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Remove parakeet-v2_423MB variant

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  1. nvidia_parakeet-v2_423MB/AudioEncoder.mlmodelc/LICENSE_NOTICE.txt +0 -7
  2. nvidia_parakeet-v2_423MB/AudioEncoder.mlmodelc/analytics/coremldata.bin +0 -3
  3. nvidia_parakeet-v2_423MB/AudioEncoder.mlmodelc/coremldata.bin +0 -3
  4. nvidia_parakeet-v2_423MB/AudioEncoder.mlmodelc/metadata.json +0 -97
  5. nvidia_parakeet-v2_423MB/AudioEncoder.mlmodelc/model.mil +0 -0
  6. nvidia_parakeet-v2_423MB/AudioEncoder.mlmodelc/weights/weight.bin +0 -3
  7. nvidia_parakeet-v2_423MB/LICENSE_NOTICE.txt +0 -7
  8. nvidia_parakeet-v2_423MB/MelSpectrogram.mlmodelc/analytics/coremldata.bin +0 -3
  9. nvidia_parakeet-v2_423MB/MelSpectrogram.mlmodelc/coremldata.bin +0 -3
  10. nvidia_parakeet-v2_423MB/MelSpectrogram.mlmodelc/metadata.json +0 -76
  11. nvidia_parakeet-v2_423MB/MelSpectrogram.mlmodelc/model.mil +0 -82
  12. nvidia_parakeet-v2_423MB/MelSpectrogram.mlmodelc/weights/weight.bin +0 -3
  13. nvidia_parakeet-v2_423MB/MultimodalLogits.mlmodelc/analytics/coremldata.bin +0 -3
  14. nvidia_parakeet-v2_423MB/MultimodalLogits.mlmodelc/coremldata.bin +0 -3
  15. nvidia_parakeet-v2_423MB/MultimodalLogits.mlmodelc/metadata.json +0 -74
  16. nvidia_parakeet-v2_423MB/MultimodalLogits.mlmodelc/model.mil +0 -15
  17. nvidia_parakeet-v2_423MB/MultimodalLogits.mlmodelc/weights/weight.bin +0 -3
  18. nvidia_parakeet-v2_423MB/TextDecoder.mlmodelc/analytics/coremldata.bin +0 -3
  19. nvidia_parakeet-v2_423MB/TextDecoder.mlmodelc/coremldata.bin +0 -3
  20. nvidia_parakeet-v2_423MB/TextDecoder.mlmodelc/metadata.json +0 -107
  21. nvidia_parakeet-v2_423MB/TextDecoder.mlmodelc/model.mil +0 -64
  22. nvidia_parakeet-v2_423MB/TextDecoder.mlmodelc/weights/weight.bin +0 -3
nvidia_parakeet-v2_423MB/AudioEncoder.mlmodelc/LICENSE_NOTICE.txt DELETED
@@ -1,7 +0,0 @@
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- Argmax proprietary and confidential. Under NDA.
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-
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- Copyright 2024 Argmax, Inc. All rights reserved.
4
-
5
- Unauthorized access, copying, use, distribution, and or commercialization of this file, via any medium or means is strictly prohibited.
6
-
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- Please contact Argmax for licensing information at [email protected].
 
 
 
 
 
 
 
 
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nvidia_parakeet-v2_423MB/AudioEncoder.mlmodelc/model.mil DELETED
The diff for this file is too large to render. See raw diff
 
nvidia_parakeet-v2_423MB/AudioEncoder.mlmodelc/weights/weight.bin DELETED
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- Argmax proprietary and confidential. Under NDA.
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-
3
- Copyright 2024 Argmax, Inc. All rights reserved.
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-
5
- Unauthorized access, copying, use, distribution, and or commercialization of this file, via any medium or means is strictly prohibited.
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-
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- Please contact Argmax for licensing information at [email protected].
