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Melspectogram.mlmodelc/analytics/coremldata.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6271a1b89644607c3ab203f79b33c86a286c041c75cb9c203332322223a398d3
+size 243

Melspectogram.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:19e161ad38b48aee83ab23e94a639fd2e4fc49aacbed47086c832bcfa65645c9
+size 396

Melspectogram.mlmodelc/model.mil

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+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3405.2.1"}, {"coremlc-version", "3405.2.1"}, {"coremltools-component-torch", "2.4.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "7.2"}})]
+{
+    func main<ios15>(tensor<int32, [1]> audio_length, tensor<fp32, [1, ?]> audio_signal) [FlexibleShapeInformation = tuple<tuple<tensor<string, []>, dict<tensor<string, []>, tensor<int32, [?]>>>, tuple<tensor<string, []>, dict<tensor<string, []>, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"audio_signal", [1, 1]}}), ("RangeDims", {{"audio_signal", [[1, 1], [1, 160000]]}})))] {
+            tensor<int32, []> var_6 = const()[name = tensor<string, []>("op_6"), val = tensor<int32, []>(512)];
+            tensor<int32, [1]> var_7 = add(x = audio_length, y = var_6)[name = tensor<string, []>("op_7")];
+            tensor<int32, []> var_9 = const()[name = tensor<string, []>("op_9"), val = tensor<int32, []>(512)];
+            tensor<int32, [1]> var_10 = sub(x = var_7, y = var_9)[name = tensor<string, []>("op_10")];
+            tensor<int32, []> var_11 = const()[name = tensor<string, []>("op_11"), val = tensor<int32, []>(160)];
+            tensor<int32, [1]> floor_div_0 = floor_div(x = var_10, y = var_11)[name = tensor<string, []>("floor_div_0")];
+            tensor<string, []> var_12_to_fp16_dtype_0 = const()[name = tensor<string, []>("op_12_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<fp16, []> var_14_promoted_to_fp16 = const()[name = tensor<string, []>("op_14_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1]> cast_15 = cast(dtype = var_12_to_fp16_dtype_0, x = floor_div_0)[name = tensor<string, []>("cast_15")];
+            tensor<fp16, [1]> seq_len_1_cast_fp16 = add(x = cast_15, y = var_14_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_28_begin_0 = const()[name = tensor<string, []>("op_28_begin_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [2]> var_28_end_0 = const()[name = tensor<string, []>("op_28_end_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<bool, [2]> var_28_end_mask_0 = const()[name = tensor<string, []>("op_28_end_mask_0"), val = tensor<bool, [2]>([true, false])];
+            tensor<bool, [2]> var_28_squeeze_mask_0 = const()[name = tensor<string, []>("op_28_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, ?]> cast_13 = cast(dtype = audio_signal_to_fp16_dtype_0, x = audio_signal)[name = tensor<string, []>("cast_13")];
+            tensor<fp16, [1]> var_28_cast_fp16 = slice_by_index(begin = var_28_begin_0, end = var_28_end_0, end_mask = var_28_end_mask_0, squeeze_mask = var_28_squeeze_mask_0, x = cast_13)[name = tensor<string, []>("op_28_cast_fp16")];
+            tensor<int32, [1]> var_30_axes_0 = const()[name = tensor<string, []>("op_30_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1]> var_30_cast_fp16 = expand_dims(axes = var_30_axes_0, x = var_28_cast_fp16)[name = tensor<string, []>("op_30_cast_fp16")];
+            tensor<int32, [2]> var_40_begin_0 = const()[name = tensor<string, []>("op_40_begin_0"), val = tensor<int32, [2]>([0, 1])];
+            tensor<int32, [2]> var_40_end_0 = const()[name = tensor<string, []>("op_40_end_0"), val = tensor<int32, [2]>([1, 0])];
+            tensor<bool, [2]> var_40_end_mask_0 = const()[name = tensor<string, []>("op_40_end_mask_0"), val = tensor<bool, [2]>([true, true])];
+            tensor<fp16, [1, ?]> var_40_cast_fp16 = slice_by_index(begin = var_40_begin_0, end = var_40_end_0, end_mask = var_40_end_mask_0, x = cast_13)[name = tensor<string, []>("op_40_cast_fp16")];
+            tensor<int32, [2]> var_50_begin_0 = const()[name = tensor<string, []>("op_50_begin_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [2]> var_50_end_0 = const()[name = tensor<string, []>("op_50_end_0"), val = tensor<int32, [2]>([1, -1])];
+            tensor<bool, [2]> var_50_end_mask_0 = const()[name = tensor<string, []>("op_50_end_mask_0"), val = tensor<bool, [2]>([true, false])];
+            tensor<fp16, [1, ?]> var_50_cast_fp16 = slice_by_index(begin = var_50_begin_0, end = var_50_end_0, end_mask = var_50_end_mask_0, x = cast_13)[name = tensor<string, []>("op_50_cast_fp16")];
+            tensor<fp16, []> var_51_to_fp16 = const()[name = tensor<string, []>("op_51_to_fp16"), val = tensor<fp16, []>(0x1.f0cp-1)];
+            tensor<fp16, [1, ?]> var_52_cast_fp16 = mul(x = var_50_cast_fp16, y = var_51_to_fp16)[name = tensor<string, []>("op_52_cast_fp16")];
