Upload 28 files

CtcDecoder.mlmodelc/analytics/coremldata.bin

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CtcDecoder.mlmodelc/coremldata.bin

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CtcDecoder.mlmodelc/metadata.json

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+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "shortDescription" : "Parakeet Japanese CTC Decoder - RAW logits (vocab=3072+1 blank)",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 188 × 3073)",
+        "shortDescription" : "",
+        "shape" : "[1, 188, 3073]",
+        "name" : "ctc_logits",
+        "type" : "MultiArray"
+      }
+    ],
+    "storagePrecision" : "Float16",
+    "modelParameters" : [
+
+    ],
+    "author" : "Fluid Inference",
+    "specificationVersion" : 8,
+    "mlProgramOperationTypeHistogram" : {
+      "Ios17.conv" : 1,
+      "Ios17.transpose" : 1,
+      "Ios17.cast" : 2
+    },
+    "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"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "inputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 1024 × 188)",
+        "shortDescription" : "",
+        "shape" : "[1, 1024, 188]",
+        "name" : "encoder_output",
+        "type" : "MultiArray"
+      }
+    ],
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2026-04-03",
+      "com.github.apple.coremltools.source" : "torch==2.7.0",
+      "com.github.apple.coremltools.version" : "9.0b1",
+      "com.github.apple.coremltools.source_dialect" : "TorchScript"
+    },
+    "generatedClassName" : "CtcDecoder",
+    "method" : "predict"
+  }
+]

CtcDecoder.mlmodelc/model.mil

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+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.7.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0b1"}})]
+{
+    func main<ios17>(tensor<fp32, [1, 1024, 188]> encoder_output) {
+            tensor<string, []> conv_output_pad_type_0 = const()[name = tensor<string, []>("conv_output_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> conv_output_strides_0 = const()[name = tensor<string, []>("conv_output_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> conv_output_pad_0 = const()[name = tensor<string, []>("conv_output_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> conv_output_dilations_0 = const()[name = tensor<string, []>("conv_output_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> conv_output_groups_0 = const()[name = tensor<string, []>("conv_output_groups_0"), val = tensor<int32, []>(1)];
+            tensor<string, []> encoder_output_to_fp16_dtype_0 = const()[name = tensor<string, []>("encoder_output_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<fp16, [3073, 1024, 1]> module_decoder_layers_0_weight_to_fp16 = const()[name = tensor<string, []>("module_decoder_layers_0_weight_to_fp16"), val = tensor<fp16, [3073, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
+            tensor<fp16, [3073]> module_decoder_layers_0_bias_to_fp16 = const()[name = tensor<string, []>("module_decoder_layers_0_bias_to_fp16"), val = tensor<fp16, [3073]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6293632)))];
+            tensor<fp16, [1, 1024, 188]> encoder_output_to_fp16 = cast(dtype = encoder_output_to_fp16_dtype_0, x = encoder_output)[name = tensor<string, []>("cast_1")];
+            tensor<fp16, [1, 3073, 188]> conv_output_cast_fp16 = conv(bias = module_decoder_layers_0_bias_to_fp16, dilations = conv_output_dilations_0, groups = conv_output_groups_0, pad = conv_output_pad_0, pad_type = conv_output_pad_type_0, strides = conv_output_strides_0, weight = module_decoder_layers_0_weight_to_fp16, x = encoder_output_to_fp16)[name = tensor<string, []>("conv_output_cast_fp16")];
+            tensor<int32, [3]> var_18_perm_0 = const()[name = tensor<string, []>("op_18_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<string, []> var_18_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("op_18_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<fp16, [1, 188, 3073]> var_18_cast_fp16 = transpose(perm = var_18_perm_0, x = conv_output_cast_fp16)[name = tensor<string, []>("transpose_0")];
+            tensor<fp32, [1, 188, 3073]> ctc_logits = cast(dtype = var_18_cast_fp16_to_fp32_dtype_0, x = var_18_cast_fp16)[name = tensor<string, []>("cast_0")];
+        } -> (ctc_logits);
+}

CtcDecoder.mlmodelc/weights/weight.bin

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CtcDecoder.mlpackage/Data/com.apple.CoreML/model.mlmodel

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CtcDecoder.mlpackage/Data/com.apple.CoreML/weights/weight.bin

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CtcDecoder.mlpackage/Manifest.json

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+{
+    "fileFormatVersion": "1.0.0",
+    "itemInfoEntries": {
+        "EA6885F7-B154-4BC4-9106-A0CF77D73264": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Weights",
+            "name": "weights",
+            "path": "com.apple.CoreML/weights"
+        },
+        "F5F3FEB7-AEC7-4D42-95AA-D91BEB11027E": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Specification",
+            "name": "model.mlmodel",
+            "path": "com.apple.CoreML/model.mlmodel"
+        }
+    },
+    "rootModelIdentifier": "F5F3FEB7-AEC7-4D42-95AA-D91BEB11027E"
+}

Encoder.mlmodelc/analytics/coremldata.bin

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Encoder.mlmodelc/coremldata.bin

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Encoder.mlmodelc/metadata.json

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+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "shortDescription" : "Parakeet Japanese Encoder (mel -> features, dim=1024)",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
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+        "shape" : "[1]",
+        "name" : "encoder_length",
+        "type" : "MultiArray"
+      }
+    ],
+    "storagePrecision" : "Float16",
+    "modelParameters" : [
+
+    ],
+    "author" : "Fluid Inference",
+    "specificationVersion" : 8,
+    "mlProgramOperationTypeHistogram" : {
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+    "userDefinedMetadata" : {
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+      "com.github.apple.coremltools.source_dialect" : "TorchScript"
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Encoder.mlmodelc/weights/weight.bin

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Encoder.mlpackage/Data/com.apple.CoreML/model.mlmodel

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Encoder.mlpackage/Data/com.apple.CoreML/weights/weight.bin

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Encoder.mlpackage/Manifest.json