 
 
 
 
 
 
 
 
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nvidia_parakeet-v2_423MB/MelSpectrogram.mlmodelc/model.mil DELETED
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- {
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- func main<ios17>(tensor<fp16, [240000]> audio) {
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- tensor<int32, [1]> var_8_begin_0 = const()[name = tensor<string, []>("op_8_begin_0"), val = tensor<int32, [1]>([1])];
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- tensor<int32, [1]> var_8_end_0 = const()[name = tensor<string, []>("op_8_end_0"), val = tensor<int32, [1]>([240000])];
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- tensor<bool, [1]> var_8_end_mask_0 = const()[name = tensor<string, []>("op_8_end_mask_0"), val = tensor<bool, [1]>([true])];
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- tensor<fp16, [239999]> var_8_cast_fp16 = slice_by_index(begin = var_8_begin_0, end = var_8_end_0, end_mask = var_8_end_mask_0, x = audio)[name = tensor<string, []>("op_8_cast_fp16")];
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- tensor<int32, [1]> var_13_begin_0 = const()[name = tensor<string, []>("op_13_begin_0"), val = tensor<int32, [1]>([0])];
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- tensor<int32, [1]> var_13_end_0 = const()[name = tensor<string, []>("op_13_end_0"), val = tensor<int32, [1]>([239999])];
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- tensor<bool, [1]> var_13_end_mask_0 = const()[name = tensor<string, []>("op_13_end_mask_0"), val = tensor<bool, [1]>([false])];
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- tensor<fp16, [239999]> var_13_cast_fp16 = slice_by_index(begin = var_13_begin_0, end = var_13_end_0, end_mask = var_13_end_mask_0, x = audio)[name = tensor<string, []>("op_13_cast_fp16")];
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- tensor<fp16, []> var_14_to_fp16 = const()[name = tensor<string, []>("op_14_to_fp16"), val = tensor<fp16, []>(0x1.f0cp-1)];
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- tensor<fp16, [239999]> var_15_cast_fp16 = mul(x = var_13_cast_fp16, y = var_14_to_fp16)[name = tensor<string, []>("op_15_cast_fp16")];
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- tensor<fp16, [239999]> input_1_cast_fp16 = sub(x = var_8_cast_fp16, y = var_15_cast_fp16)[name = tensor<string, []>("input_1_cast_fp16")];
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- tensor<int32, [2]> input_3_pad_0 = const()[name = tensor<string, []>("input_3_pad_0"), val = tensor<int32, [2]>([1, 0])];
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- tensor<string, []> input_3_mode_0 = const()[name = tensor<string, []>("input_3_mode_0"), val = tensor<string, []>("constant")];
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- tensor<fp16, []> const_0_to_fp16 = const()[name = tensor<string, []>("const_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
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- tensor<fp16, [240000]> input_3_cast_fp16 = pad(constant_val = const_0_to_fp16, mode = input_3_mode_0, pad = input_3_pad_0, x = input_1_cast_fp16)[name = tensor<string, []>("input_3_cast_fp16")];
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- tensor<int32, [3]> var_30 = const()[name = tensor<string, []>("op_30"), val = tensor<int32, [3]>([1, 1, 240000])];
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- tensor<fp16, [1, 1, 240000]> input_5_cast_fp16 = reshape(shape = var_30, x = input_3_cast_fp16)[name = tensor<string, []>("input_5_cast_fp16")];
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- tensor<int32, [6]> input_7_pad_0 = const()[name = tensor<string, []>("input_7_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 256, 256])];