+            tensor<fp16, [1, ?]> var_54_cast_fp16 = sub(x = var_40_cast_fp16, y = var_52_cast_fp16)[name = tensor<string, []>("op_54_cast_fp16")];
+            tensor<int32, []> var_56 = const()[name = tensor<string, []>("op_56"), val = tensor<int32, []>(1)];
+            tensor<bool, []> input_1_interleave_0 = const()[name = tensor<string, []>("input_1_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, ?]> input_1_cast_fp16 = concat(axis = var_56, interleave = input_1_interleave_0, values = (var_30_cast_fp16, var_54_cast_fp16))[name = tensor<string, []>("input_1_cast_fp16")];
+            tensor<int32, [3]> concat_0x = const()[name = tensor<string, []>("concat_0x"), val = tensor<int32, [3]>([1, 1, -1])];
+            tensor<fp16, [1, 1, ?]> input_3_cast_fp16 = reshape(shape = concat_0x, 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_0_to_fp16 = const()[name = tensor<string, []>("const_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 1, ?]> input_5_cast_fp16 = pad(constant_val = const_0_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]> concat_1x = const()[name = tensor<string, []>("concat_1x"), val = tensor<int32, [2]>([1, -1])];
+            tensor<fp16, [1, ?]> input_cast_fp16 = reshape(shape = concat_1x, x = input_5_cast_fp16)[name = tensor<string, []>("input_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, ?]> expand_dims_4_cast_fp16 = expand_dims(axes = expand_dims_4_axes_0, x = input_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, ?]> 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, ?]> 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, []> stack_0_axis_0 = const()[name = tensor<string, []>("stack_0_axis_0"), val = tensor<int32, []>(-1)];
+            tensor<fp16, [1, 257, ?, 2]> stack_0_cast_fp16 = stack(axis = stack_0_axis_0, values = (conv_0_cast_fp16, conv_1_cast_fp16))[name = tensor<string, []>("stack_0_cast_fp16")];
+            tensor<fp16, []> var_93_promoted_to_fp16 = const()[name = tensor<string, []>("op_93_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
+            tensor<fp16, [1, 257, ?, 2]> var_94_cast_fp16 = pow(x = stack_0_cast_fp16, y = var_93_promoted_to_fp16)[name = tensor<string, []>("op_94_cast_fp16")];
+            tensor<int32, [1]> var_96 = const()[name = tensor<string, []>("op_96"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, []> var_97 = const()[name = tensor<string, []>("op_97"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 257, ?]> var_99_cast_fp16 = reduce_sum(axes = var_96, keep_dims = var_97, x = var_94_cast_fp16)[name = tensor<string, []>("op_99_cast_fp16")];
+            tensor<fp16, [1, 257, ?]> x_7_cast_fp16 = identity(x = var_99_cast_fp16)[name = tensor<string, []>("x_7_cast_fp16")];
+            tensor<bool, []> x_9_transpose_x_0 = const()[name = tensor<string, []>("x_9_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_9_transpose_y_0 = const()[name = tensor<string, []>("x_9_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 128, 257]> filterbanks_to_fp16 = const()[name = tensor<string, []>("filterbanks_to_fp16"), val = tensor<fp16, [1, 128, 257]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(526528)))];
+            tensor<fp16, [1, 128, ?]> x_9_cast_fp16 = matmul(transpose_x = x_9_transpose_x_0, transpose_y = x_9_transpose_y_0, x = filterbanks_to_fp16, y = x_7_cast_fp16)[name = tensor<string, []>("x_9_cast_fp16")];
+            tensor<fp16, []> var_108_to_fp16 = const()[name = tensor<string, []>("op_108_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
+            tensor<fp16, [1, 128, ?]> var_109_cast_fp16 = add(x = x_9_cast_fp16, y = var_108_to_fp16)[name = tensor<string, []>("op_109_cast_fp16")];
+            tensor<fp16, []> x_11_epsilon_0_to_fp16 = const()[name = tensor<string, []>("x_11_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 128, ?]> x_11_cast_fp16 = log(epsilon = x_11_epsilon_0_to_fp16, x = var_109_cast_fp16)[name = tensor<string, []>("x_11_cast_fp16")];
+            tensor<int32, []> var_114 = const()[name = tensor<string, []>("op_114"), val = tensor<int32, []>(1)];
+            tensor<int32, [3]> var_116_shape_cast_fp16 = shape(x = x_11_cast_fp16)[name = tensor<string, []>("op_116_shape_cast_fp16")];
+            tensor<int32, []> gather_5_indices_0 = const()[name = tensor<string, []>("gather_5_indices_0"), val = tensor<int32, []>(2)];
+            tensor<int32, []> gather_5_axis_0 = const()[name = tensor<string, []>("gather_5_axis_0"), val = tensor<int32, []>(0)];
+            tensor<int32, []> gather_5 = gather(axis = gather_5_axis_0, indices = gather_5_indices_0, x = var_116_shape_cast_fp16)[name = tensor<string, []>("gather_5")];
+            tensor<int32, []> const_1 = const()[name = tensor<string, []>("const_1"), val = tensor<int32, []>(0)];
+            tensor<int32, []> const_2 = const()[name = tensor<string, []>("const_2"), val = tensor<int32, []>(1)];
