Canary-1b-v2 tokenizer backwards compitability

LICENSE_NOTICE.txt

@@ -1,7 +0,0 @@
-Argmax proprietary and confidential. Under NDA.
-
-Copyright 2025 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.

canary-1b-v2/AudioEncoder.mlmodelc/analytics/coremldata.bin

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canary-1b-v2/AudioEncoder.mlmodelc/coremldata.bin

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canary-1b-v2/AudioEncoder.mlmodelc/metadata.json

@@ -1,7 +1,7 @@
 [
   {
     "metadataOutputVersion" : "3.0",
-    "storagePrecision" : "Mixed (Float16, Palettized (4 bits), Sparse)",
+    "storagePrecision" : "Mixed (Float16, Palettized (6 bits), Sparse)",
     "outputSchema" : [
       {
         "hasShapeFlexibility" : "0",
@@ -81,7 +81,7 @@
         "type" : "MultiArray"
       }
     ],
-    "generatedClassName" : "AudioEncoder_mixedBitPalettized_4_bit_4_bit",
+    "generatedClassName" : "AudioEncoder_mixedBitPalettized_6_bit_6_bit",
     "method" : "predict"
   }
 ]

canary-1b-v2/AudioEncoder.mlmodelc/weights/weight.bin

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canary-1b-v2_474MB/AudioEncoder.mlmodelc/analytics/coremldata.bin

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canary-1b-v2_474MB/AudioEncoder.mlmodelc/coremldata.bin

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canary-1b-v2_474MB/AudioEncoder.mlmodelc/metadata.json

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-[
-  {
-    "metadataOutputVersion" : "3.0",
-    "storagePrecision" : "Mixed (Float16, Palettized (6 bits))",
-    "outputSchema" : [
-      {
-        "hasShapeFlexibility" : "0",
-        "isOptional" : "0",
-        "dataType" : "Float16",
-        "formattedType" : "MultiArray (Float16 1 × 16385 × 1 × 188)",
-        "shortDescription" : "",
-        "shape" : "[1, 16385, 1, 188]",
-        "name" : "ctc_head_raw_output",
-        "type" : "MultiArray"
-      }
-    ],
-    "modelParameters" : [
-
-    ],
-    "specificationVersion" : 8,
-    "mlProgramOperationTypeHistogram" : {
-      "Pad" : 24,
-      "Ios17.mul" : 73,
-      "Split" : 24,
-      "Ios17.transpose" : 1,
-      "Ios17.sub" : 1,
-      "Ios17.matmul" : 72,
-      "Ios17.conv" : 295,
-      "Ios16.constexprLutToDense" : 296,
-      "Ios16.sigmoid" : 24,
-      "Ios17.add" : 168,
-      "Ios17.sliceByIndex" : 48,
-      "Ios17.batchNorm" : 120,
-      "Ios16.relu" : 3,
-      "Ios16.softmax" : 24,
-      "Ios17.reshape" : 193,
-      "Ios17.layerNorm" : 120,
-      "Ios16.silu" : 72
-    },
-    "computePrecision" : "Mixed (Float16, 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" : "2025-11-17",
-      "com.github.apple.coremltools.source" : "torch==2.5.0",
-      "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 × 128)",
-        "shortDescription" : "",
-        "shape" : "[1, 1, 1501, 128]",
-        "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_6_bit",
-    "method" : "predict"
-  }
-]

canary-1b-v2_474MB/AudioEncoder.mlmodelc/weights/weight.bin

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canary-1b-v2_474MB/MelSpectrogram.mlmodelc/analytics/coremldata.bin

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canary-1b-v2_474MB/MelSpectrogram.mlmodelc/coremldata.bin

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canary-1b-v2_474MB/MelSpectrogram.mlmodelc/metadata.json

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-[
-  {
-    "metadataOutputVersion" : "3.0",
-    "storagePrecision" : "Float32",
-    "outputSchema" : [
-      {
-        "hasShapeFlexibility" : "0",
-        "isOptional" : "0",
-        "dataType" : "Float16",
-        "formattedType" : "MultiArray (Float16 1 × 1 × 1501 × 128)",
-        "shortDescription" : "",
-        "shape" : "[1, 1, 1501, 128]",
-        "name" : "melspectrogram_features",
-        "type" : "MultiArray"
-      }
-    ],
-    "modelParameters" : [
-
-    ],
-    "specificationVersion" : 8,
-    "mlProgramOperationTypeHistogram" : {
-      "Identity" : 1,
-      "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.sliceByIndex" : 2,
-      "Ios17.add" : 3,
-      "Ios16.reduceMean" : 2,
-      "Ios17.realDiv" : 1,
-      "Ios17.expandDims" : 4,
-      "Ios17.squeeze" : 2,
-      "Ios17.reshape" : 2,
-      "Ios17.cast" : 2,
-      "Pad" : 2
-    },
-    "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" : "2025-11-17",
-      "com.github.apple.coremltools.source" : "torch==2.5.0",
-      "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"
-  }
-]