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- tensor<string, []> input_7_mode_0 = const()[name = tensor<string, []>("input_7_mode_0"), val = tensor<string, []>("reflect")];
24
- tensor<fp16, []> const_2_to_fp16 = const()[name = tensor<string, []>("const_2_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
25
- tensor<fp16, [1, 1, 240512]> input_7_cast_fp16 = pad(constant_val = const_2_to_fp16, mode = input_7_mode_0, pad = input_7_pad_0, x = input_5_cast_fp16)[name = tensor<string, []>("input_7_cast_fp16")];
26
- tensor<int32, [1]> var_42 = const()[name = tensor<string, []>("op_42"), val = tensor<int32, [1]>([240512])];
27
- tensor<fp16, [240512]> input_cast_fp16 = reshape(shape = var_42, x = input_7_cast_fp16)[name = tensor<string, []>("input_cast_fp16")];
28
- tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = tensor<string, []>("expand_dims_0_axes_0"), val = tensor<int32, [1]>([0])];
29
- tensor<fp16, [1, 240512]> expand_dims_0_cast_fp16 = expand_dims(axes = expand_dims_0_axes_0, x = input_cast_fp16)[name = tensor<string, []>("expand_dims_0_cast_fp16")];
30
- tensor<int32, [1]> expand_dims_3 = const()[name = tensor<string, []>("expand_dims_3"), val = tensor<int32, [1]>([160])];
31
- tensor<int32, [1]> expand_dims_4_axes_0 = const()[name = tensor<string, []>("expand_dims_4_axes_0"), val = tensor<int32, [1]>([1])];
32
- tensor<fp16, [1, 1, 240512]> expand_dims_4_cast_fp16 = expand_dims(axes = expand_dims_4_axes_0, x = expand_dims_0_cast_fp16)[name = tensor<string, []>("expand_dims_4_cast_fp16")];
33
- tensor<string, []> conv_0_pad_type_0 = const()[name = tensor<string, []>("conv_0_pad_type_0"), val = tensor<string, []>("valid")];
34
- tensor<int32, [2]> conv_0_pad_0 = const()[name = tensor<string, []>("conv_0_pad_0"), val = tensor<int32, [2]>([0, 0])];
35
- tensor<int32, [1]> conv_0_dilations_0 = const()[name = tensor<string, []>("conv_0_dilations_0"), val = tensor<int32, [1]>([1])];
36
- tensor<int32, []> conv_0_groups_0 = const()[name = tensor<string, []>("conv_0_groups_0"), val = tensor<int32, []>(1)];
37
- tensor<fp16, [257, 1, 512]> expand_dims_1_to_fp16 = const()[name = tensor<string, []>("expand_dims_1_to_fp16"), val = tensor<fp16, [257, 1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
38
- tensor<fp16, [1, 257, 1501]> conv_0_cast_fp16 = conv(dilations = conv_0_dilations_0, groups = conv_0_groups_0, pad = conv_0_pad_0, pad_type = conv_0_pad_type_0, strides = expand_dims_3, weight = expand_dims_1_to_fp16, x = expand_dims_4_cast_fp16)[name = tensor<string, []>("conv_0_cast_fp16")];
39
- tensor<string, []> conv_1_pad_type_0 = const()[name = tensor<string, []>("conv_1_pad_type_0"), val = tensor<string, []>("valid")];
40
- tensor<int32, [2]> conv_1_pad_0 = const()[name = tensor<string, []>("conv_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
41
- tensor<int32, [1]> conv_1_dilations_0 = const()[name = tensor<string, []>("conv_1_dilations_0"), val = tensor<int32, [1]>([1])];
42
- tensor<int32, []> conv_1_groups_0 = const()[name = tensor<string, []>("conv_1_groups_0"), val = tensor<int32, []>(1)];
43
- tensor<fp16, [257, 1, 512]> expand_dims_2_to_fp16 = const()[name = tensor<string, []>("expand_dims_2_to_fp16"), val = tensor<fp16, [257, 1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(263296)))];
44
- tensor<fp16, [1, 257, 1501]> conv_1_cast_fp16 = conv(dilations = conv_1_dilations_0, groups = conv_1_groups_0, pad = conv_1_pad_0, pad_type = conv_1_pad_type_0, strides = expand_dims_3, weight = expand_dims_2_to_fp16, x = expand_dims_4_cast_fp16)[name = tensor<string, []>("conv_1_cast_fp16")];
45
- tensor<int32, [1]> squeeze_0_axes_0 = const()[name = tensor<string, []>("squeeze_0_axes_0"), val = tensor<int32, [1]>([0])];
46