+            tensor<int32, [?]> var_124 = range_1d(end = gather_5, start = const_1, step = const_2)[name = tensor<string, []>("op_124")];
+            tensor<int32, [1]> var_126_axes_0 = const()[name = tensor<string, []>("op_126_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<int32, [1, ?]> var_126 = expand_dims(axes = var_126_axes_0, x = var_124)[name = tensor<string, []>("op_126")];
+            tensor<int32, [2]> shape_0 = shape(x = var_126)[name = tensor<string, []>("shape_0")];
+            tensor<int32, []> concat_2_axis_0 = const()[name = tensor<string, []>("concat_2_axis_0"), val = tensor<int32, []>(0)];
+            tensor<bool, []> concat_2_interleave_0 = const()[name = tensor<string, []>("concat_2_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [2]> concat_2 = concat(axis = concat_2_axis_0, interleave = concat_2_interleave_0, values = (var_114, gather_5))[name = tensor<string, []>("concat_2")];
+            tensor<int32, [2]> real_div_0 = real_div(x = concat_2, y = shape_0)[name = tensor<string, []>("real_div_0")];
+            tensor<int32, [?, ?]> time_steps = tile(reps = real_div_0, x = var_126)[name = tensor<string, []>("time_steps")];
+            tensor<int32, [1]> var_131_axes_0 = const()[name = tensor<string, []>("op_131_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> melspectogram_length = cast(dtype = seq_len_dtype_0, x = seq_len_1_cast_fp16)[name = tensor<string, []>("cast_14")];
+            tensor<int32, [1, 1]> var_131 = expand_dims(axes = var_131_axes_0, x = melspectogram_length)[name = tensor<string, []>("op_131")];
+            tensor<bool, [?, ?]> valid_mask = less(x = time_steps, y = var_131)[name = tensor<string, []>("valid_mask")];
+            tensor<int32, [1]> var_134_axes_0 = const()[name = tensor<string, []>("op_134_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [?, 1, ?]> var_134 = expand_dims(axes = var_134_axes_0, x = valid_mask)[name = tensor<string, []>("op_134")];
+            tensor<fp16, []> var_135_to_fp16 = const()[name = tensor<string, []>("op_135_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 128, ?]> var_136_cast_fp16 = select(a = x_11_cast_fp16, b = var_135_to_fp16, cond = var_134)[name = tensor<string, []>("op_136_cast_fp16")];
+            tensor<int32, [1]> var_138 = const()[name = tensor<string, []>("op_138"), val = tensor<int32, [1]>([2])];
+            tensor<bool, []> var_139 = const()[name = tensor<string, []>("op_139"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 128]> x_mean_numerator_cast_fp16 = reduce_sum(axes = var_138, keep_dims = var_139, x = var_136_cast_fp16)[name = tensor<string, []>("x_mean_numerator_cast_fp16")];
+            tensor<int32, [1]> var_143 = const()[name = tensor<string, []>("op_143"), val = tensor<int32, [1]>([1])];
+            tensor<bool, []> var_144 = const()[name = tensor<string, []>("op_144"), val = tensor<bool, []>(false)];
+            tensor<string, []> cast_3_to_fp16_dtype_0 = const()[name = tensor<string, []>("cast_3_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<fp16, [?, ?]> cast_12 = cast(dtype = cast_3_to_fp16_dtype_0, x = valid_mask)[name = tensor<string, []>("cast_12")];
+            tensor<fp16, [?]> x_mean_denominator_cast_fp16 = reduce_sum(axes = var_143, keep_dims = var_144, x = cast_12)[name = tensor<string, []>("x_mean_denominator_cast_fp16")];
+            tensor<int32, [1]> var_148_axes_0 = const()[name = tensor<string, []>("op_148_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [?, 1]> var_148_cast_fp16 = expand_dims(axes = var_148_axes_0, x = x_mean_denominator_cast_fp16)[name = tensor<string, []>("op_148_cast_fp16")];
+            tensor<fp16, [?, 128]> x_mean_cast_fp16 = real_div(x = x_mean_numerator_cast_fp16, y = var_148_cast_fp16)[name = tensor<string, []>("x_mean_cast_fp16")];
+            tensor<int32, [1]> var_153_axes_0 = const()[name = tensor<string, []>("op_153_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp16, [?, 128, 1]> var_153_cast_fp16 = expand_dims(axes = var_153_axes_0, x = x_mean_cast_fp16)[name = tensor<string, []>("op_153_cast_fp16")];
+            tensor<fp16, [?, 128, ?]> var_155_cast_fp16 = sub(x = x_11_cast_fp16, y = var_153_cast_fp16)[name = tensor<string, []>("op_155_cast_fp16")];
+            tensor<fp16, []> var_156_to_fp16 = const()[name = tensor<string, []>("op_156_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [?, 128, ?]> var_157_cast_fp16 = select(a = var_155_cast_fp16, b = var_156_to_fp16, cond = var_134)[name = tensor<string, []>("op_157_cast_fp16")];
+            tensor<fp16, []> var_158_promoted_to_fp16 = const()[name = tensor<string, []>("op_158_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
+            tensor<fp16, [?, 128, ?]> var_159_cast_fp16 = pow(x = var_157_cast_fp16, y = var_158_promoted_to_fp16)[name = tensor<string, []>("op_159_cast_fp16")];
+            tensor<int32, [1]> var_161 = const()[name = tensor<string, []>("op_161"), val = tensor<int32, [1]>([2])];
