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StyleTTS-2-coreml

Text-to-Speech
Core ML
English
styletts2
apple-silicon
libritts
on-device
Model card Files
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alexwengg commited on
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  1. ANE/styletts2_ane_alignment.mlmodelc/coremldata.bin +1 -1
  2. ANE/styletts2_ane_alignment.mlmodelc/metadata.json +2 -2
  3. ANE/styletts2_ane_diffusion_step.mlmodelc/coremldata.bin +1 -1
  4. ANE/styletts2_ane_diffusion_step.mlmodelc/metadata.json +2 -2
  5. ANE/styletts2_ane_noise.mlmodelc/coremldata.bin +1 -1
  6. ANE/styletts2_ane_plbert.mlmodelc/coremldata.bin +1 -1
  7. ANE/styletts2_ane_postbert.mlmodelc/analytics/coremldata.bin +1 -1
  8. ANE/styletts2_ane_postbert.mlmodelc/coremldata.bin +1 -1
  9. ANE/styletts2_ane_postbert.mlmodelc/metadata.json +16 -15
  10. ANE/styletts2_ane_postbert.mlmodelc/model.mil +104 -104
  11. ANE/styletts2_ane_postbert.mlmodelc/weights/weight.bin +2 -2
  12. ANE/styletts2_ane_postbert.mlpackage/Data/com.apple.CoreML/model.mlmodel +2 -2
  13. ANE/styletts2_ane_postbert.mlpackage/Data/com.apple.CoreML/weights/weight.bin +2 -2
  14. ANE/styletts2_ane_postbert.mlpackage/Manifest.json +8 -8
  15. ANE/styletts2_ane_prosody.mlmodelc/coremldata.bin +1 -1
  16. ANE/styletts2_ane_vocoder.mlmodelc/analytics/coremldata.bin +1 -1
  17. ANE/styletts2_ane_vocoder.mlmodelc/coremldata.bin +2 -2
  18. ANE/styletts2_ane_vocoder.mlmodelc/metadata.json +31 -40
  19. ANE/styletts2_ane_vocoder.mlmodelc/model.mil +0 -0
  20. ANE/styletts2_ane_vocoder.mlmodelc/weights/weight.bin +2 -2
  21. ANE/voices/ref_s_1221-135767-0014.bin +3 -0
  22. ANE/voices/ref_s_1789_142896_000022_000005.bin +3 -0
  23. ANE/voices/ref_s_3.bin +3 -0
  24. ANE/voices/ref_s_4.bin +3 -0
  25. ANE/voices/ref_s_4077-13754-0000.bin +3 -0
  26. ANE/voices/ref_s_5.bin +3 -0
  27. ANE/voices/ref_s_5639-40744-0020.bin +3 -0
  28. ANE/voices/ref_s_696_92939_000016_000006.bin +3 -0
  29. ANE/voices/ref_s_908-157963-0027.bin +3 -0
  30. ANE/voices/ref_s_Gavin.bin +3 -0
  31. ANE/voices/ref_s_Nima.bin +3 -0
  32. ANE/voices/ref_s_Vinay.bin +3 -0
  33. ANE/voices/ref_s_Yinghao.bin +3 -0
  34. ANE/voices/ref_s_amused.bin +3 -0
  35. ANE/voices/ref_s_anger.bin +3 -0
  36. ANE/voices/ref_s_disgusted.bin +3 -0
  37. ANE/voices/ref_s_sleepy.bin +3 -0
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ANE/styletts2_ane_postbert.mlmodelc/model.mil CHANGED
@@ -1,69 +1,69 @@
1
  program(1.0)
2
- [buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.11.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})]
3
  {
4
  func main<ios17>(tensor<fp16, [1, ?, 768]> bert_dur, tensor<fp16, [1, 256]> style, tensor<int32, [1, ?]> tokens) [FlexibleShapeInformation = tuple<tuple<tensor<string, []>, dict<tensor<string, []>, tensor<int32, [?]>>>, tuple<tensor<string, []>, dict<tensor<string, []>, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"bert_dur", [1, 64, 768]}, {"tokens", [1, 64]}}), ("RangeDims", {{"bert_dur", [[1, 1], [2, 512], [768, 768]]}, {"tokens", [[1, 1], [2, 512]]}})))] {
5
- tensor<int32, [2]> style_begin_0 = const()[name = tensor<string, []>("style_begin_0"), val = tensor<int32, [2]>([0, 0])];
6
- tensor<int32, [2]> style_end_0 = const()[name = tensor<string, []>("style_end_0"), val = tensor<int32, [2]>([1, 128])];
7
- tensor<bool, [2]> style_end_mask_0 = const()[name = tensor<string, []>("style_end_mask_0"), val = tensor<bool, [2]>([true, false])];
8
  tensor<fp16, [1, 128]> style_cast_fp16 = slice_by_index(begin = style_begin_0, end = style_end_0, end_mask = style_end_mask_0, x = style)[name = tensor<string, []>("style_cast_fp16")];
9
  tensor<fp32, []> var_30 = const()[name = tensor<string, []>("op_30"), val = tensor<fp32, []>(0x1.99999ap-3)];
10
  tensor<int32, []> x_1_axis_0 = const()[name = tensor<string, []>("x_1_axis_0"), val = tensor<int32, []>(0)];
11
  tensor<int32, []> x_1_batch_dims_0 = const()[name = tensor<string, []>("x_1_batch_dims_0"), val = tensor<int32, []>(0)];
12
  tensor<bool, []> x_1_validate_indices_0 = const()[name = tensor<string, []>("x_1_validate_indices_0"), val = tensor<bool, []>(false)];
13
- tensor<fp16, [178, 512]> text_encoder_embedding_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_embedding_weight_to_fp16"), val = tensor<fp16, [178, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
14
  tensor<string, []> tokens_to_int16_dtype_0 = const()[name = tensor<string, []>("tokens_to_int16_dtype_0"), val = tensor<string, []>("int16")];
15
- tensor<int16, [1, ?]> tokens_to_int16 = cast(dtype = tokens_to_int16_dtype_0, x = tokens)[name = tensor<string, []>("cast_14")];
16
- tensor<fp16, [1, ?, 512]> x_1_cast_fp16_cast_uint16 = gather(axis = x_1_axis_0, batch_dims = x_1_batch_dims_0, indices = tokens_to_int16, validate_indices = x_1_validate_indices_0, x = text_encoder_embedding_weight_to_fp16)[name = tensor<string, []>("x_1_cast_fp16_cast_uint16")];
17
  tensor<int32, [3]> input_1_perm_0 = const()[name = tensor<string, []>("input_1_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
18
  tensor<string, []> x_3_pad_type_0 = const()[name = tensor<string, []>("x_3_pad_type_0"), val = tensor<string, []>("custom")];
19
  tensor<int32, [2]> x_3_pad_0 = const()[name = tensor<string, []>("x_3_pad_0"), val = tensor<int32, [2]>([2, 2])];
20
  tensor<int32, [1]> x_3_strides_0 = const()[name = tensor<string, []>("x_3_strides_0"), val = tensor<int32, [1]>([1])];
21
  tensor<int32, [1]> x_3_dilations_0 = const()[name = tensor<string, []>("x_3_dilations_0"), val = tensor<int32, [1]>([1])];
22
  tensor<int32, []> x_3_groups_0 = const()[name = tensor<string, []>("x_3_groups_0"), val = tensor<int32, []>(1)];
23
- tensor<fp16, [512, 512, 5]> weight_3_to_fp16 = const()[name = tensor<string, []>("weight_3_to_fp16"), val = tensor<fp16, [512, 512, 5]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(182400)))];
24
- tensor<fp16, [512]> text_encoder_cnn_0_0_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_cnn_0_0_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2803904)))];
25
- tensor<fp16, [1, 512, ?]> input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = x_1_cast_fp16_cast_uint16)[name = tensor<string, []>("transpose_36")];
26
- tensor<fp16, [1, 512, ?]> x_3_cast_fp16 = conv(bias = text_encoder_cnn_0_0_bias_to_fp16, dilations = x_3_dilations_0, groups = x_3_groups_0, pad = x_3_pad_0, pad_type = x_3_pad_type_0, strides = x_3_strides_0, weight = weight_3_to_fp16, x = input_1_cast_fp16)[name = tensor<string, []>("x_3_cast_fp16")];
27
  tensor<int32, [3]> input_3_perm_0 = const()[name = tensor<string, []>("input_3_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
28
  tensor<int32, [1]> x_5_axes_0 = const()[name = tensor<string, []>("x_5_axes_0"), val = tensor<int32, [1]>([-1])];
29
- tensor<fp16, [512]> text_encoder_cnn_0_1_gamma_to_fp16 = const()[name = tensor<string, []>("text_encoder_cnn_0_1_gamma_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2804992)))];
30
- tensor<fp16, [512]> text_encoder_cnn_0_1_beta_to_fp16 = const()[name = tensor<string, []>("text_encoder_cnn_0_1_beta_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2806080)))];
31
  tensor<fp16, []> var_28_to_fp16 = const()[name = tensor<string, []>("op_28_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
32
- tensor<fp16, [1, ?, 512]> input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = x_3_cast_fp16)[name = tensor<string, []>("transpose_35")];
33
  tensor<fp16, [1, ?, 512]> x_5_cast_fp16 = layer_norm(axes = x_5_axes_0, beta = text_encoder_cnn_0_1_beta_to_fp16, epsilon = var_28_to_fp16, gamma = text_encoder_cnn_0_1_gamma_to_fp16, x = input_3_cast_fp16)[name = tensor<string, []>("x_5_cast_fp16")];
34
  tensor<int32, [3]> input_5_perm_0 = const()[name = tensor<string, []>("input_5_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
35
- tensor<fp16, [1, 512, ?]> input_5_cast_fp16 = transpose(perm = input_5_perm_0, x = x_5_cast_fp16)[name = tensor<string, []>("transpose_34")];
36
  tensor<fp16, [1, 512, ?]> input_7_cast_fp16 = leaky_relu(alpha = var_30, x = input_5_cast_fp16)[name = tensor<string, []>("input_7_cast_fp16")];
37
  tensor<string, []> x_7_pad_type_0 = const()[name = tensor<string, []>("x_7_pad_type_0"), val = tensor<string, []>("custom")];
38
  tensor<int32, [2]> x_7_pad_0 = const()[name = tensor<string, []>("x_7_pad_0"), val = tensor<int32, [2]>([2, 2])];
39
  tensor<int32, [1]> x_7_strides_0 = const()[name = tensor<string, []>("x_7_strides_0"), val = tensor<int32, [1]>([1])];
40
  tensor<int32, [1]> x_7_dilations_0 = const()[name = tensor<string, []>("x_7_dilations_0"), val = tensor<int32, [1]>([1])];
41
  tensor<int32, []> x_7_groups_0 = const()[name = tensor<string, []>("x_7_groups_0"), val = tensor<int32, []>(1)];
42
- tensor<fp16, [512, 512, 5]> weight_7_to_fp16 = const()[name = tensor<string, []>("weight_7_to_fp16"), val = tensor<fp16, [512, 512, 5]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2807168)))];
43
- tensor<fp16, [512]> text_encoder_cnn_1_0_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_cnn_1_0_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5428672)))];
44
- tensor<fp16, [1, 512, ?]> x_7_cast_fp16 = conv(bias = text_encoder_cnn_1_0_bias_to_fp16, dilations = x_7_dilations_0, groups = x_7_groups_0, pad = x_7_pad_0, pad_type = x_7_pad_type_0, strides = x_7_strides_0, weight = weight_7_to_fp16, x = input_7_cast_fp16)[name = tensor<string, []>("x_7_cast_fp16")];
45
  tensor<int32, [3]> input_11_perm_0 = const()[name = tensor<string, []>("input_11_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
46
  tensor<int32, [1]> x_9_axes_0 = const()[name = tensor<string, []>("x_9_axes_0"), val = tensor<int32, [1]>([-1])];
47
- tensor<fp16, [512]> text_encoder_cnn_1_1_gamma_to_fp16 = const()[name = tensor<string, []>("text_encoder_cnn_1_1_gamma_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5429760)))];
48
- tensor<fp16, [512]> text_encoder_cnn_1_1_beta_to_fp16 = const()[name = tensor<string, []>("text_encoder_cnn_1_1_beta_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5430848)))];
49
- tensor<fp16, [1, ?, 512]> input_11_cast_fp16 = transpose(perm = input_11_perm_0, x = x_7_cast_fp16)[name = tensor<string, []>("transpose_33")];
50
  tensor<fp16, [1, ?, 512]> x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, beta = text_encoder_cnn_1_1_beta_to_fp16, epsilon = var_28_to_fp16, gamma = text_encoder_cnn_1_1_gamma_to_fp16, x = input_11_cast_fp16)[name = tensor<string, []>("x_9_cast_fp16")];
51
  tensor<int32, [3]> input_13_perm_0 = const()[name = tensor<string, []>("input_13_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
52
- tensor<fp16, [1, 512, ?]> input_13_cast_fp16 = transpose(perm = input_13_perm_0, x = x_9_cast_fp16)[name = tensor<string, []>("transpose_32")];
53
  tensor<fp16, [1, 512, ?]> input_15_cast_fp16 = leaky_relu(alpha = var_30, x = input_13_cast_fp16)[name = tensor<string, []>("input_15_cast_fp16")];
54
  tensor<string, []> x_11_pad_type_0 = const()[name = tensor<string, []>("x_11_pad_type_0"), val = tensor<string, []>("custom")];
55
  tensor<int32, [2]> x_11_pad_0 = const()[name = tensor<string, []>("x_11_pad_0"), val = tensor<int32, [2]>([2, 2])];
56
  tensor<int32, [1]> x_11_strides_0 = const()[name = tensor<string, []>("x_11_strides_0"), val = tensor<int32, [1]>([1])];