- tensor<fp16, [257, 1501]> squeeze_0_cast_fp16 = squeeze(axes = squeeze_0_axes_0, x = conv_0_cast_fp16)[name = tensor<string, []>("squeeze_0_cast_fp16")];
47
- tensor<int32, [1]> squeeze_1_axes_0 = const()[name = tensor<string, []>("squeeze_1_axes_0"), val = tensor<int32, [1]>([0])];
48
- tensor<fp16, [257, 1501]> squeeze_1_cast_fp16 = squeeze(axes = squeeze_1_axes_0, x = conv_1_cast_fp16)[name = tensor<string, []>("squeeze_1_cast_fp16")];
49
- tensor<fp16, [257, 1501]> square_1_cast_fp16 = square(x = squeeze_0_cast_fp16)[name = tensor<string, []>("square_1_cast_fp16")];
50
- tensor<fp16, [257, 1501]> square_2_cast_fp16 = square(x = squeeze_1_cast_fp16)[name = tensor<string, []>("square_2_cast_fp16")];
51
- tensor<fp16, [257, 1501]> add_1_cast_fp16 = add(x = square_1_cast_fp16, y = square_2_cast_fp16)[name = tensor<string, []>("add_1_cast_fp16")];
52
- tensor<fp16, [257, 1501]> magnitudes_cast_fp16 = identity(x = add_1_cast_fp16)[name = tensor<string, []>("magnitudes_cast_fp16")];
53
- tensor<bool, []> mel_spec_1_transpose_x_0 = const()[name = tensor<string, []>("mel_spec_1_transpose_x_0"), val = tensor<bool, []>(false)];
54
- tensor<bool, []> mel_spec_1_transpose_y_0 = const()[name = tensor<string, []>("mel_spec_1_transpose_y_0"), val = tensor<bool, []>(false)];
55
- tensor<fp16, [128, 257]> mel_filters_to_fp16 = const()[name = tensor<string, []>("mel_filters_to_fp16"), val = tensor<fp16, [128, 257]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(526528)))];
56
- tensor<fp16, [128, 1501]> mel_spec_1_cast_fp16 = matmul(transpose_x = mel_spec_1_transpose_x_0, transpose_y = mel_spec_1_transpose_y_0, x = mel_filters_to_fp16, y = magnitudes_cast_fp16)[name = tensor<string, []>("mel_spec_1_cast_fp16")];
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- tensor<fp16, []> var_56_to_fp16 = const()[name = tensor<string, []>("op_56_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
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- tensor<fp16, [128, 1501]> mel_spec_3_cast_fp16 = add(x = mel_spec_1_cast_fp16, y = var_56_to_fp16)[name = tensor<string, []>("mel_spec_3_cast_fp16")];
59
- tensor<fp32, []> mel_spec_5_epsilon_0 = const()[name = tensor<string, []>("mel_spec_5_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
60
- tensor<fp16, [128, 1501]> mel_spec_5_cast_fp16 = log(epsilon = mel_spec_5_epsilon_0, x = mel_spec_3_cast_fp16)[name = tensor<string, []>("mel_spec_5_cast_fp16")];
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- tensor<int32, [1]> per_feature_mean_axes_0 = const()[name = tensor<string, []>("per_feature_mean_axes_0"), val = tensor<int32, [1]>([-1])];
62
- tensor<bool, []> per_feature_mean_keep_dims_0 = const()[name = tensor<string, []>("per_feature_mean_keep_dims_0"), val = tensor<bool, []>(true)];
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- tensor<fp16, [128, 1]> per_feature_mean_cast_fp16 = reduce_mean(axes = per_feature_mean_axes_0, keep_dims = per_feature_mean_keep_dims_0, x = mel_spec_5_cast_fp16)[name = tensor<string, []>("per_feature_mean_cast_fp16")];
64
- tensor<fp16, [128, 1501]> sub_0_cast_fp16 = sub(x = mel_spec_5_cast_fp16, y = per_feature_mean_cast_fp16)[name = tensor<string, []>("sub_0_cast_fp16")];
65
- tensor<fp16, [128, 1501]> square_0_cast_fp16 = square(x = sub_0_cast_fp16)[name = tensor<string, []>("square_0_cast_fp16")];
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- tensor<int32, [1]> reduce_mean_1_axes_0 = const()[name = tensor<string, []>("reduce_mean_1_axes_0"), val = tensor<int32, [1]>([-1])];
67
- tensor<bool, []> reduce_mean_1_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_1_keep_dims_0"), val = tensor<bool, []>(true)];
68
- tensor<fp16, [128, 1]> reduce_mean_1_cast_fp16 = reduce_mean(axes = reduce_mean_1_axes_0, keep_dims = reduce_mean_1_keep_dims_0, x = square_0_cast_fp16)[name = tensor<string, []>("reduce_mean_1_cast_fp16")];