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Preprocessor.mlmodelc/analytics/coremldata.bin

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Preprocessor.mlmodelc/coremldata.bin

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Preprocessor.mlmodelc/metadata.json

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+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "shortDescription" : "Parakeet Japanese Preprocessor (audio -> mel, 16000Hz)",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 80 × 1501)",
+        "shortDescription" : "",
+        "shape" : "[1, 80, 1501]",
+        "name" : "mel_features",
+        "type" : "MultiArray"
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+      {
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+        "isOptional" : "0",
+        "dataType" : "Int32",
+        "formattedType" : "MultiArray (Int32 1)",
+        "shortDescription" : "",
+        "shape" : "[1]",
+        "name" : "mel_length",
+        "type" : "MultiArray"
+      }
+    ],
+    "storagePrecision" : "Float16",
+    "modelParameters" : [
+
+    ],
+    "author" : "Fluid Inference",
+    "specificationVersion" : 8,
+    "mlProgramOperationTypeHistogram" : {
+      "Ios17.reshape" : 2,
+      "Identity" : 1,
+      "Ios17.matmul" : 1,
+      "Select" : 3,
+      "Ios17.expandDims" : 8,
+      "Ios17.add" : 4,
+      "Tile" : 1,
+      "Ios17.sliceByIndex" : 3,
+      "Ios16.reduceSum" : 4,
+      "Pad" : 1,
+      "Ios17.log" : 1,
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+      "Ios17.sub" : 4,
+      "Ios17.conv" : 2,
+      "Ios17.pow" : 2,
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+      "Stack" : 1,
+      "Ios17.concat" : 1,
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+        "shortDescription" : "",
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+        "name" : "audio_signal",
+        "type" : "MultiArray"
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Preprocessor.mlmodelc/model.mil