57
  tensor<int32, [1]> x_11_dilations_0 = const()[name = tensor<string, []>("x_11_dilations_0"), val = tensor<int32, [1]>([1])];
58
  tensor<int32, []> x_11_groups_0 = const()[name = tensor<string, []>("x_11_groups_0"), val = tensor<int32, []>(1)];
59
- tensor<fp16, [512, 512, 5]> weight_11_to_fp16 = const()[name = tensor<string, []>("weight_11_to_fp16"), val = tensor<fp16, [512, 512, 5]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5431936)))];
60
- tensor<fp16, [512]> text_encoder_cnn_2_0_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_cnn_2_0_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8053440)))];
61
- tensor<fp16, [1, 512, ?]> x_11_cast_fp16 = conv(bias = text_encoder_cnn_2_0_bias_to_fp16, dilations = x_11_dilations_0, groups = x_11_groups_0, pad = x_11_pad_0, pad_type = x_11_pad_type_0, strides = x_11_strides_0, weight = weight_11_to_fp16, x = input_15_cast_fp16)[name = tensor<string, []>("x_11_cast_fp16")];
62
  tensor<int32, [3]> input_19_perm_0 = const()[name = tensor<string, []>("input_19_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
63
  tensor<int32, [1]> x_13_axes_0 = const()[name = tensor<string, []>("x_13_axes_0"), val = tensor<int32, [1]>([-1])];
64
- tensor<fp16, [512]> text_encoder_cnn_2_1_gamma_to_fp16 = const()[name = tensor<string, []>("text_encoder_cnn_2_1_gamma_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8054528)))];
65
- tensor<fp16, [512]> text_encoder_cnn_2_1_beta_to_fp16 = const()[name = tensor<string, []>("text_encoder_cnn_2_1_beta_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8055616)))];
66
- tensor<fp16, [1, ?, 512]> input_19_cast_fp16 = transpose(perm = input_19_perm_0, x = x_11_cast_fp16)[name = tensor<string, []>("transpose_31")];
67
  tensor<fp16, [1, ?, 512]> x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, beta = text_encoder_cnn_2_1_beta_to_fp16, epsilon = var_28_to_fp16, gamma = text_encoder_cnn_2_1_gamma_to_fp16, x = input_19_cast_fp16)[name = tensor<string, []>("x_13_cast_fp16")];
68
  tensor<fp16, [1, ?, 512]> input_23_cast_fp16 = leaky_relu(alpha = var_30, x = x_13_cast_fp16)[name = tensor<string, []>("input_23_cast_fp16")];
69
  tensor<int32, [3]> input_25_batch_first_transpose_perm_0 = const()[name = tensor<string, []>("input_25_batch_first_transpose_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
@@ -72,25 +72,25 @@ program(1.0)
72
  tensor<string, []> x_17_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("x_17_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
73
  tensor<string, []> x_17_batch_first_cell_activation_0 = const()[name = tensor<string, []>("x_17_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")];
74
  tensor<string, []> x_17_batch_first_activation_0 = const()[name = tensor<string, []>("x_17_batch_first_activation_0"), val = tensor<string, []>("tanh")];
75
- tensor<fp16, [1, 512]> x_17_batch_first_lstm_h0_reshaped_to_fp16 = const()[name = tensor<string, []>("x_17_batch_first_lstm_h0_reshaped_to_fp16"), val = tensor<fp16, [1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8056704)))];
76
- tensor<fp16, [1024, 512]> concat_4_to_fp16 = const()[name = tensor<string, []>("concat_4_to_fp16"), val = tensor<fp16, [1024, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8057792)))];
77
- tensor<fp16, [1024, 256]> concat_5_to_fp16 = const()[name = tensor<string, []>("concat_5_to_fp16"), val = tensor<fp16, [1024, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9106432)))];
78
- tensor<fp16, [1024]> add_1_to_fp16 = const()[name = tensor<string, []>("add_1_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9630784)))];
79
- tensor<fp16, [1024, 512]> concat_6_to_fp16 = const()[name = tensor<string, []>("concat_6_to_fp16"), val = tensor<fp16, [1024, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9632896)))];
80
- tensor<fp16, [1024, 256]> concat_7_to_fp16 = const()[name = tensor<string, []>("concat_7_to_fp16"), val = tensor<fp16, [1024, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10681536)))];
81
- tensor<fp16, [1024]> add_2_to_fp16 = const()[name = tensor<string, []>("add_2_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11205888)))];
82
- tensor<fp16, [?, 1, 512]> input_25_batch_first_transpose_cast_fp16 = transpose(perm = input_25_batch_first_transpose_perm_0, x = input_23_cast_fp16)[name = tensor<string, []>("transpose_30")];
83
- tensor<fp16, [?, 1, 512]> x_17_batch_first_cast_fp16_0, tensor<fp16, [1, 512]> x_17_batch_first_cast_fp16_1, tensor<fp16, [1, 512]> x_17_batch_first_cast_fp16_2 = lstm(activation = x_17_batch_first_activation_0, bias = add_1_to_fp16, bias_back = add_2_to_fp16, cell_activation = x_17_batch_first_cell_activation_0, direction = x_17_batch_first_direction_0, initial_c = x_17_batch_first_lstm_h0_reshaped_to_fp16, initial_h = x_17_batch_first_lstm_h0_reshaped_to_fp16, output_sequence = x_17_batch_first_output_sequence_0, recurrent_activation = x_17_batch_first_recurrent_activation_0, weight_hh = concat_5_to_fp16, weight_hh_back = concat_7_to_fp16, weight_ih = concat_4_to_fp16, weight_ih_back = concat_6_to_fp16, x = input_25_batch_first_transpose_cast_fp16)[name = tensor<string, []>("x_17_batch_first_cast_fp16")];
84
  tensor<int32, [3]> transpose_0_perm_0 = const()[name = tensor<string, []>("transpose_0_perm_0"), val = tensor<int32, [3]>([1, 2, 0])];
85
- tensor<fp16, [512, 768]> bert_encoder_weight_to_fp16 = const()[name = tensor<string, []>("bert_encoder_weight_to_fp16"), val = tensor<fp16, [512, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11208000)))];
86
- tensor<fp16, [512]> bert_encoder_bias_to_fp16 = const()[name = tensor<string, []>("bert_encoder_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11994496)))];
87
- tensor<fp16, [1, ?, 512]> linear_0_cast_fp16 = linear(bias = bert_encoder_bias_to_fp16, weight = bert_encoder_weight_to_fp16, x = bert_dur)[name = tensor<string, []>("linear_0_cast_fp16")];
88
  tensor<int32, [3]> x_19_perm_0 = const()[name = tensor<string, []>("x_19_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
89
  tensor<int32, []> var_147 = const()[name = tensor<string, []>("op_147"), val = tensor<int32, []>(1)];
90
  tensor<int32, [3]> var_163_begin_0 = const()[name = tensor<string, []>("op_163_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
91
  tensor<int32, [3]> var_163_end_0 = const()[name = tensor<string, []>("op_163_end_0"), val = tensor<int32, [3]>([1, 1, 0])];
92
  tensor<bool, [3]> var_163_end_mask_0 = const()[name = tensor<string, []>("op_163_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
93
- tensor<fp16, [1, 512, ?]> x_19_cast_fp16 = transpose(perm = x_19_perm_0, x = linear_0_cast_fp16)[name = tensor<string, []>("transpose_28")];
94
  tensor<fp16, [1, 1, ?]> var_163_cast_fp16 = slice_by_index(begin = var_163_begin_0, end = var_163_end_0, end_mask = var_163_end_mask_0, x = x_19_cast_fp16)[name = tensor<string, []>("op_163_cast_fp16")];
95
  tensor<fp16, [1, 1, ?]> sub_0_cast_fp16 = sub(x = var_163_cast_fp16, y = var_163_cast_fp16)[name = tensor<string, []>("sub_0_cast_fp16")];
96
  tensor<int32, [1]> var_166_axes_0 = const()[name = tensor<string, []>("op_166_axes_0"), val = tensor<int32, [1]>([-1])];
@@ -104,18 +104,18 @@ program(1.0)
104
  tensor<string, []> input_29_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("input_29_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
105
  tensor<string, []> input_29_batch_first_cell_activation_0 = const()[name = tensor<string, []>("input_29_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")];
106
  tensor<string, []> input_29_batch_first_activation_0 = const()[name = tensor<string, []>("input_29_batch_first_activation_0"), val = tensor<string, []>("tanh")];
107
- tensor<fp16, [1024, 640]> concat_14_to_fp16 = const()[name = tensor<string, []>("concat_14_to_fp16"), val = tensor<fp16, [1024, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11995584)))];
108
- tensor<fp16, [1024, 256]> concat_15_to_fp16 = const()[name = tensor<string, []>("concat_15_to_fp16"), val = tensor<fp16, [1024, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13306368)))];
109
- tensor<fp16, [1024]> add_4_to_fp16 = const()[name = tensor<string, []>("add_4_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13830720)))];
110
- tensor<fp16, [1024, 640]> concat_16_to_fp16 = const()[name = tensor<string, []>("concat_16_to_fp16"), val = tensor<fp16, [1024, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13832832)))];
111
- tensor<fp16, [1024, 256]> concat_17_to_fp16 = const()[name = tensor<string, []>("concat_17_to_fp16"), val = tensor<fp16, [1024, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15143616)))];
112
- tensor<fp16, [1024]> add_5_to_fp16 = const()[name = tensor<string, []>("add_5_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15667968)))];
113
- tensor<fp16, [?, 1, 640]> transpose_1_cast_fp16 = transpose(perm = transpose_1_perm_0, x = x_21_cast_fp16)[name = tensor<string, []>("transpose_27")];
114
- tensor<fp16, [?, 1, 512]> input_29_batch_first_cast_fp16_0, tensor<fp16, [1, 512]> input_29_batch_first_cast_fp16_1, tensor<fp16, [1, 512]> input_29_batch_first_cast_fp16_2 = lstm(activation = input_29_batch_first_activation_0, bias = add_4_to_fp16, bias_back = add_5_to_fp16, cell_activation = input_29_batch_first_cell_activation_0, direction = input_29_batch_first_direction_0, initial_c = x_17_batch_first_lstm_h0_reshaped_to_fp16, initial_h = x_17_batch_first_lstm_h0_reshaped_to_fp16, output_sequence = input_29_batch_first_output_sequence_0, recurrent_activation = input_29_batch_first_recurrent_activation_0, weight_hh = concat_15_to_fp16, weight_hh_back = concat_17_to_fp16, weight_ih = concat_14_to_fp16, weight_ih_back = concat_16_to_fp16, x = transpose_1_cast_fp16)[name = tensor<string, []>("input_29_batch_first_cast_fp16")];
115
  tensor<int32, [3]> transpose_7_perm_0 = const()[name = tensor<string, []>("transpose_7_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
116
- tensor<fp16, [1024, 128]> duration_encoder_norms_0_fc_weight_to_fp16 = const()[name = tensor<string, []>("duration_encoder_norms_0_fc_weight_to_fp16"), val = tensor<fp16, [1024, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15670080)))];
117
- tensor<fp16, [1024]> duration_encoder_norms_0_fc_bias_to_fp16 = const()[name = tensor<string, []>("duration_encoder_norms_0_fc_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15932288)))];
118
- tensor<fp16, [1, 1024]> linear_1_cast_fp16 = linear(bias = duration_encoder_norms_0_fc_bias_to_fp16, weight = duration_encoder_norms_0_fc_weight_to_fp16, x = style_cast_fp16)[name = tensor<string, []>("linear_1_cast_fp16")];
119
  tensor<int32, [3]> var_200 = const()[name = tensor<string, []>("op_200"), val = tensor<int32, [3]>([1, 1024, 1])];
120
  tensor<fp16, [1, 1024, 1]> h_3_cast_fp16 = reshape(shape = var_200, x = linear_1_cast_fp16)[name = tensor<string, []>("h_3_cast_fp16")];
121
  tensor<int32, [2]> var_202_split_sizes_0 = const()[name = tensor<string, []>("op_202_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
@@ -125,17 +125,17 @@ program(1.0)
125
  tensor<int32, [3]> beta_3_perm_0 = const()[name = tensor<string, []>("beta_3_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
126
  tensor<int32, [1]> x_31_axes_0 = const()[name = tensor<string, []>("x_31_axes_0"), val = tensor<int32, [1]>([-1])];
127