canary-1b-v2_474MB/MelSpectrogram.mlmodelc/model.mil

@@ -1,86 +0,0 @@
-program(1.0)
-[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3404.16.1"}, {"coremlc-version", "3404.23.1"}, {"coremltools-component-torch", "2.5.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
-{
-    func main<ios17>(tensor<fp16, [240000]> audio) {
-            tensor<string, []> cast_0_dtype_0 = const()[name = tensor<string, []>("cast_0_dtype_0"), val = tensor<string, []>("fp32")];
-            tensor<fp32, [128, 257]> mel_filters = const()[name = tensor<string, []>("mel_filters"), val = tensor<fp32, [128, 257]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
-            tensor<int32, [1]> var_8_begin_0 = const()[name = tensor<string, []>("op_8_begin_0"), val = tensor<int32, [1]>([1])];
-            tensor<int32, [1]> var_8_end_0 = const()[name = tensor<string, []>("op_8_end_0"), val = tensor<int32, [1]>([240000])];
-            tensor<bool, [1]> var_8_end_mask_0 = const()[name = tensor<string, []>("op_8_end_mask_0"), val = tensor<bool, [1]>([true])];
-            tensor<fp32, [240000]> cast_0 = cast(dtype = cast_0_dtype_0, x = audio)[name = tensor<string, []>("cast_9")];
-            tensor<fp32, [239999]> var_8 = slice_by_index(begin = var_8_begin_0, end = var_8_end_0, end_mask = var_8_end_mask_0, x = cast_0)[name = tensor<string, []>("op_8")];
-            tensor<int32, [1]> var_13_begin_0 = const()[name = tensor<string, []>("op_13_begin_0"), val = tensor<int32, [1]>([0])];
-            tensor<int32, [1]> var_13_end_0 = const()[name = tensor<string, []>("op_13_end_0"), val = tensor<int32, [1]>([239999])];
-            tensor<bool, [1]> var_13_end_mask_0 = const()[name = tensor<string, []>("op_13_end_mask_0"), val = tensor<bool, [1]>([false])];
-            tensor<fp32, [239999]> var_13 = slice_by_index(begin = var_13_begin_0, end = var_13_end_0, end_mask = var_13_end_mask_0, x = cast_0)[name = tensor<string, []>("op_13")];
-            tensor<fp32, []> var_14 = const()[name = tensor<string, []>("op_14"), val = tensor<fp32, []>(0x1.f0a3d8p-1)];
-            tensor<fp32, [239999]> var_15 = mul(x = var_13, y = var_14)[name = tensor<string, []>("op_15")];
-            tensor<fp32, [239999]> input_1 = sub(x = var_8, y = var_15)[name = tensor<string, []>("input_1")];
-            tensor<fp32, []> const_0 = const()[name = tensor<string, []>("const_0"), val = tensor<fp32, []>(0x0p+0)];
-            tensor<int32, [2]> input_3_pad_0 = const()[name = tensor<string, []>("input_3_pad_0"), val = tensor<int32, [2]>([1, 0])];
-            tensor<string, []> input_3_mode_0 = const()[name = tensor<string, []>("input_3_mode_0"), val = tensor<string, []>("constant")];
-            tensor<fp32, [240000]> input_3 = pad(constant_val = const_0, mode = input_3_mode_0, pad = input_3_pad_0, x = input_1)[name = tensor<string, []>("input_3")];
-            tensor<int32, [3]> var_30 = const()[name = tensor<string, []>("op_30"), val = tensor<int32, [3]>([1, 1, 240000])];
-            tensor<fp32, [1, 1, 240000]> input_5 = reshape(shape = var_30, x = input_3)[name = tensor<string, []>("input_5")];
-            tensor<fp32, []> const_2 = const()[name = tensor<string, []>("const_2"), val = tensor<fp32, []>(0x0p+0)];
-            tensor<int32, [6]> input_7_pad_0 = const()[name = tensor<string, []>("input_7_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 256, 256])];
-            tensor<string, []> input_7_mode_0 = const()[name = tensor<string, []>("input_7_mode_0"), val = tensor<string, []>("reflect")];
-            tensor<fp32, [1, 1, 240512]> input_7 = pad(constant_val = const_2, mode = input_7_mode_0, pad = input_7_pad_0, x = input_5)[name = tensor<string, []>("input_7")];
-            tensor<int32, [1]> var_42 = const()[name = tensor<string, []>("op_42"), val = tensor<int32, [1]>([240512])];
-            tensor<fp32, [240512]> input = reshape(shape = var_42, x = input_7)[name = tensor<string, []>("input")];
-            tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = tensor<string, []>("expand_dims_0_axes_0"), val = tensor<int32, [1]>([0])];
-            tensor<fp32, [1, 240512]> expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = input)[name = tensor<string, []>("expand_dims_0")];
-            tensor<fp32, [257, 1, 512]> expand_dims_1 = const()[name = tensor<string, []>("expand_dims_1"), val = tensor<fp32, [257, 1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(131712)))];
-            tensor<fp32, [257, 1, 512]> expand_dims_2 = const()[name = tensor<string, []>("expand_dims_2"), val = tensor<fp32, [257, 1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(658112)))];
-            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<fp32, [1, 1, 240512]> expand_dims_4 = expand_dims(axes = expand_dims_4_axes_0, x = expand_dims_0)[name = tensor<string, []>("expand_dims_4")];