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+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.7.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0b1"}})]
+{
+    func main<ios17>(tensor<fp32, [1, 240000]> audio_signal, tensor<int32, [1]> length) {
+            tensor<int32, []> var_9 = const()[name = tensor<string, []>("op_9"), val = tensor<int32, []>(1)];
+            tensor<int32, []> var_10 = const()[name = tensor<string, []>("op_10"), val = tensor<int32, []>(160)];
+            tensor<int32, []> var_34 = const()[name = tensor<string, []>("op_34"), val = tensor<int32, []>(512)];
+            tensor<int32, [1]> var_35 = add(x = length, y = var_34)[name = tensor<string, []>("op_35")];
+            tensor<int32, []> var_36 = const()[name = tensor<string, []>("op_36"), val = tensor<int32, []>(512)];
+            tensor<int32, [1]> var_37 = sub(x = var_35, y = var_36)[name = tensor<string, []>("op_37")];
+            tensor<int32, [1]> floor_div_0 = floor_div(x = var_37, y = var_10)[name = tensor<string, []>("floor_div_0")];
+            tensor<string, []> var_38_to_fp16_dtype_0 = const()[name = tensor<string, []>("op_38_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<fp16, []> var_39_promoted_to_fp16 = const()[name = tensor<string, []>("op_39_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1]> floor_div_0_to_fp16 = cast(dtype = var_38_to_fp16_dtype_0, x = floor_div_0)[name = tensor<string, []>("cast_15")];
+            tensor<fp16, [1]> seq_len_1_cast_fp16 = add(x = floor_div_0_to_fp16, y = var_39_promoted_to_fp16)[name = tensor<string, []>("seq_len_1_cast_fp16")];
+            tensor<string, []> seq_len_dtype_0 = const()[name = tensor<string, []>("seq_len_dtype_0"), val = tensor<string, []>("int32")];
+            tensor<int32, [2]> var_43_begin_0 = const()[name = tensor<string, []>("op_43_begin_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [2]> var_43_end_0 = const()[name = tensor<string, []>("op_43_end_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<bool, [2]> var_43_end_mask_0 = const()[name = tensor<string, []>("op_43_end_mask_0"), val = tensor<bool, [2]>([true, false])];
+            tensor<bool, [2]> var_43_squeeze_mask_0 = const()[name = tensor<string, []>("op_43_squeeze_mask_0"), val = tensor<bool, [2]>([false, true])];
+            tensor<string, []> audio_signal_to_fp16_dtype_0 = const()[name = tensor<string, []>("audio_signal_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<fp16, [1, 240000]> audio_signal_to_fp16 = cast(dtype = audio_signal_to_fp16_dtype_0, x = audio_signal)[name = tensor<string, []>("cast_13")];
+            tensor<fp16, [1]> var_43_cast_fp16 = slice_by_index(begin = var_43_begin_0, end = var_43_end_0, end_mask = var_43_end_mask_0, squeeze_mask = var_43_squeeze_mask_0, x = audio_signal_to_fp16)[name = tensor<string, []>("op_43_cast_fp16")];
+            tensor<int32, [1]> var_44_axes_0 = const()[name = tensor<string, []>("op_44_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1]> var_44_cast_fp16 = expand_dims(axes = var_44_axes_0, x = var_43_cast_fp16)[name = tensor<string, []>("op_44_cast_fp16")];
+            tensor<int32, [2]> var_46_begin_0 = const()[name = tensor<string, []>("op_46_begin_0"), val = tensor<int32, [2]>([0, 1])];
+            tensor<int32, [2]> var_46_end_0 = const()[name = tensor<string, []>("op_46_end_0"), val = tensor<int32, [2]>([1, 240000])];
+            tensor<bool, [2]> var_46_end_mask_0 = const()[name = tensor<string, []>("op_46_end_mask_0"), val = tensor<bool, [2]>([true, true])];
+            tensor<fp16, [1, 239999]> var_46_cast_fp16 = slice_by_index(begin = var_46_begin_0, end = var_46_end_0, end_mask = var_46_end_mask_0, x = audio_signal_to_fp16)[name = tensor<string, []>("op_46_cast_fp16")];
+            tensor<int32, [2]> var_48_begin_0 = const()[name = tensor<string, []>("op_48_begin_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [2]> var_48_end_0 = const()[name = tensor<string, []>("op_48_end_0"), val = tensor<int32, [2]>([1, 239999])];
+            tensor<bool, [2]> var_48_end_mask_0 = const()[name = tensor<string, []>("op_48_end_mask_0"), val = tensor<bool, [2]>([true, false])];
+            tensor<fp16, [1, 239999]> var_48_cast_fp16 = slice_by_index(begin = var_48_begin_0, end = var_48_end_0, end_mask = var_48_end_mask_0, x = audio_signal_to_fp16)[name = tensor<string, []>("op_48_cast_fp16")];
+            tensor<fp16, []> var_49_to_fp16 = const()[name = tensor<string, []>("op_49_to_fp16"), val = tensor<fp16, []>(0x1.f0cp-1)];
+            tensor<fp16, [1, 239999]> var_50_cast_fp16 = mul(x = var_48_cast_fp16, y = var_49_to_fp16)[name = tensor<string, []>("op_50_cast_fp16")];
+            tensor<fp16, [1, 239999]> var_51_cast_fp16 = sub(x = var_46_cast_fp16, y = var_50_cast_fp16)[name = tensor<string, []>("op_51_cast_fp16")];
+            tensor<bool, []> input_1_interleave_0 = const()[name = tensor<string, []>("input_1_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 240000]> input_1_cast_fp16 = concat(axis = var_9, interleave = input_1_interleave_0, values = (var_44_cast_fp16, var_51_cast_fp16))[name = tensor<string, []>("input_1_cast_fp16")];
+            tensor<int32, [3]> var_57 = const()[name = tensor<string, []>("op_57"), val = tensor<int32, [3]>([1, 1, 240000])];
+            tensor<fp16, [1, 1, 240000]> input_3_cast_fp16 = reshape(shape = var_57, 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_3_to_fp16 = const()[name = tensor<string, []>("const_3_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 1, 240512]> 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 = tensor<string, []>("input_5_cast_fp16")];