  tensor<fp16, []> var_136_to_fp16 = const()[name = tensor<string, []>("op_136_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
128
- tensor<fp16, [1, ?, 512]> transpose_7_cast_fp16 = transpose(perm = transpose_7_perm_0, x = input_29_batch_first_cast_fp16_0)[name = tensor<string, []>("transpose_26")];
129
  tensor<fp16, [1, ?, 512]> x_31_cast_fp16 = layer_norm(axes = x_31_axes_0, epsilon = var_136_to_fp16, x = transpose_7_cast_fp16)[name = tensor<string, []>("x_31_cast_fp16")];
130
  tensor<fp16, []> var_208_promoted_to_fp16 = const()[name = tensor<string, []>("op_208_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
131
- tensor<fp16, [1, 1, 512]> gamma_3_cast_fp16 = transpose(perm = gamma_3_perm_0, x = var_202_cast_fp16_0)[name = tensor<string, []>("transpose_25")];
132
  tensor<fp16, [1, 1, 512]> var_209_cast_fp16 = add(x = gamma_3_cast_fp16, y = var_208_promoted_to_fp16)[name = tensor<string, []>("op_209_cast_fp16")];
133
  tensor<fp16, [1, ?, 512]> var_210_cast_fp16 = mul(x = var_209_cast_fp16, y = x_31_cast_fp16)[name = tensor<string, []>("op_210_cast_fp16")];
134
- tensor<fp16, [1, 1, 512]> beta_3_cast_fp16 = transpose(perm = beta_3_perm_0, x = var_202_cast_fp16_1)[name = tensor<string, []>("transpose_24")];
135
  tensor<fp16, [1, ?, 512]> x_33_cast_fp16 = add(x = var_210_cast_fp16, y = beta_3_cast_fp16)[name = tensor<string, []>("x_33_cast_fp16")];
136
  tensor<int32, [3]> x_35_perm_0 = const()[name = tensor<string, []>("x_35_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
137
  tensor<bool, []> x_37_interleave_0 = const()[name = tensor<string, []>("x_37_interleave_0"), val = tensor<bool, []>(false)];
138
- tensor<fp16, [1, 512, ?]> x_35_cast_fp16 = transpose(perm = x_35_perm_0, x = x_33_cast_fp16)[name = tensor<string, []>("transpose_23")];
139
  tensor<fp16, [1, 640, ?]> x_37_cast_fp16 = concat(axis = var_147, interleave = x_37_interleave_0, values = (x_35_cast_fp16, s_chan_cast_fp16))[name = tensor<string, []>("x_37_cast_fp16")];
140
  tensor<int32, [3]> transpose_3_perm_0 = const()[name = tensor<string, []>("transpose_3_perm_0"), val = tensor<int32, [3]>([-1, 0, -2])];
141
  tensor<string, []> input_35_batch_first_direction_0 = const()[name = tensor<string, []>("input_35_batch_first_direction_0"), val = tensor<string, []>("bidirectional")];
@@ -143,18 +143,18 @@ program(1.0)
143
  tensor<string, []> input_35_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("input_35_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
144
  tensor<string, []> input_35_batch_first_cell_activation_0 = const()[name = tensor<string, []>("input_35_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")];
145
  tensor<string, []> input_35_batch_first_activation_0 = const()[name = tensor<string, []>("input_35_batch_first_activation_0"), val = tensor<string, []>("tanh")];
146
- tensor<fp16, [1024, 640]> concat_24_to_fp16 = const()[name = tensor<string, []>("concat_24_to_fp16"), val = tensor<fp16, [1024, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15934400)))];
147
- tensor<fp16, [1024, 256]> concat_25_to_fp16 = const()[name = tensor<string, []>("concat_25_to_fp16"), val = tensor<fp16, [1024, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17245184)))];
148
- tensor<fp16, [1024]> add_7_to_fp16 = const()[name = tensor<string, []>("add_7_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17769536)))];
149
- tensor<fp16, [1024, 640]> concat_26_to_fp16 = const()[name = tensor<string, []>("concat_26_to_fp16"), val = tensor<fp16, [1024, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17771648)))];
150
- tensor<fp16, [1024, 256]> concat_27_to_fp16 = const()[name = tensor<string, []>("concat_27_to_fp16"), val = tensor<fp16, [1024, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19082432)))];
151
- tensor<fp16, [1024]> add_8_to_fp16 = const()[name = tensor<string, []>("add_8_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19606784)))];
152
- tensor<fp16, [?, 1, 640]> transpose_3_cast_fp16 = transpose(perm = transpose_3_perm_0, x = x_37_cast_fp16)[name = tensor<string, []>("transpose_22")];
153
- tensor<fp16, [?, 1, 512]> input_35_batch_first_cast_fp16_0, tensor<fp16, [1, 512]> input_35_batch_first_cast_fp16_1, tensor<fp16, [1, 512]> input_35_batch_first_cast_fp16_2 = lstm(activation = input_35_batch_first_activation_0, bias = add_7_to_fp16, bias_back = add_8_to_fp16, cell_activation = input_35_batch_first_cell_activation_0, direction = input_35_batch_first_direction_0, initial_c = x_17_batch_first_lstm_h0_reshaped_to_fp16, initial_h = x_17_batch_first_lstm_h0_reshaped_to_fp16, output_sequence = input_35_batch_first_output_sequence_0, recurrent_activation = input_35_batch_first_recurrent_activation_0, weight_hh = concat_25_to_fp16, weight_hh_back = concat_27_to_fp16, weight_ih = concat_24_to_fp16, weight_ih_back = concat_26_to_fp16, x = transpose_3_cast_fp16)[name = tensor<string, []>("input_35_batch_first_cast_fp16")];
154
  tensor<int32, [3]> transpose_8_perm_0 = const()[name = tensor<string, []>("transpose_8_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
155
- tensor<fp16, [1024, 128]> duration_encoder_norms_1_fc_weight_to_fp16 = const()[name = tensor<string, []>("duration_encoder_norms_1_fc_weight_to_fp16"), val = tensor<fp16, [1024, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19608896)))];
156
- tensor<fp16, [1024]> duration_encoder_norms_1_fc_bias_to_fp16 = const()[name = tensor<string, []>("duration_encoder_norms_1_fc_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19871104)))];
157
- tensor<fp16, [1, 1024]> linear_2_cast_fp16 = linear(bias = duration_encoder_norms_1_fc_bias_to_fp16, weight = duration_encoder_norms_1_fc_weight_to_fp16, x = style_cast_fp16)[name = tensor<string, []>("linear_2_cast_fp16")];
158
  tensor<int32, [3]> var_247 = const()[name = tensor<string, []>("op_247"), val = tensor<int32, [3]>([1, 1024, 1])];
159
  tensor<fp16, [1, 1024, 1]> h_7_cast_fp16 = reshape(shape = var_247, x = linear_2_cast_fp16)[name = tensor<string, []>("h_7_cast_fp16")];
160
  tensor<int32, [2]> var_249_split_sizes_0 = const()[name = tensor<string, []>("op_249_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
@@ -163,17 +163,17 @@ program(1.0)
163
  tensor<int32, [3]> gamma_7_perm_0 = const()[name = tensor<string, []>("gamma_7_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
164
  tensor<int32, [3]> beta_7_perm_0 = const()[name = tensor<string, []>("beta_7_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
165
  tensor<int32, [1]> x_47_axes_0 = const()[name = tensor<string, []>("x_47_axes_0"), val = tensor<int32, [1]>([-1])];
166
- tensor<fp16, [1, ?, 512]> transpose_8_cast_fp16 = transpose(perm = transpose_8_perm_0, x = input_35_batch_first_cast_fp16_0)[name = tensor<string, []>("transpose_21")];
167
  tensor<fp16, [1, ?, 512]> x_47_cast_fp16 = layer_norm(axes = x_47_axes_0, epsilon = var_136_to_fp16, x = transpose_8_cast_fp16)[name = tensor<string, []>("x_47_cast_fp16")];
168
  tensor<fp16, []> var_255_promoted_to_fp16 = const()[name = tensor<string, []>("op_255_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
169
- tensor<fp16, [1, 1, 512]> gamma_7_cast_fp16 = transpose(perm = gamma_7_perm_0, x = var_249_cast_fp16_0)[name = tensor<string, []>("transpose_20")];
170
  tensor<fp16, [1, 1, 512]> var_256_cast_fp16 = add(x = gamma_7_cast_fp16, y = var_255_promoted_to_fp16)[name = tensor<string, []>("op_256_cast_fp16")];
171
  tensor<fp16, [1, ?, 512]> var_257_cast_fp16 = mul(x = var_256_cast_fp16, y = x_47_cast_fp16)[name = tensor<string, []>("op_257_cast_fp16")];
172
- tensor<fp16, [1, 1, 512]> beta_7_cast_fp16 = transpose(perm = beta_7_perm_0, x = var_249_cast_fp16_1)[name = tensor<string, []>("transpose_19")];
173
  tensor<fp16, [1, ?, 512]> x_49_cast_fp16 = add(x = var_257_cast_fp16, y = beta_7_cast_fp16)[name = tensor<string, []>("x_49_cast_fp16")];
174
  tensor<int32, [3]> x_51_perm_0 = const()[name = tensor<string, []>("x_51_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
175
  tensor<bool, []> x_53_interleave_0 = const()[name = tensor<string, []>("x_53_interleave_0"), val = tensor<bool, []>(false)];
176
- tensor<fp16, [1, 512, ?]> x_51_cast_fp16 = transpose(perm = x_51_perm_0, x = x_49_cast_fp16)[name = tensor<string, []>("transpose_18")];
177
  tensor<fp16, [1, 640, ?]> x_53_cast_fp16 = concat(axis = var_147, interleave = x_53_interleave_0, values = (x_51_cast_fp16, s_chan_cast_fp16))[name = tensor<string, []>("x_53_cast_fp16")];
178
  tensor<int32, [3]> transpose_5_perm_0 = const()[name = tensor<string, []>("transpose_5_perm_0"), val = tensor<int32, [3]>([-1, 0, -2])];
179
  tensor<string, []> input_41_batch_first_direction_0 = const()[name = tensor<string, []>("input_41_batch_first_direction_0"), val = tensor<string, []>("bidirectional")];
@@ -181,18 +181,18 @@ program(1.0)
181
  tensor<string, []> input_41_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("input_41_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
182
  tensor<string, []> input_41_batch_first_cell_activation_0 = const()[name = tensor<string, []>("input_41_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")];
183
  tensor<string, []> input_41_batch_first_activation_0 = const()[name = tensor<string, []>("input_41_batch_first_activation_0"), val = tensor<string, []>("tanh")];
184
- tensor<fp16, [1024, 640]> concat_34_to_fp16 = const()[name = tensor<string, []>("concat_34_to_fp16"), val = tensor<fp16, [1024, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19873216)))];
185
- tensor<fp16, [1024, 256]> concat_35_to_fp16 = const()[name = tensor<string, []>("concat_35_to_fp16"), val = tensor<fp16, [1024, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21184000)))];
186
- tensor<fp16, [1024]> add_10_to_fp16 = const()[name = tensor<string, []>("add_10_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21708352)))];
187
- tensor<fp16, [1024, 640]> concat_36_to_fp16 = const()[name = tensor<string, []>("concat_36_to_fp16"), val = tensor<fp16, [1024, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21710464)))];
188
- tensor<fp16, [1024, 256]> concat_37_to_fp16 = const()[name = tensor<string, []>("concat_37_to_fp16"), val = tensor<fp16, [1024, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(23021248)))];
189
- tensor<fp16, [1024]> add_11_to_fp16 = const()[name = tensor<string, []>("add_11_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(23545600)))];
190
- tensor<fp16, [?, 1, 640]> transpose_5_cast_fp16 = transpose(perm = transpose_5_perm_0, x = x_53_cast_fp16)[name = tensor<string, []>("transpose_17")];
191
- tensor<fp16, [?, 1, 512]> input_41_batch_first_cast_fp16_0, tensor<fp16, [1, 512]> input_41_batch_first_cast_fp16_1, tensor<fp16, [1, 512]> input_41_batch_first_cast_fp16_2 = lstm(activation = input_41_batch_first_activation_0, bias = add_10_to_fp16, bias_back = add_11_to_fp16, cell_activation = input_41_batch_first_cell_activation_0, direction = input_41_batch_first_direction_0, initial_c = x_17_batch_first_lstm_h0_reshaped_to_fp16, initial_h = x_17_batch_first_lstm_h0_reshaped_to_fp16, output_sequence = input_41_batch_first_output_sequence_0, recurrent_activation = input_41_batch_first_recurrent_activation_0, weight_hh = concat_35_to_fp16, weight_hh_back = concat_37_to_fp16, weight_ih = concat_34_to_fp16, weight_ih_back = concat_36_to_fp16, x = transpose_5_cast_fp16)[name = tensor<string, []>("input_41_batch_first_cast_fp16")];