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- tensor<fp16, []> real_div_0_to_fp16 = const()[name = tensor<string, []>("real_div_0_to_fp16"), val = tensor<fp16, []>(0x1.004p+0)];
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- tensor<fp16, [128, 1]> mul_0_cast_fp16 = mul(x = reduce_mean_1_cast_fp16, y = real_div_0_to_fp16)[name = tensor<string, []>("mul_0_cast_fp16")];
71
- tensor<fp16, [128, 1]> sqrt_0_cast_fp16 = sqrt(x = mul_0_cast_fp16)[name = tensor<string, []>("sqrt_0_cast_fp16")];
72
- tensor<fp16, []> var_70_to_fp16 = const()[name = tensor<string, []>("op_70_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
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- tensor<fp16, [128, 1]> per_feature_std_cast_fp16 = add(x = sqrt_0_cast_fp16, y = var_70_to_fp16)[name = tensor<string, []>("per_feature_std_cast_fp16")];
74
- tensor<fp16, [128, 1501]> mel_spec_cast_fp16 = real_div(x = sub_0_cast_fp16, y = per_feature_std_cast_fp16)[name = tensor<string, []>("mel_spec_cast_fp16")];
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- tensor<int32, [2]> var_75_perm_0 = const()[name = tensor<string, []>("op_75_perm_0"), val = tensor<int32, [2]>([1, 0])];
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- tensor<int32, [1]> var_77_axes_0 = const()[name = tensor<string, []>("op_77_axes_0"), val = tensor<int32, [1]>([0])];
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- tensor<fp16, [1501, 128]> var_75_cast_fp16 = transpose(perm = var_75_perm_0, x = mel_spec_cast_fp16)[name = tensor<string, []>("transpose_0")];
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- tensor<fp16, [1, 1501, 128]> var_77_cast_fp16 = expand_dims(axes = var_77_axes_0, x = var_75_cast_fp16)[name = tensor<string, []>("op_77_cast_fp16")];
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- tensor<int32, [1]> var_79_axes_0 = const()[name = tensor<string, []>("op_79_axes_0"), val = tensor<int32, [1]>([1])];
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- tensor<fp16, [1, 1, 1501, 128]> melspectrogram_features = expand_dims(axes = var_79_axes_0, x = var_77_cast_fp16)[name = tensor<string, []>("op_79_cast_fp16")];
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- } -> (melspectrogram_features);
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10
- tensor<int16, [1]> decoder_input_ids_to_int16 = cast(dtype = decoder_input_ids_to_int16_dtype_0, x = decoder_input_ids)[name = tensor<string, []>("cast_6")];
11
- tensor<fp16, [1, 640]> input_1_cast_fp16_cast_uint16 = gather(axis = input_1_axis_0, batch_dims = input_1_batch_dims_0, indices = decoder_input_ids_to_int16, validate_indices = input_1_validate_indices_0, x = prediction_embed_weight_to_fp16)[name = tensor<string, []>("input_1_cast_fp16_cast_uint16")];
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- tensor<fp16, [1, 1, 640]> input_3_cast_fp16 = expand_dims(axes = input_3_axes_0, x = input_1_cast_fp16_cast_uint16)[name = tensor<string, []>("input_3_cast_fp16")];
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- tensor<int32, [1]> hx_axes_0 = const()[name = tensor<string, []>("hx_axes_0"), val = tensor<int32, [1]>([1])];
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- tensor<fp16, [2, 1, 640]> hx_cast_fp16 = expand_dims(axes = hx_axes_0, x = state_2)[name = tensor<string, []>("hx_cast_fp16")];
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21
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22
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- tensor<int32, [1]> output_lstm_layer_0_lstm_h0_squeeze_axes_0 = const()[name = tensor<string, []>("output_lstm_layer_0_lstm_h0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
25