+            tensor<bool, []> var_162 = const()[name = tensor<string, []>("op_162"), val = tensor<bool, []>(false)];
+            tensor<fp16, [?, 128]> var_164_cast_fp16 = reduce_sum(axes = var_161, keep_dims = var_162, x = var_159_cast_fp16)[name = tensor<string, []>("op_164_cast_fp16")];
+            tensor<fp16, []> var_168_to_fp16 = const()[name = tensor<string, []>("op_168_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [?, 1]> var_169_cast_fp16 = sub(x = var_148_cast_fp16, y = var_168_to_fp16)[name = tensor<string, []>("op_169_cast_fp16")];
+            tensor<fp16, [?, 128]> var_170_cast_fp16 = real_div(x = var_164_cast_fp16, y = var_169_cast_fp16)[name = tensor<string, []>("op_170_cast_fp16")];
+            tensor<fp16, [?, 128]> x_std_1_cast_fp16 = sqrt(x = var_170_cast_fp16)[name = tensor<string, []>("x_std_1_cast_fp16")];
+            tensor<fp16, []> var_172_to_fp16 = const()[name = tensor<string, []>("op_172_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
+            tensor<fp16, [?, 128]> x_std_cast_fp16 = add(x = x_std_1_cast_fp16, y = var_172_to_fp16)[name = tensor<string, []>("x_std_cast_fp16")];
+            tensor<int32, [1]> var_180_axes_0 = const()[name = tensor<string, []>("op_180_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp16, [?, 128, 1]> var_180_cast_fp16 = expand_dims(axes = var_180_axes_0, x = x_std_cast_fp16)[name = tensor<string, []>("op_180_cast_fp16")];
+            tensor<fp16, [?, 128, ?]> x_cast_fp16 = real_div(x = var_155_cast_fp16, y = var_180_cast_fp16)[name = tensor<string, []>("x_cast_fp16")];
+            tensor<int32, [3]> var_183_shape_cast_fp16 = shape(x = x_cast_fp16)[name = tensor<string, []>("op_183_shape_cast_fp16")];
+            tensor<int32, []> gather_6_indices_0 = const()[name = tensor<string, []>("gather_6_indices_0"), val = tensor<int32, []>(-1)];
+            tensor<int32, []> gather_6_axis_0 = const()[name = tensor<string, []>("gather_6_axis_0"), val = tensor<int32, []>(0)];
+            tensor<int32, []> gather_6 = gather(axis = gather_6_axis_0, indices = gather_6_indices_0, x = var_183_shape_cast_fp16)[name = tensor<string, []>("gather_6")];
+            tensor<int32, []> const_3 = const()[name = tensor<string, []>("const_3"), val = tensor<int32, []>(0)];
+            tensor<int32, []> const_4 = const()[name = tensor<string, []>("const_4"), val = tensor<int32, []>(1)];
+            tensor<int32, [?]> mask_1 = range_1d(end = gather_6, start = const_3, step = const_4)[name = tensor<string, []>("mask_1")];
+            tensor<int32, []> gather_7_indices_0 = const()[name = tensor<string, []>("gather_7_indices_0"), val = tensor<int32, []>(0)];
+            tensor<int32, []> gather_7_axis_0 = const()[name = tensor<string, []>("gather_7_axis_0"), val = tensor<int32, []>(0)];
+            tensor<int32, []> gather_7 = gather(axis = gather_7_axis_0, indices = gather_7_indices_0, x = var_183_shape_cast_fp16)[name = tensor<string, []>("gather_7")];
+            tensor<int32, []> var_195 = const()[name = tensor<string, []>("op_195"), val = tensor<int32, []>(1)];
+            tensor<int32, []> concat_3_axis_0 = const()[name = tensor<string, []>("concat_3_axis_0"), val = tensor<int32, []>(0)];
+            tensor<bool, []> concat_3_interleave_0 = const()[name = tensor<string, []>("concat_3_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [2]> concat_3 = concat(axis = concat_3_axis_0, interleave = concat_3_interleave_0, values = (gather_7, var_195))[name = tensor<string, []>("concat_3")];
+            tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = tensor<string, []>("expand_dims_0_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<int32, [1, ?]> expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = mask_1)[name = tensor<string, []>("expand_dims_0")];
+            tensor<int32, [?, ?]> var_197 = tile(reps = concat_3, x = expand_dims_0)[name = tensor<string, []>("op_197")];
+            tensor<bool, [?, ?]> mask = greater_equal(x = var_197, y = var_131)[name = tensor<string, []>("mask")];
+            tensor<int32, [1]> var_202_axes_0 = const()[name = tensor<string, []>("op_202_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [?, 1, ?]> var_202 = expand_dims(axes = var_202_axes_0, x = mask)[name = tensor<string, []>("op_202")];
+            tensor<fp16, []> var_216_to_fp16 = const()[name = tensor<string, []>("op_216_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [?, 128, ?]> var_217_cast_fp16 = select(a = var_216_to_fp16, b = x_cast_fp16, cond = var_202)[name = tensor<string, []>("op_217_cast_fp16")];
+            tensor<string, []> var_217_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("op_217_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<fp32, [?, 128, ?]> melspectogram = cast(dtype = var_217_cast_fp16_to_fp32_dtype_0, x = var_217_cast_fp16)[name = tensor<string, []>("cast_11")];
+        } -> (melspectogram, melspectogram_length);
+}