192
  tensor<int32, [3]> transpose_9_perm_0 = const()[name = tensor<string, []>("transpose_9_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
193
- tensor<fp16, [1024, 128]> duration_encoder_norms_2_fc_weight_to_fp16 = const()[name = tensor<string, []>("duration_encoder_norms_2_fc_weight_to_fp16"), val = tensor<fp16, [1024, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(23547712)))];
194
- tensor<fp16, [1024]> duration_encoder_norms_2_fc_bias_to_fp16 = const()[name = tensor<string, []>("duration_encoder_norms_2_fc_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(23809920)))];
195
- tensor<fp16, [1, 1024]> linear_3_cast_fp16 = linear(bias = duration_encoder_norms_2_fc_bias_to_fp16, weight = duration_encoder_norms_2_fc_weight_to_fp16, x = style_cast_fp16)[name = tensor<string, []>("linear_3_cast_fp16")];
196
  tensor<int32, [3]> var_294 = const()[name = tensor<string, []>("op_294"), val = tensor<int32, [3]>([1, 1024, 1])];
197
  tensor<fp16, [1, 1024, 1]> h_cast_fp16 = reshape(shape = var_294, x = linear_3_cast_fp16)[name = tensor<string, []>("h_cast_fp16")];
198
  tensor<int32, [2]> var_296_split_sizes_0 = const()[name = tensor<string, []>("op_296_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
@@ -201,17 +201,17 @@ program(1.0)
201
  tensor<int32, [3]> gamma_11_perm_0 = const()[name = tensor<string, []>("gamma_11_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
202
  tensor<int32, [3]> beta_11_perm_0 = const()[name = tensor<string, []>("beta_11_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
203
  tensor<int32, [1]> x_63_axes_0 = const()[name = tensor<string, []>("x_63_axes_0"), val = tensor<int32, [1]>([-1])];
204
- tensor<fp16, [1, ?, 512]> transpose_9_cast_fp16 = transpose(perm = transpose_9_perm_0, x = input_41_batch_first_cast_fp16_0)[name = tensor<string, []>("transpose_16")];
205
  tensor<fp16, [1, ?, 512]> x_63_cast_fp16 = layer_norm(axes = x_63_axes_0, epsilon = var_136_to_fp16, x = transpose_9_cast_fp16)[name = tensor<string, []>("x_63_cast_fp16")];
206
  tensor<fp16, []> var_302_promoted_to_fp16 = const()[name = tensor<string, []>("op_302_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
207
- tensor<fp16, [1, 1, 512]> gamma_11_cast_fp16 = transpose(perm = gamma_11_perm_0, x = var_296_cast_fp16_0)[name = tensor<string, []>("transpose_15")];
208
  tensor<fp16, [1, 1, 512]> var_303_cast_fp16 = add(x = gamma_11_cast_fp16, y = var_302_promoted_to_fp16)[name = tensor<string, []>("op_303_cast_fp16")];
209
  tensor<fp16, [1, ?, 512]> var_304_cast_fp16 = mul(x = var_303_cast_fp16, y = x_63_cast_fp16)[name = tensor<string, []>("op_304_cast_fp16")];
210
- tensor<fp16, [1, 1, 512]> beta_11_cast_fp16 = transpose(perm = beta_11_perm_0, x = var_296_cast_fp16_1)[name = tensor<string, []>("transpose_14")];
211
  tensor<fp16, [1, ?, 512]> x_65_cast_fp16 = add(x = var_304_cast_fp16, y = beta_11_cast_fp16)[name = tensor<string, []>("x_65_cast_fp16")];
212
  tensor<int32, [3]> x_67_perm_0 = const()[name = tensor<string, []>("x_67_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
213
  tensor<bool, []> x_interleave_0 = const()[name = tensor<string, []>("x_interleave_0"), val = tensor<bool, []>(false)];
214
- tensor<fp16, [1, 512, ?]> x_67_cast_fp16 = transpose(perm = x_67_perm_0, x = x_65_cast_fp16)[name = tensor<string, []>("transpose_13")];
215
  tensor<fp16, [1, 640, ?]> x_cast_fp16 = concat(axis = var_147, interleave = x_interleave_0, values = (x_67_cast_fp16, s_chan_cast_fp16))[name = tensor<string, []>("x_cast_fp16")];
216
  tensor<int32, [3]> input_45_perm_0 = const()[name = tensor<string, []>("input_45_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
217
  tensor<int32, [3]> input_45_batch_first_transpose_perm_0 = const()[name = tensor<string, []>("input_45_batch_first_transpose_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
@@ -220,20 +220,20 @@ program(1.0)
220
  tensor<string, []> input_47_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("input_47_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
221
  tensor<string, []> input_47_batch_first_cell_activation_0 = const()[name = tensor<string, []>("input_47_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")];
222
  tensor<string, []> input_47_batch_first_activation_0 = const()[name = tensor<string, []>("input_47_batch_first_activation_0"), val = tensor<string, []>("tanh")];
223
- tensor<fp16, [1024, 640]> concat_44_to_fp16 = const()[name = tensor<string, []>("concat_44_to_fp16"), val = tensor<fp16, [1024, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(23812032)))];
224
- tensor<fp16, [1024, 256]> concat_45_to_fp16 = const()[name = tensor<string, []>("concat_45_to_fp16"), val = tensor<fp16, [1024, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25122816)))];
225
- tensor<fp16, [1024]> add_13_to_fp16 = const()[name = tensor<string, []>("add_13_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25647168)))];
226
- tensor<fp16, [1024, 640]> concat_46_to_fp16 = const()[name = tensor<string, []>("concat_46_to_fp16"), val = tensor<fp16, [1024, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25649280)))];
227
- tensor<fp16, [1024, 256]> concat_47_to_fp16 = const()[name = tensor<string, []>("concat_47_to_fp16"), val = tensor<fp16, [1024, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26960064)))];
228
- tensor<fp16, [1024]> add_14_to_fp16 = const()[name = tensor<string, []>("add_14_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(27484416)))];
229
- tensor<fp16, [1, ?, 640]> d = transpose(perm = input_45_perm_0, x = x_cast_fp16)[name = tensor<string, []>("transpose_12")];
230
- tensor<fp16, [?, 1, 640]> input_45_batch_first_transpose_cast_fp16 = transpose(perm = input_45_batch_first_transpose_perm_0, x = d)[name = tensor<string, []>("transpose_11")];
231
- tensor<fp16, [?, 1, 512]> input_47_batch_first_cast_fp16_0, tensor<fp16, [1, 512]> input_47_batch_first_cast_fp16_1, tensor<fp16, [1, 512]> input_47_batch_first_cast_fp16_2 = lstm(activation = input_47_batch_first_activation_0, bias = add_13_to_fp16, bias_back = add_14_to_fp16, cell_activation = input_47_batch_first_cell_activation_0, direction = input_47_batch_first_direction_0, initial_c = x_17_batch_first_lstm_h0_reshaped_to_fp16, initial_h = x_17_batch_first_lstm_h0_reshaped_to_fp16, output_sequence = input_47_batch_first_output_sequence_0, recurrent_activation = input_47_batch_first_recurrent_activation_0, weight_hh = concat_45_to_fp16, weight_hh_back = concat_47_to_fp16, weight_ih = concat_44_to_fp16, weight_ih_back = concat_46_to_fp16, x = input_45_batch_first_transpose_cast_fp16)[name = tensor<string, []>("input_47_batch_first_cast_fp16")];
232
  tensor<int32, [3]> input_47_perm_0 = const()[name = tensor<string, []>("input_47_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
233
- tensor<fp16, [50, 512]> duration_proj_linear_layer_weight_to_fp16 = const()[name = tensor<string, []>("duration_proj_linear_layer_weight_to_fp16"), val = tensor<fp16, [50, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(27486528)))];
234
- tensor<fp16, [50]> duration_proj_linear_layer_bias_to_fp16 = const()[name = tensor<string, []>("duration_proj_linear_layer_bias_to_fp16"), val = tensor<fp16, [50]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(27537792)))];
235
- tensor<fp16, [1, ?, 512]> input_47_cast_fp16 = transpose(perm = input_47_perm_0, x = input_47_batch_first_cast_fp16_0)[name = tensor<string, []>("transpose_10")];
236
- tensor<fp16, [1, ?, 50]> pred_dur_log = linear(bias = duration_proj_linear_layer_bias_to_fp16, weight = duration_proj_linear_layer_weight_to_fp16, x = input_47_cast_fp16)[name = tensor<string, []>("linear_4_cast_fp16")];
237
  tensor<int32, []> var_345 = const()[name = tensor<string, []>("op_345"), val = tensor<int32, []>(-1)];
238
  tensor<int32, [3]> var_355_begin_0 = const()[name = tensor<string, []>("op_355_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
239
  tensor<int32, [3]> var_355_end_0 = const()[name = tensor<string, []>("op_355_end_0"), val = tensor<int32, [3]>([1, 0, 1])];
@@ -247,19 +247,19 @@ program(1.0)
247
  tensor<fp16, [1, ?]> var_357_cast_fp16 = cumsum(axis = var_345, exclusive = var_357_exclusive_0, reverse = var_357_reverse_0, x = fill_like_0_cast_fp16)[name = tensor<string, []>("op_357_cast_fp16")];
248
  tensor<string, []> cast_6_dtype_0 = const()[name = tensor<string, []>("cast_6_dtype_0"), val = tensor<string, []>("int32")];
249
  tensor<int32, []> var_359 = const()[name = tensor<string, []>("op_359"), val = tensor<int32, []>(1)];
250
- tensor<int32, [1, ?]> var_357_cast_fp16_to_int32 = cast(dtype = cast_6_dtype_0, x = var_357_cast_fp16)[name = tensor<string, []>("cast_13")];
251
  tensor<int32, [1, ?]> indices = sub(x = var_357_cast_fp16_to_int32, y = var_359)[name = tensor<string, []>("indices")];
252
  tensor<int32, [1]> input_axes_0 = const()[name = tensor<string, []>("input_axes_0"), val = tensor<int32, [1]>([0])];
253
  tensor<string, []> indices_to_uint16_dtype_0 = const()[name = tensor<string, []>("indices_to_uint16_dtype_0"), val = tensor<string, []>("uint16")];
254
- tensor<uint16, [1, ?]> indices_to_uint16 = cast(dtype = indices_to_uint16_dtype_0, x = indices)[name = tensor<string, []>("cast_12")];
255
  tensor<uint16, [?]> input_cast_uint16 = squeeze(axes = input_axes_0, x = indices_to_uint16)[name = tensor<string, []>("input_cast_uint16")];
256
  tensor<int32, []> fe_axis_0 = const()[name = tensor<string, []>("fe_axis_0"), val = tensor<int32, []>(0)];
257
  tensor<int32, []> fe_batch_dims_0 = const()[name = tensor<string, []>("fe_batch_dims_0"), val = tensor<int32, []>(0)];
258
  tensor<bool, []> fe_validate_indices_0 = const()[name = tensor<string, []>("fe_validate_indices_0"), val = tensor<bool, []>(false)];
259
- tensor<fp16, [512, 768]> fixed_embedding_embedding_weight_to_fp16 = const()[name = tensor<string, []>("fixed_embedding_embedding_weight_to_fp16"), val = tensor<fp16, [512, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(27537984)))];
260
- tensor<fp16, [?, 768]> fe_cast_fp16_cast_uint16 = gather(axis = fe_axis_0, batch_dims = fe_batch_dims_0, indices = input_cast_uint16, validate_indices = fe_validate_indices_0, x = fixed_embedding_embedding_weight_to_fp16)[name = tensor<string, []>("fe_cast_fp16_cast_uint16")];
261
  tensor<int32, [1]> var_364_axes_0 = const()[name = tensor<string, []>("op_364_axes_0"), val = tensor<int32, [1]>([0])];
262
  tensor<fp16, [1, ?, 768]> fixed_embedding = expand_dims(axes = var_364_axes_0, x = fe_cast_fp16_cast_uint16)[name = tensor<string, []>("op_364_cast_fp16")];
263
- tensor<fp16, [1, 512, ?]> t_en = transpose(perm = transpose_0_perm_0, x = x_17_batch_first_cast_fp16_0)[name = tensor<string, []>("transpose_29")];
264
  } -> (t_en, d, pred_dur_log, fixed_embedding);
265
  }
 
1
  program(1.0)
2
+ [buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}})]
3
  {
4
  func main<ios17>(tensor<fp16, [1, ?, 768]> bert_dur, tensor<fp16, [1, 256]> style, tensor<int32, [1, ?]> tokens) [FlexibleShapeInformation = tuple<tuple<tensor<string, []>, dict<tensor<string, []>, tensor<int32, [?]>>>, tuple<tensor<string, []>, dict<tensor<string, []>, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"bert_dur", [1, 64, 768]}, {"tokens", [1, 64]}}), ("RangeDims", {{"bert_dur", [[1, 1], [2, 512], [768, 768]]}, {"tokens", [[1, 1], [2, 512]]}})))] {