+            tensor<int32, [2]> var_63 = const()[name = tensor<string, []>("op_63"), val = tensor<int32, [2]>([1, 240512])];
+            tensor<fp16, [1, 240512]> input_cast_fp16 = reshape(shape = var_63, x = input_5_cast_fp16)[name = tensor<string, []>("input_cast_fp16")];
+            tensor<int32, [1]> expand_dims_5 = const()[name = tensor<string, []>("expand_dims_5"), val = tensor<int32, [1]>([160])];
+            tensor<int32, [1]> expand_dims_6_axes_0 = const()[name = tensor<string, []>("expand_dims_6_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1, 240512]> expand_dims_6_cast_fp16 = expand_dims(axes = expand_dims_6_axes_0, x = input_cast_fp16)[name = tensor<string, []>("expand_dims_6_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_3_to_fp16 = const()[name = tensor<string, []>("expand_dims_3_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_5, weight = expand_dims_3_to_fp16, x = expand_dims_6_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_4_to_fp16 = const()[name = tensor<string, []>("expand_dims_4_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_5, weight = expand_dims_4_to_fp16, x = expand_dims_6_cast_fp16)[name = tensor<string, []>("conv_1_cast_fp16")];
+            tensor<int32, []> stack_0_axis_0 = const()[name = tensor<string, []>("stack_0_axis_0"), val = tensor<int32, []>(-1)];
+            tensor<fp16, [1, 257, 1501, 2]> stack_0_cast_fp16 = stack(axis = stack_0_axis_0, values = (conv_0_cast_fp16, conv_1_cast_fp16))[name = tensor<string, []>("stack_0_cast_fp16")];
+            tensor<fp16, []> var_17_promoted_to_fp16 = const()[name = tensor<string, []>("op_17_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
+            tensor<fp16, [1, 257, 1501, 2]> var_67_cast_fp16 = pow(x = stack_0_cast_fp16, y = var_17_promoted_to_fp16)[name = tensor<string, []>("op_67_cast_fp16")];
+            tensor<int32, [1]> var_69_axes_0 = const()[name = tensor<string, []>("op_69_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, []> var_69_keep_dims_0 = const()[name = tensor<string, []>("op_69_keep_dims_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 257, 1501]> var_69_cast_fp16 = reduce_sum(axes = var_69_axes_0, keep_dims = var_69_keep_dims_0, x = var_67_cast_fp16)[name = tensor<string, []>("op_69_cast_fp16")];
+            tensor<fp16, [1, 257, 1501]> x_9_cast_fp16 = identity(x = var_69_cast_fp16)[name = tensor<string, []>("x_9_cast_fp16")];
+            tensor<bool, []> x_11_transpose_x_0 = const()[name = tensor<string, []>("x_11_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_11_transpose_y_0 = const()[name = tensor<string, []>("x_11_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 80, 257]> const_6_to_fp16 = const()[name = tensor<string, []>("const_6_to_fp16"), val = tensor<fp16, [1, 80, 257]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(526528)))];
+            tensor<fp16, [1, 80, 1501]> x_11_cast_fp16 = matmul(transpose_x = x_11_transpose_x_0, transpose_y = x_11_transpose_y_0, x = const_6_to_fp16, y = x_9_cast_fp16)[name = tensor<string, []>("x_11_cast_fp16")];
+            tensor<fp16, []> var_76_to_fp16 = const()[name = tensor<string, []>("op_76_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
+            tensor<fp16, [1, 80, 1501]> var_77_cast_fp16 = add(x = x_11_cast_fp16, y = var_76_to_fp16)[name = tensor<string, []>("op_77_cast_fp16")];
+            tensor<fp32, []> x_13_epsilon_0 = const()[name = tensor<string, []>("x_13_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
+            tensor<fp16, [1, 80, 1501]> x_13_cast_fp16 = log(epsilon = x_13_epsilon_0, x = var_77_cast_fp16)[name = tensor<string, []>("x_13_cast_fp16")];
+            tensor<int32, [1, 1501]> var_82 = const()[name = tensor<string, []>("op_82"), val = tensor<int32, [1, 1501]>([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 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597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 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997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 1470, 1471, 1472, 1473, 1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1482, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1490, 1491, 1492, 1493, 1494, 1495, 1496, 1497, 1498, 1499, 1500]])];
+            tensor<int32, [1]> var_85_axes_0 = const()[name = tensor<string, []>("op_85_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> mel_length = cast(dtype = seq_len_dtype_0, x = seq_len_1_cast_fp16)[name = tensor<string, []>("cast_14")];
+            tensor<int32, [1, 1]> var_85 = expand_dims(axes = var_85_axes_0, x = mel_length)[name = tensor<string, []>("op_85")];
+            tensor<bool, [1, 1501]> valid_mask = less(x = var_82, y = var_85)[name = tensor<string, []>("valid_mask")];
+            tensor<int32, [1]> var_87_axes_0 = const()[name = tensor<string, []>("op_87_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 1501]> var_87 = expand_dims(axes = var_87_axes_0, x = valid_mask)[name = tensor<string, []>("op_87")];
+            tensor<int32, [3]> var_87_after_broadcast_reps_0 = const()[name = tensor<string, []>("op_87_after_broadcast_reps_0"), val = tensor<int32, [3]>([1, 80, 1])];
+            tensor<bool, [1, 80, 1501]> var_87_after_broadcast = tile(reps = var_87_after_broadcast_reps_0, x = var_87)[name = tensor<string, []>("op_87_after_broadcast")];
+            tensor<fp16, [1, 80, 1501]> var_24_after_broadcast_to_fp16 = const()[name = tensor<string, []>("op_24_after_broadcast_to_fp16"), val = tensor<fp16, [1, 80, 1501]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(567744)))];