-            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<fp32, [1, 257, 1501]> conv_0 = 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, x = expand_dims_4)[name = tensor<string, []>("conv_0")];
-            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<fp32, [1, 257, 1501]> conv_1 = 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, x = expand_dims_4)[name = tensor<string, []>("conv_1")];
-            tensor<int32, [1]> squeeze_0_axes_0 = const()[name = tensor<string, []>("squeeze_0_axes_0"), val = tensor<int32, [1]>([0])];
-            tensor<fp32, [257, 1501]> squeeze_0 = squeeze(axes = squeeze_0_axes_0, x = conv_0)[name = tensor<string, []>("squeeze_0")];
-            tensor<int32, [1]> squeeze_1_axes_0 = const()[name = tensor<string, []>("squeeze_1_axes_0"), val = tensor<int32, [1]>([0])];
-            tensor<fp32, [257, 1501]> squeeze_1 = squeeze(axes = squeeze_1_axes_0, x = conv_1)[name = tensor<string, []>("squeeze_1")];
-            tensor<fp32, [257, 1501]> square_1 = square(x = squeeze_0)[name = tensor<string, []>("square_1")];
-            tensor<fp32, [257, 1501]> square_2 = square(x = squeeze_1)[name = tensor<string, []>("square_2")];
-            tensor<fp32, [257, 1501]> add_1 = add(x = square_1, y = square_2)[name = tensor<string, []>("add_1")];
-            tensor<fp32, [257, 1501]> magnitudes = identity(x = add_1)[name = tensor<string, []>("magnitudes")];
-            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<fp32, [128, 1501]> mel_spec_1 = matmul(transpose_x = mel_spec_1_transpose_x_0, transpose_y = mel_spec_1_transpose_y_0, x = mel_filters, y = magnitudes)[name = tensor<string, []>("mel_spec_1")];
-            tensor<fp32, []> var_56 = const()[name = tensor<string, []>("op_56"), val = tensor<fp32, []>(0x1p-24)];
-            tensor<fp32, [128, 1501]> mel_spec_3 = add(x = mel_spec_1, y = var_56)[name = tensor<string, []>("mel_spec_3")];
-            tensor<fp32, []> mel_spec_5_epsilon_0 = const()[name = tensor<string, []>("mel_spec_5_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
-            tensor<fp32, [128, 1501]> mel_spec_5 = log(epsilon = mel_spec_5_epsilon_0, x = mel_spec_3)[name = tensor<string, []>("mel_spec_5")];
-            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<fp32, [128, 1]> per_feature_mean = reduce_mean(axes = per_feature_mean_axes_0, keep_dims = per_feature_mean_keep_dims_0, x = mel_spec_5)[name = tensor<string, []>("per_feature_mean")];
-            tensor<fp32, [128, 1501]> sub_0 = sub(x = mel_spec_5, y = per_feature_mean)[name = tensor<string, []>("sub_0")];
-            tensor<fp32, [128, 1501]> square_0 = square(x = sub_0)[name = tensor<string, []>("square_0")];
-            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<fp32, [128, 1]> reduce_mean_1 = reduce_mean(axes = reduce_mean_1_axes_0, keep_dims = reduce_mean_1_keep_dims_0, x = square_0)[name = tensor<string, []>("reduce_mean_1")];
-            tensor<fp32, []> real_div_0 = const()[name = tensor<string, []>("real_div_0"), val = tensor<fp32, []>(0x1.002bbp+0)];
-            tensor<fp32, [128, 1]> mul_0 = mul(x = reduce_mean_1, y = real_div_0)[name = tensor<string, []>("mul_0")];
-            tensor<fp32, [128, 1]> sqrt_0 = sqrt(x = mul_0)[name = tensor<string, []>("sqrt_0")];
-            tensor<fp32, []> var_70 = const()[name = tensor<string, []>("op_70"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
-            tensor<fp32, [128, 1]> per_feature_std = add(x = sqrt_0, y = var_70)[name = tensor<string, []>("per_feature_std")];
-            tensor<fp32, [128, 1501]> mel_spec = real_div(x = sub_0, y = per_feature_std)[name = tensor<string, []>("mel_spec")];
-            tensor<int32, [2]> var_75_perm_0 = const()[name = tensor<string, []>("op_75_perm_0"), val = tensor<int32, [2]>([1, 0])];
-            tensor<int32, [1]> var_77_axes_0 = const()[name = tensor<string, []>("op_77_axes_0"), val = tensor<int32, [1]>([0])];
-            tensor<fp32, [1501, 128]> var_75 = transpose(perm = var_75_perm_0, x = mel_spec)[name = tensor<string, []>("transpose_0")];
-            tensor<fp32, [1, 1501, 128]> var_77 = expand_dims(axes = var_77_axes_0, x = var_75)[name = tensor<string, []>("op_77")];
-            tensor<int32, [1]> var_79_axes_0 = const()[name = tensor<string, []>("op_79_axes_0"), val = tensor<int32, [1]>([1])];
-            tensor<fp32, [1, 1, 1501, 128]> var_79 = expand_dims(axes = var_79_axes_0, x = var_77)[name = tensor<string, []>("op_79")];
-            tensor<string, []> cast_7_dtype_0 = const()[name = tensor<string, []>("cast_7_dtype_0"), val = tensor<string, []>("fp16")];
-            tensor<fp16, [1, 1, 1501, 128]> melspectrogram_features = cast(dtype = cast_7_dtype_0, x = var_79)[name = tensor<string, []>("cast_8")];
-        } -> (melspectrogram_features);
-}