5
+ tensor<int32, [2]> style_begin_0 = const()[name = tensor<string, []>("style_begin_0"), val = tensor<int32, [2]>([0, 128])];
6
+ tensor<int32, [2]> style_end_0 = const()[name = tensor<string, []>("style_end_0"), val = tensor<int32, [2]>([1, 256])];
7
+ tensor<bool, [2]> style_end_mask_0 = const()[name = tensor<string, []>("style_end_mask_0"), val = tensor<bool, [2]>([true, true])];
8
  tensor<fp16, [1, 128]> style_cast_fp16 = slice_by_index(begin = style_begin_0, end = style_end_0, end_mask = style_end_mask_0, x = style)[name = tensor<string, []>("style_cast_fp16")];
9
  tensor<fp32, []> var_30 = const()[name = tensor<string, []>("op_30"), val = tensor<fp32, []>(0x1.99999ap-3)];
10
  tensor<int32, []> x_1_axis_0 = const()[name = tensor<string, []>("x_1_axis_0"), val = tensor<int32, []>(0)];
11
  tensor<int32, []> x_1_batch_dims_0 = const()[name = tensor<string, []>("x_1_batch_dims_0"), val = tensor<int32, []>(0)];
12
  tensor<bool, []> x_1_validate_indices_0 = const()[name = tensor<string, []>("x_1_validate_indices_0"), val = tensor<bool, []>(false)];
13
+ tensor<fp16, [178, 512]> text_encoder_embedding_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [91136]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(91264))), name = tensor<string, []>("text_encoder_embedding_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([178, 512])];
14
  tensor<string, []> tokens_to_int16_dtype_0 = const()[name = tensor<string, []>("tokens_to_int16_dtype_0"), val = tensor<string, []>("int16")];
15
+ tensor<int16, [1, ?]> tokens_to_int16 = cast(dtype = tokens_to_int16_dtype_0, x = tokens)[name = tensor<string, []>("cast_2")];
16
+ tensor<fp16, [1, ?, 512]> x_1_cast_fp16_cast_uint16 = gather(axis = x_1_axis_0, batch_dims = x_1_batch_dims_0, indices = tokens_to_int16, validate_indices = x_1_validate_indices_0, x = text_encoder_embedding_weight_to_fp16_palettized)[name = tensor<string, []>("x_1_cast_fp16_cast_uint16")];
17
  tensor<int32, [3]> input_1_perm_0 = const()[name = tensor<string, []>("input_1_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
18
  tensor<string, []> x_3_pad_type_0 = const()[name = tensor<string, []>("x_3_pad_type_0"), val = tensor<string, []>("custom")];
19
  tensor<int32, [2]> x_3_pad_0 = const()[name = tensor<string, []>("x_3_pad_0"), val = tensor<int32, [2]>([2, 2])];
20
  tensor<int32, [1]> x_3_strides_0 = const()[name = tensor<string, []>("x_3_strides_0"), val = tensor<int32, [1]>([1])];
21
  tensor<int32, [1]> x_3_dilations_0 = const()[name = tensor<string, []>("x_3_dilations_0"), val = tensor<int32, [1]>([1])];
22
  tensor<int32, []> x_3_groups_0 = const()[name = tensor<string, []>("x_3_groups_0"), val = tensor<int32, []>(1)];
23
+ tensor<fp16, [512, 512, 5]> weight_3_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1310720]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(91840))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1402624))), name = tensor<string, []>("weight_3_to_fp16_palettized"), shape = tensor<uint32, [3]>([512, 512, 5])];
24
+ tensor<fp16, [512]> text_encoder_cnn_0_0_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_cnn_0_0_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1403200)))];
25
+ tensor<fp16, [1, 512, ?]> input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = x_1_cast_fp16_cast_uint16)[name = tensor<string, []>("transpose_26")];
26
+ tensor<fp16, [1, 512, ?]> x_3_cast_fp16 = conv(bias = text_encoder_cnn_0_0_bias_to_fp16, dilations = x_3_dilations_0, groups = x_3_groups_0, pad = x_3_pad_0, pad_type = x_3_pad_type_0, strides = x_3_strides_0, weight = weight_3_to_fp16_palettized, x = input_1_cast_fp16)[name = tensor<string, []>("x_3_cast_fp16")];
27
  tensor<int32, [3]> input_3_perm_0 = const()[name = tensor<string, []>("input_3_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
28
  tensor<int32, [1]> x_5_axes_0 = const()[name = tensor<string, []>("x_5_axes_0"), val = tensor<int32, [1]>([-1])];
29
+ tensor<fp16, [512]> text_encoder_cnn_0_1_gamma_to_fp16 = const()[name = tensor<string, []>("text_encoder_cnn_0_1_gamma_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1404288)))];
30
+ tensor<fp16, [512]> text_encoder_cnn_0_1_beta_to_fp16 = const()[name = tensor<string, []>("text_encoder_cnn_0_1_beta_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1405376)))];
31
  tensor<fp16, []> var_28_to_fp16 = const()[name = tensor<string, []>("op_28_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
32
+ tensor<fp16, [1, ?, 512]> input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = x_3_cast_fp16)[name = tensor<string, []>("transpose_25")];
33
  tensor<fp16, [1, ?, 512]> x_5_cast_fp16 = layer_norm(axes = x_5_axes_0, beta = text_encoder_cnn_0_1_beta_to_fp16, epsilon = var_28_to_fp16, gamma = text_encoder_cnn_0_1_gamma_to_fp16, x = input_3_cast_fp16)[name = tensor<string, []>("x_5_cast_fp16")];
34
  tensor<int32, [3]> input_5_perm_0 = const()[name = tensor<string, []>("input_5_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
35
+ tensor<fp16, [1, 512, ?]> input_5_cast_fp16 = transpose(perm = input_5_perm_0, x = x_5_cast_fp16)[name = tensor<string, []>("transpose_24")];
36
  tensor<fp16, [1, 512, ?]> input_7_cast_fp16 = leaky_relu(alpha = var_30, x = input_5_cast_fp16)[name = tensor<string, []>("input_7_cast_fp16")];
37
  tensor<string, []> x_7_pad_type_0 = const()[name = tensor<string, []>("x_7_pad_type_0"), val = tensor<string, []>("custom")];
38
  tensor<int32, [2]> x_7_pad_0 = const()[name = tensor<string, []>("x_7_pad_0"), val = tensor<int32, [2]>([2, 2])];
39
  tensor<int32, [1]> x_7_strides_0 = const()[name = tensor<string, []>("x_7_strides_0"), val = tensor<int32, [1]>([1])];
40
  tensor<int32, [1]> x_7_dilations_0 = const()[name = tensor<string, []>("x_7_dilations_0"), val = tensor<int32, [1]>([1])];
41
  tensor<int32, []> x_7_groups_0 = const()[name = tensor<string, []>("x_7_groups_0"), val = tensor<int32, []>(1)];
42
+ tensor<fp16, [512, 512, 5]> weight_7_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1310720]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1406464))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2717248))), name = tensor<string, []>("weight_7_to_fp16_palettized"), shape = tensor<uint32, [3]>([512, 512, 5])];
43
+ tensor<fp16, [512]> text_encoder_cnn_1_0_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_cnn_1_0_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2717824)))];
44
+ tensor<fp16, [1, 512, ?]> x_7_cast_fp16 = conv(bias = text_encoder_cnn_1_0_bias_to_fp16, dilations = x_7_dilations_0, groups = x_7_groups_0, pad = x_7_pad_0, pad_type = x_7_pad_type_0, strides = x_7_strides_0, weight = weight_7_to_fp16_palettized, x = input_7_cast_fp16)[name = tensor<string, []>("x_7_cast_fp16")];
45
  tensor<int32, [3]> input_11_perm_0 = const()[name = tensor<string, []>("input_11_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
46
  tensor<int32, [1]> x_9_axes_0 = const()[name = tensor<string, []>("x_9_axes_0"), val = tensor<int32, [1]>([-1])];
47
+ tensor<fp16, [512]> text_encoder_cnn_1_1_gamma_to_fp16 = const()[name = tensor<string, []>("text_encoder_cnn_1_1_gamma_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2718912)))];
48
+ tensor<fp16, [512]> text_encoder_cnn_1_1_beta_to_fp16 = const()[name = tensor<string, []>("text_encoder_cnn_1_1_beta_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2720000)))];
49
+ tensor<fp16, [1, ?, 512]> input_11_cast_fp16 = transpose(perm = input_11_perm_0, x = x_7_cast_fp16)[name = tensor<string, []>("transpose_23")];
50
  tensor<fp16, [1, ?, 512]> x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, beta = text_encoder_cnn_1_1_beta_to_fp16, epsilon = var_28_to_fp16, gamma = text_encoder_cnn_1_1_gamma_to_fp16, x = input_11_cast_fp16)[name = tensor<string, []>("x_9_cast_fp16")];
51
  tensor<int32, [3]> input_13_perm_0 = const()[name = tensor<string, []>("input_13_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
52
+ tensor<fp16, [1, 512, ?]> input_13_cast_fp16 = transpose(perm = input_13_perm_0, x = x_9_cast_fp16)[name = tensor<string, []>("transpose_22")];
53
  tensor<fp16, [1, 512, ?]> input_15_cast_fp16 = leaky_relu(alpha = var_30, x = input_13_cast_fp16)[name = tensor<string, []>("input_15_cast_fp16")];
54
  tensor<string, []> x_11_pad_type_0 = const()[name = tensor<string, []>("x_11_pad_type_0"), val = tensor<string, []>("custom")];
55
  tensor<int32, [2]> x_11_pad_0 = const()[name = tensor<string, []>("x_11_pad_0"), val = tensor<int32, [2]>([2, 2])];
56
  tensor<int32, [1]> x_11_strides_0 = const()[name = tensor<string, []>("x_11_strides_0"), val = tensor<int32, [1]>([1])];
57
  tensor<int32, [1]> x_11_dilations_0 = const()[name = tensor<string, []>("x_11_dilations_0"), val = tensor<int32, [1]>([1])];
58
  tensor<int32, []> x_11_groups_0 = const()[name = tensor<string, []>("x_11_groups_0"), val = tensor<int32, []>(1)];
59
+ tensor<fp16, [512, 512, 5]> weight_11_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1310720]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2721088))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4031872))), name = tensor<string, []>("weight_11_to_fp16_palettized"), shape = tensor<uint32, [3]>([512, 512, 5])];
60
+ tensor<fp16, [512]> text_encoder_cnn_2_0_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_cnn_2_0_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4032448)))];
61
+ tensor<fp16, [1, 512, ?]> x_11_cast_fp16 = conv(bias = text_encoder_cnn_2_0_bias_to_fp16, dilations = x_11_dilations_0, groups = x_11_groups_0, pad = x_11_pad_0, pad_type = x_11_pad_type_0, strides = x_11_strides_0, weight = weight_11_to_fp16_palettized, x = input_15_cast_fp16)[name = tensor<string, []>("x_11_cast_fp16")];
62
  tensor<int32, [3]> input_19_perm_0 = const()[name = tensor<string, []>("input_19_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
63
  tensor<int32, [1]> x_13_axes_0 = const()[name = tensor<string, []>("x_13_axes_0"), val = tensor<int32, [1]>([-1])];
64
+ tensor<fp16, [512]> text_encoder_cnn_2_1_gamma_to_fp16 = const()[name = tensor<string, []>("text_encoder_cnn_2_1_gamma_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4033536)))];
65
+ tensor<fp16, [512]> text_encoder_cnn_2_1_beta_to_fp16 = const()[name = tensor<string, []>("text_encoder_cnn_2_1_beta_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4034624)))];
66
+ tensor<fp16, [1, ?, 512]> input_19_cast_fp16 = transpose(perm = input_19_perm_0, x = x_11_cast_fp16)[name = tensor<string, []>("transpose_21")];
67
  tensor<fp16, [1, ?, 512]> x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, beta = text_encoder_cnn_2_1_beta_to_fp16, epsilon = var_28_to_fp16, gamma = text_encoder_cnn_2_1_gamma_to_fp16, x = input_19_cast_fp16)[name = tensor<string, []>("x_13_cast_fp16")];
68
  tensor<fp16, [1, ?, 512]> input_23_cast_fp16 = leaky_relu(alpha = var_30, x = x_13_cast_fp16)[name = tensor<string, []>("input_23_cast_fp16")];
69
  tensor<int32, [3]> input_25_batch_first_transpose_perm_0 = const()[name = tensor<string, []>("input_25_batch_first_transpose_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
 
72
  tensor<string, []> x_17_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("x_17_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
73