+            tensor<fp16, [1, 80, 1501]> var_88_cast_fp16 = select(a = x_13_cast_fp16, b = var_24_after_broadcast_to_fp16, cond = var_87_after_broadcast)[name = tensor<string, []>("op_88_cast_fp16")];
+            tensor<int32, [1]> x_mean_numerator_axes_0 = const()[name = tensor<string, []>("x_mean_numerator_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<bool, []> x_mean_numerator_keep_dims_0 = const()[name = tensor<string, []>("x_mean_numerator_keep_dims_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 80]> x_mean_numerator_cast_fp16 = reduce_sum(axes = x_mean_numerator_axes_0, keep_dims = x_mean_numerator_keep_dims_0, x = var_88_cast_fp16)[name = tensor<string, []>("x_mean_numerator_cast_fp16")];
+            tensor<int32, [1]> x_mean_denominator_axes_0 = const()[name = tensor<string, []>("x_mean_denominator_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, []> x_mean_denominator_keep_dims_0 = const()[name = tensor<string, []>("x_mean_denominator_keep_dims_0"), val = tensor<bool, []>(false)];
+            tensor<string, []> cast_2_to_fp16_dtype_0 = const()[name = tensor<string, []>("cast_2_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<fp16, [1, 1501]> valid_mask_to_fp16 = cast(dtype = cast_2_to_fp16_dtype_0, x = valid_mask)[name = tensor<string, []>("cast_12")];
+            tensor<fp16, [1]> x_mean_denominator_cast_fp16 = reduce_sum(axes = x_mean_denominator_axes_0, keep_dims = x_mean_denominator_keep_dims_0, x = valid_mask_to_fp16)[name = tensor<string, []>("x_mean_denominator_cast_fp16")];
+            tensor<int32, [1]> var_93_axes_0 = const()[name = tensor<string, []>("op_93_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1]> var_93_cast_fp16 = expand_dims(axes = var_93_axes_0, x = x_mean_denominator_cast_fp16)[name = tensor<string, []>("op_93_cast_fp16")];
+            tensor<fp16, [1, 80]> x_mean_cast_fp16 = real_div(x = x_mean_numerator_cast_fp16, y = var_93_cast_fp16)[name = tensor<string, []>("x_mean_cast_fp16")];
+            tensor<int32, [1]> var_96_axes_0 = const()[name = tensor<string, []>("op_96_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp16, [1, 80, 1]> var_96_cast_fp16 = expand_dims(axes = var_96_axes_0, x = x_mean_cast_fp16)[name = tensor<string, []>("op_96_cast_fp16")];
+            tensor<fp16, [1, 80, 1501]> var_97_cast_fp16 = sub(x = x_13_cast_fp16, y = var_96_cast_fp16)[name = tensor<string, []>("op_97_cast_fp16")];
+            tensor<fp16, [1, 80, 1501]> var_98_cast_fp16 = select(a = var_97_cast_fp16, b = var_24_after_broadcast_to_fp16, cond = var_87_after_broadcast)[name = tensor<string, []>("op_98_cast_fp16")];
+            tensor<fp16, []> var_17_promoted_1_to_fp16 = const()[name = tensor<string, []>("op_17_promoted_1_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
+            tensor<fp16, [1, 80, 1501]> var_99_cast_fp16 = pow(x = var_98_cast_fp16, y = var_17_promoted_1_to_fp16)[name = tensor<string, []>("op_99_cast_fp16")];
+            tensor<int32, [1]> var_101_axes_0 = const()[name = tensor<string, []>("op_101_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<bool, []> var_101_keep_dims_0 = const()[name = tensor<string, []>("op_101_keep_dims_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 80]> var_101_cast_fp16 = reduce_sum(axes = var_101_axes_0, keep_dims = var_101_keep_dims_0, x = var_99_cast_fp16)[name = tensor<string, []>("op_101_cast_fp16")];
+            tensor<fp16, []> var_103_to_fp16 = const()[name = tensor<string, []>("op_103_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 1]> var_104_cast_fp16 = sub(x = var_93_cast_fp16, y = var_103_to_fp16)[name = tensor<string, []>("op_104_cast_fp16")];
+            tensor<fp16, [1, 80]> var_105_cast_fp16 = real_div(x = var_101_cast_fp16, y = var_104_cast_fp16)[name = tensor<string, []>("op_105_cast_fp16")];
+            tensor<fp16, [1, 80]> x_std_1_cast_fp16 = sqrt(x = var_105_cast_fp16)[name = tensor<string, []>("x_std_1_cast_fp16")];
+            tensor<fp16, []> var_25_to_fp16 = const()[name = tensor<string, []>("op_25_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
+            tensor<fp16, [1, 80]> x_std_cast_fp16 = add(x = x_std_1_cast_fp16, y = var_25_to_fp16)[name = tensor<string, []>("x_std_cast_fp16")];
+            tensor<int32, [1]> var_110_axes_0 = const()[name = tensor<string, []>("op_110_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp16, [1, 80, 1]> var_110_cast_fp16 = expand_dims(axes = var_110_axes_0, x = x_std_cast_fp16)[name = tensor<string, []>("op_110_cast_fp16")];
+            tensor<fp16, [1, 80, 1501]> x_cast_fp16 = real_div(x = var_97_cast_fp16, y = var_110_cast_fp16)[name = tensor<string, []>("x_cast_fp16")];
+            tensor<bool, [1, 1501]> mask = greater_equal(x = var_82, y = var_85)[name = tensor<string, []>("mask")];
+            tensor<int32, [1]> var_119_axes_0 = const()[name = tensor<string, []>("op_119_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 1501]> var_119 = expand_dims(axes = var_119_axes_0, x = mask)[name = tensor<string, []>("op_119")];
+            tensor<fp16, []> cast_9_to_fp16 = const()[name = tensor<string, []>("cast_9_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 80, 1501]> processed_signal_cast_fp16 = select(a = cast_9_to_fp16, b = x_cast_fp16, cond = var_119)[name = tensor<string, []>("processed_signal_cast_fp16")];
+            tensor<string, []> processed_signal_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("processed_signal_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<fp32, [1, 80, 1501]> mel_features = cast(dtype = processed_signal_cast_fp16_to_fp32_dtype_0, x = processed_signal_cast_fp16)[name = tensor<string, []>("cast_11")];
+        } -> (mel_features, mel_length);
+}