- tensor<fp16, [1, 640]> output_lstm_layer_0_lstm_h0_squeeze_cast_fp16 = squeeze(axes = output_lstm_layer_0_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_0)[name = tensor<string, []>("output_lstm_layer_0_lstm_h0_squeeze_cast_fp16")];
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- tensor<int32, [1]> output_lstm_layer_0_lstm_c0_squeeze_axes_0 = const()[name = tensor<string, []>("output_lstm_layer_0_lstm_c0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
27
- tensor<fp16, [1, 640]> output_lstm_layer_0_lstm_c0_squeeze_cast_fp16 = squeeze(axes = output_lstm_layer_0_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_0)[name = tensor<string, []>("output_lstm_layer_0_lstm_c0_squeeze_cast_fp16")];
28
- tensor<string, []> output_lstm_layer_0_direction_0 = const()[name = tensor<string, []>("output_lstm_layer_0_direction_0"), val = tensor<string, []>("forward")];
29
- tensor<bool, []> output_lstm_layer_0_output_sequence_0 = const()[name = tensor<string, []>("output_lstm_layer_0_output_sequence_0"), val = tensor<bool, []>(true)];
30
- tensor<string, []> output_lstm_layer_0_recurrent_activation_0 = const()[name = tensor<string, []>("output_lstm_layer_0_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
31
- tensor<string, []> output_lstm_layer_0_cell_activation_0 = const()[name = tensor<string, []>("output_lstm_layer_0_cell_activation_0"), val = tensor<string, []>("tanh")];
32
- tensor<string, []> output_lstm_layer_0_activation_0 = const()[name = tensor<string, []>("output_lstm_layer_0_activation_0"), val = tensor<string, []>("tanh")];
33
- tensor<fp16, [2560, 640]> concat_1_to_fp16 = const()[name = tensor<string, []>("concat_1_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1312128)))];
34
- tensor<fp16, [2560, 640]> concat_2_to_fp16 = const()[name = tensor<string, []>("concat_2_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4588992)))];
35
- tensor<fp16, [2560]> concat_0_to_fp16 = const()[name = tensor<string, []>("concat_0_to_fp16"), val = tensor<fp16, [2560]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7865856)))];
36
- tensor<fp16, [1, 1, 640]> output_lstm_layer_0_cast_fp16_0, tensor<fp16, [1, 640]> output_lstm_layer_0_cast_fp16_1, tensor<fp16, [1, 640]> output_lstm_layer_0_cast_fp16_2 = lstm(activation = output_lstm_layer_0_activation_0, bias = concat_0_to_fp16, cell_activation = output_lstm_layer_0_cell_activation_0, direction = output_lstm_layer_0_direction_0, initial_c = output_lstm_layer_0_lstm_c0_squeeze_cast_fp16, initial_h = output_lstm_layer_0_lstm_h0_squeeze_cast_fp16, output_sequence = output_lstm_layer_0_output_sequence_0, recurrent_activation = output_lstm_layer_0_recurrent_activation_0, weight_hh = concat_2_to_fp16, weight_ih = concat_1_to_fp16, x = input_3_cast_fp16)[name = tensor<string, []>("output_lstm_layer_0_cast_fp16")];
37
- tensor<int32, [1]> output_lstm_h0_squeeze_axes_0 = const()[name = tensor<string, []>("output_lstm_h0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
38
- tensor<fp16, [1, 640]> output_lstm_h0_squeeze_cast_fp16 = squeeze(axes = output_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_1)[name = tensor<string, []>("output_lstm_h0_squeeze_cast_fp16")];
39
- tensor<int32, [1]> output_lstm_c0_squeeze_axes_0 = const()[name = tensor<string, []>("output_lstm_c0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
40
- tensor<fp16, [1, 640]> output_lstm_c0_squeeze_cast_fp16 = squeeze(axes = output_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_1)[name = tensor<string, []>("output_lstm_c0_squeeze_cast_fp16")];
41
- tensor<string, []> output_direction_0 = const()[name = tensor<string, []>("output_direction_0"), val = tensor<string, []>("forward")];
42
- tensor<bool, []> output_output_sequence_0 = const()[name = tensor<string, []>("output_output_sequence_0"), val = tensor<bool, []>(true)];