Melspectogram.mlmodelc/weights/weight.bin

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+size 592384

ParakeetDecoder.mlmodelc/analytics/coremldata.bin

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ParakeetDecoder.mlmodelc/coremldata.bin

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ParakeetDecoder.mlmodelc/model.mil

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+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3405.2.1"}, {"coremlc-version", "3405.2.1"}, {"coremltools-component-torch", "2.4.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "7.2"}})]
+{
+    func main<ios15>(tensor<fp32, [2, 1, 640]> c_in, tensor<fp32, [2, 1, 640]> h_in, tensor<int32, [1]> target_lengths, tensor<int32, [1, ?]> targets) [FlexibleShapeInformation = tuple<tuple<tensor<string, []>, dict<tensor<string, []>, tensor<int32, [?]>>>, tuple<tensor<string, []>, dict<tensor<string, []>, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"targets", [1, 1]}}), ("RangeDims", {{"targets", [[1, 1], [1, 1000]]}})))] {
+            tensor<int32, []> input_axis_0 = const()[name = tensor<string, []>("input_axis_0"), val = tensor<int32, []>(0)];
+            tensor<fp16, [1025, 640]> embed_weight_to_fp16 = const()[name = tensor<string, []>("embed_weight_to_fp16"), val = tensor<fp16, [1025, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
+            tensor<fp16, [1, ?, 640]> input_cast_fp16 = gather(axis = input_axis_0, indices = targets, x = embed_weight_to_fp16)[name = tensor<string, []>("input_cast_fp16")];
+            tensor<string, []> input_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("input_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
+            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<string, []> h_in_to_fp16_dtype_0 = const()[name = tensor<string, []>("h_in_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<fp16, [2, 1, 640]> cast_12 = cast(dtype = h_in_to_fp16_dtype_0, x = h_in)[name = tensor<string, []>("cast_12")];
+            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 = cast_12)[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<string, []> c_in_to_fp16_dtype_0 = const()[name = tensor<string, []>("c_in_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<fp16, [2, 1, 640]> cast_11 = cast(dtype = c_in_to_fp16_dtype_0, x = c_in)[name = tensor<string, []>("cast_11")];
+            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 = cast_11)[name = tensor<string, []>("split_1_cast_fp16")];
+            tensor<fp32, [2560]> concat_0 = const()[name = tensor<string, []>("concat_0"), val = tensor<fp32, [2560]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1312128)))];
+            tensor<fp32, [2560, 640]> concat_1 = const()[name = tensor<string, []>("concat_1"), val = tensor<fp32, [2560, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1322432)))];
+            tensor<fp32, [2560, 640]> concat_2 = const()[name = tensor<string, []>("concat_2"), val = tensor<fp32, [2560, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7876096)))];
+            tensor<int32, [1]> var_25_lstm_layer_0_lstm_h0_squeeze_axes_0 = const()[name = tensor<string, []>("op_25_lstm_layer_0_lstm_h0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 640]> var_25_lstm_layer_0_lstm_h0_squeeze_cast_fp16 = squeeze(axes = var_25_lstm_layer_0_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_0)[name = tensor<string, []>("op_25_lstm_layer_0_lstm_h0_squeeze_cast_fp16")];
+            tensor<string, []> var_25_lstm_layer_0_lstm_h0_squeeze_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("op_25_lstm_layer_0_lstm_h0_squeeze_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<int32, [1]> var_25_lstm_layer_0_lstm_c0_squeeze_axes_0 = const()[name = tensor<string, []>("op_25_lstm_layer_0_lstm_c0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 640]> var_25_lstm_layer_0_lstm_c0_squeeze_cast_fp16 = squeeze(axes = var_25_lstm_layer_0_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_0)[name = tensor<string, []>("op_25_lstm_layer_0_lstm_c0_squeeze_cast_fp16")];