Preprocessor.mlmodelc/weights/weight.bin

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Preprocessor.mlpackage/Data/com.apple.CoreML/model.mlmodel

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Preprocessor.mlpackage/Data/com.apple.CoreML/weights/weight.bin

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Preprocessor.mlpackage/Manifest.json

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+{
+    "fileFormatVersion": "1.0.0",
+    "itemInfoEntries": {
+        "3FB75408-29CA-46B1-91EF-B6112692C650": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Specification",
+            "name": "model.mlmodel",
+            "path": "com.apple.CoreML/model.mlmodel"
+        },
+        "E0D74DDD-D2AD-4D0D-BBEA-13CA019A1977": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Weights",
+            "name": "weights",
+            "path": "com.apple.CoreML/weights"
+        }
+    },
+    "rootModelIdentifier": "3FB75408-29CA-46B1-91EF-B6112692C650"
+}

README.md

@@ -0,0 +1,250 @@
+---
+language:
+- ja
+license: cc-by-4.0
+tags:
+- speech
+- audio
+- automatic-speech-recognition
+- coreml
+- parakeet
+- ctc
+- japanese
+library_name: coreml
+pipeline_tag: automatic-speech-recognition
+---
+
+# Parakeet CTC 0.6B Japanese - CoreML
+
+CoreML conversion of [nvidia/parakeet-tdt_ctc-0.6b-ja](https://huggingface.co/nvidia/parakeet-tdt_ctc-0.6b-ja) for on-device Japanese speech recognition on Apple Silicon.
+
+## Model Description
+
+- **Language**: Japanese (日本語)
+- **Parameters**: 600M (0.6B)
+- **Architecture**: Hybrid FastConformer-TDT-CTC
+- **Vocabulary**: 3,072 Japanese SentencePiece BPE tokens
+- **Sample Rate**: 16 kHz
+- **Max Duration**: 15 seconds per chunk
+- **Platform**: iOS 17+ / macOS 14+ (Apple Silicon recommended)
+- **ANE Utilization**: 100% (0 CPU fallbacks)
+
+## Performance
+
+**Benchmark on FluidInference/fleurs-full (650 Japanese samples)**:
+- **CER**: 10.29% (within expected 10-13% range)
+- **RTFx**: 136.85x (far exceeds real-time)
+- **Avg Latency**: 91.34ms per sample on M-series chips
+
+**Expected CER by Dataset** (from NeMo paper):
+| Dataset | CER |
+|---------|-----|
+| JSUT basic5000 | 6.5% |
+| Mozilla Common Voice 8.0 test | 7.2% |
+| Mozilla Common Voice 16.1 dev | 10.2% |
+| Mozilla Common Voice 16.1 test | 13.3% |
+| TEDxJP-10k | 9.1% |
+
+## Critical Implementation Note: Raw Logits Output
+
+**IMPORTANT**: The CTC decoder outputs **raw logits** (not log-probabilities). You **must** apply `log_softmax` before CTC decoding.
+
+### Why?
+
+During CoreML conversion, we discovered that `log_softmax` failed to convert correctly, producing extreme values (-45440 instead of -67). The solution was to output raw logits and apply `log_softmax` in post-processing.
+
+### Usage Example
+
+```python
+import coremltools as ct
+import numpy as np
+import torch
+
+# Load the three CoreML models
+preprocessor = ct.models.MLModel('Preprocessor.mlpackage')
+encoder = ct.models.MLModel('Encoder.mlpackage')
+ctc_decoder = ct.models.MLModel('CtcDecoder.mlpackage')
+
+# Prepare audio (16kHz, mono, max 15 seconds)
+audio = np.array(audio_samples, dtype=np.float32).reshape(1, -1)
+audio_length = np.array([audio.shape[1]], dtype=np.int32)
+
+# Pad or truncate to 240,000 samples (15 seconds)
+if audio.shape[1] < 240000:
+    audio = np.pad(audio, ((0, 0), (0, 240000 - audio.shape[1])))
+else:
+    audio = audio[:, :240000]
+
+# Step 1: Preprocessor (audio → mel)
+prep_out = preprocessor.predict({
+    'audio_signal': audio,
+    'length': audio_length
+})
+
+# Step 2: Encoder (mel → features)
+enc_out = encoder.predict({
+    'mel_features': prep_out['mel_features'],
+    'mel_length': prep_out['mel_length']
+})
+
+# Step 3: CTC Decoder (features → raw logits)
+ctc_out = ctc_decoder.predict({
+    'encoder_output': enc_out['encoder_output']
+})
+raw_logits = ctc_out['ctc_logits']  # [1, 188, 3073]
+
+# Apply log_softmax (CRITICAL!)
+logits_tensor = torch.from_numpy(raw_logits)
+log_probs = torch.nn.functional.log_softmax(logits_tensor, dim=-1)
+
+# Now use log_probs for CTC decoding
+# Greedy decoding example:
+labels = torch.argmax(log_probs, dim=-1)[0].numpy()  # [188]
+
+# Collapse repeats and remove blanks
+blank_id = 3072
+decoded = []
+prev = None
+for label in labels:
+    if label != blank_id and label != prev:
+        decoded.append(label)
+    prev = label
+
+# Convert to text using vocabulary
+import json
+with open('vocab.json', 'r') as f:
+    vocab = json.load(f)
+tokens = [vocab[i] for i in decoded if i < len(vocab)]
+text = ''.join(tokens).replace('▁', ' ').strip()
+print(text)
+```
+
+## Files Included
+
+### CoreML Models
+
+- **Preprocessor.mlpackage** - Audio → Mel spectrogram
+  - Input: `audio_signal` [1, 240000], `length` [1]
+  - Output: `mel_features` [1, 80, 1501], `mel_length` [1]
+
+- **Encoder.mlpackage** - Mel → Encoder features (FastConformer)
+  - Input: `mel_features` [1, 80, 1501], `mel_length` [1]
+  - Output: `encoder_output` [1, 1024, 188]
+
+- **CtcDecoder.mlpackage** - Features → Raw CTC logits
+  - Input: `encoder_output` [1, 1024, 188]
+  - Output: `ctc_logits` [1, 188, 3073] (RAW logits, not log-softmax!)
+
+**Note**: Chain these three components together for full audio → text transcription (see usage example above).
+
+### Supporting Files
+
+- **vocab.json** - 3,072 Japanese SentencePiece BPE tokens (index → token mapping)
+- **metadata.json** - Model metadata and shapes
+
+## Model Architecture
+
+```
+Audio [1, 240000] @ 16kHz
+  ↓ Preprocessor (STFT, Mel filterbank)
+Mel Spectrogram [1, 80, 1501]
+  ↓ Encoder (FastConformer, 8x downsampling)
+Encoder Features [1, 1024, 188]
+  ↓ CTC Decoder (Conv1d 1024→3073, kernel_size=1)
+Raw Logits [1, 188, 3073]
+  ↓ log_softmax (YOUR CODE - required!)
+Log Probabilities [1, 188, 3073]
+  ↓ CTC Beam Search / Greedy Decoding
+Transcription
+```
+
+## Compilation (Optional but Recommended)
+
+Compile models for faster loading:
+
+```bash
+xcrun coremlcompiler compile Preprocessor.mlpackage .
+xcrun coremlcompiler compile Encoder.mlpackage .
+xcrun coremlcompiler compile CtcDecoder.mlpackage .
+```
+
+This generates `.mlmodelc` directories that load ~20x faster on first run.
+
+## Validation Results
+
+All models validated against original NeMo implementation:
+
+| Component | Max Diff | Relative Error | ANE % |
+|-----------|----------|----------------|-------|
+| Preprocessor | 0.148 | < 0.001% | 100% |
+| Encoder | 0.109 | 1.03e-07% | 100% |
+| CTC Decoder | 0.011 | < 0.001% | 100% |
+| Full Pipeline | 0.482 | 1.44% | 100% |
+
+## System Requirements
+
+- **Minimum**: macOS 14.0 / iOS 17.0
+- **Recommended**: Apple Silicon (M1/M2/M3/M4) for optimal performance
+- **Intel Macs**: Will run on CPU only (slower, higher power consumption)
+
+## Conversion Details
+
+This CoreML conversion includes a critical fix for `log_softmax` conversion failure:
+
+### The Problem
+
+Initial attempts to convert the CTC decoder's `forward()` method (which includes `log_softmax`) produced catastrophically wrong outputs:
+- Expected: `[-67.31, -0.00]`
+- CoreML: `[-45440.00, 0.00]`
+- Max difference: **45,422** ❌
+
+### The Solution
+
+Bypass NeMo's `forward()` method and access only the underlying `decoder_layers` (Conv1d):
+
+```python
+# Instead of:
+log_probs = ctc_decoder(encoder_output)  # Broken in CoreML
+
+# We do:
+raw_logits = ctc_decoder_layers(encoder_output)  # Works perfectly
+log_probs = torch.nn.functional.log_softmax(raw_logits, dim=-1)
+```
+
+This achieves identical results (0.011 max diff) while avoiding the CoreML conversion bug.
+
+## Citation
+
+```bibtex
+@misc{parakeet-ctc-ja-coreml,
+  title={Parakeet CTC 0.6B Japanese - CoreML},
+  author={FluidInference},
+  year={2026},
+  publisher={HuggingFace},
+  howpublished={\url{https://huggingface.co/FluidInference/parakeet-ctc-0.6b-ja-coreml}}
+}
+
+@misc{parakeet2024,
+  title={Parakeet: NVIDIA's Automatic Speech Recognition Toolkit},
+  author={NVIDIA},
+  year={2024},
+  publisher={HuggingFace},
+  howpublished={\url{https://huggingface.co/nvidia/parakeet-tdt_ctc-0.6b-ja}}
+}
+```
+
+## License
+
+CC-BY-4.0 (following the original NVIDIA Parakeet model license)
+
+## Acknowledgments
+
+- Original model by NVIDIA NeMo team
+- Converted to CoreML by FluidInference
+- Benchmarked on FluidInference/fleurs-full dataset
+
+## Links
+
+- **Original Model**: https://huggingface.co/nvidia/parakeet-tdt_ctc-0.6b-ja
+- **Benchmark Dataset**: https://huggingface.co/datasets/FluidInference/fleurs-full
+- **Conversion Repository**: https://github.com/FluidInference/mobius