canary-1b-v2_474MB/MelSpectrogram.mlmodelc/weights/weight.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1570ad9beb3077cf61314606d8e4c30fc1d7b971e3bb73ee9444467faa9dd671
-size 1184512

canary-1b-v2_474MB/config.json

@@ -1,38 +0,0 @@
-{
-  "architectures": [
-    "ParakeetForCTC"
-  ],
-  "ctc_loss_reduction": "mean",
-  "ctc_zero_infinity": true,
-  "dtype": "float32",
-  "encoder_config": {
-    "activation_dropout": 0.1,
-    "attention_bias": true,
-    "attention_dropout": 0.1,
-    "conv_kernel_size": 9,
-    "dropout": 0.1,
-    "dropout_positions": 0.0,
-    "hidden_act": "silu",
-    "hidden_size": 1024,
-    "initializer_range": 0.02,
-    "intermediate_size": 4096,
-    "layerdrop": 0.1,
-    "max_position_embeddings": 5000,
-    "model_type": "parakeet_encoder",
-    "num_attention_heads": 8,
-    "num_hidden_layers": 24,
-    "num_key_value_heads": 8,
-    "num_mel_bins": 128,
-    "scale_input": false,
-    "subsampling_conv_channels": 256,
-    "subsampling_conv_kernel_size": 3,
-    "subsampling_conv_stride": 2,
-    "subsampling_factor": 8
-  },
-  "bos_token_id": 4,
-  "eos_token_id": 3,
-  "nemo_model_type": "parakeet",
-  "initializer_range": 0.02,
-  "pad_token_id": 1024,
-  "vocab_size": 16385
-}

canary-1b-v2_474MB/preprocessor_config.json

@@ -1,13 +0,0 @@
-{
-  "feature_extractor_type": "ParakeetFeatureExtractor",
-  "feature_size": 128,
-  "hop_length": 160,
-  "n_fft": 512,
-  "padding_side": "right",
-  "padding_value": 0.0,
-  "preemphasis": 0.97,
-  "processor_class": "ParakeetProcessor",
-  "return_attention_mask": true,
-  "sampling_rate": 16000,
-  "win_length": 400
-}