+            tensor<string, []> var_25_lstm_layer_0_lstm_c0_squeeze_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("op_25_lstm_layer_0_lstm_c0_squeeze_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<string, []> var_25_lstm_layer_0_direction_0 = const()[name = tensor<string, []>("op_25_lstm_layer_0_direction_0"), val = tensor<string, []>("forward")];
+            tensor<bool, []> var_25_lstm_layer_0_output_sequence_0 = const()[name = tensor<string, []>("op_25_lstm_layer_0_output_sequence_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> var_25_lstm_layer_0_recurrent_activation_0 = const()[name = tensor<string, []>("op_25_lstm_layer_0_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
+            tensor<string, []> var_25_lstm_layer_0_cell_activation_0 = const()[name = tensor<string, []>("op_25_lstm_layer_0_cell_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<string, []> var_25_lstm_layer_0_activation_0 = const()[name = tensor<string, []>("op_25_lstm_layer_0_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<fp32, [1, 640]> cast_9 = cast(dtype = var_25_lstm_layer_0_lstm_c0_squeeze_cast_fp16_to_fp32_dtype_0, x = var_25_lstm_layer_0_lstm_c0_squeeze_cast_fp16)[name = tensor<string, []>("cast_9")];
+            tensor<fp32, [1, 640]> cast_10 = cast(dtype = var_25_lstm_layer_0_lstm_h0_squeeze_cast_fp16_to_fp32_dtype_0, x = var_25_lstm_layer_0_lstm_h0_squeeze_cast_fp16)[name = tensor<string, []>("cast_10")];
+            tensor<fp32, [1, ?, 640]> cast_13 = cast(dtype = input_cast_fp16_to_fp32_dtype_0, x = input_cast_fp16)[name = tensor<string, []>("cast_13")];
+            tensor<fp32, [1, ?, 640]> var_25_lstm_layer_0_0, tensor<fp32, [?, 640]> var_25_lstm_layer_0_1, tensor<fp32, [?, 640]> var_25_lstm_layer_0_2 = lstm(activation = var_25_lstm_layer_0_activation_0, bias = concat_0, cell_activation = var_25_lstm_layer_0_cell_activation_0, direction = var_25_lstm_layer_0_direction_0, initial_c = cast_9, initial_h = cast_10, output_sequence = var_25_lstm_layer_0_output_sequence_0, recurrent_activation = var_25_lstm_layer_0_recurrent_activation_0, weight_hh = concat_2, weight_ih = concat_1, x = cast_13)[name = tensor<string, []>("op_25_lstm_layer_0")];
+            tensor<fp32, [2560]> concat_3 = const()[name = tensor<string, []>("concat_3"), val = tensor<fp32, [2560]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14429760)))];
+            tensor<fp32, [2560, 640]> concat_4 = const()[name = tensor<string, []>("concat_4"), val = tensor<fp32, [2560, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14440064)))];
+            tensor<fp32, [2560, 640]> concat_5 = const()[name = tensor<string, []>("concat_5"), val = tensor<fp32, [2560, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20993728)))];
+            tensor<int32, [1]> var_25_lstm_h0_squeeze_axes_0 = const()[name = tensor<string, []>("op_25_lstm_h0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 640]> var_25_lstm_h0_squeeze_cast_fp16 = squeeze(axes = var_25_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_1)[name = tensor<string, []>("op_25_lstm_h0_squeeze_cast_fp16")];
+            tensor<string, []> var_25_lstm_h0_squeeze_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("op_25_lstm_h0_squeeze_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<int32, [1]> var_25_lstm_c0_squeeze_axes_0 = const()[name = tensor<string, []>("op_25_lstm_c0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 640]> var_25_lstm_c0_squeeze_cast_fp16 = squeeze(axes = var_25_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_1)[name = tensor<string, []>("op_25_lstm_c0_squeeze_cast_fp16")];
+            tensor<string, []> var_25_lstm_c0_squeeze_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("op_25_lstm_c0_squeeze_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<string, []> var_25_direction_0 = const()[name = tensor<string, []>("op_25_direction_0"), val = tensor<string, []>("forward")];