metadata.json

@@ -0,0 +1,51 @@
+{
+  "model": "parakeet-tdt_ctc-0.6b-ja",
+  "language": "ja (Japanese)",
+  "source": "nvidia/parakeet-tdt_ctc-0.6b-ja",
+  "sample_rate": 16000,
+  "max_audio_seconds": 15.0,
+  "max_samples": 240000,
+  "vocab_size": 3072,
+  "blank_id": 3072,
+  "mel_features": 80,
+  "mel_frames": 1501,
+  "encoder_dim": 1024,
+  "time_steps": 188,
+  "components": {
+    "preprocessor": {
+      "input": [
+        1,
+        240000
+      ],
+      "output": [
+        1,
+        80,
+        1501
+      ]
+    },
+    "encoder": {
+      "input": [
+        1,
+        80,
+        1501
+      ],
+      "output": [
+        1,
+        1024,
+        188
+      ]
+    },
+    "ctc_decoder": {
+      "input": [
+        1,
+        1024,
+        188
+      ],
+      "output": [
+        1,
+        188,
+        3073
+      ]
+    }
+  }
+}

requirements.txt

@@ -0,0 +1,5 @@
+# Requirements for example_usage.py
+coremltools>=8.0
+librosa>=0.10.0
+torch>=2.0.0
+numpy>=1.24.0