43
- tensor<string, []> output_recurrent_activation_0 = const()[name = tensor<string, []>("output_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
44
- tensor<string, []> output_cell_activation_0 = const()[name = tensor<string, []>("output_cell_activation_0"), val = tensor<string, []>("tanh")];
45
- tensor<string, []> output_activation_0 = const()[name = tensor<string, []>("output_activation_0"), val = tensor<string, []>("tanh")];
46
- tensor<fp16, [2560, 640]> concat_4_to_fp16 = const()[name = tensor<string, []>("concat_4_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7871040)))];
47
- tensor<fp16, [2560, 640]> concat_5_to_fp16 = const()[name = tensor<string, []>("concat_5_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11147904)))];
48
- tensor<fp16, [2560]> concat_3_to_fp16 = const()[name = tensor<string, []>("concat_3_to_fp16"), val = tensor<fp16, [2560]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14424768)))];
49
- tensor<fp16, [1, 1, 640]> output_cast_fp16_0, tensor<fp16, [1, 640]> output_cast_fp16_1, tensor<fp16, [1, 640]> output_cast_fp16_2 = lstm(activation = output_activation_0, bias = concat_3_to_fp16, cell_activation = output_cell_activation_0, direction = output_direction_0, initial_c = output_lstm_c0_squeeze_cast_fp16, initial_h = output_lstm_h0_squeeze_cast_fp16, output_sequence = output_output_sequence_0, recurrent_activation = output_recurrent_activation_0, weight_hh = concat_5_to_fp16, weight_ih = concat_4_to_fp16, x = output_lstm_layer_0_cast_fp16_0)[name = tensor<string, []>("output_cast_fp16")];
50
- tensor<int32, []> var_32_axis_0 = const()[name = tensor<string, []>("op_32_axis_0"), val = tensor<int32, []>(0)];
51
- tensor<fp16, [2, 1, 640]> var_32_cast_fp16 = stack(axis = var_32_axis_0, values = (output_lstm_layer_0_cast_fp16_1, output_cast_fp16_1))[name = tensor<string, []>("op_32_cast_fp16")];
52
- tensor<int32, []> var_33_axis_0 = const()[name = tensor<string, []>("op_33_axis_0"), val = tensor<int32, []>(0)];
53
- tensor<fp16, [2, 1, 640]> var_33_cast_fp16 = stack(axis = var_33_axis_0, values = (output_lstm_layer_0_cast_fp16_2, output_cast_fp16_2))[name = tensor<string, []>("op_33_cast_fp16")];
54
- tensor<int32, [1]> input_axes_0 = const()[name = tensor<string, []>("input_axes_0"), val = tensor<int32, [1]>([1])];
55
- tensor<fp16, [1, 640]> input_cast_fp16 = squeeze(axes = input_axes_0, x = output_cast_fp16_0)[name = tensor<string, []>("input_cast_fp16")];
56
- tensor<int32, [1]> var_35_axes_0 = const()[name = tensor<string, []>("op_35_axes_0"), val = tensor<int32, [1]>([1])];
57
- tensor<fp16, [2, 640]> new_state_1 = squeeze(axes = var_35_axes_0, x = var_32_cast_fp16)[name = tensor<string, []>("op_35_cast_fp16")];
58
- tensor<int32, [1]> var_36_axes_0 = const()[name = tensor<string, []>("op_36_axes_0"), val = tensor<int32, [1]>([1])];
59
- tensor<fp16, [2, 640]> new_state_2 = squeeze(axes = var_36_axes_0, x = var_33_cast_fp16)[name = tensor<string, []>("op_36_cast_fp16")];
60
- tensor<fp16, [640, 640]> joint_projection_weight_to_fp16 = const()[name = tensor<string, []>("joint_projection_weight_to_fp16"), val = tensor<fp16, [640, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14429952)))];
61
- tensor<fp16, [640]> joint_projection_bias_to_fp16 = const()[name = tensor<string, []>("joint_projection_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15249216)))];
62
- tensor<fp16, [1, 640]> decoder_output_projected = linear(bias = joint_projection_bias_to_fp16, weight = joint_projection_weight_to_fp16, x = input_cast_fp16)[name = tensor<string, []>("linear_0_cast_fp16")];
63
- } -> (decoder_output_projected, new_state_1, new_state_2);
64
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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