+            tensor<bool, []> var_25_output_sequence_0 = const()[name = tensor<string, []>("op_25_output_sequence_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> var_25_recurrent_activation_0 = const()[name = tensor<string, []>("op_25_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
+            tensor<string, []> var_25_cell_activation_0 = const()[name = tensor<string, []>("op_25_cell_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<string, []> var_25_activation_0 = const()[name = tensor<string, []>("op_25_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<fp32, [1, 640]> cast_7 = cast(dtype = var_25_lstm_c0_squeeze_cast_fp16_to_fp32_dtype_0, x = var_25_lstm_c0_squeeze_cast_fp16)[name = tensor<string, []>("cast_7")];
+            tensor<fp32, [1, 640]> cast_8 = cast(dtype = var_25_lstm_h0_squeeze_cast_fp16_to_fp32_dtype_0, x = var_25_lstm_h0_squeeze_cast_fp16)[name = tensor<string, []>("cast_8")];
+            tensor<fp32, [1, ?, 640]> decoder_output, tensor<fp32, [?, 640]> var_25_1, tensor<fp32, [?, 640]> var_25_2 = lstm(activation = var_25_activation_0, bias = concat_3, cell_activation = var_25_cell_activation_0, direction = var_25_direction_0, initial_c = cast_7, initial_h = cast_8, output_sequence = var_25_output_sequence_0, recurrent_activation = var_25_recurrent_activation_0, weight_hh = concat_5, weight_ih = concat_4, x = var_25_lstm_layer_0_0)[name = tensor<string, []>("op_25")];
+            tensor<int32, []> var_26_axis_0 = const()[name = tensor<string, []>("op_26_axis_0"), val = tensor<int32, []>(0)];
+            tensor<string, []> var_25_lstm_layer_0_1_to_fp16_dtype_0 = const()[name = tensor<string, []>("op_25_lstm_layer_0_1_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<string, []> var_25_1_to_fp16_dtype_0 = const()[name = tensor<string, []>("op_25_1_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<fp16, [?, 640]> cast_5 = cast(dtype = var_25_1_to_fp16_dtype_0, x = var_25_1)[name = tensor<string, []>("cast_5")];
+            tensor<fp16, [?, 640]> cast_6 = cast(dtype = var_25_lstm_layer_0_1_to_fp16_dtype_0, x = var_25_lstm_layer_0_1)[name = tensor<string, []>("cast_6")];
+            tensor<fp16, [2, ?, 640]> var_26_cast_fp16 = stack(axis = var_26_axis_0, values = (cast_6, cast_5))[name = tensor<string, []>("op_26_cast_fp16")];
+            tensor<string, []> var_26_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("op_26_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<int32, []> var_27_axis_0 = const()[name = tensor<string, []>("op_27_axis_0"), val = tensor<int32, []>(0)];
+            tensor<string, []> var_25_lstm_layer_0_2_to_fp16_dtype_0 = const()[name = tensor<string, []>("op_25_lstm_layer_0_2_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<string, []> var_25_2_to_fp16_dtype_0 = const()[name = tensor<string, []>("op_25_2_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<fp16, [?, 640]> cast_2 = cast(dtype = var_25_2_to_fp16_dtype_0, x = var_25_2)[name = tensor<string, []>("cast_2")];
+            tensor<fp16, [?, 640]> cast_3 = cast(dtype = var_25_lstm_layer_0_2_to_fp16_dtype_0, x = var_25_lstm_layer_0_2)[name = tensor<string, []>("cast_3")];
+            tensor<fp16, [2, ?, 640]> var_27_cast_fp16 = stack(axis = var_27_axis_0, values = (cast_3, cast_2))[name = tensor<string, []>("op_27_cast_fp16")];
+            tensor<string, []> var_27_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("op_27_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<fp32, [2, ?, 640]> c_out = cast(dtype = var_27_cast_fp16_to_fp32_dtype_0, x = var_27_cast_fp16)[name = tensor<string, []>("cast_1")];
+            tensor<fp32, [2, ?, 640]> h_out = cast(dtype = var_26_cast_fp16_to_fp32_dtype_0, x = var_26_cast_fp16)[name = tensor<string, []>("cast_4")];
+            tensor<int32, [1]> target_lengths_tmp = identity(x = target_lengths)[name = tensor<string, []>("target_lengths_tmp")];
+        } -> (decoder_output, h_out, c_out);
+}