  tensor<string, []> x_17_batch_first_cell_activation_0 = const()[name = tensor<string, []>("x_17_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")];
74
  tensor<string, []> x_17_batch_first_activation_0 = const()[name = tensor<string, []>("x_17_batch_first_activation_0"), val = tensor<string, []>("tanh")];
75
+ tensor<fp16, [1, 512]> x_17_batch_first_lstm_h0_reshaped_to_fp16 = const()[name = tensor<string, []>("x_17_batch_first_lstm_h0_reshaped_to_fp16"), val = tensor<fp16, [1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4035712)))];
76
+ tensor<fp16, [1024, 512]> concat_4_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [524288]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4036800))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4561152))), name = tensor<string, []>("concat_4_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 512])];
77
+ tensor<fp16, [1024, 256]> concat_5_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4561728))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4823936))), name = tensor<string, []>("concat_5_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 256])];
78
+ tensor<fp16, [1024]> add_1_to_fp16 = const()[name = tensor<string, []>("add_1_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4824512)))];
79
+ tensor<fp16, [1024, 512]> concat_6_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [524288]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4826624))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5350976))), name = tensor<string, []>("concat_6_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 512])];
80
+ tensor<fp16, [1024, 256]> concat_7_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5351552))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5613760))), name = tensor<string, []>("concat_7_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 256])];
81
+ tensor<fp16, [1024]> add_2_to_fp16 = const()[name = tensor<string, []>("add_2_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5614336)))];
82
+ tensor<fp16, [?, 1, 512]> input_25_batch_first_transpose_cast_fp16 = transpose(perm = input_25_batch_first_transpose_perm_0, x = input_23_cast_fp16)[name = tensor<string, []>("transpose_20")];
83
+ tensor<fp16, [?, 1, 512]> x_17_batch_first_cast_fp16_0, tensor<fp16, [1, 512]> x_17_batch_first_cast_fp16_1, tensor<fp16, [1, 512]> x_17_batch_first_cast_fp16_2 = lstm(activation = x_17_batch_first_activation_0, bias = add_1_to_fp16, bias_back = add_2_to_fp16, cell_activation = x_17_batch_first_cell_activation_0, direction = x_17_batch_first_direction_0, initial_c = x_17_batch_first_lstm_h0_reshaped_to_fp16, initial_h = x_17_batch_first_lstm_h0_reshaped_to_fp16, output_sequence = x_17_batch_first_output_sequence_0, recurrent_activation = x_17_batch_first_recurrent_activation_0, weight_hh = concat_5_to_fp16_palettized, weight_hh_back = concat_7_to_fp16_palettized, weight_ih = concat_4_to_fp16_palettized, weight_ih_back = concat_6_to_fp16_palettized, x = input_25_batch_first_transpose_cast_fp16)[name = tensor<string, []>("x_17_batch_first_cast_fp16")];
84
  tensor<int32, [3]> transpose_0_perm_0 = const()[name = tensor<string, []>("transpose_0_perm_0"), val = tensor<int32, [3]>([1, 2, 0])];
85
+ tensor<fp16, [512, 768]> bert_encoder_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [393216]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5616448))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6009728))), name = tensor<string, []>("bert_encoder_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([512, 768])];
86
+ tensor<fp16, [512]> bert_encoder_bias_to_fp16 = const()[name = tensor<string, []>("bert_encoder_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6010304)))];
87
+ tensor<fp16, [1, ?, 512]> linear_0_cast_fp16 = linear(bias = bert_encoder_bias_to_fp16, weight = bert_encoder_weight_to_fp16_palettized, x = bert_dur)[name = tensor<string, []>("linear_0_cast_fp16")];
88
  tensor<int32, [3]> x_19_perm_0 = const()[name = tensor<string, []>("x_19_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
89
  tensor<int32, []> var_147 = const()[name = tensor<string, []>("op_147"), val = tensor<int32, []>(1)];
90
  tensor<int32, [3]> var_163_begin_0 = const()[name = tensor<string, []>("op_163_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
91
  tensor<int32, [3]> var_163_end_0 = const()[name = tensor<string, []>("op_163_end_0"), val = tensor<int32, [3]>([1, 1, 0])];
92
  tensor<bool, [3]> var_163_end_mask_0 = const()[name = tensor<string, []>("op_163_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
93
+ tensor<fp16, [1, 512, ?]> x_19_cast_fp16 = transpose(perm = x_19_perm_0, x = linear_0_cast_fp16)[name = tensor<string, []>("transpose_19")];
94
  tensor<fp16, [1, 1, ?]> var_163_cast_fp16 = slice_by_index(begin = var_163_begin_0, end = var_163_end_0, end_mask = var_163_end_mask_0, x = x_19_cast_fp16)[name = tensor<string, []>("op_163_cast_fp16")];
95
  tensor<fp16, [1, 1, ?]> sub_0_cast_fp16 = sub(x = var_163_cast_fp16, y = var_163_cast_fp16)[name = tensor<string, []>("sub_0_cast_fp16")];
96
  tensor<int32, [1]> var_166_axes_0 = const()[name = tensor<string, []>("op_166_axes_0"), val = tensor<int32, [1]>([-1])];
 
104
  tensor<string, []> input_29_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("input_29_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
105
  tensor<string, []> input_29_batch_first_cell_activation_0 = const()[name = tensor<string, []>("input_29_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")];
106
  tensor<string, []> input_29_batch_first_activation_0 = const()[name = tensor<string, []>("input_29_batch_first_activation_0"), val = tensor<string, []>("tanh")];
107
+ tensor<fp16, [1024, 640]> concat_14_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6011392))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6666816))), name = tensor<string, []>("concat_14_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 640])];
108
+ tensor<fp16, [1024, 256]> concat_15_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6667392))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6929600))), name = tensor<string, []>("concat_15_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 256])];
109
+ tensor<fp16, [1024]> add_4_to_fp16 = const()[name = tensor<string, []>("add_4_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6930176)))];
110
+ tensor<fp16, [1024, 640]> concat_16_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6932288))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7587712))), name = tensor<string, []>("concat_16_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 640])];
111
+ tensor<fp16, [1024, 256]> concat_17_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7588288))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7850496))), name = tensor<string, []>("concat_17_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 256])];
112
+ tensor<fp16, [1024]> add_5_to_fp16 = const()[name = tensor<string, []>("add_5_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7851072)))];
113
+ tensor<fp16, [?, 1, 640]> transpose_1_cast_fp16 = transpose(perm = transpose_1_perm_0, x = x_21_cast_fp16)[name = tensor<string, []>("transpose_18")];
114
+ tensor<fp16, [?, 1, 512]> input_29_batch_first_cast_fp16_0, tensor<fp16, [1, 512]> input_29_batch_first_cast_fp16_1, tensor<fp16, [1, 512]> input_29_batch_first_cast_fp16_2 = lstm(activation = input_29_batch_first_activation_0, bias = add_4_to_fp16, bias_back = add_5_to_fp16, cell_activation = input_29_batch_first_cell_activation_0, direction = input_29_batch_first_direction_0, initial_c = x_17_batch_first_lstm_h0_reshaped_to_fp16, initial_h = x_17_batch_first_lstm_h0_reshaped_to_fp16, output_sequence = input_29_batch_first_output_sequence_0, recurrent_activation = input_29_batch_first_recurrent_activation_0, weight_hh = concat_15_to_fp16_palettized, weight_hh_back = concat_17_to_fp16_palettized, weight_ih = concat_14_to_fp16_palettized, weight_ih_back = concat_16_to_fp16_palettized, x = transpose_1_cast_fp16)[name = tensor<string, []>("input_29_batch_first_cast_fp16")];
115
  tensor<int32, [3]> transpose_7_perm_0 = const()[name = tensor<string, []>("transpose_7_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
116
+ tensor<fp16, [1024, 128]> duration_encoder_norms_0_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [131072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7853184))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7984320))), name = tensor<string, []>("duration_encoder_norms_0_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 128])];
117
+ tensor<fp16, [1024]> duration_encoder_norms_0_fc_bias_to_fp16 = const()[name = tensor<string, []>("duration_encoder_norms_0_fc_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7984896)))];
118
+ tensor<fp16, [1, 1024]> linear_1_cast_fp16 = linear(bias = duration_encoder_norms_0_fc_bias_to_fp16, weight = duration_encoder_norms_0_fc_weight_to_fp16_palettized, x = style_cast_fp16)[name = tensor<string, []>("linear_1_cast_fp16")];
119
  tensor<int32, [3]> var_200 = const()[name = tensor<string, []>("op_200"), val = tensor<int32, [3]>([1, 1024, 1])];
120
  tensor<fp16, [1, 1024, 1]> h_3_cast_fp16 = reshape(shape = var_200, x = linear_1_cast_fp16)[name = tensor<string, []>("h_3_cast_fp16")];
121
  tensor<int32, [2]> var_202_split_sizes_0 = const()[name = tensor<string, []>("op_202_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
 
125
  tensor<int32, [3]> beta_3_perm_0 = const()[name = tensor<string, []>("beta_3_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
126
  tensor<int32, [1]> x_31_axes_0 = const()[name = tensor<string, []>("x_31_axes_0"), val = tensor<int32, [1]>([-1])];
127
  tensor<fp16, []> var_136_to_fp16 = const()[name = tensor<string, []>("op_136_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
128
+ tensor<fp16, [1, ?, 512]> transpose_7_cast_fp16 = transpose(perm = transpose_7_perm_0, x = input_29_batch_first_cast_fp16_0)[name = tensor<string, []>("transpose_17")];
129
  tensor<fp16, [1, ?, 512]> x_31_cast_fp16 = layer_norm(axes = x_31_axes_0, epsilon = var_136_to_fp16, x = transpose_7_cast_fp16)[name = tensor<string, []>("x_31_cast_fp16")];
130
  tensor<fp16, []> var_208_promoted_to_fp16 = const()[name = tensor<string, []>("op_208_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
131
+ tensor<fp16, [1, 1, 512]> gamma_3_cast_fp16 = transpose(perm = gamma_3_perm_0, x = var_202_cast_fp16_0)[name = tensor<string, []>("transpose_16")];
132
  tensor<fp16, [1, 1, 512]> var_209_cast_fp16 = add(x = gamma_3_cast_fp16, y = var_208_promoted_to_fp16)[name = tensor<string, []>("op_209_cast_fp16")];
133
  tensor<fp16, [1, ?, 512]> var_210_cast_fp16 = mul(x = var_209_cast_fp16, y = x_31_cast_fp16)[name = tensor<string, []>("op_210_cast_fp16")];
134
+ tensor<fp16, [1, 1, 512]> beta_3_cast_fp16 = transpose(perm = beta_3_perm_0, x = var_202_cast_fp16_1)[name = tensor<string, []>("transpose_15")];
135
  tensor<fp16, [1, ?, 512]> x_33_cast_fp16 = add(x = var_210_cast_fp16, y = beta_3_cast_fp16)[name = tensor<string, []>("x_33_cast_fp16")];
136
  tensor<int32, [3]> x_35_perm_0 = const()[name = tensor<string, []>("x_35_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
137
  tensor<bool, []> x_37_interleave_0 = const()[name = tensor<string, []>("x_37_interleave_0"), val = tensor<bool, []>(false)];
