3 @@
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