ParakeetDecoder.mlmodelc/weights/weight.bin

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ParakeetEncoder.mlmodelc/analytics/coremldata.bin

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ParakeetEncoder.mlmodelc/coremldata.bin

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RNNTJoint.mlmodelc/analytics/coremldata.bin

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RNNTJoint.mlmodelc/model.mil

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+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3405.2.1"}, {"coremlc-version", "3405.2.1"}, {"coremltools-component-torch", "2.4.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "7.2"}})]
+{
+    func main<ios15>(tensor<fp32, [?, ?, ?]> decoder_outputs, tensor<fp32, [?, ?, ?]> encoder_outputs) [FlexibleShapeInformation = tuple<tuple<tensor<string, []>, dict<tensor<string, []>, tensor<int32, [?]>>>, tuple<tensor<string, []>, dict<tensor<string, []>, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"decoder_outputs", [1, 1, 1]}, {"encoder_outputs", [1, 1, 1]}}), ("RangeDims", {{"decoder_outputs", [[1, 100], [1, 1025], [1, 640]]}, {"encoder_outputs", [[1, 100], [1, 1025], [1, 1024]]}})))] {
+            tensor<string, []> encoder_outputs_to_fp16_dtype_0 = const()[name = tensor<string, []>("encoder_outputs_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<fp16, [640, 1024]> joint_enc_weight_to_fp16 = const()[name = tensor<string, []>("joint_enc_weight_to_fp16"), val = tensor<fp16, [640, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
+            tensor<fp16, [640]> joint_enc_bias_to_fp16 = const()[name = tensor<string, []>("joint_enc_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1310848)))];
+            tensor<fp16, [?, ?, ?]> cast_2 = cast(dtype = encoder_outputs_to_fp16_dtype_0, x = encoder_outputs)[name = tensor<string, []>("cast_2")];
+            tensor<fp16, [?, ?, 640]> linear_0_cast_fp16 = linear(bias = joint_enc_bias_to_fp16, weight = joint_enc_weight_to_fp16, x = cast_2)[name = tensor<string, []>("linear_0_cast_fp16")];
+            tensor<string, []> decoder_outputs_to_fp16_dtype_0 = const()[name = tensor<string, []>("decoder_outputs_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<fp16, [640, 640]> joint_pred_weight_to_fp16 = const()[name = tensor<string, []>("joint_pred_weight_to_fp16"), val = tensor<fp16, [640, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1312192)))];
+            tensor<fp16, [640]> joint_pred_bias_to_fp16 = const()[name = tensor<string, []>("joint_pred_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2131456)))];
+            tensor<fp16, [?, ?, ?]> cast_1 = cast(dtype = decoder_outputs_to_fp16_dtype_0, x = decoder_outputs)[name = tensor<string, []>("cast_1")];
+            tensor<fp16, [?, ?, 640]> linear_1_cast_fp16 = linear(bias = joint_pred_bias_to_fp16, weight = joint_pred_weight_to_fp16, x = cast_1)[name = tensor<string, []>("linear_1_cast_fp16")];
+            tensor<int32, [1]> f_axes_0 = const()[name = tensor<string, []>("f_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp16, [?, ?, 1, 640]> f_cast_fp16 = expand_dims(axes = f_axes_0, x = linear_0_cast_fp16)[name = tensor<string, []>("f_cast_fp16")];
+            tensor<int32, [1]> g_axes_0 = const()[name = tensor<string, []>("g_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [?, 1, ?, 640]> g_cast_fp16 = expand_dims(axes = g_axes_0, x = linear_1_cast_fp16)[name = tensor<string, []>("g_cast_fp16")];
+            tensor<fp16, [?, ?, ?, 640]> input_1_cast_fp16 = add(x = f_cast_fp16, y = g_cast_fp16)[name = tensor<string, []>("input_1_cast_fp16")];
+            tensor<fp16, [?, ?, ?, 640]> input_3_cast_fp16 = relu(x = input_1_cast_fp16)[name = tensor<string, []>("input_3_cast_fp16")];
+            tensor<fp16, [1030, 640]> joint_joint_net_2_weight_to_fp16 = const()[name = tensor<string, []>("joint_joint_net_2_weight_to_fp16"), val = tensor<fp16, [1030, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2132800)))];
+            tensor<fp16, [1030]> joint_joint_net_2_bias_to_fp16 = const()[name = tensor<string, []>("joint_joint_net_2_bias_to_fp16"), val = tensor<fp16, [1030]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3451264)))];
+            tensor<fp16, [?, ?, ?, 1030]> linear_2_cast_fp16 = linear(bias = joint_joint_net_2_bias_to_fp16, weight = joint_joint_net_2_weight_to_fp16, x = input_3_cast_fp16)[name = tensor<string, []>("linear_2_cast_fp16")];
+            tensor<int32, []> var_29 = const()[name = tensor<string, []>("op_29"), val = tensor<int32, []>(-1)];
+            tensor<fp16, [?, ?, ?, 1030]> var_31_softmax_cast_fp16 = softmax(axis = var_29, x = linear_2_cast_fp16)[name = tensor<string, []>("op_31_softmax_cast_fp16")];
+            tensor<fp16, []> var_31_epsilon_0_to_fp16 = const()[name = tensor<string, []>("op_31_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [?, ?, ?, 1030]> var_31_cast_fp16 = log(epsilon = var_31_epsilon_0_to_fp16, x = var_31_softmax_cast_fp16)[name = tensor<string, []>("op_31_cast_fp16")];
+            tensor<string, []> var_31_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("op_31_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<fp32, [?, ?, ?, 1030]> logits = cast(dtype = var_31_cast_fp16_to_fp32_dtype_0, x = var_31_cast_fp16)[name = tensor<string, []>("cast_0")];
+        } -> (logits);
+}

RNNTJoint.mlmodelc/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:863c0b9d0f23533bf3dc20986b917293000bed662f778976b33e1cb0fb3ee1f3
+size 3453388