138
+ tensor<fp16, [1, 512, ?]> x_35_cast_fp16 = transpose(perm = x_35_perm_0, x = x_33_cast_fp16)[name = tensor<string, []>("transpose_14")];
139
  tensor<fp16, [1, 640, ?]> x_37_cast_fp16 = concat(axis = var_147, interleave = x_37_interleave_0, values = (x_35_cast_fp16, s_chan_cast_fp16))[name = tensor<string, []>("x_37_cast_fp16")];
140
  tensor<int32, [3]> transpose_3_perm_0 = const()[name = tensor<string, []>("transpose_3_perm_0"), val = tensor<int32, [3]>([-1, 0, -2])];
141
  tensor<string, []> input_35_batch_first_direction_0 = const()[name = tensor<string, []>("input_35_batch_first_direction_0"), val = tensor<string, []>("bidirectional")];
 
143
  tensor<string, []> input_35_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("input_35_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
144
  tensor<string, []> input_35_batch_first_cell_activation_0 = const()[name = tensor<string, []>("input_35_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")];
145
  tensor<string, []> input_35_batch_first_activation_0 = const()[name = tensor<string, []>("input_35_batch_first_activation_0"), val = tensor<string, []>("tanh")];
146
+ tensor<fp16, [1024, 640]> concat_24_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7987008))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8642432))), name = tensor<string, []>("concat_24_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 640])];
147
+ tensor<fp16, [1024, 256]> concat_25_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8643008))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8905216))), name = tensor<string, []>("concat_25_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 256])];
148
+ tensor<fp16, [1024]> add_7_to_fp16 = const()[name = tensor<string, []>("add_7_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8905792)))];
149
+ tensor<fp16, [1024, 640]> concat_26_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8907904))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9563328))), name = tensor<string, []>("concat_26_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 640])];
150
+ tensor<fp16, [1024, 256]> concat_27_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9563904))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9826112))), name = tensor<string, []>("concat_27_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 256])];
151
+ tensor<fp16, [1024]> add_8_to_fp16 = const()[name = tensor<string, []>("add_8_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9826688)))];
152
+ tensor<fp16, [?, 1, 640]> transpose_3_cast_fp16 = transpose(perm = transpose_3_perm_0, x = x_37_cast_fp16)[name = tensor<string, []>("transpose_13")];
153
+ tensor<fp16, [?, 1, 512]> input_35_batch_first_cast_fp16_0, tensor<fp16, [1, 512]> input_35_batch_first_cast_fp16_1, tensor<fp16, [1, 512]> input_35_batch_first_cast_fp16_2 = lstm(activation = input_35_batch_first_activation_0, bias = add_7_to_fp16, bias_back = add_8_to_fp16, cell_activation = input_35_batch_first_cell_activation_0, direction = input_35_batch_first_direction_0, initial_c = x_17_batch_first_lstm_h0_reshaped_to_fp16, initial_h = x_17_batch_first_lstm_h0_reshaped_to_fp16, output_sequence = input_35_batch_first_output_sequence_0, recurrent_activation = input_35_batch_first_recurrent_activation_0, weight_hh = concat_25_to_fp16_palettized, weight_hh_back = concat_27_to_fp16_palettized, weight_ih = concat_24_to_fp16_palettized, weight_ih_back = concat_26_to_fp16_palettized, x = transpose_3_cast_fp16)[name = tensor<string, []>("input_35_batch_first_cast_fp16")];
154
  tensor<int32, [3]> transpose_8_perm_0 = const()[name = tensor<string, []>("transpose_8_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
155
+ tensor<fp16, [1024, 128]> duration_encoder_norms_1_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [131072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9828800))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9959936))), name = tensor<string, []>("duration_encoder_norms_1_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 128])];
156
+ tensor<fp16, [1024]> duration_encoder_norms_1_fc_bias_to_fp16 = const()[name = tensor<string, []>("duration_encoder_norms_1_fc_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9960512)))];
157
+ tensor<fp16, [1, 1024]> linear_2_cast_fp16 = linear(bias = duration_encoder_norms_1_fc_bias_to_fp16, weight = duration_encoder_norms_1_fc_weight_to_fp16_palettized, x = style_cast_fp16)[name = tensor<string, []>("linear_2_cast_fp16")];
158
  tensor<int32, [3]> var_247 = const()[name = tensor<string, []>("op_247"), val = tensor<int32, [3]>([1, 1024, 1])];
159
  tensor<fp16, [1, 1024, 1]> h_7_cast_fp16 = reshape(shape = var_247, x = linear_2_cast_fp16)[name = tensor<string, []>("h_7_cast_fp16")];
160
  tensor<int32, [2]> var_249_split_sizes_0 = const()[name = tensor<string, []>("op_249_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
 
163
  tensor<int32, [3]> gamma_7_perm_0 = const()[name = tensor<string, []>("gamma_7_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
164
  tensor<int32, [3]> beta_7_perm_0 = const()[name = tensor<string, []>("beta_7_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
165
  tensor<int32, [1]> x_47_axes_0 = const()[name = tensor<string, []>("x_47_axes_0"), val = tensor<int32, [1]>([-1])];
166
+ tensor<fp16, [1, ?, 512]> transpose_8_cast_fp16 = transpose(perm = transpose_8_perm_0, x = input_35_batch_first_cast_fp16_0)[name = tensor<string, []>("transpose_12")];
167
  tensor<fp16, [1, ?, 512]> x_47_cast_fp16 = layer_norm(axes = x_47_axes_0, epsilon = var_136_to_fp16, x = transpose_8_cast_fp16)[name = tensor<string, []>("x_47_cast_fp16")];
168
  tensor<fp16, []> var_255_promoted_to_fp16 = const()[name = tensor<string, []>("op_255_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
169
+ tensor<fp16, [1, 1, 512]> gamma_7_cast_fp16 = transpose(perm = gamma_7_perm_0, x = var_249_cast_fp16_0)[name = tensor<string, []>("transpose_11")];
170
  tensor<fp16, [1, 1, 512]> var_256_cast_fp16 = add(x = gamma_7_cast_fp16, y = var_255_promoted_to_fp16)[name = tensor<string, []>("op_256_cast_fp16")];
171
  tensor<fp16, [1, ?, 512]> var_257_cast_fp16 = mul(x = var_256_cast_fp16, y = x_47_cast_fp16)[name = tensor<string, []>("op_257_cast_fp16")];
172
+ tensor<fp16, [1, 1, 512]> beta_7_cast_fp16 = transpose(perm = beta_7_perm_0, x = var_249_cast_fp16_1)[name = tensor<string, []>("transpose_10")];
173
  tensor<fp16, [1, ?, 512]> x_49_cast_fp16 = add(x = var_257_cast_fp16, y = beta_7_cast_fp16)[name = tensor<string, []>("x_49_cast_fp16")];
174
  tensor<int32, [3]> x_51_perm_0 = const()[name = tensor<string, []>("x_51_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
175
  tensor<bool, []> x_53_interleave_0 = const()[name = tensor<string, []>("x_53_interleave_0"), val = tensor<bool, []>(false)];
176
+ tensor<fp16, [1, 512, ?]> x_51_cast_fp16 = transpose(perm = x_51_perm_0, x = x_49_cast_fp16)[name = tensor<string, []>("transpose_9")];
177
  tensor<fp16, [1, 640, ?]> x_53_cast_fp16 = concat(axis = var_147, interleave = x_53_interleave_0, values = (x_51_cast_fp16, s_chan_cast_fp16))[name = tensor<string, []>("x_53_cast_fp16")];
178
  tensor<int32, [3]> transpose_5_perm_0 = const()[name = tensor<string, []>("transpose_5_perm_0"), val = tensor<int32, [3]>([-1, 0, -2])];
179
  tensor<string, []> input_41_batch_first_direction_0 = const()[name = tensor<string, []>("input_41_batch_first_direction_0"), val = tensor<string, []>("bidirectional")];
 
181
  tensor<string, []> input_41_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("input_41_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
182
  tensor<string, []> input_41_batch_first_cell_activation_0 = const()[name = tensor<string, []>("input_41_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")];
183
  tensor<string, []> input_41_batch_first_activation_0 = const()[name = tensor<string, []>("input_41_batch_first_activation_0"), val = tensor<string, []>("tanh")];
184
+ tensor<fp16, [1024, 640]> concat_34_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9962624))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10618048))), name = tensor<string, []>("concat_34_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 640])];
185
+ tensor<fp16, [1024, 256]> concat_35_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10618624))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10880832))), name = tensor<string, []>("concat_35_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 256])];
186
+ tensor<fp16, [1024]> add_10_to_fp16 = const()[name = tensor<string, []>("add_10_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10881408)))];
187
+ tensor<fp16, [1024, 640]> concat_36_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10883520))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11538944))), name = tensor<string, []>("concat_36_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 640])];
188
+ tensor<fp16, [1024, 256]> concat_37_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11539520))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11801728))), name = tensor<string, []>("concat_37_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 256])];
189
+ tensor<fp16, [1024]> add_11_to_fp16 = const()[name = tensor<string, []>("add_11_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11802304)))];
190
+ tensor<fp16, [?, 1, 640]> transpose_5_cast_fp16 = transpose(perm = transpose_5_perm_0, x = x_53_cast_fp16)[name = tensor<string, []>("transpose_8")];
191
+ tensor<fp16, [?, 1, 512]> input_41_batch_first_cast_fp16_0, tensor<fp16, [1, 512]> input_41_batch_first_cast_fp16_1, tensor<fp16, [1, 512]> input_41_batch_first_cast_fp16_2 = lstm(activation = input_41_batch_first_activation_0, bias = add_10_to_fp16, bias_back = add_11_to_fp16, cell_activation = input_41_batch_first_cell_activation_0, direction = input_41_batch_first_direction_0, initial_c = x_17_batch_first_lstm_h0_reshaped_to_fp16, initial_h = x_17_batch_first_lstm_h0_reshaped_to_fp16, output_sequence = input_41_batch_first_output_sequence_0, recurrent_activation = input_41_batch_first_recurrent_activation_0, weight_hh = concat_35_to_fp16_palettized, weight_hh_back = concat_37_to_fp16_palettized, weight_ih = concat_34_to_fp16_palettized, weight_ih_back = concat_36_to_fp16_palettized, x = transpose_5_cast_fp16)[name = tensor<string, []>("input_41_batch_first_cast_fp16")];
192
  tensor<int32, [3]> transpose_9_perm_0 = const()[name = tensor<string, []>("transpose_9_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
193
+ tensor<fp16, [1024, 128]> duration_encoder_norms_2_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [131072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11804416))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11935552))), name = tensor<string, []>("duration_encoder_norms_2_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 128])];
194
+ tensor<fp16, [1024]> duration_encoder_norms_2_fc_bias_to_fp16 = const()[name = tensor<string, []>("duration_encoder_norms_2_fc_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11936128)))];
195
+ tensor<fp16, [1, 1024]> linear_3_cast_fp16 = linear(bias = duration_encoder_norms_2_fc_bias_to_fp16, weight = duration_encoder_norms_2_fc_weight_to_fp16_palettized, x = style_cast_fp16)[name = tensor<string, []>("linear_3_cast_fp16")];