vocab.json

@@ -0,0 +1,3074 @@
+[
+  "<unk>",
+  "。",
+  "▁",
+  "の",
+  "が",
+  "を",
+  "に",
+  "は",
+  "、",
+  "で",
+  "と",
+  "も",
+  "い",
+  "か",
+  "る",
+  "な",
+  "し",
+  "って",
+  "り",
+  "から",
+  "ー",
+  "て",
+  "た",
+  "ン",
+  "です",
+  "?",
+  "く",
+  "!",
+  "お",
+  "ス",
+  "して",
+  "ね",
+  "という",
+  "ん",
+  "や",
+  "1",
+  "き",
+  "ない",
+  "人",
+  "つ",
+  "した",
+  "ら",
+  "ます",
+  "だ",
+  "2",
+  "さ",
+  "さん",
+  "ル",
+  "ました",
+  "う",
+  "する",
+  "中",
+  "わ",
+  "え",
+  "あ",
+  "す",
+  "った",
+  "大",
+  "日",
+  "み",
+  "ア",
+  "ま",
+  "見",
+  "イ",
+  "出",
+  "います",
+  "こと",
+  "け",
+  "っ",
+  "よ",
+  "リ",
+  "ク",
+  "ラ",
+  "ト",
+  "れ",
+  "一",
+  "3",
+  "め",
+  "では",
+  "この",
+  "ド",
+  "年",
+  "せ",
+  "ですね",
+  "いい",
+  "行",
+  "方",
+  "など",
+  "事",
+  "分",
+  "上",
+  "手",
+  "者",
+  "気",
+  "カ",
+  "4",
+  "いた",
+  "前",
+  "マ",
+  "国",
+  "目",
+  "時",
+  "ナ",
+  "いて",
+  "日本",
+  "こ",
+  "月",
+  "体",
+  "今",
+  "んで",
+  "これ",
+  "本",
+  "間",
+  "生",
+  "▁この",
+  "ご",
+  "その",
+  "ちょっと",
+  "まで",
+  "でも",
+  "度",
+  "んだ",
+  "5",
+  "ています",
+  "フ",
+  "来",
+  "地",
+  "ジ",
+  "回",
+  "作",
+  "どう",
+  "けど",
+  "とか",
+  "発",
+  "には",
+  "ロ",
+  "コ",
+  "ッ",
+  "レ",
+  "会",
+  "ば",
+  "パ",
+  "しました",
+  "ず",
+  "ある",
+  "子",
+  "タ",
+  "メ",
+  "として",
+  "っていう",
+  "長",
+  "高",
+  "サ",
+  "バ",
+  "そう",
+  "シ",
+  "▁これ",
+  "ウ",
+  "ち",
+  "戦",
+  "物",
+  "話",
+  "じ",
+  "キ",
+  "7",
+  "それ",
+  "プ",
+  "内",
+  "6",
+  "市",
+  "力",
+  "ところ",
+  "なんです",
+  "ミ",
+  "かな",
+  "げ",
+  "よう",
+  "ろ",
+  "ている",
+  "▁それ",
+  "ですか",
+  "▁お",
+  "0",
+  "ット",
+  "ので",
+  "開",
+  "ここ",
+  "ると",
+  "下",
+  "もの",
+  "山",
+  "水",
+  "実",
+  "自",
+  "動",
+  "ハ",
+  "んです",
+  "ック",
+  "こう",
+  "だった",
+  "何",
+  "チ",
+  "しい",
+  "じゃない",
+  "もう",
+  "ため",
+  "オ",
+  "▁そう",
+  "▁その",
+  "ど",
+  "エ",
+  "ません",
+  "ニ",
+  "ム",
+  "がある",
+  "部",
+  "リー",
+  "金",
+  "8",
+  "全",
+  "▁そして",
+  "家",
+  "入",
+  "ブ",
+  "合",
+  "場",
+  "言",
+  "後",
+  "び",
+  "先",
+  "選手",
+  "ビ",
+  "今日",
+  "田",
+  "最",
+  "外",
+  "あります",
+  "新",
+  "通",
+  "▁はい",
+  "▁今",
+  "点",
+  "します",
+  "思います",
+  "している",
+  "スト",
+  "ように",
+  "多",
+  "ちゃん",
+  "グ",
+  "でした",
+  "使",
+  "雨",
+  "ズ",
+  "だけ",
+  "ください",
+  "野",
+  "テ",
+  "▁あ",
+  "いました",
+  "ツ",
+  "当",
+  "になって",
+  "られ",
+  "いる",
+  "車",
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+  "芳",
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+  "砕",
+  "絆",
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+  "瑠",
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+  "柿",
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+  "縛",
+  "嘉",
+  "遮",
+  "炉",
+  "齋",
+  "匂",
+  "辻",
+  "陵",
+  "慰",
+  "陳",
+  "顕",
+  "峡",
+  "晶",
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+  "鷲",
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+  "掌",
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+  "芯",
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+  "括",
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+  "錠",
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+  "冗",
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+  "琉",
+  "醸",
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+  "藻",
+  "揃",
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+  "憎",
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+  "薫",
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+  "暢",
+  "璃",
+  "朱",
+  "乙",
+  "猶",
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+  "譜",
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+  "蔭",
+  "甚",
+  "胴",
+  "宴",
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+  "舘",
+  "痴",
+  "喚",
+  "哀",
+  "傑",
+  "銚",
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+  "煎",
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+  "碑",
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+  "宛",
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+  "轄",
+  "窟",
+  "芦",
+  "俗",
+  "瑞",
+  "痘",
+  "辱",
+  "湘",
+  "姓",
+  "麒",
+  "膳",
+  "槻",
+  "汐",
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+  "腎",
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+  "翠",
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+  "鯛",
+  "堺",
+  "呉",
+  "韻",
+  "鯉",
+  "訂",
+  "渇",
+  "蒲",
+  "苑",
+  "窒",
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+  "弄",
+  "塀",
+  "稜",
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+  "秦",
+  "赴",
+  "渓",
+  "獅",
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+  "栓",
+  "窯",
+  "頬",
+  "茅",
+  "畜",
+  "諏",
+  "岬",
+  "匿",
+  "礁",
+  "髄",
+  "侑",
+  "幽",
+  "昧",
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+  "隈",
+  "雌",
+  "凛",
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+  "磐",
+  "嶺",
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+  "綻",
+  "曹",
+  "ヱ",
+  "侶",
+  "硫",
+  "叔",
+  "蒼",
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+  "郁",
+  "厘",
+  "閲",
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+  "鮭",
+  "鳳",
+  "噛",
+  "崇",
+  "樽",
+  "妬",
+  "幣",
+  "瞳",
+  "勲",
+  "釧",
+  "湊",
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+  "繕",
+  "楓",
+  "堆",
+  "臼",
+  "貌",
+  "憂",
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+  "巾",
+  "臆",
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+  "凸",
+  "薩",
+  "閑",
+  "窪",
+  "妄",
+  "琶",
+  "脊",
+  "唄",
+  "楠",
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+  "蔡",
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+  "椒",
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+  "尺",
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+  "柊",
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+  "吟",
+  "ぢ",
+  "茉",
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+  "學",
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+  "疱",
+  "姜",
+  "瞑",
+  "腱",
+  "惣",
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+  "扶",
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+  "惹",
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+  "薪",
+  "蕎",
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+  "灘",
+  "喧",
+  "煩",
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+  "騨",
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+  "蚕",
+  "勅",
+  "哨",
+  "冥",
+  "瓜",
+  "來",
+  "畔",
+  "鉾",
+  "綺",
+  "腔",
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+  "涌",
+  "逐",
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+  "享",
+  "騙",
+  "廻",
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+  "帥",
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+  "雛",
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+  "睦",
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+  "栞",
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+  "蠣",
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+  "鎧",
+  "怨",
+  "吊",
+  "炙",
+  "牢",
+  "毀",
+  "紬",
+  "賜",
+  "飴",
+  "萬",
+  "桝",
+  "諒",
+  "掴",
+  "零",
+  "凄",
+  "鴻",
+  "兎",
+  "吠",
+  "婿",
+  "縣",
+  "杭",
+  "囃",
+  "椛",
+  "錫",
+  "彌",
+  "餡",
+  "楼",
+  "喋",
+  "爵",
+  "渚",
+  "翁",
+  "楚",
+  "冶",
+  "舛",
+  "卜",
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+  "硝",
+  "鸞",
+  "冴",
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+  "檀",
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+  "釉",
+  "煌",
+  "燻",
+  "燕",
+  "鞍",
+  "巌",
+  "劉",
+  "某",
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+  "鬱",
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+  "竿",
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+  "扮",
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+  "薮",
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+  "蟹",
+  "膀",
+  "砥",
+  "詮",
+  "滉",
+  "牟",
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+  "卯",
+  "鞠",
+  "矯",
+  "宋",
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+  "距",
+  "胱",
+  "淹",
+  "峙",
+  "嘩",
+  "擢",
+  "拷",
+  "娯",
+  "訃",
+  "錮",
+  "誹",
+  "醐",
+  "琵",
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+  "摯",
+  "酎",
+  "踪",
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+  "嚇",
+  "惧",
+  "妥",
+  "践",
+  "娠",
+  "祉",
+  "氾",
+  "批",
+  "ュ",
+  "雰"
+]