196
  tensor<int32, [3]> var_294 = const()[name = tensor<string, []>("op_294"), val = tensor<int32, [3]>([1, 1024, 1])];
197
  tensor<fp16, [1, 1024, 1]> h_cast_fp16 = reshape(shape = var_294, x = linear_3_cast_fp16)[name = tensor<string, []>("h_cast_fp16")];
198
  tensor<int32, [2]> var_296_split_sizes_0 = const()[name = tensor<string, []>("op_296_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
 
201
  tensor<int32, [3]> gamma_11_perm_0 = const()[name = tensor<string, []>("gamma_11_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
202
  tensor<int32, [3]> beta_11_perm_0 = const()[name = tensor<string, []>("beta_11_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
203
  tensor<int32, [1]> x_63_axes_0 = const()[name = tensor<string, []>("x_63_axes_0"), val = tensor<int32, [1]>([-1])];
204
+ tensor<fp16, [1, ?, 512]> transpose_9_cast_fp16 = transpose(perm = transpose_9_perm_0, x = input_41_batch_first_cast_fp16_0)[name = tensor<string, []>("transpose_7")];
205
  tensor<fp16, [1, ?, 512]> x_63_cast_fp16 = layer_norm(axes = x_63_axes_0, epsilon = var_136_to_fp16, x = transpose_9_cast_fp16)[name = tensor<string, []>("x_63_cast_fp16")];
206
  tensor<fp16, []> var_302_promoted_to_fp16 = const()[name = tensor<string, []>("op_302_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
207
+ tensor<fp16, [1, 1, 512]> gamma_11_cast_fp16 = transpose(perm = gamma_11_perm_0, x = var_296_cast_fp16_0)[name = tensor<string, []>("transpose_6")];
208
  tensor<fp16, [1, 1, 512]> var_303_cast_fp16 = add(x = gamma_11_cast_fp16, y = var_302_promoted_to_fp16)[name = tensor<string, []>("op_303_cast_fp16")];
209
  tensor<fp16, [1, ?, 512]> var_304_cast_fp16 = mul(x = var_303_cast_fp16, y = x_63_cast_fp16)[name = tensor<string, []>("op_304_cast_fp16")];
210
+ tensor<fp16, [1, 1, 512]> beta_11_cast_fp16 = transpose(perm = beta_11_perm_0, x = var_296_cast_fp16_1)[name = tensor<string, []>("transpose_5")];
211
  tensor<fp16, [1, ?, 512]> x_65_cast_fp16 = add(x = var_304_cast_fp16, y = beta_11_cast_fp16)[name = tensor<string, []>("x_65_cast_fp16")];
212
  tensor<int32, [3]> x_67_perm_0 = const()[name = tensor<string, []>("x_67_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
213
  tensor<bool, []> x_interleave_0 = const()[name = tensor<string, []>("x_interleave_0"), val = tensor<bool, []>(false)];
214
+ tensor<fp16, [1, 512, ?]> x_67_cast_fp16 = transpose(perm = x_67_perm_0, x = x_65_cast_fp16)[name = tensor<string, []>("transpose_4")];
215
  tensor<fp16, [1, 640, ?]> x_cast_fp16 = concat(axis = var_147, interleave = x_interleave_0, values = (x_67_cast_fp16, s_chan_cast_fp16))[name = tensor<string, []>("x_cast_fp16")];
216
  tensor<int32, [3]> input_45_perm_0 = const()[name = tensor<string, []>("input_45_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
217
  tensor<int32, [3]> input_45_batch_first_transpose_perm_0 = const()[name = tensor<string, []>("input_45_batch_first_transpose_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
 
220
  tensor<string, []> input_47_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("input_47_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
221
  tensor<string, []> input_47_batch_first_cell_activation_0 = const()[name = tensor<string, []>("input_47_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")];
222
  tensor<string, []> input_47_batch_first_activation_0 = const()[name = tensor<string, []>("input_47_batch_first_activation_0"), val = tensor<string, []>("tanh")];
223
+ tensor<fp16, [1024, 640]> concat_44_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11938240))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12593664))), name = tensor<string, []>("concat_44_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 640])];
224
+ tensor<fp16, [1024, 256]> concat_45_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12594240))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12856448))), name = tensor<string, []>("concat_45_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 256])];
225
+ tensor<fp16, [1024]> add_13_to_fp16 = const()[name = tensor<string, []>("add_13_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12857024)))];
226
+ tensor<fp16, [1024, 640]> concat_46_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12859136))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13514560))), name = tensor<string, []>("concat_46_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 640])];
227
+ tensor<fp16, [1024, 256]> concat_47_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13515136))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13777344))), name = tensor<string, []>("concat_47_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 256])];
228
+ tensor<fp16, [1024]> add_14_to_fp16 = const()[name = tensor<string, []>("add_14_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13777920)))];
229
+ tensor<fp16, [1, ?, 640]> d = transpose(perm = input_45_perm_0, x = x_cast_fp16)[name = tensor<string, []>("transpose_3")];
230
+ tensor<fp16, [?, 1, 640]> input_45_batch_first_transpose_cast_fp16 = transpose(perm = input_45_batch_first_transpose_perm_0, x = d)[name = tensor<string, []>("transpose_2")];
231
+ tensor<fp16, [?, 1, 512]> input_47_batch_first_cast_fp16_0, tensor<fp16, [1, 512]> input_47_batch_first_cast_fp16_1, tensor<fp16, [1, 512]> input_47_batch_first_cast_fp16_2 = lstm(activation = input_47_batch_first_activation_0, bias = add_13_to_fp16, bias_back = add_14_to_fp16, cell_activation = input_47_batch_first_cell_activation_0, direction = input_47_batch_first_direction_0, initial_c = x_17_batch_first_lstm_h0_reshaped_to_fp16, initial_h = x_17_batch_first_lstm_h0_reshaped_to_fp16, output_sequence = input_47_batch_first_output_sequence_0, recurrent_activation = input_47_batch_first_recurrent_activation_0, weight_hh = concat_45_to_fp16_palettized, weight_hh_back = concat_47_to_fp16_palettized, weight_ih = concat_44_to_fp16_palettized, weight_ih_back = concat_46_to_fp16_palettized, x = input_45_batch_first_transpose_cast_fp16)[name = tensor<string, []>("input_47_batch_first_cast_fp16")];
232
  tensor<int32, [3]> input_47_perm_0 = const()[name = tensor<string, []>("input_47_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
233
+ tensor<fp16, [50, 512]> duration_proj_linear_layer_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [25600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13780032))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13805696))), name = tensor<string, []>("duration_proj_linear_layer_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([50, 512])];
234
+ tensor<fp16, [50]> duration_proj_linear_layer_bias_to_fp16 = const()[name = tensor<string, []>("duration_proj_linear_layer_bias_to_fp16"), val = tensor<fp16, [50]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13806272)))];
235
+ tensor<fp16, [1, ?, 512]> input_47_cast_fp16 = transpose(perm = input_47_perm_0, x = input_47_batch_first_cast_fp16_0)[name = tensor<string, []>("transpose_1")];
236
+ tensor<fp16, [1, ?, 50]> pred_dur_log = linear(bias = duration_proj_linear_layer_bias_to_fp16, weight = duration_proj_linear_layer_weight_to_fp16_palettized, x = input_47_cast_fp16)[name = tensor<string, []>("linear_4_cast_fp16")];
237
  tensor<int32, []> var_345 = const()[name = tensor<string, []>("op_345"), val = tensor<int32, []>(-1)];
238
  tensor<int32, [3]> var_355_begin_0 = const()[name = tensor<string, []>("op_355_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
239
  tensor<int32, [3]> var_355_end_0 = const()[name = tensor<string, []>("op_355_end_0"), val = tensor<int32, [3]>([1, 0, 1])];
 
247
  tensor<fp16, [1, ?]> var_357_cast_fp16 = cumsum(axis = var_345, exclusive = var_357_exclusive_0, reverse = var_357_reverse_0, x = fill_like_0_cast_fp16)[name = tensor<string, []>("op_357_cast_fp16")];
248
  tensor<string, []> cast_6_dtype_0 = const()[name = tensor<string, []>("cast_6_dtype_0"), val = tensor<string, []>("int32")];
249
  tensor<int32, []> var_359 = const()[name = tensor<string, []>("op_359"), val = tensor<int32, []>(1)];
250
+ tensor<int32, [1, ?]> var_357_cast_fp16_to_int32 = cast(dtype = cast_6_dtype_0, x = var_357_cast_fp16)[name = tensor<string, []>("cast_1")];
251
  tensor<int32, [1, ?]> indices = sub(x = var_357_cast_fp16_to_int32, y = var_359)[name = tensor<string, []>("indices")];
252
  tensor<int32, [1]> input_axes_0 = const()[name = tensor<string, []>("input_axes_0"), val = tensor<int32, [1]>([0])];
253
  tensor<string, []> indices_to_uint16_dtype_0 = const()[name = tensor<string, []>("indices_to_uint16_dtype_0"), val = tensor<string, []>("uint16")];
254
+ tensor<uint16, [1, ?]> indices_to_uint16 = cast(dtype = indices_to_uint16_dtype_0, x = indices)[name = tensor<string, []>("cast_0")];
255
  tensor<uint16, [?]> input_cast_uint16 = squeeze(axes = input_axes_0, x = indices_to_uint16)[name = tensor<string, []>("input_cast_uint16")];
256
  tensor<int32, []> fe_axis_0 = const()[name = tensor<string, []>("fe_axis_0"), val = tensor<int32, []>(0)];
257
  tensor<int32, []> fe_batch_dims_0 = const()[name = tensor<string, []>("fe_batch_dims_0"), val = tensor<int32, []>(0)];
258
  tensor<bool, []> fe_validate_indices_0 = const()[name = tensor<string, []>("fe_validate_indices_0"), val = tensor<bool, []>(false)];
259
+ tensor<fp16, [512, 768]> fixed_embedding_embedding_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [393216]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13806464))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14199744))), name = tensor<string, []>("fixed_embedding_embedding_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([512, 768])];
260
+ tensor<fp16, [?, 768]> fe_cast_fp16_cast_uint16 = gather(axis = fe_axis_0, batch_dims = fe_batch_dims_0, indices = input_cast_uint16, validate_indices = fe_validate_indices_0, x = fixed_embedding_embedding_weight_to_fp16_palettized)[name = tensor<string, []>("fe_cast_fp16_cast_uint16")];
261
  tensor<int32, [1]> var_364_axes_0 = const()[name = tensor<string, []>("op_364_axes_0"), val = tensor<int32, [1]>([0])];
262
  tensor<fp16, [1, ?, 768]> fixed_embedding = expand_dims(axes = var_364_axes_0, x = fe_cast_fp16_cast_uint16)[name = tensor<string, []>("op_364_cast_fp16")];
263
+ tensor<fp16, [1, 512, ?]> t_en = transpose(perm = transpose_0_perm_0, x = x_17_batch_first_cast_fp16_0)[name = tensor<string, []>("transpose_0")];
264
  } -> (t_en, d, pred_dur_log, fixed_embedding);
265
  }
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20
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113
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114
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115
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116
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117
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118
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119
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120
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121
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122
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123
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124
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125
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